diff --git a/.dev_scripts/diff_images.py b/.dev_scripts/diff_images.py
index e21cae214e..5208ed41ec 100644
--- a/.dev_scripts/diff_images.py
+++ b/.dev_scripts/diff_images.py
@@ -20,13 +20,13 @@ def calc_images_mean_L1(image1_path, image2_path):
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('image1_path')
-    parser.add_argument('image2_path')
+    parser.add_argument("image1_path")
+    parser.add_argument("image2_path")
     args = parser.parse_args()
     return args
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     args = parse_args()
     mean_L1 = calc_images_mean_L1(args.image1_path, args.image2_path)
     print(mean_L1)
diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index 5afd17b417..c6b833cf58 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -1 +1,2 @@
 b3dccfaeb636599c02effc377cdd8a87d658256c
+218b6d0546b990fc449c876fb99f44b50c4daa35
diff --git a/.github/workflows/style-checks.yml b/.github/workflows/style-checks.yml
new file mode 100644
index 0000000000..8aceb6469e
--- /dev/null
+++ b/.github/workflows/style-checks.yml
@@ -0,0 +1,27 @@
+name: Black  # TODO: add isort and flake8 later
+
+on:
+  pull_request: {}
+  push:
+    branches: master
+    tags: "*"
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies with pip
+        run: |
+          pip install --upgrade pip wheel
+          pip install .[test]
+
+      # - run: isort --check-only .
+      - run: black --check .
+      # - run: flake8
diff --git a/.gitignore b/.gitignore
index b7b5c1ae75..cc000de20e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -38,7 +38,6 @@ develop-eggs/
 downloads/
 eggs/
 .eggs/
-lib/
 lib64/
 parts/
 sdist/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000000..2b16b274d6
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,10 @@
+# See https://pre-commit.com/ for usage and config
+repos:
+- repo: local
+  hooks:
+  - id: black
+    name: black
+    stages: [commit]
+    language: system
+    entry: black
+    types: [python]
diff --git a/LICENSE-ModelWeights.txt b/LICENSE-SD1+SD2.txt
similarity index 100%
rename from LICENSE-ModelWeights.txt
rename to LICENSE-SD1+SD2.txt
diff --git a/LICENSE-SDXL.txt b/LICENSE-SDXL.txt
new file mode 100644
index 0000000000..05fbe3abb6
--- /dev/null
+++ b/LICENSE-SDXL.txt
@@ -0,0 +1,290 @@
+Copyright (c) 2023 Stability AI
+CreativeML Open RAIL++-M License dated July 26, 2023
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and
+have the potential to transform the way artists, among other
+individuals, conceive and benefit from AI or ML technologies as a tool
+for content creation.
+
+Notwithstanding the current and potential benefits that these
+artifacts can bring to society at large, there are also concerns about
+potential misuses of them, either due to their technical limitations
+or ethical considerations.
+
+In short, this license strives for both the open and responsible
+downstream use of the accompanying model. When it comes to the open
+character, we took inspiration from open source permissive licenses
+regarding the grant of IP rights. Referring to the downstream
+responsible use, we added use-based restrictions not permitting the
+use of the model in very specific scenarios, in order for the licensor
+to be able to enforce the license in case potential misuses of the
+Model may occur. At the same time, we strive to promote open and
+responsible research on generative models for art and content
+generation.
+
+Even though downstream derivative versions of the model could be
+released under different licensing terms, the latter will always have
+to include - at minimum - the same use-based restrictions as the ones
+in the original license (this license). We believe in the intersection
+between open and responsible AI development; thus, this agreement aims
+to strike a balance between both in order to enable responsible
+open-science in the field of AI.
+
+This CreativeML Open RAIL++-M License governs the use of the model
+(and its derivatives) and is informed by the model card associated
+with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+Definitions
+
+"License" means the terms and conditions for use, reproduction, and
+Distribution as defined in this document.
+
+"Data" means a collection of information and/or content extracted from
+the dataset used with the Model, including to train, pretrain, or
+otherwise evaluate the Model. The Data is not licensed under this
+License.
+
+"Output" means the results of operating a Model as embodied in
+informational content resulting therefrom.
+
+"Model" means any accompanying machine-learning based assemblies
+(including checkpoints), consisting of learnt weights, parameters
+(including optimizer states), corresponding to the model architecture
+as embodied in the Complementary Material, that have been trained or
+tuned, in whole or in part on the Data, using the Complementary
+Material.
+
+"Derivatives of the Model" means all modifications to the Model, works
+based on the Model, or any other model which is created or initialized
+by transfer of patterns of the weights, parameters, activations or
+output of the Model, to the other model, in order to cause the other
+model to perform similarly to the Model, including - but not limited
+to - distillation methods entailing the use of intermediate data
+representations or methods based on the generation of synthetic data
+by the Model for training the other model.
+
+"Complementary Material" means the accompanying source code and
+scripts used to define, run, load, benchmark or evaluate the Model,
+and used to prepare data for training or evaluation, if any. This
+includes any accompanying documentation, tutorials, examples, etc, if
+any.
+
+"Distribution" means any transmission, reproduction, publication or
+other sharing of the Model or Derivatives of the Model to a third
+party, including providing the Model as a hosted service made
+available by electronic or other remote means - e.g. API-based or web
+access.
+
+"Licensor" means the copyright owner or entity authorized by the
+copyright owner that is granting the License, including the persons or
+entities that may have rights in the Model and/or distributing the
+Model.
+
+"You" (or "Your") means an individual or Legal Entity exercising
+permissions granted by this License and/or making use of the Model for
+whichever purpose and in any field of use, including usage of the
+Model in an end-use application - e.g. chatbot, translator, image
+generator.
+
+"Third Parties" means individuals or legal entities that are not under
+common control with Licensor or You.
+
+"Contribution" means any work of authorship, including the original
+version of the Model and any modifications or additions to that Model
+or Derivatives of the Model thereof, that is intentionally submitted
+to Licensor for inclusion in the Model by the copyright owner or by an
+individual or Legal Entity authorized to submit on behalf of the
+copyright owner. For the purposes of this definition, "submitted"
+means any form of electronic, verbal, or written communication sent to
+the Licensor or its representatives, including but not limited to
+communication on electronic mailing lists, source code control
+systems, and issue tracking systems that are managed by, or on behalf
+of, the Licensor for the purpose of discussing and improving the
+Model, but excluding communication that is conspicuously marked or
+otherwise designated in writing by the copyright owner as "Not a
+Contribution."
+
+"Contributor" means Licensor and any individual or Legal Entity on
+behalf of whom a Contribution has been received by Licensor and
+subsequently incorporated within the Model.
+
+Section II: INTELLECTUAL PROPERTY RIGHTS
+
+Both copyright and patent grants apply to the Model, Derivatives of
+the Model and Complementary Material. The Model and Derivatives of the
+Model are subject to additional terms as described in
+
+Section III.
+
+Grant of Copyright License. Subject to the terms and conditions of
+this License, each Contributor hereby grants to You a perpetual,
+worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+copyright license to reproduce, prepare, publicly display, publicly
+perform, sublicense, and distribute the Complementary Material, the
+Model, and Derivatives of the Model.
+
+Grant of Patent License. Subject to the terms and conditions of this
+License and where and as applicable, each Contributor hereby grants to
+You a perpetual, worldwide, non-exclusive, no-charge, royalty-free,
+irrevocable (except as stated in this paragraph) patent license to
+make, have made, use, offer to sell, sell, import, and otherwise
+transfer the Model and the Complementary Material, where such license
+applies only to those patent claims licensable by such Contributor
+that are necessarily infringed by their Contribution(s) alone or by
+combination of their Contribution(s) with the Model to which such
+Contribution(s) was submitted. If You institute patent litigation
+against any entity (including a cross-claim or counterclaim in a
+lawsuit) alleging that the Model and/or Complementary Material or a
+Contribution incorporated within the Model and/or Complementary
+Material constitutes direct or contributory patent infringement, then
+any patent licenses granted to You under this License for the Model
+and/or Work shall terminate as of the date such litigation is asserted
+or filed.
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+Distribution and Redistribution. You may host for Third Party remote
+access purposes (e.g. software-as-a-service), reproduce and distribute
+copies of the Model or Derivatives of the Model thereof in any medium,
+with or without modifications, provided that You meet the following
+conditions: Use-based restrictions as referenced in paragraph 5 MUST
+be included as an enforceable provision by You in any type of legal
+agreement (e.g. a license) governing the use and/or distribution of
+the Model or Derivatives of the Model, and You shall give notice to
+subsequent users You Distribute to, that the Model or Derivatives of
+the Model are subject to paragraph 5. This provision does not apply to
+the use of Complementary Material. You must give any Third Party
+recipients of the Model or Derivatives of the Model a copy of this
+License; You must cause any modified files to carry prominent notices
+stating that You changed the files; You must retain all copyright,
+patent, trademark, and attribution notices excluding those notices
+that do not pertain to any part of the Model, Derivatives of the
+Model. You may add Your own copyright statement to Your modifications
+and may provide additional or different license terms and conditions -
+respecting paragraph 4.a. - for use, reproduction, or Distribution of
+Your modifications, or for any such Derivatives of the Model as a
+whole, provided Your use, reproduction, and Distribution of the Model
+otherwise complies with the conditions stated in this License.
+
+Use-based restrictions. The restrictions set forth in Attachment A are
+considered Use-based restrictions. Therefore You cannot use the Model
+and the Derivatives of the Model for the specified restricted
+uses. You may use the Model subject to this License, including only
+for lawful purposes and in accordance with the License. Use may
+include creating any content with, finetuning, updating, running,
+training, evaluating and/or reparametrizing the Model. You shall
+require all of Your users who use the Model or a Derivative of the
+Model to comply with the terms of this paragraph (paragraph 5).
+
+The Output You Generate.  Except as set forth herein, Licensor claims
+no rights in the Output You generate using the Model. You are
+accountable for the Output you generate and its subsequent uses. No
+use of the output can contravene any provision as stated in the
+License.
+
+Section IV: OTHER PROVISIONS
+
+Updates and Runtime Restrictions. To the maximum extent permitted by
+law, Licensor reserves the right to restrict (remotely or otherwise)
+usage of the Model in violation of this License.
+
+Trademarks and related. Nothing in this License permits You to make
+use of Licensors’ trademarks, trade names, logos or to otherwise
+suggest endorsement or misrepresent the relationship between the
+parties; and any rights not expressly granted herein are reserved by
+the Licensors.
+
+Disclaimer of Warranty. Unless required by applicable law or agreed to
+in writing, Licensor provides the Model and the Complementary Material
+(and each Contributor provides its Contributions) on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied, including, without limitation, any warranties or conditions
+of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+PARTICULAR PURPOSE. You are solely responsible for determining the
+appropriateness of using or redistributing the Model, Derivatives of
+the Model, and the Complementary Material and assume any risks
+associated with Your exercise of permissions under this License.
+
+Limitation of Liability. In no event and under no legal theory,
+whether in tort (including negligence), contract, or otherwise, unless
+required by applicable law (such as deliberate and grossly negligent
+acts) or agreed to in writing, shall any Contributor be liable to You
+for damages, including any direct, indirect, special, incidental, or
+consequential damages of any character arising as a result of this
+License or out of the use or inability to use the Model and the
+Complementary Material (including but not limited to damages for loss
+of goodwill, work stoppage, computer failure or malfunction, or any
+and all other commercial damages or losses), even if such Contributor
+has been advised of the possibility of such damages.
+
+Accepting Warranty or Additional Liability. While redistributing the
+Model, Derivatives of the Model and the Complementary Material
+thereof, You may choose to offer, and charge a fee for, acceptance of
+support, warranty, indemnity, or other liability obligations and/or
+rights consistent with this License. However, in accepting such
+obligations, You may act only on Your own behalf and on Your sole
+responsibility, not on behalf of any other Contributor, and only if
+You agree to indemnify, defend, and hold each Contributor harmless for
+any liability incurred by, or claims asserted against, such
+Contributor by reason of your accepting any such warranty or
+additional liability.
+
+If any provision of this License is held to be invalid, illegal or
+unenforceable, the remaining provisions shall be unaffected thereby
+and remain valid as if such provision had not been set forth herein.
+
+
+END OF TERMS AND CONDITIONS
+
+Attachment A
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
+* In any way that violates any applicable national, federal, state,
+local or international law or regulation;
+
+* For the purpose of exploiting, harming or attempting to exploit or
+harm minors in any way;
+
+* To generate or disseminate verifiably false information and/or
+  content with the purpose of harming others;
+
+* To generate or disseminate personal identifiable information that
+  can be used to harm an individual;
+
+* To defame, disparage or otherwise harass others;
+
+* For fully automated decision making that adversely impacts an
+  individual’s legal rights or otherwise creates or modifies a
+  binding, enforceable obligation;
+
+* For any use intended to or which has the effect of discriminating
+  against or harming individuals or groups based on online or offline
+  social behavior or known or predicted personal or personality
+  characteristics;
+
+* To exploit any of the vulnerabilities of a specific group of persons
+  based on their age, social, physical or mental characteristics, in
+  order to materially distort the behavior of a person pertaining to
+  that group in a manner that causes or is likely to cause that person
+  or another person physical or psychological harm;
+
+* For any use intended to or which has the effect of discriminating
+  against individuals or groups based on legally protected
+  characteristics or categories;
+
+* To provide medical advice and medical results interpretation;
+
+* To generate or disseminate information for the purpose to be used
+  for administration of justice, law enforcement, immigration or
+  asylum processes, such as predicting an individual will commit
+  fraud/crime commitment (e.g. by text profiling, drawing causal
+  relationships between assertions made in documents, indiscriminate
+  and arbitrarily-targeted use).
+
diff --git a/README.md b/README.md
index 6f6bd2a0b8..3ca1ca1f26 100644
--- a/README.md
+++ b/README.md
@@ -123,7 +123,7 @@ and go to http://localhost:9090.
 
 ### Command-Line Installation (for developers and users familiar with Terminals)
 
-You must have Python 3.9 or 3.10 installed on your machine. Earlier or
+You must have Python 3.9 through 3.11 installed on your machine. Earlier or
 later versions are not supported.
 Node.js also needs to be installed along with yarn (can be installed with
 the command `npm install -g yarn` if needed)
diff --git a/docs/assets/troubleshooting/broken-dependency.png b/docs/assets/troubleshooting/broken-dependency.png
new file mode 100644
index 0000000000..2841508920
Binary files /dev/null and b/docs/assets/troubleshooting/broken-dependency.png differ
diff --git a/docs/features/CONFIGURATION.md b/docs/features/CONFIGURATION.md
index 68c5ced59b..09e6143e95 100644
--- a/docs/features/CONFIGURATION.md
+++ b/docs/features/CONFIGURATION.md
@@ -65,7 +65,6 @@ InvokeAI:
     esrgan: true
     internet_available: true
     log_tokenization: false
-    nsfw_checker: false
     patchmatch: true
     restore: true
 ...
@@ -136,19 +135,16 @@ command-line options by giving the `--help` argument:
 
 ```
 (.venv) > invokeai-web --help
-usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials]
-                [--allow_methods [ALLOW_METHODS ...]] [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan]
-                [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
-                [--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
-                [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE]
-                [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--precision {auto,float16,float32,autocast}]
-                [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled]
-                [--tiled_decode | --no-tiled_decode] [--root ROOT] [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR]
-                [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] [--models_dir MODELS_DIR]
-                [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
-                [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]]
-                [--log_format {plain,color,syslog,legacy}] [--log_level {debug,info,warning,error,critical}]
-...
+usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] [--allow_methods [ALLOW_METHODS ...]]
+                [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
+                [--patchmatch | --no-patchmatch] [--restore | --no-restore]
+                [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_loaded_models MAX_LOADED_MODELS] [--max_cache_size MAX_CACHE_SIZE]
+                [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--gpu_mem_reserved GPU_MEM_RESERVED] [--precision {auto,float16,float32,autocast}]
+                [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] [--tiled_decode | --no-tiled_decode] [--root ROOT]
+                [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH]
+                [--models_dir MODELS_DIR] [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
+                [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] [--log_format {plain,color,syslog,legacy}]
+                [--log_level {debug,info,warning,error,critical}] [--version | --no-version]
 ```
 
 ## The Configuration Settings
@@ -178,7 +174,6 @@ These configuration settings allow you to enable and disable various InvokeAI fe
 | `esrgan`     | `true`      | Activate the ESRGAN upscaling options|
 | `internet_available` | `true`     | When a resource is not available locally, try to fetch it via the internet |
 | `log_tokenization` | `false`      | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
-| `nsfw_checker` | `true`     | Activate the NSFW checker to blur out risque images |
 | `patchmatch` | `true`     | Activate the "patchmatch" algorithm for improved inpainting |
 | `restore`    | `true`     | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
 
diff --git a/docs/features/NODES.md b/docs/features/NODES.md
index 94663d9f1d..eef71eb974 100644
--- a/docs/features/NODES.md
+++ b/docs/features/NODES.md
@@ -61,11 +61,13 @@ A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from
 | ImageInverseLerp                   | Inverse linear interpolation of all pixels of an image |
 | ImageLerp                          | Linear interpolation of all pixels of an image |
 | ImageMultiply                      | Multiplies two images together using `PIL.ImageChops.Multiply()` |
+| ImageNSFWBlurInvocation            | Detects and blurs images that may contain sexually explicit content |
 | ImagePaste                         | Pastes an image into another image |
 | ImageProcessor                     | Base class for invocations that reprocess images for ControlNet |
 | ImageResize                        | Resizes an image to specific dimensions |
 | ImageScale                         | Scales an image by a factor |
 | ImageToLatents                     | Scales latents by a given factor |
+| ImageWatermarkInvocation           | Adds an invisible watermark to images |
 | InfillColor                        | Infills transparent areas of an image with a solid color |
 | InfillPatchMatch                   | Infills transparent areas of an image using the PatchMatch algorithm |
 | InfillTile                         | Infills transparent areas of an image with tiles of the image |
diff --git a/docs/features/OTHER.md b/docs/features/OTHER.md
index 2401f644ba..00bfd05e14 100644
--- a/docs/features/OTHER.md
+++ b/docs/features/OTHER.md
@@ -16,21 +16,24 @@ Output Example:
 
 ---
 
-## **Seamless Tiling**
+## **Invisible Watermark**
 
-The seamless tiling mode causes generated images to seamlessly tile
-with itself creating repetitive wallpaper-like patterns. To use it,
-activate the Seamless Tiling option in the Web GUI and then select
-whether to tile on the X (horizontal) and/or Y (vertical) axes. Tiling
-will then be active for the next set of generations.
+In keeping with the principles for responsible AI generation, and to
+help AI researchers avoid synthetic images contaminating their
+training sets, InvokeAI adds an invisible watermark to each of the
+final images it generates. The watermark consists of the text
+"InvokeAI" and can be viewed using the
+[invisible-watermarks](https://github.com/ShieldMnt/invisible-watermark)
+tool.
 
-A nice prompt to test seamless tiling with is:
+Watermarking is controlled using the `invisible-watermark` setting in
+`invokeai.yaml`. To turn it off, add the following line under the `Features`
+category.
 
 ```
-pond garden with lotus by claude monet"
+invisible_watermark: false
 ```
 
----
 
 ## **Weighted Prompts**
 
@@ -39,34 +42,10 @@ priority to them, by adding `:<percent>` to the end of the section you wish to u
 example consider this prompt:
 
 ```bash
-tabby cat:0.25 white duck:0.75 hybrid
+(tabby cat):0.25 (white duck):0.75 hybrid
 ```
 
 This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
 on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
 combination of integers and floating point numbers, and they do not need to add up to 1.
 
-## **Thresholding and Perlin Noise Initialization Options**
-
-Under the Noise section of the Web UI, you will find two options named
-Perlin Noise and Noise Threshold. [Perlin
-noise](https://en.wikipedia.org/wiki/Perlin_noise) is a type of
-structured noise used to simulate terrain and other natural
-textures. The slider controls the percentage of perlin noise that will
-be mixed into the image at the beginning of generation. Adding a little
-perlin noise to a generation will alter the image substantially.
-
-The noise threshold limits the range of the latent values during
-sampling and helps combat the oversharpening seem with higher CFG
-scale values.
-
-For better intuition into what these options do in practice:
-
-![here is a graphic demonstrating them both](../assets/truncation_comparison.jpg)
-
-In generating this graphic, perlin noise at initialization was
-programmatically varied going across on the diagram by values 0.0,
-0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied
-going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are
-fixed using the prompt "a portrait of a beautiful young lady" a CFG of
-20, 100 steps, and a seed of 1950357039.
diff --git a/docs/features/NSFW.md b/docs/features/WATERMARK+NSFW.md
similarity index 62%
rename from docs/features/NSFW.md
rename to docs/features/WATERMARK+NSFW.md
index f0539cc6a9..c837d66b57 100644
--- a/docs/features/NSFW.md
+++ b/docs/features/WATERMARK+NSFW.md
@@ -1,12 +1,40 @@
 ---
-title: The NSFW Checker
+title: Watermarking, NSFW Image Checking
 ---
 
-# :material-image-off: NSFW Checker
+# :material-image-off: Invisible Watermark and the NSFW Checker
+
+## Watermarking
+
+InvokeAI does not apply watermarking to images by default. However,
+many computer scientists working in the field of generative AI worry
+that a flood of computer-generated imagery will contaminate the image
+data sets needed to train future generations of generative models.
+
+InvokeAI offers an optional watermarking mode that writes a small bit
+of text, **InvokeAI**, into each image that it generates using an
+"invisible" watermarking library that spreads the information
+throughout the image in a way that is not perceptible to the human
+eye. If you are planning to share your generated images on
+internet-accessible services, we encourage you to activate the
+invisible watermark mode in order to help preserve the digital image
+environment.
+
+The downside of watermarking is that it increases the size of the
+image moderately, and has been reported by some individuals to degrade
+image quality. Your mileage may vary.
+
+To read the watermark in an image, activate the InvokeAI virtual
+environment (called the "developer's console" in the launcher) and run
+the command:
+
+```
+invisible-watermark -a decode -t bytes -m dwtDct -l 64 /path/to/image.png
+```
 
 ## The NSFW ("Safety") Checker
 
-The Stable Diffusion image generation models will produce sexual
+Stable Diffusion 1.5-based image generation models will produce sexual
 imagery if deliberately prompted, and will occasionally produce such
 images when this is not intended. Such images are colloquially known
 as "Not Safe for Work" (NSFW). This behavior is due to the nature of
@@ -18,35 +46,17 @@ jurisdictions it may be illegal to publicly distribute such imagery,
 including mounting a publicly-available server that provides
 unfiltered images to the public. Furthermore, the [Stable Diffusion
 weights
-License](https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-ModelWeights.txt)
-forbids the model from being used to "exploit any of the
+License](https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-SD1+SD2.txt),
+and the [Stable Diffusion XL
+License][https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-SDXL.txt]
+both forbid the models from being used to "exploit any of the
 vulnerabilities of a specific group of persons."
 
 For these reasons Stable Diffusion offers a "safety checker," a
 machine learning model trained to recognize potentially disturbing
 imagery. When a potentially NSFW image is detected, the checker will
 blur the image and paste a warning icon on top. The checker can be
-turned on and off on the command line using `--nsfw_checker` and
-`--no-nsfw_checker`.
-
-At installation time, InvokeAI will ask whether the checker should be
-activated by default (neither argument given on the command line). The
-response is stored in the InvokeAI initialization file
-(`invokeai.yaml` in the InvokeAI root directory).  You can change the
-default at any time by opening this file in a text editor and
-changing the line `nsfw_checker:` from true to false or vice-versa:
-
-
-```
-...
-  Features:
-    esrgan: true
-    internet_available: true
-    log_tokenization: false
-    nsfw_checker: true
-    patchmatch: true
-    restore: true
-```
+turned on and off in the Web interface under Settings.
 
 ## Caveats
 
@@ -84,10 +94,3 @@ are encouraged to turn **off** intermediate image rendering when you
 are using the checker. Future versions of InvokeAI will apply
 additional blurring to intermediate images when the checker is active.
 
-### Watermarking
-
-InvokeAI does not apply any sort of watermark to images it
-generates. However, it does write metadata into the PNG data area,
-including the prompt used to generate the image and relevant parameter
-settings. These fields can be examined using the `sd-metadata.py`
-script that comes with the InvokeAI package.
diff --git a/docs/index.md b/docs/index.md
index ce26f1f1f3..5e62445f89 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -80,11 +80,11 @@ Q&A</a>]
 
 !!! note
 
-    This fork is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
+    This software is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
 
 ## :octicons-package-dependencies-24: Installation
 
-This fork is supported across Linux, Windows and Macintosh. Linux users can use
+This software is supported across Linux, Windows and Macintosh. Linux users can use
 either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
@@ -95,6 +95,8 @@ driver).
 This method is recommended for experienced users and developers
 #### [Docker Installation](installation/040_INSTALL_DOCKER.md)
 This method is recommended for those familiar with running Docker containers
+#### [Installation Troubleshooting](installation/010_INSTALL_AUTOMATED.md#troubleshooting)
+Installation troubleshooting guide.
 ### Other Installation Guides
   - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
   - [XFormers](installation/070_INSTALL_XFORMERS.md)
@@ -148,7 +150,7 @@ images in full-precision mode:
 - [Model Merging](features/MODEL_MERGING.md)
 - [ControlNet Models](features/CONTROLNET.md)
 - [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
-- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
+- [Watermarking and the Not Safe for Work (NSFW) Checker](features/WATERMARK+NSFW.md)
 <!-- seperator -->
 ### Prompt Engineering
 - [Prompt Syntax](features/PROMPTS.md)
@@ -230,7 +232,7 @@ encouraged to do so.
 
 ## :octicons-person-24: Contributors
 
-This fork is a combined effort of various people from across the world.
+This software is a combined effort of various people from across the world.
 [Check out the list of all these amazing people](other/CONTRIBUTORS.md). We
 thank them for their time, hard work and effort.
 
diff --git a/docs/installation/010_INSTALL_AUTOMATED.md b/docs/installation/010_INSTALL_AUTOMATED.md
index 8072364b55..79203f066c 100644
--- a/docs/installation/010_INSTALL_AUTOMATED.md
+++ b/docs/installation/010_INSTALL_AUTOMATED.md
@@ -40,10 +40,8 @@ experimental versions later.
     this, open up a command-line window ("Terminal" on Linux and
     Macintosh, "Command" or "Powershell" on Windows) and type `python
     --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.*` or `3.10.*`, you meet
-    requirements. We do not recommend using Python 3.11 or higher,
-    as not all the libraries that InvokeAI depends on work properly
-    with this version.
+    number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
+    requirements.
 
     !!! warning "What to do if you have an unsupported version"
 
@@ -215,17 +213,6 @@ experimental versions later.
     Generally the defaults are fine, and you can come back to this screen at
     any time to tweak your system. Here are the options you can adjust:
 
-    - ***Output directory for images***
-      This is the path to a directory in which InvokeAI will store all its
-      generated images.
-
-    - ***NSFW checker***
-      If checked, InvokeAI will test images for potential sexual content
-      and blur them out if found. Note that the NSFW checker consumes
-      an additional 0.6 GB of VRAM on top of the 2-3 GB of VRAM used
-      by most image models. If you have a low VRAM GPU (4-6 GB), you
-      can reduce out of memory errors by disabling the checker.
-
     - ***HuggingFace Access Token***
       InvokeAI has the ability to download embedded styles and subjects
       from the HuggingFace Concept Library on-demand. However, some of
@@ -257,20 +244,30 @@ experimental versions later.
 	and graphics cards. The "autocast" option is deprecated and
 	shouldn't be used unless you are asked to by a member of the team.
 
-    - ***Number of models to cache in CPU memory***
+    - **Size of the RAM cache used for fast model switching***
         This allows you to keep models in memory and switch rapidly among
 	them rather than having them load from disk each time. This slider
-	controls how many models to keep loaded at once. Each
-	model will use 2-4 GB of RAM, so use this cautiously
+	controls how many models to keep loaded at once. A typical SD-1 or SD-2 model
+	uses 2-3 GB of memory. A typical SDXL model uses 6-7 GB. Providing more
+	RAM will allow more models to be co-resident.
 
-    - ***Directory containing embedding/textual inversion files***
-        This is the directory in which you can place custom embedding
-	files (.pt or .bin). During startup, this directory will be
-	scanned and InvokeAI will print out the text terms that
-	are available to trigger the embeddings.
+    - ***Output directory for images***
+      This is the path to a directory in which InvokeAI will store all its
+      generated images.
+
+    - ***Autoimport Folder***
+      This is the directory in which you can place models you have
+	  downloaded and wish to load into InvokeAI. You can place a variety
+	  of models in this directory, including diffusers folders, .ckpt files,
+	  .safetensors files, as well as LoRAs, ControlNet and Textual Inversion
+	  files (both folder and file versions). To help organize this folder,
+	  you can create several levels of subfolders and drop your models into
+      whichever ones you want.
+	
+    - ***Autoimport FolderLICENSE***     
 
     At the bottom of the screen you will see a checkbox for accepting
-    the CreativeML Responsible AI License. You need to accept the license
+    the CreativeML Responsible AI Licenses. You need to accept the license
     in order to download Stable Diffusion models from the next screen.
 
     _You can come back to the startup options form_ as many times as you like.
@@ -375,8 +372,71 @@ experimental versions later.
         Once InvokeAI is installed, do not move or remove this directory."
 
 
+<a name="troubleshooting"></a>
 ## Troubleshooting
 
+### _OSErrors on Windows while installing dependencies_
+
+During a zip file installation or an online update, installation stops
+with an error like this:
+
+![broken-dependency-screenshot](../assets/troubleshooting/broken-dependency.png){:width="800px"}
+
+This seems to happen particularly often with the `pydantic` and
+`numpy` packages. The most reliable solution requires several manual
+steps to complete installation.
+
+Open up a Powershell window and navigate to the `invokeai` directory
+created by the installer. Then give the following series of commands:
+
+```cmd
+rm .\.venv -r -force
+python -mvenv .venv
+.\.venv\Scripts\activate
+pip install invokeai
+invokeai-configure --root .
+```
+
+If you see anything marked as an error during this process please stop
+and seek help on the Discord [installation support
+channel](https://discord.com/channels/1020123559063990373/1041391462190956654). A
+few warning messages are OK.
+
+If you are updating from a previous version, this should restore your
+system to a working state. If you are installing from scratch, there
+is one additional command to give:
+
+```cmd
+wget -O invoke.bat https://raw.githubusercontent.com/invoke-ai/InvokeAI/main/installer/templates/invoke.bat.in
+```
+
+This will create the `invoke.bat` script needed to launch InvokeAI and
+its related programs.
+
+
+### _Stable Diffusion XL Generation Fails after Trying to Load unet_
+
+InvokeAI is working in other respects, but when trying to generate
+images with Stable Diffusion XL you get a "Server Error". The text log
+in the launch window contains this log line above several more lines of
+error messages:
+
+```INFO --> Loading model:D:\LONG\PATH\TO\MODEL, type sdxl:main:unet```
+
+This failure mode occurs when there is a network glitch during
+downloading the very large SDXL model.
+
+To address this, first go to the Web Model Manager and delete the
+Stable-Diffusion-XL-base-1.X model. Then navigate to HuggingFace and
+manually download the .safetensors version of the model. The 1.0
+version is located at
+https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main
+and the file is named `sd_xl_base_1.0.safetensors`.
+
+Save this file to disk and then reenter the Model Manager. Navigate to
+Import Models->Add Model, then type (or drag-and-drop) the path to the
+.safetensors file. Press "Add Model".
+
 ### _Package dependency conflicts_
 
 If you have previously installed InvokeAI or another Stable Diffusion
diff --git a/docs/installation/020_INSTALL_MANUAL.md b/docs/installation/020_INSTALL_MANUAL.md
index 75309c5fea..c0fb4c046f 100644
--- a/docs/installation/020_INSTALL_MANUAL.md
+++ b/docs/installation/020_INSTALL_MANUAL.md
@@ -32,7 +32,7 @@ gaming):
 
 * **Python**
 
-    version 3.9 or 3.10 (3.11 is not recommended).
+    version 3.9 through 3.11
 
 * **CUDA Tools**
 
@@ -65,7 +65,7 @@ gaming):
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Please make sure you are using Python 3.9 or 3.10. The rest of the install
+1.  Please make sure you are using Python 3.9 through 3.11. The rest of the install
     procedure depends on this and will not work with other versions:
 
     ```bash
diff --git a/installer/install.sh.in b/installer/install.sh.in
index 0063c0ebbf..1b8ba92ea6 100755
--- a/installer/install.sh.in
+++ b/installer/install.sh.in
@@ -9,16 +9,20 @@ cd $scriptdir
 function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
 
 MINIMUM_PYTHON_VERSION=3.9.0
-MAXIMUM_PYTHON_VERSION=3.11.0
+MAXIMUM_PYTHON_VERSION=3.11.100
 PYTHON=""
-for candidate in python3.10 python3.9 python3 python ; do
+for candidate in python3.11 python3.10 python3.9 python3 python ; do
     if ppath=`which $candidate`; then
+        # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
+        # we check that this found executable can actually run
+        if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
+
         python_version=$($ppath -V | awk '{ print $2 }')
         if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
-	    if [ $(version $python_version) -lt $(version "$MAXIMUM_PYTHON_VERSION") ]; then
-		PYTHON=$ppath
-		break
-	    fi
+            if [ $(version $python_version) -le $(version "$MAXIMUM_PYTHON_VERSION") ]; then
+                PYTHON=$ppath
+                break
+            fi
         fi
     fi
 done
diff --git a/installer/lib/installer.py b/installer/lib/installer.py
index 5904a71968..e1ca8c2e8f 100644
--- a/installer/lib/installer.py
+++ b/installer/lib/installer.py
@@ -141,15 +141,16 @@ class Installer:
 
         # upgrade pip in Python 3.9 environments
         if int(platform.python_version_tuple()[1]) == 9:
-
             from plumbum import FG, local
 
             pip = local[get_pip_from_venv(venv_dir)]
-            pip[ "install", "--upgrade", "pip"] & FG
+            pip["install", "--upgrade", "pip"] & FG
 
         return venv_dir
 
-    def install(self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None) -> None:
+    def install(
+        self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None
+    ) -> None:
         """
         Install the InvokeAI application into the given runtime path
 
@@ -175,7 +176,7 @@ class Installer:
         self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
 
         # install dependencies and the InvokeAI application
-        (extra_index_url,optional_modules) = get_torch_source() if not yes_to_all else (None,None)
+        (extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
         self.instance.install(
             extra_index_url,
             optional_modules,
@@ -188,6 +189,7 @@ class Installer:
         # run through the configuration flow
         self.instance.configure()
 
+
 class InvokeAiInstance:
     """
     Manages an installed instance of InvokeAI, comprising a virtual environment and a runtime directory.
@@ -196,7 +198,6 @@ class InvokeAiInstance:
     """
 
     def __init__(self, runtime: Path, venv: Path, version: str) -> None:
-
         self.runtime = runtime
         self.venv = venv
         self.pip = get_pip_from_venv(venv)
@@ -312,7 +313,7 @@ class InvokeAiInstance:
                 "install",
                 "--require-virtualenv",
                 "--use-pep517",
-                str(src)+(optional_modules if optional_modules else ''),
+                str(src) + (optional_modules if optional_modules else ""),
                 "--find-links" if find_links is not None else None,
                 find_links,
                 "--extra-index-url" if extra_index_url is not None else None,
@@ -329,15 +330,15 @@ class InvokeAiInstance:
 
         # set sys.argv to a consistent state
         new_argv = [sys.argv[0]]
-        for i in range(1,len(sys.argv)):
+        for i in range(1, len(sys.argv)):
             el = sys.argv[i]
-            if el in ['-r','--root']:
+            if el in ["-r", "--root"]:
                 new_argv.append(el)
-                new_argv.append(sys.argv[i+1])
-            elif el in ['-y','--yes','--yes-to-all']:
+                new_argv.append(sys.argv[i + 1])
+            elif el in ["-y", "--yes", "--yes-to-all"]:
                 new_argv.append(el)
         sys.argv = new_argv
-        
+
         import requests  # to catch download exceptions
         from messages import introduction
 
@@ -353,16 +354,16 @@ class InvokeAiInstance:
             invokeai_configure()
             succeeded = True
         except requests.exceptions.ConnectionError as e:
-            print(f'\nA network error was encountered during configuration and download: {str(e)}')
+            print(f"\nA network error was encountered during configuration and download: {str(e)}")
         except OSError as e:
-            print(f'\nAn OS error was encountered during configuration and download: {str(e)}')
+            print(f"\nAn OS error was encountered during configuration and download: {str(e)}")
         except Exception as e:
-            print(f'\nA problem was encountered during the configuration and download steps: {str(e)}')
+            print(f"\nA problem was encountered during the configuration and download steps: {str(e)}")
         finally:
             if not succeeded:
                 print('To try again, find the "invokeai" directory, run the script "invoke.sh" or "invoke.bat"')
-                print('and choose option 7 to fix a broken install, optionally followed by option 5 to install models.')
-                print('Alternatively you can relaunch the installer.')
+                print("and choose option 7 to fix a broken install, optionally followed by option 5 to install models.")
+                print("Alternatively you can relaunch the installer.")
 
     def install_user_scripts(self):
         """
@@ -371,11 +372,11 @@ class InvokeAiInstance:
 
         ext = "bat" if OS == "Windows" else "sh"
 
-        #scripts = ['invoke', 'update']
-        scripts = ['invoke']
-        
+        # scripts = ['invoke', 'update']
+        scripts = ["invoke"]
+
         for script in scripts:
-            src = Path(__file__).parent / '..' / "templates" / f"{script}.{ext}.in"
+            src = Path(__file__).parent / ".." / "templates" / f"{script}.{ext}.in"
             dest = self.runtime / f"{script}.{ext}"
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
@@ -420,11 +421,7 @@ def set_sys_path(venv_path: Path) -> None:
     # filter out any paths in sys.path that may be system- or user-wide
     # but leave the temporary bootstrap virtualenv as it contains packages we
     # temporarily need at install time
-    sys.path = list(filter(
-        lambda p: not p.endswith("-packages")
-        or p.find(BOOTSTRAP_VENV_PREFIX) != -1,
-        sys.path
-    ))
+    sys.path = list(filter(lambda p: not p.endswith("-packages") or p.find(BOOTSTRAP_VENV_PREFIX) != -1, sys.path))
 
     # determine site-packages/lib directory location for the venv
     lib = "Lib" if OS == "Windows" else f"lib/python{sys.version_info.major}.{sys.version_info.minor}"
@@ -433,7 +430,7 @@ def set_sys_path(venv_path: Path) -> None:
     sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
 
 
-def get_torch_source() -> (Union[str, None],str):
+def get_torch_source() -> (Union[str, None], str):
     """
     Determine the extra index URL for pip to use for torch installation.
     This depends on the OS and the graphics accelerator in use.
@@ -461,9 +458,9 @@ def get_torch_source() -> (Union[str, None],str):
         elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 
-    if device == 'cuda':
-        url = 'https://download.pytorch.org/whl/cu117'
-        optional_modules = '[xformers]'
+    if device == "cuda":
+        url = "https://download.pytorch.org/whl/cu117"
+        optional_modules = "[xformers]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 
diff --git a/installer/lib/main.py b/installer/lib/main.py
index 42460bb0e8..b442f49255 100644
--- a/installer/lib/main.py
+++ b/installer/lib/main.py
@@ -41,7 +41,7 @@ if __name__ == "__main__":
         type=Path,
         default=None,
     )
-    
+
     args = parser.parse_args()
 
     inst = Installer()
diff --git a/installer/lib/messages.py b/installer/lib/messages.py
index 196c0e1eb0..3687b52d32 100644
--- a/installer/lib/messages.py
+++ b/installer/lib/messages.py
@@ -36,13 +36,15 @@ else:
 
 
 def welcome():
-
     @group()
     def text():
         if (platform_specific := _platform_specific_help()) != "":
             yield platform_specific
             yield ""
-        yield Text.from_markup("Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.", justify="center")
+        yield Text.from_markup(
+            "Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
+            justify="center",
+        )
 
     console.rule()
     print(
@@ -58,6 +60,7 @@ def welcome():
     )
     console.line()
 
+
 def confirm_install(dest: Path) -> bool:
     if dest.exists():
         print(f":exclamation: Directory {dest} already exists :exclamation:")
@@ -92,7 +95,6 @@ def dest_path(dest=None) -> Path:
     dest_confirmed = confirm_install(dest)
 
     while not dest_confirmed:
-
         # if the given destination already exists, the starting point for browsing is its parent directory.
         # the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
         # if the destination dir does NOT exist, then the user must have changed their mind about the selection.
@@ -300,15 +302,20 @@ def introduction() -> None:
     )
     console.line(2)
 
-def _platform_specific_help()->str:
+
+def _platform_specific_help() -> str:
     if OS == "Darwin":
-        text = Text.from_markup("""[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/].""")
+        text = Text.from_markup(
+            """[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
+        )
     elif OS == "Windows":
-        text = Text.from_markup("""[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
+        text = Text.from_markup(
+            """[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
   1. Double-click on the file [b wheat1]WinLongPathsEnabled.reg[/] in order to
      enable long path support on your system.
   2. Make sure you have the [b wheat1]Visual C++ core libraries[/] installed. If not, install from
-     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]""")
+     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
+        )
     else:
         text = ""
     return text
diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py
index 050d1b091f..a186daedf5 100644
--- a/invokeai/app/api/dependencies.py
+++ b/invokeai/app/api/dependencies.py
@@ -78,9 +78,7 @@ class ApiDependencies:
         image_record_storage = SqliteImageRecordStorage(db_location)
         image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
         names = SimpleNameService()
-        latents = ForwardCacheLatentsStorage(
-            DiskLatentsStorage(f"{output_folder}/latents")
-        )
+        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
 
         board_record_storage = SqliteBoardRecordStorage(db_location)
         board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
@@ -125,9 +123,7 @@ class ApiDependencies:
             boards=boards,
             board_images=board_images,
             queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](
-                filename=db_location, table_name="graphs"
-            ),
+            graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
             graph_execution_manager=graph_execution_manager,
             processor=DefaultInvocationProcessor(),
             configuration=config,
diff --git a/invokeai/app/api/routers/app_info.py b/invokeai/app/api/routers/app_info.py
index e1bfeda4a1..9d9e47d2ef 100644
--- a/invokeai/app/api/routers/app_info.py
+++ b/invokeai/app/api/routers/app_info.py
@@ -1,14 +1,21 @@
+import typing
 from enum import Enum
 from fastapi import Body
 from fastapi.routing import APIRouter
+from pathlib import Path
 from pydantic import BaseModel, Field
 
 from invokeai.backend.image_util.patchmatch import PatchMatch
+from invokeai.backend.image_util.safety_checker import SafetyChecker
+from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
+from invokeai.app.invocations.upscale import ESRGAN_MODELS
+
 from invokeai.version import __version__
 
 from ..dependencies import ApiDependencies
 from invokeai.backend.util.logging import logging
 
+
 class LogLevel(int, Enum):
     NotSet = logging.NOTSET
     Debug = logging.DEBUG
@@ -16,7 +23,13 @@ class LogLevel(int, Enum):
     Warning = logging.WARNING
     Error = logging.ERROR
     Critical = logging.CRITICAL
-    
+
+
+class Upscaler(BaseModel):
+    upscaling_method: str = Field(description="Name of upscaling method")
+    upscaling_models: list[str] = Field(description="List of upscaling models for this method")
+
+
 app_router = APIRouter(prefix="/v1/app", tags=["app"])
 
 
@@ -30,43 +43,62 @@ class AppConfig(BaseModel):
     """App Config Response"""
 
     infill_methods: list[str] = Field(description="List of available infill methods")
+    upscaling_methods: list[Upscaler] = Field(description="List of upscaling methods")
+    nsfw_methods: list[str] = Field(description="List of NSFW checking methods")
+    watermarking_methods: list[str] = Field(description="List of invisible watermark methods")
 
 
-@app_router.get(
-    "/version", operation_id="app_version", status_code=200, response_model=AppVersion
-)
+@app_router.get("/version", operation_id="app_version", status_code=200, response_model=AppVersion)
 async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
-@app_router.get(
-    "/config", operation_id="get_config", status_code=200, response_model=AppConfig
-)
+@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
-    infill_methods = ['tile']
+    infill_methods = ["tile"]
     if PatchMatch.patchmatch_available():
-        infill_methods.append('patchmatch')
-    return AppConfig(infill_methods=infill_methods)
+        infill_methods.append("patchmatch")
+
+    upscaling_models = []
+    for model in typing.get_args(ESRGAN_MODELS):
+        upscaling_models.append(str(Path(model).stem))
+    upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
+
+    nsfw_methods = []
+    if SafetyChecker.safety_checker_available():
+        nsfw_methods.append("nsfw_checker")
+
+    watermarking_methods = []
+    if InvisibleWatermark.invisible_watermark_available():
+        watermarking_methods.append("invisible_watermark")
+
+    return AppConfig(
+        infill_methods=infill_methods,
+        upscaling_methods=[upscaler],
+        nsfw_methods=nsfw_methods,
+        watermarking_methods=watermarking_methods,
+    )
+
 
 @app_router.get(
     "/logging",
     operation_id="get_log_level",
-    responses={200: {"description" : "The operation was successful"}},
-    response_model = LogLevel,
+    responses={200: {"description": "The operation was successful"}},
+    response_model=LogLevel,
 )
-async def get_log_level(
-) -> LogLevel:
+async def get_log_level() -> LogLevel:
     """Returns the log level"""
     return LogLevel(ApiDependencies.invoker.services.logger.level)
 
+
 @app_router.post(
     "/logging",
     operation_id="set_log_level",
-    responses={200: {"description" : "The operation was successful"}},
-    response_model = LogLevel,
+    responses={200: {"description": "The operation was successful"}},
+    response_model=LogLevel,
 )
 async def set_log_level(
-        level: LogLevel = Body(description="New log verbosity level"),
+    level: LogLevel = Body(description="New log verbosity level"),
 ) -> LogLevel:
     """Sets the log verbosity level"""
     ApiDependencies.invoker.services.logger.setLevel(level)
diff --git a/invokeai/app/api/routers/board_images.py b/invokeai/app/api/routers/board_images.py
index 651310af24..6cb073ca7c 100644
--- a/invokeai/app/api/routers/board_images.py
+++ b/invokeai/app/api/routers/board_images.py
@@ -52,4 +52,3 @@ async def remove_board_image(
         return result
     except Exception as e:
         raise HTTPException(status_code=500, detail="Failed to update board")
-
diff --git a/invokeai/app/api/routers/boards.py b/invokeai/app/api/routers/boards.py
index f3de7f4952..69f4a8b3a7 100644
--- a/invokeai/app/api/routers/boards.py
+++ b/invokeai/app/api/routers/boards.py
@@ -18,9 +18,7 @@ class DeleteBoardResult(BaseModel):
     deleted_board_images: list[str] = Field(
         description="The image names of the board-images relationships that were deleted."
     )
-    deleted_images: list[str] = Field(
-        description="The names of the images that were deleted."
-    )
+    deleted_images: list[str] = Field(description="The names of the images that were deleted.")
 
 
 @boards_router.post(
@@ -73,22 +71,16 @@ async def update_board(
 ) -> BoardDTO:
     """Updates a board"""
     try:
-        result = ApiDependencies.invoker.services.boards.update(
-            board_id=board_id, changes=changes
-        )
+        result = ApiDependencies.invoker.services.boards.update(board_id=board_id, changes=changes)
         return result
     except Exception as e:
         raise HTTPException(status_code=500, detail="Failed to update board")
 
 
-@boards_router.delete(
-    "/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult
-)
+@boards_router.delete("/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult)
 async def delete_board(
     board_id: str = Path(description="The id of board to delete"),
-    include_images: Optional[bool] = Query(
-        description="Permanently delete all images on the board", default=False
-    ),
+    include_images: Optional[bool] = Query(description="Permanently delete all images on the board", default=False),
 ) -> DeleteBoardResult:
     """Deletes a board"""
     try:
@@ -96,9 +88,7 @@ async def delete_board(
             deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
                 board_id=board_id
             )
-            ApiDependencies.invoker.services.images.delete_images_on_board(
-                board_id=board_id
-            )
+            ApiDependencies.invoker.services.images.delete_images_on_board(board_id=board_id)
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
             return DeleteBoardResult(
                 board_id=board_id,
@@ -127,9 +117,7 @@ async def delete_board(
 async def list_boards(
     all: Optional[bool] = Query(default=None, description="Whether to list all boards"),
     offset: Optional[int] = Query(default=None, description="The page offset"),
-    limit: Optional[int] = Query(
-        default=None, description="The number of boards per page"
-    ),
+    limit: Optional[int] = Query(default=None, description="The number of boards per page"),
 ) -> Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]]:
     """Gets a list of boards"""
     if all:
diff --git a/invokeai/app/api/routers/images.py b/invokeai/app/api/routers/images.py
index 36e2e3d75d..498a1139e4 100644
--- a/invokeai/app/api/routers/images.py
+++ b/invokeai/app/api/routers/images.py
@@ -40,15 +40,9 @@ async def upload_image(
     response: Response,
     image_category: ImageCategory = Query(description="The category of the image"),
     is_intermediate: bool = Query(description="Whether this is an intermediate image"),
-    board_id: Optional[str] = Query(
-        default=None, description="The board to add this image to, if any"
-    ),
-    session_id: Optional[str] = Query(
-        default=None, description="The session ID associated with this upload, if any"
-    ),
-    crop_visible: Optional[bool] = Query(
-        default=False, description="Whether to crop the image"
-    ),
+    board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
+    session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
+    crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type.startswith("image"):
@@ -115,9 +109,7 @@ async def clear_intermediates() -> int:
 )
 async def update_image(
     image_name: str = Path(description="The name of the image to update"),
-    image_changes: ImageRecordChanges = Body(
-        description="The changes to apply to the image"
-    ),
+    image_changes: ImageRecordChanges = Body(description="The changes to apply to the image"),
 ) -> ImageDTO:
     """Updates an image"""
 
@@ -212,15 +204,11 @@ async def get_image_thumbnail(
     """Gets a thumbnail image file"""
 
     try:
-        path = ApiDependencies.invoker.services.images.get_path(
-            image_name, thumbnail=True
-        )
+        path = ApiDependencies.invoker.services.images.get_path(image_name, thumbnail=True)
         if not ApiDependencies.invoker.services.images.validate_path(path):
             raise HTTPException(status_code=404)
 
-        response = FileResponse(
-            path, media_type="image/webp", content_disposition_type="inline"
-        )
+        response = FileResponse(path, media_type="image/webp", content_disposition_type="inline")
         response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
         return response
     except Exception as e:
@@ -239,9 +227,7 @@ async def get_image_urls(
 
     try:
         image_url = ApiDependencies.invoker.services.images.get_url(image_name)
-        thumbnail_url = ApiDependencies.invoker.services.images.get_url(
-            image_name, thumbnail=True
-        )
+        thumbnail_url = ApiDependencies.invoker.services.images.get_url(image_name, thumbnail=True)
         return ImageUrlsDTO(
             image_name=image_name,
             image_url=image_url,
@@ -257,15 +243,9 @@ async def get_image_urls(
     response_model=OffsetPaginatedResults[ImageDTO],
 )
 async def list_image_dtos(
-    image_origin: Optional[ResourceOrigin] = Query(
-        default=None, description="The origin of images to list."
-    ),
-    categories: Optional[list[ImageCategory]] = Query(
-        default=None, description="The categories of image to include."
-    ),
-    is_intermediate: Optional[bool] = Query(
-        default=None, description="Whether to list intermediate images."
-    ),
+    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
+    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
+    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
     board_id: Optional[str] = Query(
         default=None,
         description="The board id to filter by. Use 'none' to find images without a board.",
diff --git a/invokeai/app/api/routers/models.py b/invokeai/app/api/routers/models.py
index 759f6c9f59..2f59f1dd0f 100644
--- a/invokeai/app/api/routers/models.py
+++ b/invokeai/app/api/routers/models.py
@@ -28,49 +28,52 @@ ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 
+
 class ModelsList(BaseModel):
     models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
 
+
 @models_router.get(
     "/",
     operation_id="list_models",
-    responses={200: {"model": ModelsList }},
+    responses={200: {"model": ModelsList}},
 )
 async def list_models(
     base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
     model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
 ) -> ModelsList:
     """Gets a list of models"""
-    if base_models and len(base_models)>0:
+    if base_models and len(base_models) > 0:
         models_raw = list()
         for base_model in base_models:
             models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
     else:
         models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
-    models = parse_obj_as(ModelsList, { "models": models_raw })
+    models = parse_obj_as(ModelsList, {"models": models_raw})
     return models
 
+
 @models_router.patch(
     "/{base_model}/{model_type}/{model_name}",
     operation_id="update_model",
-    responses={200: {"description" : "The model was updated successfully"},
-               400: {"description" : "Bad request"},
-               404: {"description" : "The model could not be found"},
-               409: {"description" : "There is already a model corresponding to the new name"},
-               },
-    status_code = 200,
-    response_model = UpdateModelResponse,
+    responses={
+        200: {"description": "The model was updated successfully"},
+        400: {"description": "Bad request"},
+        404: {"description": "The model could not be found"},
+        409: {"description": "There is already a model corresponding to the new name"},
+    },
+    status_code=200,
+    response_model=UpdateModelResponse,
 )
 async def update_model(
-        base_model: BaseModelType = Path(description="Base model"),
-        model_type: ModelType = Path(description="The type of model"),
-        model_name: str = Path(description="model name"),
-        info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
+    base_model: BaseModelType = Path(description="Base model"),
+    model_type: ModelType = Path(description="The type of model"),
+    model_name: str = Path(description="model name"),
+    info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
 ) -> UpdateModelResponse:
-    """ Update model contents with a new config. If the model name or base fields are changed, then the model is renamed. """
+    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
     logger = ApiDependencies.invoker.services.logger
 
-    
     try:
         previous_info = ApiDependencies.invoker.services.model_manager.list_model(
             model_name=model_name,
@@ -81,13 +84,13 @@ async def update_model(
         # rename operation requested
         if info.model_name != model_name or info.base_model != base_model:
             ApiDependencies.invoker.services.model_manager.rename_model(
-                base_model = base_model,
-                model_type = model_type,
-                model_name = model_name,
-                new_name = info.model_name,
-                new_base = info.base_model,
+                base_model=base_model,
+                model_type=model_type,
+                model_name=model_name,
+                new_name=info.model_name,
+                new_base=info.base_model,
             )
-            logger.info(f'Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}')
+            logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
             # update information to support an update of attributes
             model_name = info.model_name
             base_model = info.base_model
@@ -96,16 +99,15 @@ async def update_model(
                 base_model=base_model,
                 model_type=model_type,
             )
-            if new_info.get('path') != previous_info.get('path'):  # model manager moved model path during rename - don't overwrite it
-                info.path = new_info.get('path')
-            
+            if new_info.get("path") != previous_info.get(
+                "path"
+            ):  # model manager moved model path during rename - don't overwrite it
+                info.path = new_info.get("path")
+
         ApiDependencies.invoker.services.model_manager.update_model(
-            model_name=model_name,
-            base_model=base_model,
-            model_type=model_type,
-            model_attributes=info.dict()
+            model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info.dict()
         )
-            
+
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
             model_name=model_name,
             base_model=base_model,
@@ -123,49 +125,48 @@ async def update_model(
 
     return model_response
 
+
 @models_router.post(
     "/import",
     operation_id="import_model",
-    responses= {
-        201: {"description" : "The model imported successfully"},
-        404: {"description" : "The model could not be found"},
-        415: {"description" : "Unrecognized file/folder format"},
-        424: {"description" : "The model appeared to import successfully, but could not be found in the model manager"},
-        409: {"description" : "There is already a model corresponding to this path or repo_id"},
+    responses={
+        201: {"description": "The model imported successfully"},
+        404: {"description": "The model could not be found"},
+        415: {"description": "Unrecognized file/folder format"},
+        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
     },
     status_code=201,
-    response_model=ImportModelResponse
+    response_model=ImportModelResponse,
 )
 async def import_model(
-        location: str = Body(description="A model path, repo_id or URL to import"),
-        prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = \
-                Body(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
+    location: str = Body(description="A model path, repo_id or URL to import"),
+    prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
+        description="Prediction type for SDv2 checkpoint files", default="v_prediction"
+    ),
 ) -> ImportModelResponse:
-    """ Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically """
-    
+    """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
+
     items_to_import = {location}
-    prediction_types = { x.value: x for x in SchedulerPredictionType }
+    prediction_types = {x.value: x for x in SchedulerPredictionType}
     logger = ApiDependencies.invoker.services.logger
 
     try:
         installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
-            items_to_import = items_to_import,
-            prediction_type_helper = lambda x: prediction_types.get(prediction_type)
+            items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
         )
         info = installed_models.get(location)
 
         if not info:
             logger.error("Import failed")
             raise HTTPException(status_code=415)
-        
-        logger.info(f'Successfully imported {location}, got {info}')
+
+        logger.info(f"Successfully imported {location}, got {info}")
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=info.name,
-            base_model=info.base_model,
-            model_type=info.model_type
+            model_name=info.name, base_model=info.base_model, model_type=info.model_type
         )
         return parse_obj_as(ImportModelResponse, model_raw)
-    
+
     except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
@@ -175,38 +176,34 @@ async def import_model(
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
-        
+
+
 @models_router.post(
     "/add",
     operation_id="add_model",
-    responses= {
-        201: {"description" : "The model added successfully"},
-        404: {"description" : "The model could not be found"},
-        424: {"description" : "The model appeared to add successfully, but could not be found in the model manager"},
-        409: {"description" : "There is already a model corresponding to this path or repo_id"},
+    responses={
+        201: {"description": "The model added successfully"},
+        404: {"description": "The model could not be found"},
+        424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
     },
     status_code=201,
-    response_model=ImportModelResponse
+    response_model=ImportModelResponse,
 )
 async def add_model(
-        info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
+    info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
 ) -> ImportModelResponse:
-    """ Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
-    
+    """Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
+
     logger = ApiDependencies.invoker.services.logger
 
     try:
         ApiDependencies.invoker.services.model_manager.add_model(
-            info.model_name,
-            info.base_model,
-            info.model_type,
-            model_attributes = info.dict()
+            info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
         )
-        logger.info(f'Successfully added {info.model_name}')
+        logger.info(f"Successfully added {info.model_name}")
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=info.model_name,
-            base_model=info.base_model,
-            model_type=info.model_type
+            model_name=info.model_name, base_model=info.base_model, model_type=info.model_type
         )
         return parse_obj_as(ImportModelResponse, model_raw)
     except ModelNotFoundException as e:
@@ -216,66 +213,66 @@ async def add_model(
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
 
-    
+
 @models_router.delete(
     "/{base_model}/{model_type}/{model_name}",
     operation_id="del_model",
-    responses={
-        204: { "description": "Model deleted successfully" }, 
-        404: { "description": "Model not found" }
-    },
-    status_code = 204,
-    response_model = None,
+    responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
+    status_code=204,
+    response_model=None,
 )
 async def delete_model(
-        base_model: BaseModelType = Path(description="Base model"),
-        model_type: ModelType = Path(description="The type of model"),
-        model_name: str = Path(description="model name"),
+    base_model: BaseModelType = Path(description="Base model"),
+    model_type: ModelType = Path(description="The type of model"),
+    model_name: str = Path(description="model name"),
 ) -> Response:
     """Delete Model"""
     logger = ApiDependencies.invoker.services.logger
-    
+
     try:
-        ApiDependencies.invoker.services.model_manager.del_model(model_name,
-                                                                 base_model = base_model,
-                                                                 model_type = model_type
-                                                                 )
+        ApiDependencies.invoker.services.model_manager.del_model(
+            model_name, base_model=base_model, model_type=model_type
+        )
         logger.info(f"Deleted model: {model_name}")
         return Response(status_code=204)
     except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
 
+
 @models_router.put(
     "/convert/{base_model}/{model_type}/{model_name}",
     operation_id="convert_model",
     responses={
-        200: { "description": "Model converted successfully" },
-        400: {"description" : "Bad request"  },
-        404: { "description": "Model not found"  },
+        200: {"description": "Model converted successfully"},
+        400: {"description": "Bad request"},
+        404: {"description": "Model not found"},
     },
-    status_code = 200,
-    response_model = ConvertModelResponse,
+    status_code=200,
+    response_model=ConvertModelResponse,
 )
 async def convert_model(
-        base_model: BaseModelType = Path(description="Base model"),
-        model_type: ModelType = Path(description="The type of model"),
-        model_name: str = Path(description="model name"),
-        convert_dest_directory: Optional[str] = Query(default=None, description="Save the converted model to the designated directory"),
+    base_model: BaseModelType = Path(description="Base model"),
+    model_type: ModelType = Path(description="The type of model"),
+    model_name: str = Path(description="model name"),
+    convert_dest_directory: Optional[str] = Query(
+        default=None, description="Save the converted model to the designated directory"
+    ),
 ) -> ConvertModelResponse:
     """Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
     logger = ApiDependencies.invoker.services.logger
     try:
         logger.info(f"Converting model: {model_name}")
         dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
-        ApiDependencies.invoker.services.model_manager.convert_model(model_name,
-                                                                     base_model = base_model,
-                                                                     model_type = model_type,
-                                                                     convert_dest_directory = dest,
-                                                                     )
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(model_name,
-                                                                              base_model = base_model,
-                                                                              model_type = model_type)
+        ApiDependencies.invoker.services.model_manager.convert_model(
+            model_name,
+            base_model=base_model,
+            model_type=model_type,
+            convert_dest_directory=dest,
+        )
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name, base_model=base_model, model_type=model_type
+        )
         response = parse_obj_as(ConvertModelResponse, model_raw)
     except ModelNotFoundException as e:
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
@@ -283,91 +280,101 @@ async def convert_model(
         raise HTTPException(status_code=400, detail=str(e))
     return response
 
+
 @models_router.get(
     "/search",
     operation_id="search_for_models",
     responses={
-        200: { "description": "Directory searched successfully" },
-        404: { "description": "Invalid directory path"  },
+        200: {"description": "Directory searched successfully"},
+        404: {"description": "Invalid directory path"},
     },
-    status_code = 200,
-    response_model = List[pathlib.Path]
+    status_code=200,
+    response_model=List[pathlib.Path],
 )
 async def search_for_models(
-        search_path: pathlib.Path = Query(description="Directory path to search for models")
-)->List[pathlib.Path]:
+    search_path: pathlib.Path = Query(description="Directory path to search for models"),
+) -> List[pathlib.Path]:
     if not search_path.is_dir():
-        raise HTTPException(status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory")
+        raise HTTPException(
+            status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
+        )
     return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
 
+
 @models_router.get(
     "/ckpt_confs",
     operation_id="list_ckpt_configs",
     responses={
-        200: { "description" : "paths retrieved successfully" },
+        200: {"description": "paths retrieved successfully"},
     },
-    status_code = 200,
-    response_model = List[pathlib.Path]
+    status_code=200,
+    response_model=List[pathlib.Path],
 )
-async def list_ckpt_configs(
-)->List[pathlib.Path]:
+async def list_ckpt_configs() -> List[pathlib.Path]:
     """Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
     return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
-    
-        
+
+
 @models_router.post(
     "/sync",
     operation_id="sync_to_config",
     responses={
-        201: { "description": "synchronization successful" },
+        201: {"description": "synchronization successful"},
     },
-    status_code = 201,
-    response_model = bool
+    status_code=201,
+    response_model=bool,
 )
-async def sync_to_config(
-)->bool:
+async def sync_to_config() -> bool:
     """Call after making changes to models.yaml, autoimport directories or models directory to synchronize
     in-memory data structures with disk data structures."""
     ApiDependencies.invoker.services.model_manager.sync_to_config()
     return True
-        
+
+
 @models_router.put(
     "/merge/{base_model}",
     operation_id="merge_models",
     responses={
-        200: { "description": "Model converted successfully" },
-        400: { "description": "Incompatible models"  },
-        404: { "description": "One or more models not found"  },
+        200: {"description": "Model converted successfully"},
+        400: {"description": "Incompatible models"},
+        404: {"description": "One or more models not found"},
     },
-    status_code = 200,
-    response_model = MergeModelResponse,
+    status_code=200,
+    response_model=MergeModelResponse,
 )
 async def merge_models(
-        base_model: BaseModelType                  = Path(description="Base model"),
-        model_names: List[str]                     = Body(description="model name", min_items=2, max_items=3),
-        merged_model_name: Optional[str]           = Body(description="Name of destination model"),
-        alpha: Optional[float]                     = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
-        interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
-        force: Optional[bool]                      = Body(description="Force merging of models created with different versions of diffusers", default=False),
-        merge_dest_directory: Optional[str]       = Body(description="Save the merged model to the designated directory (with 'merged_model_name' appended)", default=None)
+    base_model: BaseModelType = Path(description="Base model"),
+    model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
+    merged_model_name: Optional[str] = Body(description="Name of destination model"),
+    alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
+    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
+    force: Optional[bool] = Body(
+        description="Force merging of models created with different versions of diffusers", default=False
+    ),
+    merge_dest_directory: Optional[str] = Body(
+        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
+        default=None,
+    ),
 ) -> MergeModelResponse:
     """Convert a checkpoint model into a diffusers model"""
     logger = ApiDependencies.invoker.services.logger
     try:
         logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
         dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
-        result = ApiDependencies.invoker.services.model_manager.merge_models(model_names,
-                                                                             base_model,
-                                                                             merged_model_name=merged_model_name or "+".join(model_names),
-                                                                             alpha=alpha,
-                                                                             interp=interp,
-                                                                             force=force,
-                                                                             merge_dest_directory = dest
-                                                                             )
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(result.name,
-                                                                              base_model = base_model,
-                                                                              model_type = ModelType.Main,
-                                                                              )
+        result = ApiDependencies.invoker.services.model_manager.merge_models(
+            model_names,
+            base_model,
+            merged_model_name=merged_model_name or "+".join(model_names),
+            alpha=alpha,
+            interp=interp,
+            force=force,
+            merge_dest_directory=dest,
+        )
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            result.name,
+            base_model=base_model,
+            model_type=ModelType.Main,
+        )
         response = parse_obj_as(ConvertModelResponse, model_raw)
     except ModelNotFoundException:
         raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
diff --git a/invokeai/app/api/routers/sessions.py b/invokeai/app/api/routers/sessions.py
index da842a3968..e4ba2a353e 100644
--- a/invokeai/app/api/routers/sessions.py
+++ b/invokeai/app/api/routers/sessions.py
@@ -30,9 +30,7 @@ session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
     },
 )
 async def create_session(
-    graph: Optional[Graph] = Body(
-        default=None, description="The graph to initialize the session with"
-    )
+    graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with")
 ) -> GraphExecutionState:
     """Creates a new session, optionally initializing it with an invocation graph"""
     session = ApiDependencies.invoker.create_execution_state(graph)
@@ -51,13 +49,9 @@ async def list_sessions(
 ) -> PaginatedResults[GraphExecutionState]:
     """Gets a list of sessions, optionally searching"""
     if query == "":
-        result = ApiDependencies.invoker.services.graph_execution_manager.list(
-            page, per_page
-        )
+        result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
     else:
-        result = ApiDependencies.invoker.services.graph_execution_manager.search(
-            query, page, per_page
-        )
+        result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
     return result
 
 
@@ -91,9 +85,9 @@ async def get_session(
 )
 async def add_node(
     session_id: str = Path(description="The id of the session"),
-    node: Annotated[
-        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
-    ] = Body(description="The node to add"),
+    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
+        description="The node to add"
+    ),
 ) -> str:
     """Adds a node to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -124,9 +118,9 @@ async def add_node(
 async def update_node(
     session_id: str = Path(description="The id of the session"),
     node_path: str = Path(description="The path to the node in the graph"),
-    node: Annotated[
-        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
-    ] = Body(description="The new node"),
+    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
+        description="The new node"
+    ),
 ) -> GraphExecutionState:
     """Updates a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -230,7 +224,7 @@ async def delete_edge(
     try:
         edge = Edge(
             source=EdgeConnection(node_id=from_node_id, field=from_field),
-            destination=EdgeConnection(node_id=to_node_id, field=to_field)
+            destination=EdgeConnection(node_id=to_node_id, field=to_field),
         )
         session.delete_edge(edge)
         ApiDependencies.invoker.services.graph_execution_manager.set(
@@ -255,9 +249,7 @@ async def delete_edge(
 )
 async def invoke_session(
     session_id: str = Path(description="The id of the session to invoke"),
-    all: bool = Query(
-        default=False, description="Whether or not to invoke all remaining invocations"
-    ),
+    all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
 ) -> Response:
     """Invokes a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -274,9 +266,7 @@ async def invoke_session(
 @session_router.delete(
     "/{session_id}/invoke",
     operation_id="cancel_session_invoke",
-    responses={
-        202: {"description": "The invocation is canceled"}
-    },
+    responses={202: {"description": "The invocation is canceled"}},
 )
 async def cancel_session_invoke(
     session_id: str = Path(description="The id of the session to cancel"),
diff --git a/invokeai/app/api/sockets.py b/invokeai/app/api/sockets.py
index f70d7a6609..4591bac540 100644
--- a/invokeai/app/api/sockets.py
+++ b/invokeai/app/api/sockets.py
@@ -16,9 +16,7 @@ class SocketIO:
         self.__sio.on("subscribe", handler=self._handle_sub)
         self.__sio.on("unsubscribe", handler=self._handle_unsub)
 
-        local_handler.register(
-            event_name=EventServiceBase.session_event, _func=self._handle_session_event
-        )
+        local_handler.register(event_name=EventServiceBase.session_event, _func=self._handle_session_event)
 
     async def _handle_session_event(self, event: Event):
         await self.__sio.emit(
diff --git a/invokeai/app/api_app.py b/invokeai/app/api_app.py
index 824b697d54..6b875d37ce 100644
--- a/invokeai/app/api_app.py
+++ b/invokeai/app/api_app.py
@@ -3,6 +3,7 @@ import asyncio
 import sys
 from inspect import signature
 
+import logging
 import uvicorn
 import socket
 
@@ -16,9 +17,10 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
 from pathlib import Path
 from pydantic.schema import schema
 
-#This should come early so that modules can log their initialization properly
+# This should come early so that modules can log their initialization properly
 from .services.config import InvokeAIAppConfig
 from ..backend.util.logging import InvokeAILogger
+
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
 logger = InvokeAILogger.getLogger(config=app_config)
@@ -27,7 +29,7 @@ from invokeai.version.invokeai_version import __version__
 # we call this early so that the message appears before
 # other invokeai initialization messages
 if app_config.version:
-    print(f'InvokeAI version {__version__}')
+    print(f"InvokeAI version {__version__}")
     sys.exit(0)
 
 import invokeai.frontend.web as web_dir
@@ -37,17 +39,18 @@ from .api.dependencies import ApiDependencies
 from .api.routers import sessions, models, images, boards, board_images, app_info
 from .api.sockets import SocketIO
 from .invocations.baseinvocation import BaseInvocation
-    
+
 
 import torch
 import invokeai.backend.util.hotfixes
+
 if torch.backends.mps.is_available():
     import invokeai.backend.util.mps_fixes
 
 # fix for windows mimetypes registry entries being borked
 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
-mimetypes.add_type('application/javascript', '.js')
-mimetypes.add_type('text/css', '.css')
+mimetypes.add_type("application/javascript", ".js")
+mimetypes.add_type("text/css", ".css")
 
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
@@ -57,14 +60,13 @@ app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
 event_handler_id: int = id(app)
 app.add_middleware(
     EventHandlerASGIMiddleware,
-    handlers=[
-        local_handler
-    ],  # TODO: consider doing this in services to support different configurations
+    handlers=[local_handler],  # TODO: consider doing this in services to support different configurations
     middleware_id=event_handler_id,
 )
 
 socket_io = SocketIO(app)
 
+
 # Add startup event to load dependencies
 @app.on_event("startup")
 async def startup_event():
@@ -76,9 +78,7 @@ async def startup_event():
         allow_headers=app_config.allow_headers,
     )
 
-    ApiDependencies.initialize(
-        config=app_config, event_handler_id=event_handler_id, logger=logger
-    )
+    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
 
 
 # Shut down threads
@@ -103,7 +103,8 @@ app.include_router(boards.boards_router, prefix="/api")
 
 app.include_router(board_images.board_images_router, prefix="/api")
 
-app.include_router(app_info.app_router, prefix='/api')
+app.include_router(app_info.app_router, prefix="/api")
+
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
@@ -144,6 +145,7 @@ def custom_openapi():
         invoker_schema["output"] = outputs_ref
 
     from invokeai.backend.model_management.models import get_model_config_enums
+
     for model_config_format_enum in set(get_model_config_enums()):
         name = model_config_format_enum.__qualname__
 
@@ -166,7 +168,8 @@ def custom_openapi():
 app.openapi = custom_openapi
 
 # Override API doc favicons
-app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], 'static/dream_web')), name="static")
+app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
+
 
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
@@ -187,11 +190,8 @@ def overridden_redoc():
 
 
 # Must mount *after* the other routes else it borks em
-app.mount("/", 
-          StaticFiles(directory=Path(web_dir.__path__[0],"dist"), 
-                      html=True
-                     ), name="ui"
-         )
+app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
+
 
 def invoke_api():
     def find_port(port: int):
@@ -204,15 +204,34 @@ def invoke_api():
             else:
                 return port
 
+    from invokeai.backend.install.check_root import check_invokeai_root
+
+    check_invokeai_root(app_config)  # note, may exit with an exception if root not set up
+
     port = find_port(app_config.port)
     if port != app_config.port:
         logger.warn(f"Port {app_config.port} in use, using port {port}")
+
     # Start our own event loop for eventing usage
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(app=app, host=app_config.host, port=port, loop=loop)
-    # Use access_log to turn off logging
+    config = uvicorn.Config(
+        app=app,
+        host=app_config.host,
+        port=port,
+        loop=loop,
+        log_level=app_config.log_level,
+    )
     server = uvicorn.Server(config)
+
+    # replace uvicorn's loggers with InvokeAI's for consistent appearance
+    for logname in ["uvicorn.access", "uvicorn"]:
+        l = logging.getLogger(logname)
+        l.handlers.clear()
+        for ch in logger.handlers:
+            l.addHandler(ch)
+
     loop.run_until_complete(server.serve())
 
+
 if __name__ == "__main__":
     invoke_api()
diff --git a/invokeai/assets/web/caution.png b/invokeai/app/assets/images/caution.png
similarity index 100%
rename from invokeai/assets/web/caution.png
rename to invokeai/app/assets/images/caution.png
diff --git a/invokeai/app/cli/commands.py b/invokeai/app/cli/commands.py
index bffb2988dc..64ea6034fc 100644
--- a/invokeai/app/cli/commands.py
+++ b/invokeai/app/cli/commands.py
@@ -14,8 +14,14 @@ from ..services.graph import GraphExecutionState, LibraryGraph, Edge
 from ..services.invoker import Invoker
 
 
-def add_field_argument(command_parser, name: str, field, default_override = None):
-    default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
+def add_field_argument(command_parser, name: str, field, default_override=None):
+    default = (
+        default_override
+        if default_override is not None
+        else field.default
+        if field.default_factory is None
+        else field.default_factory()
+    )
     if get_origin(field.type_) == Literal:
         allowed_values = get_args(field.type_)
         allowed_types = set()
@@ -47,8 +53,8 @@ def add_parsers(
     commands: list[type],
     command_field: str = "type",
     exclude_fields: list[str] = ["id", "type"],
-    add_arguments: Union[Callable[[argparse.ArgumentParser], None],None] = None
-    ):
+    add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None,
+):
     """Adds parsers for each command to the subparsers"""
 
     # Create subparsers for each command
@@ -61,7 +67,7 @@ def add_parsers(
             add_arguments(command_parser)
 
         # Convert all fields to arguments
-        fields = command.__fields__ # type: ignore
+        fields = command.__fields__  # type: ignore
         for name, field in fields.items():
             if name in exclude_fields:
                 continue
@@ -70,13 +76,11 @@ def add_parsers(
 
 
 def add_graph_parsers(
-    subparsers,
-    graphs: list[LibraryGraph],
-    add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
+    subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
 ):
     for graph in graphs:
         command_parser = subparsers.add_parser(graph.name, help=graph.description)
-        
+
         if add_arguments is not None:
             add_arguments(command_parser)
 
@@ -128,6 +132,7 @@ class CliContext:
 
 class ExitCli(Exception):
     """Exception to exit the CLI"""
+
     pass
 
 
@@ -155,7 +160,7 @@ class BaseCommand(ABC, BaseModel):
     @classmethod
     def get_commands_map(cls):
         # Get the type strings out of the literals and into a dictionary
-        return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseCommand.get_all_subclasses()))
+        return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses()))
 
     @abstractmethod
     def run(self, context: CliContext) -> None:
@@ -165,7 +170,8 @@ class BaseCommand(ABC, BaseModel):
 
 class ExitCommand(BaseCommand):
     """Exits the CLI"""
-    type: Literal['exit'] = 'exit'
+
+    type: Literal["exit"] = "exit"
 
     def run(self, context: CliContext) -> None:
         raise ExitCli()
@@ -173,7 +179,8 @@ class ExitCommand(BaseCommand):
 
 class HelpCommand(BaseCommand):
     """Shows help"""
-    type: Literal['help'] = 'help'
+
+    type: Literal["help"] = "help"
 
     def run(self, context: CliContext) -> None:
         context.parser.print_help()
@@ -183,11 +190,7 @@ def get_graph_execution_history(
     graph_execution_state: GraphExecutionState,
 ) -> Iterable[str]:
     """Gets the history of fully-executed invocations for a graph execution"""
-    return (
-        n
-        for n in reversed(graph_execution_state.executed_history)
-        if n in graph_execution_state.graph.nodes
-    )
+    return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
 
 
 def get_invocation_command(invocation) -> str:
@@ -218,7 +221,8 @@ def get_invocation_command(invocation) -> str:
 
 class HistoryCommand(BaseCommand):
     """Shows the invocation history"""
-    type: Literal['history'] = 'history'
+
+    type: Literal["history"] = "history"
 
     # Inputs
     # fmt: off
@@ -235,7 +239,8 @@ class HistoryCommand(BaseCommand):
 
 class SetDefaultCommand(BaseCommand):
     """Sets a default value for a field"""
-    type: Literal['default'] = 'default'
+
+    type: Literal["default"] = "default"
 
     # Inputs
     # fmt: off
@@ -253,7 +258,8 @@ class SetDefaultCommand(BaseCommand):
 
 class DrawGraphCommand(BaseCommand):
     """Debugs a graph"""
-    type: Literal['draw_graph'] = 'draw_graph'
+
+    type: Literal["draw_graph"] = "draw_graph"
 
     def run(self, context: CliContext) -> None:
         session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
@@ -271,7 +277,8 @@ class DrawGraphCommand(BaseCommand):
 
 class DrawExecutionGraphCommand(BaseCommand):
     """Debugs an execution graph"""
-    type: Literal['draw_xgraph'] = 'draw_xgraph'
+
+    type: Literal["draw_xgraph"] = "draw_xgraph"
 
     def run(self, context: CliContext) -> None:
         session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
@@ -286,6 +293,7 @@ class DrawExecutionGraphCommand(BaseCommand):
         plt.axis("off")
         plt.show()
 
+
 class SortedHelpFormatter(argparse.HelpFormatter):
     def _iter_indented_subactions(self, action):
         try:
diff --git a/invokeai/app/cli/completer.py b/invokeai/app/cli/completer.py
index 79274dab8c..fd10034dd7 100644
--- a/invokeai/app/cli/completer.py
+++ b/invokeai/app/cli/completer.py
@@ -19,8 +19,8 @@ from ..services.invocation_services import InvocationServices
 # singleton object, class variable
 completer = None
 
+
 class Completer(object):
-    
     def __init__(self, model_manager: ModelManager):
         self.commands = self.get_commands()
         self.matches = None
@@ -43,7 +43,7 @@ class Completer(object):
             except IndexError:
                 pass
             options = options or list(self.parse_commands().keys())
-            
+
             if not text:  # first time
                 self.matches = options
             else:
@@ -56,17 +56,17 @@ class Completer(object):
         return match
 
     @classmethod
-    def get_commands(self)->List[object]:
+    def get_commands(self) -> List[object]:
         """
         Return a list of all the client commands and invocations.
         """
         return BaseCommand.get_commands() + BaseInvocation.get_invocations()
 
-    def get_current_command(self, buffer: str)->tuple[str, str]:
+    def get_current_command(self, buffer: str) -> tuple[str, str]:
         """
         Parse the readline buffer to find the most recent command and its switch.
         """
-        if len(buffer)==0:
+        if len(buffer) == 0:
             return None, None
         tokens = shlex.split(buffer)
         command = None
@@ -78,11 +78,11 @@ class Completer(object):
             else:
                 switch = t
         # don't try to autocomplete switches that are already complete
-        if switch and buffer.endswith(' '):
-            switch=None
-        return command or '', switch or ''
+        if switch and buffer.endswith(" "):
+            switch = None
+        return command or "", switch or ""
 
-    def parse_commands(self)->Dict[str, List[str]]:
+    def parse_commands(self) -> Dict[str, List[str]]:
         """
         Return a dict in which the keys are the command name
         and the values are the parameters the command takes.
@@ -90,11 +90,11 @@ class Completer(object):
         result = dict()
         for command in self.commands:
             hints = get_type_hints(command)
-            name = get_args(hints['type'])[0]
-            result.update({name:hints})
+            name = get_args(hints["type"])[0]
+            result.update({name: hints})
         return result
 
-    def get_command_options(self, command: str, switch: str)->List[str]:
+    def get_command_options(self, command: str, switch: str) -> List[str]:
         """
         Return all the parameters that can be passed to the command as
         command-line switches. Returns None if the command is unrecognized.
@@ -102,42 +102,46 @@ class Completer(object):
         parsed_commands = self.parse_commands()
         if command not in parsed_commands:
             return None
-        
+
         # handle switches in the format "-foo=bar"
         argument = None
-        if switch and '=' in switch:
-            switch, argument = switch.split('=')
-            
-        parameter = switch.strip('-')
+        if switch and "=" in switch:
+            switch, argument = switch.split("=")
+
+        parameter = switch.strip("-")
         if parameter in parsed_commands[command]:
             if argument is None:
                 return self.get_parameter_options(parameter, parsed_commands[command][parameter])
             else:
-                return [f"--{parameter}={x}" for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])]
+                return [
+                    f"--{parameter}={x}"
+                    for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])
+                ]
         else:
             return [f"--{x}" for x in parsed_commands[command].keys()]
 
-    def get_parameter_options(self, parameter: str, typehint)->List[str]:
+    def get_parameter_options(self, parameter: str, typehint) -> List[str]:
         """
         Given a parameter type (such as Literal), offers autocompletions.
         """
         if get_origin(typehint) == Literal:
             return get_args(typehint)
-        if parameter == 'model':
+        if parameter == "model":
             return self.manager.model_names()
-        
+
     def _pre_input_hook(self):
         if self.linebuffer:
             readline.insert_text(self.linebuffer)
             readline.redisplay()
             self.linebuffer = None
-    
+
+
 def set_autocompleter(services: InvocationServices) -> Completer:
     global completer
-    
+
     if completer:
         return completer
-    
+
     completer = Completer(services.model_manager)
 
     readline.set_completer(completer.complete)
@@ -162,8 +166,6 @@ def set_autocompleter(services: InvocationServices) -> Completer:
         pass
     except OSError:  # file likely corrupted
         newname = f"{histfile}.old"
-        logger.error(
-            f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
-        )
+        logger.error(f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}")
         histfile.replace(Path(newname))
     atexit.register(readline.write_history_file, histfile)
diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py
index 341e9e5b7e..bad95bb559 100644
--- a/invokeai/app/cli_app.py
+++ b/invokeai/app/cli_app.py
@@ -13,6 +13,7 @@ from pydantic.fields import Field
 # This should come early so that the logger can pick up its configuration options
 from .services.config import InvokeAIAppConfig
 from invokeai.backend.util.logging import InvokeAILogger
+
 config = InvokeAIAppConfig.get_config()
 config.parse_args()
 logger = InvokeAILogger().getLogger(config=config)
@@ -20,7 +21,7 @@ from invokeai.version.invokeai_version import __version__
 
 # we call this early so that the message appears before other invokeai initialization messages
 if config.version:
-    print(f'InvokeAI version {__version__}')
+    print(f"InvokeAI version {__version__}")
     sys.exit(0)
 
 from invokeai.app.services.board_image_record_storage import (
@@ -36,18 +37,21 @@ from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
-from .services.default_graphs import (default_text_to_image_graph_id,
-                                      create_system_graphs)
+from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
 from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
-from .cli.commands import (BaseCommand, CliContext, ExitCli,
-                           SortedHelpFormatter, add_graph_parsers, add_parsers)
+from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
 from .cli.completer import set_autocompleter
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
-from .services.graph import (Edge, EdgeConnection, GraphExecutionState,
-                             GraphInvocation, LibraryGraph,
-                             are_connection_types_compatible)
+from .services.graph import (
+    Edge,
+    EdgeConnection,
+    GraphExecutionState,
+    GraphInvocation,
+    LibraryGraph,
+    are_connection_types_compatible,
+)
 from .services.image_file_storage import DiskImageFileStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
@@ -58,6 +62,7 @@ from .services.sqlite import SqliteItemStorage
 
 import torch
 import invokeai.backend.util.hotfixes
+
 if torch.backends.mps.is_available():
     import invokeai.backend.util.mps_fixes
 
@@ -69,6 +74,7 @@ class CliCommand(BaseModel):
 class InvalidArgs(Exception):
     pass
 
+
 def add_invocation_args(command_parser):
     # Add linking capability
     command_parser.add_argument(
@@ -113,7 +119,7 @@ def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     return parser
 
 
-class NodeField():
+class NodeField:
     alias: str
     node_path: str
     field: str
@@ -126,15 +132,20 @@ class NodeField():
         self.field_type = field_type
 
 
-def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str,NodeField]:
-    return {k:NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
+def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str, NodeField]:
+    return {k: NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
 
 
 def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
     """Gets the node field for the specified field alias"""
     exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
     node_type = type(graph.graph.get_node(exposed_input.node_path))
-    return NodeField(alias=exposed_input.alias, node_path=f'{node_id}.{exposed_input.node_path}', field=exposed_input.field, field_type=get_type_hints(node_type)[exposed_input.field])
+    return NodeField(
+        alias=exposed_input.alias,
+        node_path=f"{node_id}.{exposed_input.node_path}",
+        field=exposed_input.field,
+        field_type=get_type_hints(node_type)[exposed_input.field],
+    )
 
 
 def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
@@ -142,7 +153,12 @@ def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -
     exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
     node_type = type(graph.graph.get_node(exposed_output.node_path))
     node_output_type = node_type.get_output_type()
-    return NodeField(alias=exposed_output.alias, node_path=f'{node_id}.{exposed_output.node_path}', field=exposed_output.field, field_type=get_type_hints(node_output_type)[exposed_output.field])
+    return NodeField(
+        alias=exposed_output.alias,
+        node_path=f"{node_id}.{exposed_output.node_path}",
+        field=exposed_output.field,
+        field_type=get_type_hints(node_output_type)[exposed_output.field],
+    )
 
 
 def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
@@ -165,9 +181,7 @@ def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[st
         return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
 
 
-def generate_matching_edges(
-    a: BaseInvocation, b: BaseInvocation, context: CliContext
-) -> list[Edge]:
+def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliContext) -> list[Edge]:
     """Generates all possible edges between two invocations"""
     afields = get_node_outputs(a, context)
     bfields = get_node_inputs(b, context)
@@ -179,12 +193,14 @@ def generate_matching_edges(
     matching_fields = matching_fields.difference(invalid_fields)
 
     # Validate types
-    matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)]
+    matching_fields = [
+        f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)
+    ]
 
     edges = [
         Edge(
             source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
-            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
+            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field),
         )
         for alias in matching_fields
     ]
@@ -193,6 +209,7 @@ def generate_matching_edges(
 
 class SessionError(Exception):
     """Raised when a session error has occurred"""
+
     pass
 
 
@@ -209,22 +226,23 @@ def invoke_all(context: CliContext):
             context.invoker.services.logger.error(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
-        
+
         raise SessionError()
 
+
 def invoke_cli():
-    logger.info(f'InvokeAI version {__version__}')
+    logger.info(f"InvokeAI version {__version__}")
     # get the optional list of invocations to execute on the command line
     parser = config.get_parser()
-    parser.add_argument('commands',nargs='*')
+    parser.add_argument("commands", nargs="*")
     invocation_commands = parser.parse_args().commands
 
     # get the optional file to read commands from.
     # Simplest is to use it for STDIN
     if infile := config.from_file:
-        sys.stdin = open(infile,"r")
-    
-    model_manager = ModelManagerService(config,logger)
+        sys.stdin = open(infile, "r")
+
+    model_manager = ModelManagerService(config, logger)
 
     events = EventServiceBase()
     output_folder = config.output_path
@@ -234,13 +252,13 @@ def invoke_cli():
         db_location = ":memory:"
     else:
         db_location = config.db_path
-        db_location.parent.mkdir(parents=True,exist_ok=True)
+        db_location.parent.mkdir(parents=True, exist_ok=True)
 
     logger.info(f'InvokeAI database location is "{db_location}"')
 
     graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-            filename=db_location, table_name="graph_executions"
-        )
+        filename=db_location, table_name="graph_executions"
+    )
 
     urls = LocalUrlService()
     image_record_storage = SqliteImageRecordStorage(db_location)
@@ -281,24 +299,21 @@ def invoke_cli():
             graph_execution_manager=graph_execution_manager,
         )
     )
-        
+
     services = InvocationServices(
         model_manager=model_manager,
         events=events,
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
+        latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")),
         images=images,
         boards=boards,
         board_images=board_images,
         queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=db_location, table_name="graphs"
-        ),
+        graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
         graph_execution_manager=graph_execution_manager,
         processor=DefaultInvocationProcessor(),
         logger=logger,
         configuration=config,
     )
-    
 
     system_graphs = create_system_graphs(services.graph_library)
     system_graph_names = set([g.name for g in system_graphs])
@@ -308,7 +323,7 @@ def invoke_cli():
     session: GraphExecutionState = invoker.create_execution_state()
     parser = get_command_parser(services)
 
-    re_negid = re.compile('^-[0-9]+$')
+    re_negid = re.compile("^-[0-9]+$")
 
     # Uncomment to print out previous sessions at startup
     # print(services.session_manager.list())
@@ -318,7 +333,7 @@ def invoke_cli():
 
     command_line_args_exist = len(invocation_commands) > 0
     done = False
-    
+
     while not done:
         try:
             if command_line_args_exist:
@@ -332,7 +347,7 @@ def invoke_cli():
 
         try:
             # Refresh the state of the session
-            #history = list(get_graph_execution_history(context.session))
+            # history = list(get_graph_execution_history(context.session))
             history = list(reversed(context.nodes_added))
 
             # Split the command for piping
@@ -353,17 +368,17 @@ def invoke_cli():
                         args[field_name] = field_default
 
                 # Parse invocation
-                command: CliCommand = None # type:ignore
+                command: CliCommand = None  # type:ignore
                 system_graph: Optional[LibraryGraph] = None
-                if args['type'] in system_graph_names:
-                    system_graph = next(filter(lambda g: g.name == args['type'], system_graphs))
+                if args["type"] in system_graph_names:
+                    system_graph = next(filter(lambda g: g.name == args["type"], system_graphs))
                     invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
                     for exposed_input in system_graph.exposed_inputs:
                         if exposed_input.alias in args:
                             node = invocation.graph.get_node(exposed_input.node_path)
                             field = exposed_input.field
                             setattr(node, field, args[exposed_input.alias])
-                    command = CliCommand(command = invocation)
+                    command = CliCommand(command=invocation)
                     context.graph_nodes[invocation.id] = system_graph.id
                 else:
                     args["id"] = current_id
@@ -385,17 +400,13 @@ def invoke_cli():
                 # Pipe previous command output (if there was a previous command)
                 edges: list[Edge] = list()
                 if len(history) > 0 or current_id != start_id:
-                    from_id = (
-                        history[0] if current_id == start_id else str(current_id - 1)
-                    )
+                    from_id = history[0] if current_id == start_id else str(current_id - 1)
                     from_node = (
                         next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
                         if current_id != start_id
                         else context.session.graph.get_node(from_id)
                     )
-                    matching_edges = generate_matching_edges(
-                        from_node, command.command, context
-                    )
+                    matching_edges = generate_matching_edges(from_node, command.command, context)
                     edges.extend(matching_edges)
 
                 # Parse provided links
@@ -406,16 +417,18 @@ def invoke_cli():
                             node_id = str(current_id + int(node_id))
 
                         link_node = context.session.graph.get_node(node_id)
-                        matching_edges = generate_matching_edges(
-                            link_node, command.command, context
-                        )
+                        matching_edges = generate_matching_edges(link_node, command.command, context)
                         matching_destinations = [e.destination for e in matching_edges]
                         edges = [e for e in edges if e.destination not in matching_destinations]
                         edges.extend(matching_edges)
 
                 if "link" in args and args["link"]:
                     for link in args["link"]:
-                        edges = [e for e in edges if e.destination.node_id != command.command.id or e.destination.field != link[2]]
+                        edges = [
+                            e
+                            for e in edges
+                            if e.destination.node_id != command.command.id or e.destination.field != link[2]
+                        ]
 
                         node_id = link[0]
                         if re_negid.match(node_id):
@@ -428,7 +441,7 @@ def invoke_cli():
                         edges.append(
                             Edge(
                                 source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
-                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
+                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field),
                             )
                         )
 
diff --git a/invokeai/app/invocations/__init__.py b/invokeai/app/invocations/__init__.py
index 0a451ff618..6407a1cdee 100644
--- a/invokeai/app/invocations/__init__.py
+++ b/invokeai/app/invocations/__init__.py
@@ -4,9 +4,5 @@ __all__ = []
 
 dirname = os.path.dirname(os.path.abspath(__file__))
 for f in os.listdir(dirname):
-    if (
-        f != "__init__.py"
-        and os.path.isfile("%s/%s" % (dirname, f))
-        and f[-3:] == ".py"
-    ):
+    if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
         __all__.append(f[:-3])
diff --git a/invokeai/app/invocations/baseinvocation.py b/invokeai/app/invocations/baseinvocation.py
index 4c7314bd2b..758ab2e787 100644
--- a/invokeai/app/invocations/baseinvocation.py
+++ b/invokeai/app/invocations/baseinvocation.py
@@ -4,8 +4,7 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import (TYPE_CHECKING, Dict, List, Literal, TypedDict, get_args,
-                    get_type_hints)
+from typing import TYPE_CHECKING, Dict, List, Literal, TypedDict, get_args, get_type_hints
 
 from pydantic import BaseConfig, BaseModel, Field
 
diff --git a/invokeai/app/invocations/collections.py b/invokeai/app/invocations/collections.py
index 5446757eb0..01c003da96 100644
--- a/invokeai/app/invocations/collections.py
+++ b/invokeai/app/invocations/collections.py
@@ -8,8 +8,7 @@ from pydantic import Field, validator
 from invokeai.app.models.image import ImageField
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext, UIConfig)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext, UIConfig
 
 
 class IntCollectionOutput(BaseInvocationOutput):
@@ -27,8 +26,7 @@ class FloatCollectionOutput(BaseInvocationOutput):
     type: Literal["float_collection"] = "float_collection"
 
     # Outputs
-    collection: list[float] = Field(
-        default=[], description="The float collection")
+    collection: list[float] = Field(default=[], description="The float collection")
 
 
 class ImageCollectionOutput(BaseInvocationOutput):
@@ -37,8 +35,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
     type: Literal["image_collection"] = "image_collection"
 
     # Outputs
-    collection: list[ImageField] = Field(
-        default=[], description="The output images")
+    collection: list[ImageField] = Field(default=[], description="The output images")
 
     class Config:
         schema_extra = {"required": ["type", "collection"]}
@@ -56,10 +53,7 @@ class RangeInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Range",
-                "tags": ["range", "integer", "collection"]
-            },
+            "ui": {"title": "Range", "tags": ["range", "integer", "collection"]},
         }
 
     @validator("stop")
@@ -69,9 +63,7 @@ class RangeInvocation(BaseInvocation):
         return v
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        return IntCollectionOutput(
-            collection=list(range(self.start, self.stop, self.step))
-        )
+        return IntCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
 
 
 class RangeOfSizeInvocation(BaseInvocation):
@@ -86,18 +78,11 @@ class RangeOfSizeInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Sized Range",
-                "tags": ["range", "integer", "size", "collection"]
-            },
+            "ui": {"title": "Sized Range", "tags": ["range", "integer", "size", "collection"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        return IntCollectionOutput(
-            collection=list(
-                range(
-                    self.start, self.start + self.size,
-                    self.step)))
+        return IntCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
 
 
 class RandomRangeInvocation(BaseInvocation):
@@ -107,9 +92,7 @@ class RandomRangeInvocation(BaseInvocation):
 
     # Inputs
     low: int = Field(default=0, description="The inclusive low value")
-    high: int = Field(
-        default=np.iinfo(np.int32).max, description="The exclusive high value"
-    )
+    high: int = Field(default=np.iinfo(np.int32).max, description="The exclusive high value")
     size: int = Field(default=1, description="The number of values to generate")
     seed: int = Field(
         ge=0,
@@ -120,19 +103,12 @@ class RandomRangeInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Random Range",
-                "tags": ["range", "integer", "random", "collection"]
-            },
+            "ui": {"title": "Random Range", "tags": ["range", "integer", "random", "collection"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:
         rng = np.random.default_rng(self.seed)
-        return IntCollectionOutput(
-            collection=list(
-                rng.integers(
-                    low=self.low, high=self.high,
-                    size=self.size)))
+        return IntCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))
 
 
 class ImageCollectionInvocation(BaseInvocation):
diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py
index 6aadbf509d..fb29e01628 100644
--- a/invokeai/app/invocations/compel.py
+++ b/invokeai/app/invocations/compel.py
@@ -3,64 +3,63 @@ from pydantic import BaseModel, Field
 import re
 import torch
 from compel import Compel, ReturnedEmbeddingsType
-from compel.prompt_parser import (Blend, Conjunction,
-                                  CrossAttentionControlSubstitute,
-                                  FlattenedPrompt, Fragment)
+from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 from ...backend.util.devices import torch_dtype
 from ...backend.model_management import ModelType
 from ...backend.model_management.models import ModelNotFoundException
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 from .model import ClipField
 from dataclasses import dataclass
 
 
 class ConditioningField(BaseModel):
-    conditioning_name: Optional[str] = Field(
-        default=None, description="The name of conditioning data")
+    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
 
     class Config:
         schema_extra = {"required": ["conditioning_name"]}
 
+
 @dataclass
 class BasicConditioningInfo:
-    #type: Literal["basic_conditioning"] = "basic_conditioning"
+    # type: Literal["basic_conditioning"] = "basic_conditioning"
     embeds: torch.Tensor
     extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
     # weight: float
     # mode: ConditioningAlgo
 
+
 @dataclass
 class SDXLConditioningInfo(BasicConditioningInfo):
-    #type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
+    # type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
     pooled_embeds: torch.Tensor
     add_time_ids: torch.Tensor
 
-ConditioningInfoType = Annotated[
-    Union[BasicConditioningInfo, SDXLConditioningInfo],
-    Field(discriminator="type")
-]
+
+ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")]
+
 
 @dataclass
 class ConditioningFieldData:
     conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
-    #unconditioned: Optional[torch.Tensor]
+    # unconditioned: Optional[torch.Tensor]
 
-#class ConditioningAlgo(str, Enum):
+
+# class ConditioningAlgo(str, Enum):
 #    Compose = "compose"
 #    ComposeEx = "compose_ex"
 #    PerpNeg = "perp_neg"
 
+
 class CompelOutput(BaseInvocationOutput):
     """Compel parser output"""
 
-    #fmt: off
+    # fmt: off
     type: Literal["compel_output"] = "compel_output"
 
     conditioning: ConditioningField = Field(default=None, description="Conditioning")
-    #fmt: on
+    # fmt: on
 
 
 class CompelInvocation(BaseInvocation):
@@ -74,33 +73,28 @@ class CompelInvocation(BaseInvocation):
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Prompt (Compel)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {
-                    "model": "model"
-                }
-            },
+            "ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
         }
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
         tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.dict(), context=context,
+            **self.clip.tokenizer.dict(),
+            context=context,
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.dict(), context=context,
+            **self.clip.text_encoder.dict(),
+            context=context,
         )
 
         def _lora_loader():
             for lora in self.clip.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context)
+                lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
@@ -116,15 +110,18 @@ class CompelInvocation(BaseInvocation):
                 )
             except ModelNotFoundException:
                 # print(e)
-                #import traceback
-                #print(traceback.format_exc())
-                print(f"Warn: trigger: \"{trigger}\" not found")
-
-        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
-                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
-                ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),\
-                text_encoder_info as text_encoder:
+                # import traceback
+                # print(traceback.format_exc())
+                print(f'Warn: trigger: "{trigger}" not found')
 
+        with ModelPatcher.apply_lora_text_encoder(
+            text_encoder_info.context.model, _lora_loader()
+        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+            tokenizer,
+            ti_manager,
+        ), ModelPatcher.apply_clip_skip(
+            text_encoder_info.context.model, self.clip.skipped_layers
+        ), text_encoder_info as text_encoder:
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -139,14 +136,12 @@ class CompelInvocation(BaseInvocation):
             if context.services.configuration.log_tokenization:
                 log_tokenization_for_prompt_object(prompt, tokenizer)
 
-            c, options = compel.build_conditioning_tensor_for_prompt_object(
-                prompt)
+            c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
 
             ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-                tokens_count_including_eos_bos=get_max_token_count(
-                    tokenizer, conjunction),
-                cross_attention_control_args=options.get(
-                    "cross_attention_control", None),)
+                tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
+                cross_attention_control_args=options.get("cross_attention_control", None),
+            )
 
         c = c.detach().to("cpu")
 
@@ -168,24 +163,26 @@ class CompelInvocation(BaseInvocation):
             ),
         )
 
+
 class SDXLPromptInvocationBase:
     def run_clip_raw(self, context, clip_field, prompt, get_pooled):
         tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.dict(), context=context,
+            **clip_field.tokenizer.dict(),
+            context=context,
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.dict(), context=context,
+            **clip_field.text_encoder.dict(),
+            context=context,
         )
 
         def _lora_loader():
             for lora in clip_field.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context)
+                lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
@@ -201,15 +198,18 @@ class SDXLPromptInvocationBase:
                 )
             except ModelNotFoundException:
                 # print(e)
-                #import traceback
-                #print(traceback.format_exc())
-                print(f"Warn: trigger: \"{trigger}\" not found")
-
-        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
-                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
-                ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
-                text_encoder_info as text_encoder:
+                # import traceback
+                # print(traceback.format_exc())
+                print(f'Warn: trigger: "{trigger}" not found')
 
+        with ModelPatcher.apply_lora_text_encoder(
+            text_encoder_info.context.model, _lora_loader()
+        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+            tokenizer,
+            ti_manager,
+        ), ModelPatcher.apply_clip_skip(
+            text_encoder_info.context.model, clip_field.skipped_layers
+        ), text_encoder_info as text_encoder:
             text_inputs = tokenizer(
                 prompt,
                 padding="max_length",
@@ -241,21 +241,22 @@ class SDXLPromptInvocationBase:
 
     def run_clip_compel(self, context, clip_field, prompt, get_pooled):
         tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.dict(), context=context,
+            **clip_field.tokenizer.dict(),
+            context=context,
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.dict(), context=context,
+            **clip_field.text_encoder.dict(),
+            context=context,
         )
 
         def _lora_loader():
             for lora in clip_field.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context)
+                lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
@@ -271,22 +272,25 @@ class SDXLPromptInvocationBase:
                 )
             except ModelNotFoundException:
                 # print(e)
-                #import traceback
-                #print(traceback.format_exc())
-                print(f"Warn: trigger: \"{trigger}\" not found")
-
-        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
-                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
-                ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
-                text_encoder_info as text_encoder:
+                # import traceback
+                # print(traceback.format_exc())
+                print(f'Warn: trigger: "{trigger}" not found')
 
+        with ModelPatcher.apply_lora_text_encoder(
+            text_encoder_info.context.model, _lora_loader()
+        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+            tokenizer,
+            ti_manager,
+        ), ModelPatcher.apply_clip_skip(
+            text_encoder_info.context.model, clip_field.skipped_layers
+        ), text_encoder_info as text_encoder:
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
                 dtype_for_device_getter=torch_dtype,
                 truncate_long_prompts=True,  # TODO:
-                returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
+                returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=True,
             )
 
@@ -320,6 +324,7 @@ class SDXLPromptInvocationBase:
 
         return c, c_pooled, ec
 
+
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
 
@@ -339,13 +344,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "SDXL Prompt (Compel)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {
-                    "model": "model"
-                }
-            },
+            "ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
         }
 
     @torch.no_grad()
@@ -360,9 +359,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
         crop_coords = (self.crop_top, self.crop_left)
         target_size = (self.target_height, self.target_width)
 
-        add_time_ids = torch.tensor([
-            original_size + crop_coords + target_size
-        ])
+        add_time_ids = torch.tensor([original_size + crop_coords + target_size])
 
         conditioning_data = ConditioningFieldData(
             conditionings=[
@@ -384,12 +381,13 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
             ),
         )
 
+
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
 
     type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
 
-    style: str = Field(default="", description="Style prompt") # TODO: ?
+    style: str = Field(default="", description="Style prompt")  # TODO: ?
     original_width: int = Field(1024, description="")
     original_height: int = Field(1024, description="")
     crop_top: int = Field(0, description="")
@@ -403,9 +401,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
             "ui": {
                 "title": "SDXL Refiner Prompt (Compel)",
                 "tags": ["prompt", "compel"],
-                "type_hints": {
-                    "model": "model"
-                }
+                "type_hints": {"model": "model"},
             },
         }
 
@@ -416,9 +412,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)
 
-        add_time_ids = torch.tensor([
-            original_size + crop_coords + (self.aesthetic_score,)
-        ])
+        add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
 
         conditioning_data = ConditioningFieldData(
             conditionings=[
@@ -426,7 +420,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
                     embeds=c2,
                     pooled_embeds=c2_pooled,
                     add_time_ids=add_time_ids,
-                    extra_conditioning=ec2, # or None
+                    extra_conditioning=ec2,  # or None
                 )
             ]
         )
@@ -440,6 +434,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
             ),
         )
 
+
 class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Pass unmodified prompt to conditioning without compel processing."""
 
@@ -459,13 +454,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "SDXL Prompt (Raw)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {
-                    "model": "model"
-                }
-            },
+            "ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
         }
 
     @torch.no_grad()
@@ -480,9 +469,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
         crop_coords = (self.crop_top, self.crop_left)
         target_size = (self.target_height, self.target_width)
 
-        add_time_ids = torch.tensor([
-            original_size + crop_coords + target_size
-        ])
+        add_time_ids = torch.tensor([original_size + crop_coords + target_size])
 
         conditioning_data = ConditioningFieldData(
             conditionings=[
@@ -504,12 +491,13 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
             ),
         )
 
+
 class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
 
     type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
 
-    style: str = Field(default="", description="Style prompt") # TODO: ?
+    style: str = Field(default="", description="Style prompt")  # TODO: ?
     original_width: int = Field(1024, description="")
     original_height: int = Field(1024, description="")
     crop_top: int = Field(0, description="")
@@ -523,9 +511,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
             "ui": {
                 "title": "SDXL Refiner Prompt (Raw)",
                 "tags": ["prompt", "compel"],
-                "type_hints": {
-                    "model": "model"
-                }
+                "type_hints": {"model": "model"},
             },
         }
 
@@ -536,9 +522,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)
 
-        add_time_ids = torch.tensor([
-            original_size + crop_coords + (self.aesthetic_score,)
-        ])
+        add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
 
         conditioning_data = ConditioningFieldData(
             conditionings=[
@@ -546,7 +530,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
                     embeds=c2,
                     pooled_embeds=c2_pooled,
                     add_time_ids=add_time_ids,
-                    extra_conditioning=ec2, # or None
+                    extra_conditioning=ec2,  # or None
                 )
             ]
         )
@@ -563,11 +547,14 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
 class ClipSkipInvocationOutput(BaseInvocationOutput):
     """Clip skip node output"""
+
     type: Literal["clip_skip_output"] = "clip_skip_output"
     clip: ClipField = Field(None, description="Clip with skipped layers")
 
+
 class ClipSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""
+
     type: Literal["clip_skip"] = "clip_skip"
 
     clip: ClipField = Field(None, description="Clip to use")
@@ -575,10 +562,7 @@ class ClipSkipInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "CLIP Skip",
-                "tags": ["clip", "skip"]
-            },
+            "ui": {"title": "CLIP Skip", "tags": ["clip", "skip"]},
         }
 
     def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
@@ -589,46 +573,26 @@ class ClipSkipInvocation(BaseInvocation):
 
 
 def get_max_token_count(
-        tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],
-        truncate_if_too_long=False) -> int:
+    tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
+) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
-        return max(
-            [
-                get_max_token_count(tokenizer, p, truncate_if_too_long)
-                for p in blend.prompts
-            ]
-        )
+        return max([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in blend.prompts])
     elif type(prompt) is Conjunction:
         conjunction: Conjunction = prompt
-        return sum(
-            [
-                get_max_token_count(tokenizer, p, truncate_if_too_long)
-                for p in conjunction.prompts
-            ]
-        )
+        return sum([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in conjunction.prompts])
     else:
-        return len(
-            get_tokens_for_prompt_object(
-                tokenizer, prompt, truncate_if_too_long))
+        return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
 
 
-def get_tokens_for_prompt_object(
-    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
-) -> List[str]:
+def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> List[str]:
     if type(parsed_prompt) is Blend:
-        raise ValueError(
-            "Blend is not supported here - you need to get tokens for each of its .children"
-        )
+        raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
 
     text_fragments = [
         x.text
         if type(x) is Fragment
-        else (
-            " ".join([f.text for f in x.original])
-            if type(x) is CrossAttentionControlSubstitute
-            else str(x)
-        )
+        else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
         for x in parsed_prompt.children
     ]
     text = " ".join(text_fragments)
@@ -639,25 +603,17 @@ def get_tokens_for_prompt_object(
     return tokens
 
 
-def log_tokenization_for_conjunction(
-    c: Conjunction, tokenizer, display_label_prefix=None
-):
+def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=None):
     display_label_prefix = display_label_prefix or ""
     for i, p in enumerate(c.prompts):
         if len(c.prompts) > 1:
             this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
         else:
             this_display_label_prefix = display_label_prefix
-        log_tokenization_for_prompt_object(
-            p,
-            tokenizer,
-            display_label_prefix=this_display_label_prefix
-        )
+        log_tokenization_for_prompt_object(p, tokenizer, display_label_prefix=this_display_label_prefix)
 
 
-def log_tokenization_for_prompt_object(
-    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
-):
+def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
     display_label_prefix = display_label_prefix or ""
     if type(p) is Blend:
         blend: Blend = p
@@ -694,13 +650,10 @@ def log_tokenization_for_prompt_object(
             )
         else:
             text = " ".join([x.text for x in flattened_prompt.children])
-            log_tokenization_for_text(
-                text, tokenizer, display_label=display_label_prefix
-            )
+            log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
 
 
-def log_tokenization_for_text(
-        text, tokenizer, display_label=None, truncate_if_too_long=False):
+def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
     """shows how the prompt is tokenized
     # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '
diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index 911fede8fb..d2b2d44526 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -6,21 +6,30 @@ from typing import Dict, List, Literal, Optional, Union
 
 import cv2
 import numpy as np
-from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
-                            LeresDetector, LineartAnimeDetector,
-                            LineartDetector, MediapipeFaceDetector,
-                            MidasDetector, MLSDdetector, NormalBaeDetector,
-                            OpenposeDetector, PidiNetDetector, SamDetector,
-                            ZoeDetector)
+from controlnet_aux import (
+    CannyDetector,
+    ContentShuffleDetector,
+    HEDdetector,
+    LeresDetector,
+    LineartAnimeDetector,
+    LineartDetector,
+    MediapipeFaceDetector,
+    MidasDetector,
+    MLSDdetector,
+    NormalBaeDetector,
+    OpenposeDetector,
+    PidiNetDetector,
+    SamDetector,
+    ZoeDetector,
+)
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
 from pydantic import BaseModel, Field, validator
 
 from ...backend.model_management import BaseModelType, ModelType
 from ..models.image import ImageCategory, ImageField, ResourceOrigin
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
-from .image import ImageOutput, PILInvocationConfig
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
+from ..models.image import ImageOutput, PILInvocationConfig
 
 CONTROLNET_DEFAULT_MODELS = [
     ###########################################
@@ -34,7 +43,6 @@ CONTROLNET_DEFAULT_MODELS = [
     "lllyasviel/sd-controlnet-scribble",
     "lllyasviel/sd-controlnet-normal",
     "lllyasviel/sd-controlnet-mlsd",
-
     #############################################
     # lllyasviel sd v1.5, ControlNet v1.1 models
     #############################################
@@ -56,7 +64,6 @@ CONTROLNET_DEFAULT_MODELS = [
     "lllyasviel/control_v11e_sd15_shuffle",
     "lllyasviel/control_v11e_sd15_ip2p",
     "lllyasviel/control_v11f1e_sd15_tile",
-
     #################################################
     #  thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
     ##################################################
@@ -71,7 +78,6 @@ CONTROLNET_DEFAULT_MODELS = [
     "thibaud/controlnet-sd21-lineart-diffusers",
     "thibaud/controlnet-sd21-normalbae-diffusers",
     "thibaud/controlnet-sd21-ade20k-diffusers",
-
     ##############################################
     #  ControlNetMediaPipeface, ControlNet v1.1
     ##############################################
@@ -83,10 +89,17 @@ CONTROLNET_DEFAULT_MODELS = [
 ]
 
 CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
-CONTROLNET_MODE_VALUES = Literal[tuple(
-    ["balanced", "more_prompt", "more_control", "unbalanced"])]
-CONTROLNET_RESIZE_VALUES = Literal[tuple(
-    ["just_resize", "crop_resize", "fill_resize", "just_resize_simple",])]
+CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
+CONTROLNET_RESIZE_VALUES = Literal[
+    tuple(
+        [
+            "just_resize",
+            "crop_resize",
+            "fill_resize",
+            "just_resize_simple",
+        ]
+    )
+]
 
 
 class ControlNetModelField(BaseModel):
@@ -98,21 +111,17 @@ class ControlNetModelField(BaseModel):
 
 class ControlField(BaseModel):
     image: ImageField = Field(default=None, description="The control image")
-    control_model: Optional[ControlNetModelField] = Field(
-        default=None, description="The ControlNet model to use")
+    control_model: Optional[ControlNetModelField] = Field(default=None, description="The ControlNet model to use")
     # control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
-    control_weight: Union[float, List[float]] = Field(
-        default=1, description="The weight given to the ControlNet")
+    control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
-        default=0, ge=0, le=1,
-        description="When the ControlNet is first applied (% of total steps)")
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
     end_step_percent: float = Field(
-        default=1, ge=0, le=1,
-        description="When the ControlNet is last applied (% of total steps)")
-    control_mode: CONTROLNET_MODE_VALUES = Field(
-        default="balanced", description="The control mode to use")
-    resize_mode: CONTROLNET_RESIZE_VALUES = Field(
-        default="just_resize", description="The resize mode to use")
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
     @validator("control_weight")
     def validate_control_weight(cls, v):
@@ -120,11 +129,10 @@ class ControlField(BaseModel):
         if isinstance(v, list):
             for i in v:
                 if i < -1 or i > 2:
-                    raise ValueError(
-                        'Control weights must be within -1 to 2 range')
+                    raise ValueError("Control weights must be within -1 to 2 range")
         else:
             if v < -1 or v > 2:
-                raise ValueError('Control weights must be within -1 to 2 range')
+                raise ValueError("Control weights must be within -1 to 2 range")
         return v
 
     class Config:
@@ -136,12 +144,13 @@ class ControlField(BaseModel):
                     "control_model": "controlnet_model",
                     # "control_weight": "number",
                 }
-            }
+            },
         }
 
 
 class ControlOutput(BaseInvocationOutput):
     """node output for ControlNet info"""
+
     # fmt: off
     type: Literal["control_output"] = "control_output"
     control: ControlField = Field(default=None, description="The control info")
@@ -150,6 +159,7 @@ class ControlOutput(BaseInvocationOutput):
 
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
+
     # fmt: off
     type: Literal["controlnet"] = "controlnet"
     # Inputs
@@ -176,7 +186,7 @@ class ControlNetInvocation(BaseInvocation):
                     # "cfg_scale": "float",
                     "cfg_scale": "number",
                     "control_weight": "float",
-                }
+                },
             },
         }
 
@@ -205,10 +215,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Image Processor",
-                "tags": ["image", "processor"]
-            },
+            "ui": {"title": "Image Processor", "tags": ["image", "processor"]},
         }
 
     def run_processor(self, image):
@@ -233,7 +240,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
             image_category=ImageCategory.CONTROL,
             session_id=context.graph_execution_state_id,
             node_id=self.id,
-            is_intermediate=self.is_intermediate
+            is_intermediate=self.is_intermediate,
         )
 
         """Builds an ImageOutput and its ImageField"""
@@ -248,9 +255,9 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
         )
 
 
-class CannyImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Canny edge detection for ControlNet"""
+
     # fmt: off
     type: Literal["canny_image_processor"] = "canny_image_processor"
     # Input
@@ -260,22 +267,18 @@ class CannyImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Canny Processor",
-                "tags": ["controlnet", "canny", "image", "processor"]
-            },
+            "ui": {"title": "Canny Processor", "tags": ["controlnet", "canny", "image", "processor"]},
         }
 
     def run_processor(self, image):
         canny_processor = CannyDetector()
-        processed_image = canny_processor(
-            image, self.low_threshold, self.high_threshold)
+        processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
         return processed_image
 
 
-class HedImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies HED edge detection to image"""
+
     # fmt: off
     type: Literal["hed_image_processor"] = "hed_image_processor"
     # Inputs
@@ -288,27 +291,25 @@ class HedImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Softedge(HED) Processor",
-                "tags": ["controlnet", "softedge", "hed", "image", "processor"]
-            },
+            "ui": {"title": "Softedge(HED) Processor", "tags": ["controlnet", "softedge", "hed", "image", "processor"]},
         }
 
     def run_processor(self, image):
         hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = hed_processor(image,
-                                        detect_resolution=self.detect_resolution,
-                                        image_resolution=self.image_resolution,
-                                        # safe not supported in controlnet_aux v0.0.3
-                                        # safe=self.safe,
-                                        scribble=self.scribble,
-                                        )
+        processed_image = hed_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            # safe not supported in controlnet_aux v0.0.3
+            # safe=self.safe,
+            scribble=self.scribble,
+        )
         return processed_image
 
 
-class LineartImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies line art processing to image"""
+
     # fmt: off
     type: Literal["lineart_image_processor"] = "lineart_image_processor"
     # Inputs
@@ -319,24 +320,20 @@ class LineartImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Lineart Processor",
-                "tags": ["controlnet", "lineart", "image", "processor"]
-            },
+            "ui": {"title": "Lineart Processor", "tags": ["controlnet", "lineart", "image", "processor"]},
         }
 
     def run_processor(self, image):
-        lineart_processor = LineartDetector.from_pretrained(
-            "lllyasviel/Annotators")
+        lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = lineart_processor(
-            image, detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution, coarse=self.coarse)
+            image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution, coarse=self.coarse
+        )
         return processed_image
 
 
-class LineartAnimeImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies line art anime processing to image"""
+
     # fmt: off
     type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
     # Inputs
@@ -348,23 +345,23 @@ class LineartAnimeImageProcessorInvocation(
         schema_extra = {
             "ui": {
                 "title": "Lineart Anime Processor",
-                "tags": ["controlnet", "lineart", "anime", "image", "processor"]
+                "tags": ["controlnet", "lineart", "anime", "image", "processor"],
             },
         }
 
     def run_processor(self, image):
-        processor = LineartAnimeDetector.from_pretrained(
-            "lllyasviel/Annotators")
-        processed_image = processor(image,
-                                    detect_resolution=self.detect_resolution,
-                                    image_resolution=self.image_resolution,
-                                    )
+        processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+        )
         return processed_image
 
 
-class OpenposeImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies Openpose processing to image"""
+
     # fmt: off
     type: Literal["openpose_image_processor"] = "openpose_image_processor"
     # Inputs
@@ -375,25 +372,23 @@ class OpenposeImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Openpose Processor",
-                "tags": ["controlnet", "openpose", "image", "processor"]
-            },
+            "ui": {"title": "Openpose Processor", "tags": ["controlnet", "openpose", "image", "processor"]},
         }
 
     def run_processor(self, image):
-        openpose_processor = OpenposeDetector.from_pretrained(
-            "lllyasviel/Annotators")
+        openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = openpose_processor(
-            image, detect_resolution=self.detect_resolution,
+            image,
+            detect_resolution=self.detect_resolution,
             image_resolution=self.image_resolution,
-            hand_and_face=self.hand_and_face,)
+            hand_and_face=self.hand_and_face,
+        )
         return processed_image
 
 
-class MidasDepthImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies Midas depth processing to image"""
+
     # fmt: off
     type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
     # Inputs
@@ -405,26 +400,24 @@ class MidasDepthImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Midas (Depth) Processor",
-                "tags": ["controlnet", "midas", "depth", "image", "processor"]
-            },
+            "ui": {"title": "Midas (Depth) Processor", "tags": ["controlnet", "midas", "depth", "image", "processor"]},
         }
 
     def run_processor(self, image):
         midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = midas_processor(image,
-                                          a=np.pi * self.a_mult,
-                                          bg_th=self.bg_th,
-                                          # dept_and_normal not supported in controlnet_aux v0.0.3
-                                          # depth_and_normal=self.depth_and_normal,
-                                          )
+        processed_image = midas_processor(
+            image,
+            a=np.pi * self.a_mult,
+            bg_th=self.bg_th,
+            # dept_and_normal not supported in controlnet_aux v0.0.3
+            # depth_and_normal=self.depth_and_normal,
+        )
         return processed_image
 
 
-class NormalbaeImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies NormalBae processing to image"""
+
     # fmt: off
     type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
     # Inputs
@@ -434,24 +427,20 @@ class NormalbaeImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Normal BAE Processor",
-                "tags": ["controlnet", "normal", "bae", "image", "processor"]
-            },
+            "ui": {"title": "Normal BAE Processor", "tags": ["controlnet", "normal", "bae", "image", "processor"]},
         }
 
     def run_processor(self, image):
-        normalbae_processor = NormalBaeDetector.from_pretrained(
-            "lllyasviel/Annotators")
+        normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = normalbae_processor(
-            image, detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution)
+            image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution
+        )
         return processed_image
 
 
-class MlsdImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies MLSD processing to image"""
+
     # fmt: off
     type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
     # Inputs
@@ -463,24 +452,24 @@ class MlsdImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "MLSD Processor",
-                "tags": ["controlnet", "mlsd", "image", "processor"]
-            },
+            "ui": {"title": "MLSD Processor", "tags": ["controlnet", "mlsd", "image", "processor"]},
         }
 
     def run_processor(self, image):
         mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
         processed_image = mlsd_processor(
-            image, detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution, thr_v=self.thr_v,
-            thr_d=self.thr_d)
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            thr_v=self.thr_v,
+            thr_d=self.thr_d,
+        )
         return processed_image
 
 
-class PidiImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies PIDI processing to image"""
+
     # fmt: off
     type: Literal["pidi_image_processor"] = "pidi_image_processor"
     # Inputs
@@ -492,25 +481,24 @@ class PidiImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "PIDI Processor",
-                "tags": ["controlnet", "pidi", "image", "processor"]
-            },
+            "ui": {"title": "PIDI Processor", "tags": ["controlnet", "pidi", "image", "processor"]},
         }
 
     def run_processor(self, image):
-        pidi_processor = PidiNetDetector.from_pretrained(
-            "lllyasviel/Annotators")
+        pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = pidi_processor(
-            image, detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution, safe=self.safe,
-            scribble=self.scribble)
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            safe=self.safe,
+            scribble=self.scribble,
+        )
         return processed_image
 
 
-class ContentShuffleImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies content shuffle processing to image"""
+
     # fmt: off
     type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
     # Inputs
@@ -525,48 +513,45 @@ class ContentShuffleImageProcessorInvocation(
         schema_extra = {
             "ui": {
                 "title": "Content Shuffle Processor",
-                "tags": ["controlnet", "contentshuffle", "image", "processor"]
+                "tags": ["controlnet", "contentshuffle", "image", "processor"],
             },
         }
 
     def run_processor(self, image):
         content_shuffle_processor = ContentShuffleDetector()
-        processed_image = content_shuffle_processor(image,
-                                                    detect_resolution=self.detect_resolution,
-                                                    image_resolution=self.image_resolution,
-                                                    h=self.h,
-                                                    w=self.w,
-                                                    f=self.f
-                                                    )
+        processed_image = content_shuffle_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            h=self.h,
+            w=self.w,
+            f=self.f,
+        )
         return processed_image
 
 
 # should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
-class ZoeDepthImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies Zoe depth processing to image"""
+
     # fmt: off
     type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
     # fmt: on
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Zoe (Depth) Processor",
-                "tags": ["controlnet", "zoe", "depth", "image", "processor"]
-            },
+            "ui": {"title": "Zoe (Depth) Processor", "tags": ["controlnet", "zoe", "depth", "image", "processor"]},
         }
 
     def run_processor(self, image):
-        zoe_depth_processor = ZoeDetector.from_pretrained(
-            "lllyasviel/Annotators")
+        zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = zoe_depth_processor(image)
         return processed_image
 
 
-class MediapipeFaceProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies mediapipe face processing to image"""
+
     # fmt: off
     type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
     # Inputs
@@ -576,26 +561,22 @@ class MediapipeFaceProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Mediapipe Processor",
-                "tags": ["controlnet", "mediapipe", "image", "processor"]
-            },
+            "ui": {"title": "Mediapipe Processor", "tags": ["controlnet", "mediapipe", "image", "processor"]},
         }
 
     def run_processor(self, image):
         # MediaPipeFaceDetector throws an error if image has alpha channel
         #     so convert to RGB if needed
-        if image.mode == 'RGBA':
-            image = image.convert('RGB')
+        if image.mode == "RGBA":
+            image = image.convert("RGB")
         mediapipe_face_processor = MediapipeFaceDetector()
-        processed_image = mediapipe_face_processor(
-            image, max_faces=self.max_faces, min_confidence=self.min_confidence)
+        processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
         return processed_image
 
 
-class LeresImageProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies leres processing to image"""
+
     # fmt: off
     type: Literal["leres_image_processor"] = "leres_image_processor"
     # Inputs
@@ -608,24 +589,23 @@ class LeresImageProcessorInvocation(
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Leres (Depth) Processor",
-                "tags": ["controlnet", "leres", "depth", "image", "processor"]
-            },
+            "ui": {"title": "Leres (Depth) Processor", "tags": ["controlnet", "leres", "depth", "image", "processor"]},
         }
 
     def run_processor(self, image):
         leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = leres_processor(
-            image, thr_a=self.thr_a, thr_b=self.thr_b, boost=self.boost,
+            image,
+            thr_a=self.thr_a,
+            thr_b=self.thr_b,
+            boost=self.boost,
             detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution)
+            image_resolution=self.image_resolution,
+        )
         return processed_image
 
 
-class TileResamplerProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
-
+class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     # fmt: off
     type: Literal["tile_image_processor"] = "tile_image_processor"
     # Inputs
@@ -637,16 +617,17 @@ class TileResamplerProcessorInvocation(
         schema_extra = {
             "ui": {
                 "title": "Tile Resample Processor",
-                "tags": ["controlnet", "tile", "resample", "image", "processor"]
+                "tags": ["controlnet", "tile", "resample", "image", "processor"],
             },
         }
 
     # tile_resample copied from sd-webui-controlnet/scripts/processor.py
-    def tile_resample(self,
-                      np_img: np.ndarray,
-                      res=512,   # never used?
-                      down_sampling_rate=1.0,
-                      ):
+    def tile_resample(
+        self,
+        np_img: np.ndarray,
+        res=512,  # never used?
+        down_sampling_rate=1.0,
+    ):
         np_img = HWC3(np_img)
         if down_sampling_rate < 1.1:
             return np_img
@@ -658,36 +639,41 @@ class TileResamplerProcessorInvocation(
 
     def run_processor(self, img):
         np_img = np.array(img, dtype=np.uint8)
-        processed_np_image = self.tile_resample(np_img,
-                                                # res=self.tile_size,
-                                                down_sampling_rate=self.down_sampling_rate
-                                                )
+        processed_np_image = self.tile_resample(
+            np_img,
+            # res=self.tile_size,
+            down_sampling_rate=self.down_sampling_rate,
+        )
         processed_image = Image.fromarray(processed_np_image)
         return processed_image
 
 
-class SegmentAnythingProcessorInvocation(
-        ImageProcessorInvocation, PILInvocationConfig):
+class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies segment anything processing to image"""
+
     # fmt: off
     type: Literal["segment_anything_processor"] = "segment_anything_processor"
     # fmt: on
 
     class Config(InvocationConfig):
-        schema_extra = {"ui": {"title": "Segment Anything Processor", "tags": [
-            "controlnet", "segment", "anything", "sam", "image", "processor"]}, }
+        schema_extra = {
+            "ui": {
+                "title": "Segment Anything Processor",
+                "tags": ["controlnet", "segment", "anything", "sam", "image", "processor"],
+            },
+        }
 
     def run_processor(self, image):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
         segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(
-            "ybelkada/segment-anything", subfolder="checkpoints")
+            "ybelkada/segment-anything", subfolder="checkpoints"
+        )
         np_img = np.array(image, dtype=np.uint8)
         processed_image = segment_anything_processor(np_img)
         return processed_image
 
 
 class SamDetectorReproducibleColors(SamDetector):
-
     # overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
     #     base class show_anns() method randomizes colors,
     #     which seems to also lead to non-reproducible image generation
@@ -695,19 +681,15 @@ class SamDetectorReproducibleColors(SamDetector):
     def show_anns(self, anns: List[Dict]):
         if len(anns) == 0:
             return
-        sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
-        h, w = anns[0]['segmentation'].shape
-        final_img = Image.fromarray(
-            np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
+        sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
+        h, w = anns[0]["segmentation"].shape
+        final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
         palette = ade_palette()
         for i, ann in enumerate(sorted_anns):
-            m = ann['segmentation']
+            m = ann["segmentation"]
             img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
             # doing modulo just in case number of annotated regions exceeds number of colors in palette
             ann_color = palette[i % len(palette)]
             img[:, :] = ann_color
-            final_img.paste(
-                Image.fromarray(img, mode="RGB"),
-                (0, 0),
-                Image.fromarray(np.uint8(m * 255)))
+            final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
         return np.array(final_img, dtype=np.uint8)
diff --git a/invokeai/app/invocations/cv.py b/invokeai/app/invocations/cv.py
index cd7eaebeec..bd3a4adbe4 100644
--- a/invokeai/app/invocations/cv.py
+++ b/invokeai/app/invocations/cv.py
@@ -37,10 +37,7 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "OpenCV Inpaint",
-                "tags": ["opencv", "inpaint"]
-            },
+            "ui": {"title": "OpenCV Inpaint", "tags": ["opencv", "inpaint"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py
index 910a7edf8b..d48c9f922e 100644
--- a/invokeai/app/invocations/generate.py
+++ b/invokeai/app/invocations/generate.py
@@ -6,8 +6,7 @@ from typing import Literal, Optional, get_args
 import torch
 from pydantic import Field
 
-from invokeai.app.models.image import (ColorField, ImageCategory, ImageField,
-                                       ResourceOrigin)
+from invokeai.app.models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.generator.inpaint import infill_methods
 
@@ -25,13 +24,12 @@ from contextlib import contextmanager, ExitStack, ContextDecorator
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
 INFILL_METHODS = Literal[tuple(infill_methods())]
-DEFAULT_INFILL_METHOD = (
-    "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
-)
+DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
 
 
 from .latent import get_scheduler
 
+
 class OldModelContext(ContextDecorator):
     model: StableDiffusionGeneratorPipeline
 
@@ -44,6 +42,7 @@ class OldModelContext(ContextDecorator):
     def __exit__(self, *exc):
         return False
 
+
 class OldModelInfo:
     name: str
     hash: str
@@ -64,20 +63,34 @@ class InpaintInvocation(BaseInvocation):
 
     positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
     negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
-    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
-    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    seed: int = Field(
+        ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed
+    )
+    steps: int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
+    width: int = Field(
+        default=512,
+        multiple_of=8,
+        gt=0,
+        description="The width of the resulting image",
+    )
+    height: int = Field(
+        default=512,
+        multiple_of=8,
+        gt=0,
+        description="The height of the resulting image",
+    )
+    cfg_scale: float = Field(
+        default=7.5,
+        ge=1,
+        description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt",
+    )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use")
     unet: UNetField = Field(default=None, description="UNet model")
     vae: VaeField = Field(default=None, description="Vae model")
 
     # Inputs
     image: Optional[ImageField] = Field(description="The input image")
-    strength: float = Field(
-        default=0.75, gt=0, le=1, description="The strength of the original image"
-    )
+    strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the original image")
     fit: bool = Field(
         default=True,
         description="Whether or not the result should be fit to the aspect ratio of the input image",
@@ -86,18 +99,10 @@ class InpaintInvocation(BaseInvocation):
     # Inputs
     mask: Optional[ImageField] = Field(description="The mask")
     seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
-    seam_blur: int = Field(
-        default=16, ge=0, description="The seam inpaint blur radius (px)"
-    )
-    seam_strength: float = Field(
-        default=0.75, gt=0, le=1, description="The seam inpaint strength"
-    )
-    seam_steps: int = Field(
-        default=30, ge=1, description="The number of steps to use for seam inpaint"
-    )
-    tile_size: int = Field(
-        default=32, ge=1, description="The tile infill method size (px)"
-    )
+    seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
+    seam_strength: float = Field(default=0.75, gt=0, le=1, description="The seam inpaint strength")
+    seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
+    tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
     infill_method: INFILL_METHODS = Field(
         default=DEFAULT_INFILL_METHOD,
         description="The method used to infill empty regions (px)",
@@ -128,10 +133,7 @@ class InpaintInvocation(BaseInvocation):
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "tags": ["stable-diffusion", "image"],
-                "title": "Inpaint"
-            },
+            "ui": {"tags": ["stable-diffusion", "image"], "title": "Inpaint"},
         }
 
     def dispatch_progress(
@@ -162,18 +164,23 @@ class InpaintInvocation(BaseInvocation):
         def _lora_loader():
             for lora in self.unet.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context,)
+                    **lora.dict(exclude={"weight"}),
+                    context=context,
+                )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
-        
-        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context,)
-        vae_info = context.services.model_manager.get_model(**self.vae.vae.dict(), context=context,)
 
-        with vae_info as vae,\
-                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
-                unet_info as unet:
+        unet_info = context.services.model_manager.get_model(
+            **self.unet.unet.dict(),
+            context=context,
+        )
+        vae_info = context.services.model_manager.get_model(
+            **self.vae.vae.dict(),
+            context=context,
+        )
 
+        with vae_info as vae, ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
             device = context.services.model_manager.mgr.cache.execution_device
             dtype = context.services.model_manager.mgr.cache.precision
 
@@ -197,21 +204,11 @@ class InpaintInvocation(BaseInvocation):
             )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = (
-            None
-            if self.image is None
-            else context.services.images.get_pil_image(self.image.image_name)
-        )
-        mask = (
-            None
-            if self.mask is None
-            else context.services.images.get_pil_image(self.mask.image_name)
-        )
+        image = None if self.image is None else context.services.images.get_pil_image(self.image.image_name)
+        mask = None if self.mask is None else context.services.images.get_pil_image(self.mask.image_name)
 
         # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
         source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
         scheduler = get_scheduler(
diff --git a/invokeai/app/invocations/image.py b/invokeai/app/invocations/image.py
index 8405aa6daf..3f40ea3cbe 100644
--- a/invokeai/app/invocations/image.py
+++ b/invokeai/app/invocations/image.py
@@ -4,60 +4,25 @@ from typing import Literal, Optional
 
 import numpy
 from PIL import Image, ImageFilter, ImageOps, ImageChops
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pathlib import Path
 from typing import Union
-
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
+from invokeai.app.invocations.metadata import CoreMetadata
+from ..models.image import (
+    ImageCategory,
+    ImageField,
+    ResourceOrigin,
+    PILInvocationConfig,
+    ImageOutput,
+    MaskOutput,
+)
 from .baseinvocation import (
     BaseInvocation,
-    BaseInvocationOutput,
     InvocationContext,
     InvocationConfig,
 )
-
-
-class PILInvocationConfig(BaseModel):
-    """Helper class to provide all PIL invocations with additional config"""
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["PIL", "image"],
-            },
-        }
-
-
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
-
-
-class MaskOutput(BaseInvocationOutput):
-    """Base class for invocations that output a mask"""
-
-    # fmt: off
-    type: Literal["mask"] = "mask"
-    mask:      ImageField = Field(default=None, description="The output mask")
-    width:            int = Field(description="The width of the mask in pixels")
-    height:           int = Field(description="The height of the mask in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": [
-                "type",
-                "mask",
-            ]
-        }
+from invokeai.backend.image_util.safety_checker import SafetyChecker
+from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 
 
 class LoadImageInvocation(BaseInvocation):
@@ -74,10 +39,7 @@ class LoadImageInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Load Image",
-                "tags": ["image", "load"]
-            },
+            "ui": {"title": "Load Image", "tags": ["image", "load"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -96,16 +58,11 @@ class ShowImageInvocation(BaseInvocation):
     type: Literal["show_image"] = "show_image"
 
     # Inputs
-    image: Optional[ImageField] = Field(
-        default=None, description="The image to show"
-    )
+    image: Optional[ImageField] = Field(default=None, description="The image to show")
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Show Image",
-                "tags": ["image", "show"]
-            },
+            "ui": {"title": "Show Image", "tags": ["image", "show"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -138,18 +95,13 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Crop Image",
-                "tags": ["image", "crop"]
-            },
+            "ui": {"title": "Crop Image", "tags": ["image", "crop"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
 
-        image_crop = Image.new(
-            mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0)
-        )
+        image_crop = Image.new(mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0))
         image_crop.paste(image, (-self.x, -self.y))
 
         image_dto = context.services.images.create(
@@ -184,21 +136,14 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Paste Image",
-                "tags": ["image", "paste"]
-            },
+            "ui": {"title": "Paste Image", "tags": ["image", "paste"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         base_image = context.services.images.get_pil_image(self.base_image.image_name)
         image = context.services.images.get_pil_image(self.image.image_name)
         mask = (
-            None
-            if self.mask is None
-            else ImageOps.invert(
-                context.services.images.get_pil_image(self.mask.image_name)
-            )
+            None if self.mask is None else ImageOps.invert(context.services.images.get_pil_image(self.mask.image_name))
         )
         # TODO: probably shouldn't invert mask here... should user be required to do it?
 
@@ -207,9 +152,7 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
         max_x = max(base_image.width, image.width + self.x)
         max_y = max(base_image.height, image.height + self.y)
 
-        new_image = Image.new(
-            mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0)
-        )
+        new_image = Image.new(mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0))
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
         new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
 
@@ -242,10 +185,7 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Mask From Alpha",
-                "tags": ["image", "mask", "alpha"]
-            },
+            "ui": {"title": "Mask From Alpha", "tags": ["image", "mask", "alpha"]},
         }
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
@@ -284,10 +224,7 @@ class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Multiply Images",
-                "tags": ["image", "multiply"]
-            },
+            "ui": {"title": "Multiply Images", "tags": ["image", "multiply"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -328,10 +265,7 @@ class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Image Channel",
-                "tags": ["image", "channel"]
-            },
+            "ui": {"title": "Image Channel", "tags": ["image", "channel"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -371,10 +305,7 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Convert Image",
-                "tags": ["image", "convert"]
-            },
+            "ui": {"title": "Convert Image", "tags": ["image", "convert"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -412,19 +343,14 @@ class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Blur Image",
-                "tags": ["image", "blur"]
-            },
+            "ui": {"title": "Blur Image", "tags": ["image", "blur"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
 
         blur = (
-            ImageFilter.GaussianBlur(self.radius)
-            if self.blur_type == "gaussian"
-            else ImageFilter.BoxBlur(self.radius)
+            ImageFilter.GaussianBlur(self.radius) if self.blur_type == "gaussian" else ImageFilter.BoxBlur(self.radius)
         )
         blur_image = image.filter(blur)
 
@@ -479,10 +405,7 @@ class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Resize Image",
-                "tags": ["image", "resize"]
-            },
+            "ui": {"title": "Resize Image", "tags": ["image", "resize"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -525,10 +448,7 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Scale Image",
-                "tags": ["image", "scale"]
-            },
+            "ui": {"title": "Scale Image", "tags": ["image", "scale"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -573,10 +493,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Image Linear Interpolation",
-                "tags": ["image", "linear", "interpolation", "lerp"]
-            },
+            "ui": {"title": "Image Linear Interpolation", "tags": ["image", "linear", "interpolation", "lerp"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -619,7 +536,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
         schema_extra = {
             "ui": {
                 "title": "Image Inverse Linear Interpolation",
-                "tags": ["image", "linear", "interpolation", "inverse"]
+                "tags": ["image", "linear", "interpolation", "inverse"],
             },
         }
 
@@ -627,12 +544,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
         image = context.services.images.get_pil_image(self.image.image_name)
 
         image_arr = numpy.asarray(image, dtype=numpy.float32)
-        image_arr = (
-            numpy.minimum(
-                numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1
-            )
-            * 255
-        )
+        image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
 
         ilerp_image = Image.fromarray(numpy.uint8(image_arr))
 
@@ -650,3 +562,91 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
             width=image_dto.width,
             height=image_dto.height,
         )
+
+
+class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
+    """Add blur to NSFW-flagged images"""
+
+    # fmt: off
+    type: Literal["img_nsfw"] = "img_nsfw"
+
+    # Inputs
+    image: Optional[ImageField]  = Field(default=None, description="The image to check")
+    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")    
+    # fmt: on
+
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {"title": "Blur NSFW Images", "tags": ["image", "nsfw", "checker"]},
+        }
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+
+        logger = context.services.logger
+        logger.debug("Running NSFW checker")
+        if SafetyChecker.has_nsfw_concept(image):
+            logger.info("A potentially NSFW image has been detected. Image will be blurred.")
+            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
+            caution = self._get_caution_img()
+            blurry_image.paste(caution, (0, 0), caution)
+            image = blurry_image
+
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata.dict() if self.metadata else None,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
+    def _get_caution_img(self) -> Image:
+        import invokeai.app.assets.images as image_assets
+
+        caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
+        return caution.resize((caution.width // 2, caution.height // 2))
+
+
+class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
+    """Add an invisible watermark to an image"""
+
+    # fmt: off
+    type: Literal["img_watermark"] = "img_watermark"
+
+    # Inputs
+    image: Optional[ImageField]  = Field(default=None, description="The image to check")
+    text: str = Field(default='InvokeAI', description="Watermark text")
+    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")    
+    # fmt: on
+
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {"title": "Add Invisible Watermark", "tags": ["image", "watermark", "invisible"]},
+        }
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        new_image = InvisibleWatermark.add_watermark(image, self.text)
+        image_dto = context.services.images.create(
+            image=new_image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata.dict() if self.metadata else None,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
diff --git a/invokeai/app/invocations/infill.py b/invokeai/app/invocations/infill.py
index ff8900fc50..cd5b2f9a11 100644
--- a/invokeai/app/invocations/infill.py
+++ b/invokeai/app/invocations/infill.py
@@ -30,9 +30,7 @@ def infill_methods() -> list[str]:
 
 
 INFILL_METHODS = Literal[tuple(infill_methods())]
-DEFAULT_INFILL_METHOD = (
-    "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
-)
+DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
 
 
 def infill_patchmatch(im: Image.Image) -> Image.Image:
@@ -44,9 +42,7 @@ def infill_patchmatch(im: Image.Image) -> Image.Image:
         return im
 
     # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
-    im_patched_np = PatchMatch.inpaint(
-        im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
-    )
+    im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3)
     im_patched = Image.fromarray(im_patched_np, mode="RGB")
     return im_patched
 
@@ -68,9 +64,7 @@ def get_tile_images(image: np.ndarray, width=8, height=8):
     )
 
 
-def tile_fill_missing(
-    im: Image.Image, tile_size: int = 16, seed: Optional[int] = None
-) -> Image.Image:
+def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image:
     # Only fill if there's an alpha layer
     if im.mode != "RGBA":
         return im
@@ -103,9 +97,7 @@ def tile_fill_missing(
     # Find all invalid tiles and replace with a random valid tile
     replace_count = (tiles_mask == False).sum()
     rng = np.random.default_rng(seed=seed)
-    tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
-        rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
-    ]
+    tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count), :, :, :]
 
     # Convert back to an image
     tiles_all = tiles_all.reshape(tshape)
@@ -126,9 +118,7 @@ class InfillColorInvocation(BaseInvocation):
     """Infills transparent areas of an image with a solid color"""
 
     type: Literal["infill_rgba"] = "infill_rgba"
-    image: Optional[ImageField] = Field(
-        default=None, description="The image to infill"
-    )
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
     color: ColorField = Field(
         default=ColorField(r=127, g=127, b=127, a=255),
         description="The color to use to infill",
@@ -136,10 +126,7 @@ class InfillColorInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Color Infill",
-                "tags": ["image", "inpaint", "color", "infill"]
-            },
+            "ui": {"title": "Color Infill", "tags": ["image", "inpaint", "color", "infill"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -171,9 +158,7 @@ class InfillTileInvocation(BaseInvocation):
 
     type: Literal["infill_tile"] = "infill_tile"
 
-    image: Optional[ImageField] = Field(
-        default=None, description="The image to infill"
-    )
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
     tile_size: int = Field(default=32, ge=1, description="The tile size (px)")
     seed: int = Field(
         ge=0,
@@ -184,18 +169,13 @@ class InfillTileInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Tile Infill",
-                "tags": ["image", "inpaint", "tile", "infill"]
-            },
+            "ui": {"title": "Tile Infill", "tags": ["image", "inpaint", "tile", "infill"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
 
-        infilled = tile_fill_missing(
-            image.copy(), seed=self.seed, tile_size=self.tile_size
-        )
+        infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
         infilled.paste(image, (0, 0), image.split()[-1])
 
         image_dto = context.services.images.create(
@@ -219,16 +199,11 @@ class InfillPatchMatchInvocation(BaseInvocation):
 
     type: Literal["infill_patchmatch"] = "infill_patchmatch"
 
-    image: Optional[ImageField] = Field(
-        default=None, description="The image to infill"
-    )
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Patch Match Infill",
-                "tags": ["image", "inpaint", "patchmatch", "infill"]
-            },
+            "ui": {"title": "Patch Match Infill", "tags": ["image", "inpaint", "patchmatch", "infill"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 6082057bd3..d6391e4f77 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -12,20 +12,21 @@ from pydantic import BaseModel, Field, validator
 
 from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend.model_management.models.base import ModelType
+from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
-    ConditioningData, ControlNetData, StableDiffusionGeneratorPipeline,
-    image_resized_to_grid_as_tensor)
-from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
-    PostprocessingSettings
+    ConditioningData,
+    ControlNetData,
+    StableDiffusionGeneratorPipeline,
+    image_resized_to_grid_as_tensor,
+)
+from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
 from ..models.image import ImageCategory, ImageField, ResourceOrigin
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .image import ImageOutput
@@ -46,8 +47,7 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 class LatentsField(BaseModel):
     """A latents field used for passing latents between invocations"""
 
-    latents_name: Optional[str] = Field(
-        default=None, description="The name of the latents")
+    latents_name: Optional[str] = Field(default=None, description="The name of the latents")
 
     class Config:
         schema_extra = {"required": ["latents_name"]}
@@ -55,14 +55,15 @@ class LatentsField(BaseModel):
 
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["latents_output"] = "latents_output"
 
     # Inputs
     latents: LatentsField          = Field(default=None, description="The output latents")
     width:                     int = Field(description="The width of the latents in pixels")
     height:                    int = Field(description="The height of the latents in pixels")
-    #fmt: on
+    # fmt: on
 
 
 def build_latents_output(latents_name: str, latents: torch.Tensor):
@@ -73,9 +74,7 @@ def build_latents_output(latents_name: str, latents: torch.Tensor):
     )
 
 
-SAMPLER_NAME_VALUES = Literal[
-    tuple(list(SCHEDULER_MAP.keys()))
-]
+SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
 def get_scheduler(
@@ -83,11 +82,10 @@ def get_scheduler(
     scheduler_info: ModelInfo,
     scheduler_name: str,
 ) -> Scheduler:
-    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(
-        scheduler_name, SCHEDULER_MAP['ddim']
-    )
+    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
     orig_scheduler_info = context.services.model_manager.get_model(
-        **scheduler_info.dict(), context=context,
+        **scheduler_info.dict(),
+        context=context,
     )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
@@ -102,7 +100,7 @@ def get_scheduler(
     scheduler = scheduler_class.from_config(scheduler_config)
 
     # hack copied over from generate.py
-    if not hasattr(scheduler, 'uses_inpainting_model'):
+    if not hasattr(scheduler, "uses_inpainting_model"):
         scheduler.uses_inpainting_model = lambda: False
     return scheduler
 
@@ -123,8 +121,8 @@ class TextToLatentsInvocation(BaseInvocation):
     scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
     unet: UNetField = Field(default=None, description="UNet submodel")
     control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
-    #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    #seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    # seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
     # fmt: on
 
     @validator("cfg_scale")
@@ -133,10 +131,10 @@ class TextToLatentsInvocation(BaseInvocation):
         if isinstance(v, list):
             for i in v:
                 if i < 1:
-                    raise ValueError('cfg_scale must be greater than 1')
+                    raise ValueError("cfg_scale must be greater than 1")
         else:
             if v < 1:
-                raise ValueError('cfg_scale must be greater than 1')
+                raise ValueError("cfg_scale must be greater than 1")
         return v
 
     # Schema customisation
@@ -149,8 +147,8 @@ class TextToLatentsInvocation(BaseInvocation):
                     "model": "model",
                     "control": "control",
                     # "cfg_scale": "float",
-                    "cfg_scale": "number"
-                }
+                    "cfg_scale": "number",
+                },
             },
         }
 
@@ -190,16 +188,14 @@ class TextToLatentsInvocation(BaseInvocation):
                 threshold=0.0,  # threshold,
                 warmup=0.2,  # warmup,
                 h_symmetry_time_pct=None,  # h_symmetry_time_pct,
-                v_symmetry_time_pct=None  # v_symmetry_time_pct,
+                v_symmetry_time_pct=None,  # v_symmetry_time_pct,
             ),
         )
 
         conditioning_data = conditioning_data.add_scheduler_args_if_applicable(
             scheduler,
-
             # for ddim scheduler
             eta=0.0,  # ddim_eta
-
             # for ancestral and sde schedulers
             generator=torch.Generator(device=unet.device).manual_seed(0),
         )
@@ -247,7 +243,6 @@ class TextToLatentsInvocation(BaseInvocation):
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> List[ControlNetData]:
-
         # assuming fixed dimensional scaling of 8:1 for image:latents
         control_height_resize = latents_shape[2] * 8
         control_width_resize = latents_shape[3] * 8
@@ -261,7 +256,7 @@ class TextToLatentsInvocation(BaseInvocation):
             control_list = control_input
         else:
             control_list = None
-        if (control_list is None):
+        if control_list is None:
             control_data = None
             # from above handling, any control that is not None should now be of type list[ControlField]
         else:
@@ -281,9 +276,7 @@ class TextToLatentsInvocation(BaseInvocation):
 
                 control_models.append(control_model)
                 control_image_field = control_info.image
-                input_image = context.services.images.get_pil_image(
-                    control_image_field.image_name
-                )
+                input_image = context.services.images.get_pil_image(control_image_field.image_name)
                 # self.image.image_type, self.image.image_name
                 # FIXME: still need to test with different widths, heights, devices, dtypes
                 #        and add in batch_size, num_images_per_prompt?
@@ -318,69 +311,71 @@ class TextToLatentsInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
+        with SilenceWarnings():
+            noise = context.services.latents.get(self.noise.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            # Get the source node id (we are invoking the prepared node)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+            def step_callback(state: PipelineIntermediateState):
+                self.dispatch_progress(context, source_node_id, state)
 
-        def _lora_loader():
-            for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context,
-                )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
+            def _lora_loader():
+                for lora in self.unet.loras:
+                    lora_info = context.services.model_manager.get_model(
+                        **lora.dict(exclude={"weight"}),
+                        context=context,
+                    )
+                    yield (lora_info.context.model, lora.weight)
+                    del lora_info
+                return
 
-        unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict(), context=context,
-        )
-        with ExitStack() as exit_stack,\
-                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
-                unet_info as unet:
-
-            noise = noise.to(device=unet.device, dtype=unet.dtype)
-
-            scheduler = get_scheduler(
+            unet_info = context.services.model_manager.get_model(
+                **self.unet.unet.dict(),
                 context=context,
-                scheduler_info=self.unet.scheduler,
-                scheduler_name=self.scheduler,
             )
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), unet_info as unet:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
 
-            pipeline = self.create_pipeline(unet, scheduler)
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet)
+                scheduler = get_scheduler(
+                    context=context,
+                    scheduler_info=self.unet.scheduler,
+                    scheduler_name=self.scheduler,
+                )
 
-            control_data = self.prep_control_data(
-                model=pipeline, context=context, control_input=self.control,
-                latents_shape=noise.shape,
-                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                do_classifier_free_guidance=True,
-                exit_stack=exit_stack,
-            )
+                pipeline = self.create_pipeline(unet, scheduler)
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet)
 
-            # TODO: Verify the noise is the right size
-            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-                latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
-                noise=noise,
-                num_inference_steps=self.steps,
-                conditioning_data=conditioning_data,
-                control_data=control_data,  # list[ControlNetData]
-                callback=step_callback,
-            )
+                control_data = self.prep_control_data(
+                    model=pipeline,
+                    context=context,
+                    control_input=self.control,
+                    latents_shape=noise.shape,
+                    # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                    do_classifier_free_guidance=True,
+                    exit_stack=exit_stack,
+                )
 
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        result_latents = result_latents.to("cpu")
-        torch.cuda.empty_cache()
+                # TODO: Verify the noise is the right size
+                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+                    latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
+                    noise=noise,
+                    num_inference_steps=self.steps,
+                    conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
+                    callback=step_callback,
+                )
 
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents)
+            # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+            result_latents = result_latents.to("cpu")
+            torch.cuda.empty_cache()
+
+            name = f"{context.graph_execution_state_id}__{self.id}"
+            context.services.latents.save(name, result_latents)
+            return build_latents_output(latents_name=name, latents=result_latents)
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -389,11 +384,8 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
     type: Literal["l2l"] = "l2l"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(
-        description="The latents to use as a base image")
-    strength: float = Field(
-        default=0.7, ge=0, le=1,
-        description="The strength of the latents to use")
+    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
+    strength: float = Field(default=0.7, ge=0, le=1, description="The strength of the latents to use")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -405,87 +397,89 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
                     "model": "model",
                     "control": "control",
                     "cfg_scale": "number",
-                }
+                },
             },
         }
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-        latent = context.services.latents.get(self.latents.latents_name)
+        with SilenceWarnings():  # this quenches NSFW nag from diffusers
+            noise = context.services.latents.get(self.noise.latents_name)
+            latent = context.services.latents.get(self.latents.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            # Get the source node id (we are invoking the prepared node)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+            def step_callback(state: PipelineIntermediateState):
+                self.dispatch_progress(context, source_node_id, state)
 
-        def _lora_loader():
-            for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context,
-                )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
+            def _lora_loader():
+                for lora in self.unet.loras:
+                    lora_info = context.services.model_manager.get_model(
+                        **lora.dict(exclude={"weight"}),
+                        context=context,
+                    )
+                    yield (lora_info.context.model, lora.weight)
+                    del lora_info
+                return
 
-        unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict(), context=context,
-        )
-        with ExitStack() as exit_stack,\
-                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
-                unet_info as unet:
-
-            noise = noise.to(device=unet.device, dtype=unet.dtype)
-            latent = latent.to(device=unet.device, dtype=unet.dtype)
-
-            scheduler = get_scheduler(
+            unet_info = context.services.model_manager.get_model(
+                **self.unet.unet.dict(),
                 context=context,
-                scheduler_info=self.unet.scheduler,
-                scheduler_name=self.scheduler,
             )
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), unet_info as unet:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
+                latent = latent.to(device=unet.device, dtype=unet.dtype)
 
-            pipeline = self.create_pipeline(unet, scheduler)
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet)
+                scheduler = get_scheduler(
+                    context=context,
+                    scheduler_info=self.unet.scheduler,
+                    scheduler_name=self.scheduler,
+                )
 
-            control_data = self.prep_control_data(
-                model=pipeline, context=context, control_input=self.control,
-                latents_shape=noise.shape,
-                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                do_classifier_free_guidance=True,
-                exit_stack=exit_stack,
-            )
+                pipeline = self.create_pipeline(unet, scheduler)
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet)
 
-            # TODO: Verify the noise is the right size
-            initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
-                latent, device=unet.device, dtype=latent.dtype
-            )
+                control_data = self.prep_control_data(
+                    model=pipeline,
+                    context=context,
+                    control_input=self.control,
+                    latents_shape=noise.shape,
+                    # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                    do_classifier_free_guidance=True,
+                    exit_stack=exit_stack,
+                )
 
-            timesteps, _ = pipeline.get_img2img_timesteps(
-                self.steps,
-                self.strength,
-                device=unet.device,
-            )
+                # TODO: Verify the noise is the right size
+                initial_latents = (
+                    latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
+                )
 
-            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-                latents=initial_latents,
-                timesteps=timesteps,
-                noise=noise,
-                num_inference_steps=self.steps,
-                conditioning_data=conditioning_data,
-                control_data=control_data,  # list[ControlNetData]
-                callback=step_callback
-            )
+                timesteps, _ = pipeline.get_img2img_timesteps(
+                    self.steps,
+                    self.strength,
+                    device=unet.device,
+                )
 
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        result_latents = result_latents.to("cpu")
-        torch.cuda.empty_cache()
+                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+                    latents=initial_latents,
+                    timesteps=timesteps,
+                    noise=noise,
+                    num_inference_steps=self.steps,
+                    conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
+                    callback=step_callback,
+                )
 
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, result_latents)
+            # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+            result_latents = result_latents.to("cpu")
+            torch.cuda.empty_cache()
+
+            name = f"{context.graph_execution_state_id}__{self.id}"
+            context.services.latents.save(name, result_latents)
         return build_latents_output(latents_name=name, latents=result_latents)
 
 
@@ -496,14 +490,13 @@ class LatentsToImageInvocation(BaseInvocation):
     type: Literal["l2i"] = "l2i"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(
-        description="The latents to generate an image from")
+    latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    tiled: bool = Field(
-        default=False,
-        description="Decode latents by overlaping tiles(less memory consumption)")
-    fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
-    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
+    tiled: bool = Field(default=False, description="Decode latents by overlaping tiles (less memory consumption)")
+    fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
+    metadata: Optional[CoreMetadata] = Field(
+        default=None, description="Optional core metadata to be written to the image"
+    )
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -519,7 +512,8 @@ class LatentsToImageInvocation(BaseInvocation):
         latents = context.services.latents.get(self.latents.latents_name)
 
         vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(), context=context,
+            **self.vae.vae.dict(),
+            context=context,
         )
 
         with vae_info as vae:
@@ -586,8 +580,7 @@ class LatentsToImageInvocation(BaseInvocation):
         )
 
 
-LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear",
-                                     "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
+LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
 
 
 class ResizeLatentsInvocation(BaseInvocation):
@@ -596,36 +589,30 @@ class ResizeLatentsInvocation(BaseInvocation):
     type: Literal["lresize"] = "lresize"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(
-        description="The latents to resize")
-    width:                         Union[int, None] = Field(default=512,
-        ge=64, multiple_of=8, description="The width to resize to (px)")
-    height:                        Union[int, None] = Field(default=512,
-        ge=64, multiple_of=8, description="The height to resize to (px)")
-    mode:   LATENTS_INTERPOLATION_MODE = Field(
-        default="bilinear", description="The interpolation mode")
+    latents: Optional[LatentsField] = Field(description="The latents to resize")
+    width: Union[int, None] = Field(default=512, ge=64, multiple_of=8, description="The width to resize to (px)")
+    height: Union[int, None] = Field(default=512, ge=64, multiple_of=8, description="The height to resize to (px)")
+    mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
     antialias: bool = Field(
-        default=False,
-        description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+        default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)"
+    )
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Resize Latents",
-                "tags": ["latents", "resize"]
-            },
+            "ui": {"title": "Resize Latents", "tags": ["latents", "resize"]},
         }
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
         # TODO:
-        device=choose_torch_device()
+        device = choose_torch_device()
 
         resized_latents = torch.nn.functional.interpolate(
-            latents.to(device), size=(self.height // 8, self.width // 8),
-            mode=self.mode, antialias=self.antialias
-            if self.mode in ["bilinear", "bicubic"] else False,
+            latents.to(device),
+            size=(self.height // 8, self.width // 8),
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@@ -644,35 +631,30 @@ class ScaleLatentsInvocation(BaseInvocation):
     type: Literal["lscale"] = "lscale"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(
-        description="The latents to scale")
-    scale_factor:               float = Field(
-        gt=0, description="The factor by which to scale the latents")
-    mode:  LATENTS_INTERPOLATION_MODE = Field(
-        default="bilinear", description="The interpolation mode")
+    latents: Optional[LatentsField] = Field(description="The latents to scale")
+    scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
+    mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
     antialias: bool = Field(
-        default=False,
-        description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+        default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)"
+    )
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Scale Latents",
-                "tags": ["latents", "scale"]
-            },
+            "ui": {"title": "Scale Latents", "tags": ["latents", "scale"]},
         }
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
         # TODO:
-        device=choose_torch_device()
+        device = choose_torch_device()
 
         # resizing
         resized_latents = torch.nn.functional.interpolate(
-            latents.to(device), scale_factor=self.scale_factor, mode=self.mode,
-            antialias=self.antialias
-            if self.mode in ["bilinear", "bicubic"] else False,
+            latents.to(device),
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@@ -693,19 +675,13 @@ class ImageToLatentsInvocation(BaseInvocation):
     # Inputs
     image: Optional[ImageField] = Field(description="The image to encode")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    tiled: bool = Field(
-        default=False,
-        description="Encode latents by overlaping tiles(less memory consumption)")
-    fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
-
+    tiled: bool = Field(default=False, description="Encode latents by overlaping tiles(less memory consumption)")
+    fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
 
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Image To Latents",
-                "tags": ["latents", "image"]
-            },
+            "ui": {"title": "Image To Latents", "tags": ["latents", "image"]},
         }
 
     @torch.no_grad()
@@ -715,9 +691,10 @@ class ImageToLatentsInvocation(BaseInvocation):
         # )
         image = context.services.images.get_pil_image(self.image.image_name)
 
-        #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
+        # vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
         vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(), context=context,
+            **self.vae.vae.dict(),
+            context=context,
         )
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
@@ -744,12 +721,12 @@ class ImageToLatentsInvocation(BaseInvocation):
                     vae.post_quant_conv.to(orig_dtype)
                     vae.decoder.conv_in.to(orig_dtype)
                     vae.decoder.mid_block.to(orig_dtype)
-                #else:
+                # else:
                 #    latents = latents.float()
 
             else:
                 vae.to(dtype=torch.float16)
-                #latents = latents.half()
+                # latents = latents.half()
 
             if self.tiled:
                 vae.enable_tiling()
@@ -760,9 +737,7 @@ class ImageToLatentsInvocation(BaseInvocation):
             image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
             with torch.inference_mode():
                 image_tensor_dist = vae.encode(image_tensor).latent_dist
-                latents = image_tensor_dist.sample().to(
-                    dtype=vae.dtype
-                )  # FIXME: uses torch.randn. make reproducible!
+                latents = image_tensor_dist.sample().to(dtype=vae.dtype)  # FIXME: uses torch.randn. make reproducible!
 
             latents = vae.config.scaling_factor * latents
             latents = latents.to(dtype=orig_dtype)
diff --git a/invokeai/app/invocations/math.py b/invokeai/app/invocations/math.py
index 92cff04bf7..32b1ab2a39 100644
--- a/invokeai/app/invocations/math.py
+++ b/invokeai/app/invocations/math.py
@@ -54,10 +54,7 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Add",
-                "tags": ["math", "add"]
-            },
+            "ui": {"title": "Add", "tags": ["math", "add"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntOutput:
@@ -75,10 +72,7 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Subtract",
-                "tags": ["math", "subtract"]
-            },
+            "ui": {"title": "Subtract", "tags": ["math", "subtract"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntOutput:
@@ -96,10 +90,7 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Multiply",
-                "tags": ["math", "multiply"]
-            },
+            "ui": {"title": "Multiply", "tags": ["math", "multiply"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntOutput:
@@ -117,10 +108,7 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Divide",
-                "tags": ["math", "divide"]
-            },
+            "ui": {"title": "Divide", "tags": ["math", "divide"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntOutput:
@@ -140,10 +128,7 @@ class RandomIntInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Random Integer",
-                "tags": ["math", "random", "integer"]
-            },
+            "ui": {"title": "Random Integer", "tags": ["math", "random", "integer"]},
         }
 
     def invoke(self, context: InvocationContext) -> IntOutput:
diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py
index ab02487cdc..3588ef4ebe 100644
--- a/invokeai/app/invocations/metadata.py
+++ b/invokeai/app/invocations/metadata.py
@@ -38,9 +38,7 @@ class CoreMetadata(BaseModel):
         description="The number of skipped CLIP layers",
     )
     model: MainModelField = Field(description="The main model used for inference")
-    controlnets: list[ControlField] = Field(
-        description="The ControlNets used for inference"
-    )
+    controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
     loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
     vae: Union[VAEModelField, None] = Field(
         default=None,
@@ -52,38 +50,24 @@ class CoreMetadata(BaseModel):
         default=None,
         description="The strength used for latents-to-latents",
     )
-    init_image: Union[str, None] = Field(
-        default=None, description="The name of the initial image"
-    )
+    init_image: Union[str, None] = Field(default=None, description="The name of the initial image")
 
     # SDXL
-    positive_style_prompt: Union[str, None] = Field(
-        default=None, description="The positive style prompt parameter"
-    )
-    negative_style_prompt: Union[str, None] = Field(
-        default=None, description="The negative style prompt parameter"
-    )
+    positive_style_prompt: Union[str, None] = Field(default=None, description="The positive style prompt parameter")
+    negative_style_prompt: Union[str, None] = Field(default=None, description="The negative style prompt parameter")
 
     # SDXL Refiner
-    refiner_model: Union[MainModelField, None] = Field(
-        default=None, description="The SDXL Refiner model used"
-    )
+    refiner_model: Union[MainModelField, None] = Field(default=None, description="The SDXL Refiner model used")
     refiner_cfg_scale: Union[float, None] = Field(
         default=None,
         description="The classifier-free guidance scale parameter used for the refiner",
     )
-    refiner_steps: Union[int, None] = Field(
-        default=None, description="The number of steps used for the refiner"
-    )
-    refiner_scheduler: Union[str, None] = Field(
-        default=None, description="The scheduler used for the refiner"
-    )
+    refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
+    refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
     refiner_aesthetic_store: Union[float, None] = Field(
         default=None, description="The aesthetic score used for the refiner"
     )
-    refiner_start: Union[float, None] = Field(
-        default=None, description="The start value used for refiner denoising"
-    )
+    refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
 
 
 class ImageMetadata(BaseModel):
@@ -93,9 +77,7 @@ class ImageMetadata(BaseModel):
         default=None,
         description="The image's core metadata, if it was created in the Linear or Canvas UI",
     )
-    graph: Optional[dict] = Field(
-        default=None, description="The graph that created the image"
-    )
+    graph: Optional[dict] = Field(default=None, description="The graph that created the image")
 
 
 class MetadataAccumulatorOutput(BaseInvocationOutput):
@@ -127,50 +109,34 @@ class MetadataAccumulatorInvocation(BaseInvocation):
         description="The number of skipped CLIP layers",
     )
     model: MainModelField = Field(description="The main model used for inference")
-    controlnets: list[ControlField] = Field(
-        description="The ControlNets used for inference"
-    )
+    controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
     loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
     strength: Union[float, None] = Field(
         default=None,
         description="The strength used for latents-to-latents",
     )
-    init_image: Union[str, None] = Field(
-        default=None, description="The name of the initial image"
-    )
+    init_image: Union[str, None] = Field(default=None, description="The name of the initial image")
     vae: Union[VAEModelField, None] = Field(
         default=None,
         description="The VAE used for decoding, if the main model's default was not used",
     )
 
     # SDXL
-    positive_style_prompt: Union[str, None] = Field(
-        default=None, description="The positive style prompt parameter"
-    )
-    negative_style_prompt: Union[str, None] = Field(
-        default=None, description="The negative style prompt parameter"
-    )
+    positive_style_prompt: Union[str, None] = Field(default=None, description="The positive style prompt parameter")
+    negative_style_prompt: Union[str, None] = Field(default=None, description="The negative style prompt parameter")
 
     # SDXL Refiner
-    refiner_model: Union[MainModelField, None] = Field(
-        default=None, description="The SDXL Refiner model used"
-    )
+    refiner_model: Union[MainModelField, None] = Field(default=None, description="The SDXL Refiner model used")
     refiner_cfg_scale: Union[float, None] = Field(
         default=None,
         description="The classifier-free guidance scale parameter used for the refiner",
     )
-    refiner_steps: Union[int, None] = Field(
-        default=None, description="The number of steps used for the refiner"
-    )
-    refiner_scheduler: Union[str, None] = Field(
-        default=None, description="The scheduler used for the refiner"
-    )
+    refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
+    refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
     refiner_aesthetic_store: Union[float, None] = Field(
         default=None, description="The aesthetic score used for the refiner"
     )
-    refiner_start: Union[float, None] = Field(
-        default=None, description="The start value used for refiner denoising"
-    )
+    refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
 
     class Config(InvocationConfig):
         schema_extra = {
diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index 5f1a91ae3b..2388d12e25 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -4,17 +4,14 @@ from typing import List, Literal, Optional, Union
 from pydantic import BaseModel, Field
 
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 
 
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
     model_type: ModelType = Field(description="Info to load submodel")
-    submodel: Optional[SubModelType] = Field(
-        default=None, description="Info to load submodel"
-    )
+    submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
 
 
 class LoraInfo(ModelInfo):
@@ -33,6 +30,7 @@ class ClipField(BaseModel):
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
 
+
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
@@ -49,6 +47,7 @@ class ModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = Field(default=None, description="Vae submodel")
     # fmt: on
 
+
 class MainModelField(BaseModel):
     """Main model field"""
 
@@ -62,6 +61,7 @@ class LoRAModelField(BaseModel):
     model_name: str = Field(description="Name of the LoRA model")
     base_model: BaseModelType = Field(description="Base model")
 
+
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
@@ -180,7 +180,7 @@ class MainModelLoaderInvocation(BaseInvocation):
             ),
         )
 
-    
+
 class LoraLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
@@ -197,9 +197,7 @@ class LoraLoaderInvocation(BaseInvocation):
 
     type: Literal["lora_loader"] = "lora_loader"
 
-    lora: Union[LoRAModelField, None] = Field(
-        default=None, description="Lora model name"
-    )
+    lora: Union[LoRAModelField, None] = Field(default=None, description="Lora model name")
     weight: float = Field(default=0.75, description="With what weight to apply lora")
 
     unet: Optional[UNetField] = Field(description="UNet model for applying lora")
@@ -228,14 +226,10 @@ class LoraLoaderInvocation(BaseInvocation):
         ):
             raise Exception(f"Unkown lora name: {lora_name}!")
 
-        if self.unet is not None and any(
-            lora.model_name == lora_name for lora in self.unet.loras
-        ):
+        if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
             raise Exception(f'Lora "{lora_name}" already applied to unet')
 
-        if self.clip is not None and any(
-            lora.model_name == lora_name for lora in self.clip.loras
-        ):
+        if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
             raise Exception(f'Lora "{lora_name}" already applied to clip')
 
         output = LoraLoaderOutput()
diff --git a/invokeai/app/invocations/param_easing.py b/invokeai/app/invocations/param_easing.py
index 70d3ddc7d2..f910e5379c 100644
--- a/invokeai/app/invocations/param_easing.py
+++ b/invokeai/app/invocations/param_easing.py
@@ -12,16 +12,37 @@ import matplotlib.pyplot as plt
 
 from easing_functions import (
     LinearInOut,
-    QuadEaseInOut, QuadEaseIn, QuadEaseOut,
-    CubicEaseInOut, CubicEaseIn, CubicEaseOut,
-    QuarticEaseInOut, QuarticEaseIn, QuarticEaseOut,
-    QuinticEaseInOut, QuinticEaseIn, QuinticEaseOut,
-    SineEaseInOut, SineEaseIn, SineEaseOut,
-    CircularEaseIn, CircularEaseInOut, CircularEaseOut,
-    ExponentialEaseInOut, ExponentialEaseIn, ExponentialEaseOut,
-    ElasticEaseIn, ElasticEaseInOut, ElasticEaseOut,
-    BackEaseIn, BackEaseInOut, BackEaseOut,
-    BounceEaseIn, BounceEaseInOut, BounceEaseOut)
+    QuadEaseInOut,
+    QuadEaseIn,
+    QuadEaseOut,
+    CubicEaseInOut,
+    CubicEaseIn,
+    CubicEaseOut,
+    QuarticEaseInOut,
+    QuarticEaseIn,
+    QuarticEaseOut,
+    QuinticEaseInOut,
+    QuinticEaseIn,
+    QuinticEaseOut,
+    SineEaseInOut,
+    SineEaseIn,
+    SineEaseOut,
+    CircularEaseIn,
+    CircularEaseInOut,
+    CircularEaseOut,
+    ExponentialEaseInOut,
+    ExponentialEaseIn,
+    ExponentialEaseOut,
+    ElasticEaseIn,
+    ElasticEaseInOut,
+    ElasticEaseOut,
+    BackEaseIn,
+    BackEaseInOut,
+    BackEaseOut,
+    BounceEaseIn,
+    BounceEaseInOut,
+    BounceEaseOut,
+)
 
 from .baseinvocation import (
     BaseInvocation,
@@ -45,17 +66,12 @@ class FloatLinearRangeInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Linear Range (Float)",
-                "tags": ["math", "float", "linear", "range"]
-            },
+            "ui": {"title": "Linear Range (Float)", "tags": ["math", "float", "linear", "range"]},
         }
 
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         param_list = list(np.linspace(self.start, self.stop, self.steps))
-        return FloatCollectionOutput(
-            collection=param_list
-        )
+        return FloatCollectionOutput(collection=param_list)
 
 
 EASING_FUNCTIONS_MAP = {
@@ -92,9 +108,7 @@ EASING_FUNCTIONS_MAP = {
     "BounceInOut": BounceEaseInOut,
 }
 
-EASING_FUNCTION_KEYS: Any = Literal[
-    tuple(list(EASING_FUNCTIONS_MAP.keys()))
-]
+EASING_FUNCTION_KEYS: Any = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
 
 
 # actually I think for now could just use CollectionOutput (which is list[Any]
@@ -123,13 +137,9 @@ class StepParamEasingInvocation(BaseInvocation):
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Param Easing By Step",
-                "tags": ["param", "step", "easing"]
-            },
+            "ui": {"title": "Param Easing By Step", "tags": ["param", "step", "easing"]},
         }
 
-
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         log_diagnostics = False
         # convert from start_step_percent to nearest step <= (steps * start_step_percent)
@@ -170,12 +180,13 @@ class StepParamEasingInvocation(BaseInvocation):
             # and create reverse copy of list[1:end-1]
             # but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
 
-            base_easing_duration = int(np.ceil(num_easing_steps/2.0))
-            if log_diagnostics: context.services.logger.debug("base easing duration: " + str(base_easing_duration))
-            even_num_steps = (num_easing_steps % 2 == 0)  # even number of steps
-            easing_function = easing_class(start=self.start_value,
-                                           end=self.end_value,
-                                           duration=base_easing_duration - 1)
+            base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
+            if log_diagnostics:
+                context.services.logger.debug("base easing duration: " + str(base_easing_duration))
+            even_num_steps = num_easing_steps % 2 == 0  # even number of steps
+            easing_function = easing_class(
+                start=self.start_value, end=self.end_value, duration=base_easing_duration - 1
+            )
             base_easing_vals = list()
             for step_index in range(base_easing_duration):
                 easing_val = easing_function.ease(step_index)
@@ -214,9 +225,7 @@ class StepParamEasingInvocation(BaseInvocation):
         #
 
         else:  # no mirroring (default)
-            easing_function = easing_class(start=self.start_value,
-                                           end=self.end_value,
-                                           duration=num_easing_steps - 1)
+            easing_function = easing_class(start=self.start_value, end=self.end_value, duration=num_easing_steps - 1)
             for step_index in range(num_easing_steps):
                 step_val = easing_function.ease(step_index)
                 easing_list.append(step_val)
@@ -240,13 +249,11 @@ class StepParamEasingInvocation(BaseInvocation):
             ax = plt.gca()
             ax.xaxis.set_major_locator(MaxNLocator(integer=True))
             buf = io.BytesIO()
-            plt.savefig(buf, format='png')
+            plt.savefig(buf, format="png")
             buf.seek(0)
             im = PIL.Image.open(buf)
             im.show()
             buf.close()
 
         # output array of size steps, each entry list[i] is param value for step i
-        return FloatCollectionOutput(
-            collection=param_list
-        )
+        return FloatCollectionOutput(collection=param_list)
diff --git a/invokeai/app/invocations/params.py b/invokeai/app/invocations/params.py
index 0f01d65948..127eefa21d 100644
--- a/invokeai/app/invocations/params.py
+++ b/invokeai/app/invocations/params.py
@@ -4,67 +4,63 @@ from typing import Literal
 
 from pydantic import Field
 
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 from .math import FloatOutput, IntOutput
 
 # Pass-through parameter nodes - used by subgraphs
 
+
 class ParamIntInvocation(BaseInvocation):
     """An integer parameter"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["param_int"] = "param_int"
     a: int = Field(default=0, description="The integer value")
-    #fmt: on
+    # fmt: on
 
     class Config(InvocationConfig):
-      schema_extra = {
-          "ui": {
-              "tags": ["param", "integer"],
-              "title": "Integer Parameter"
-          },
-      }
+        schema_extra = {
+            "ui": {"tags": ["param", "integer"], "title": "Integer Parameter"},
+        }
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a)
 
+
 class ParamFloatInvocation(BaseInvocation):
     """A float parameter"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["param_float"] = "param_float"
     param: float = Field(default=0.0, description="The float value")
-    #fmt: on
+    # fmt: on
 
     class Config(InvocationConfig):
-      schema_extra = {
-          "ui": {
-              "tags": ["param", "float"],
-              "title": "Float Parameter"
-          },
-      }
+        schema_extra = {
+            "ui": {"tags": ["param", "float"], "title": "Float Parameter"},
+        }
 
     def invoke(self, context: InvocationContext) -> FloatOutput:
         return FloatOutput(param=self.param)
 
+
 class StringOutput(BaseInvocationOutput):
     """A string output"""
+
     type: Literal["string_output"] = "string_output"
     text: str = Field(default=None, description="The output string")
 
 
 class ParamStringInvocation(BaseInvocation):
     """A string parameter"""
-    type: Literal['param_string'] = 'param_string'
-    text: str = Field(default='', description='The string value')
+
+    type: Literal["param_string"] = "param_string"
+    text: str = Field(default="", description="The string value")
 
     class Config(InvocationConfig):
-      schema_extra = {
-          "ui": {
-              "tags": ["param", "string"],
-              "title": "String Parameter"
-          },
-      }
+        schema_extra = {
+            "ui": {"tags": ["param", "string"], "title": "String Parameter"},
+        }
 
     def invoke(self, context: InvocationContext) -> StringOutput:
         return StringOutput(text=self.text)
-    
\ No newline at end of file
diff --git a/invokeai/app/invocations/prompt.py b/invokeai/app/invocations/prompt.py
index 5d07a88759..83a397ddcf 100644
--- a/invokeai/app/invocations/prompt.py
+++ b/invokeai/app/invocations/prompt.py
@@ -7,19 +7,21 @@ from pydantic import Field, validator
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
 
+
 class PromptOutput(BaseInvocationOutput):
     """Base class for invocations that output a prompt"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["prompt"] = "prompt"
 
     prompt: str = Field(default=None, description="The output prompt")
-    #fmt: on
+    # fmt: on
 
     class Config:
         schema_extra = {
-            'required': [
-                'type',
-                'prompt',
+            "required": [
+                "type",
+                "prompt",
             ]
         }
 
@@ -44,16 +46,11 @@ class DynamicPromptInvocation(BaseInvocation):
     type: Literal["dynamic_prompt"] = "dynamic_prompt"
     prompt: str = Field(description="The prompt to parse with dynamicprompts")
     max_prompts: int = Field(default=1, description="The number of prompts to generate")
-    combinatorial: bool = Field(
-        default=False, description="Whether to use the combinatorial generator"
-    )
+    combinatorial: bool = Field(default=False, description="Whether to use the combinatorial generator")
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Dynamic Prompt",
-                "tags": ["prompt", "dynamic"]
-            },
+            "ui": {"title": "Dynamic Prompt", "tags": ["prompt", "dynamic"]},
         }
 
     def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
@@ -65,10 +62,11 @@ class DynamicPromptInvocation(BaseInvocation):
             prompts = generator.generate(self.prompt, num_images=self.max_prompts)
 
         return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
-    
+
 
 class PromptsFromFileInvocation(BaseInvocation):
-    '''Loads prompts from a text file'''
+    """Loads prompts from a text file"""
+
     # fmt: off
     type: Literal['prompt_from_file'] = 'prompt_from_file'
 
@@ -78,14 +76,11 @@ class PromptsFromFileInvocation(BaseInvocation):
     post_prompt: Optional[str] = Field(description="String to append to each prompt")
     start_line: int = Field(default=1, ge=1, description="Line in the file to start start from")
     max_prompts: int = Field(default=1, ge=0, description="Max lines to read from file (0=all)")
-    #fmt: on
+    # fmt: on
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Prompts From File",
-                "tags": ["prompt", "file"]
-            },
+            "ui": {"title": "Prompts From File", "tags": ["prompt", "file"]},
         }
 
     @validator("file_path")
@@ -103,11 +98,13 @@ class PromptsFromFileInvocation(BaseInvocation):
         with open(file_path) as f:
             for i, line in enumerate(f):
                 if i >= start_line and i < end_line:
-                    prompts.append((pre_prompt or '') + line.strip() + (post_prompt or ''))
+                    prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))
                 if i >= end_line:
                     break
         return prompts
 
     def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
-        prompts = self.promptsFromFile(self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts)
+        prompts = self.promptsFromFile(
+            self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts
+        )
         return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
diff --git a/invokeai/app/invocations/sdxl.py b/invokeai/app/invocations/sdxl.py
index 3b4d3a9d86..7dfceba853 100644
--- a/invokeai/app/invocations/sdxl.py
+++ b/invokeai/app/invocations/sdxl.py
@@ -7,13 +7,13 @@ from pydantic import Field, validator
 
 from ...backend.model_management import ModelType, SubModelType
 from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 
 from .model import UNetField, ClipField, VaeField, MainModelField, ModelInfo
 from .compel import ConditioningField
 from .latent import LatentsField, SAMPLER_NAME_VALUES, LatentsOutput, get_scheduler, build_latents_output
 
+
 class SDXLModelLoaderOutput(BaseInvocationOutput):
     """SDXL base model loader output"""
 
@@ -26,16 +26,19 @@ class SDXLModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = Field(default=None, description="Vae submodel")
     # fmt: on
 
+
 class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     """SDXL refiner model loader output"""
+
     # fmt: off
     type: Literal["sdxl_refiner_model_loader_output"] = "sdxl_refiner_model_loader_output"
     unet: UNetField = Field(default=None, description="UNet submodel")
     clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
     vae: VaeField = Field(default=None, description="Vae submodel")
     # fmt: on
-    #fmt: on
-    
+    # fmt: on
+
+
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -125,8 +128,10 @@ class SDXLModelLoaderInvocation(BaseInvocation):
             ),
         )
 
+
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
+
     type: Literal["sdxl_refiner_model_loader"] = "sdxl_refiner_model_loader"
 
     model: MainModelField = Field(description="The model to load")
@@ -196,7 +201,8 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
                 ),
             ),
         )
-    
+
+
 # Text to image
 class SDXLTextToLatentsInvocation(BaseInvocation):
     """Generates latents from conditionings."""
@@ -213,9 +219,9 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
     scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
     unet: UNetField = Field(default=None, description="UNet submodel")
     denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
-    #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
-    #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    #seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    # control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
+    # seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
     # fmt: on
 
     @validator("cfg_scale")
@@ -224,10 +230,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
         if isinstance(v, list):
             for i in v:
                 if i < 1:
-                    raise ValueError('cfg_scale must be greater than 1')
+                    raise ValueError("cfg_scale must be greater than 1")
         else:
             if v < 1:
-                raise ValueError('cfg_scale must be greater than 1')
+                raise ValueError("cfg_scale must be greater than 1")
         return v
 
     # Schema customisation
@@ -237,10 +243,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                 "title": "SDXL Text To Latents",
                 "tags": ["latents"],
                 "type_hints": {
-                  "model": "model",
-                  # "cfg_scale": "float",
-                  "cfg_scale": "number"
-                }
+                    "model": "model",
+                    # "cfg_scale": "float",
+                    "cfg_scale": "number",
+                },
             },
         }
 
@@ -265,9 +271,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
     # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
         source_node_id = graph_execution_state.prepared_source_mapping[self.id]
         latents = context.services.latents.get(self.noise.latents_name)
 
@@ -288,18 +292,15 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
         )
 
         num_inference_steps = self.steps
-        scheduler.set_timesteps(num_inference_steps)
-        timesteps = scheduler.timesteps
 
-        latents = latents * scheduler.init_noise_sigma
-
-
-        unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict(), context=context
-        )
+        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
         with unet_info as unet:
+            scheduler.set_timesteps(num_inference_steps, device=unet.device)
+            timesteps = scheduler.timesteps
+
+            latents = latents.to(device=unet.device, dtype=unet.dtype) * scheduler.init_noise_sigma
 
             extra_step_kwargs = dict()
             if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
@@ -350,10 +351,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                         if do_classifier_free_guidance:
                             noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                             noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
-                            #del noise_pred_uncond
-                            #del noise_pred_text
+                            # del noise_pred_uncond
+                            # del noise_pred_text
 
-                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        # if do_classifier_free_guidance and guidance_rescale > 0.0:
                         #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                         #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
 
@@ -364,7 +365,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
                             self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
-                            #if callback is not None and i % callback_steps == 0:
+                            # if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
             else:
                 negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
@@ -378,13 +379,13 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                 with tqdm(total=num_inference_steps) as progress_bar:
                     for i, t in enumerate(timesteps):
                         # expand the latents if we are doing classifier free guidance
-                        #latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                        # latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
 
                         latent_model_input = scheduler.scale_model_input(latents, t)
 
-                        #import gc
-                        #gc.collect()
-                        #torch.cuda.empty_cache()
+                        # import gc
+                        # gc.collect()
+                        # torch.cuda.empty_cache()
 
                         # predict the noise residual
 
@@ -411,42 +412,41 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                         # perform guidance
                         noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
 
-                        #del noise_pred_text
-                        #del noise_pred_uncond
-                        #import gc
-                        #gc.collect()
-                        #torch.cuda.empty_cache()
+                        # del noise_pred_text
+                        # del noise_pred_uncond
+                        # import gc
+                        # gc.collect()
+                        # torch.cuda.empty_cache()
 
-                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        # if do_classifier_free_guidance and guidance_rescale > 0.0:
                         #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                         #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
 
                         # compute the previous noisy sample x_t -> x_t-1
                         latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
 
-                        #del noise_pred
-                        #import gc
-                        #gc.collect()
-                        #torch.cuda.empty_cache()
+                        # del noise_pred
+                        # import gc
+                        # gc.collect()
+                        # torch.cuda.empty_cache()
 
                         # call the callback, if provided
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
                             self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
-                            #if callback is not None and i % callback_steps == 0:
+                            # if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
 
-
-
         #################
 
         latents = latents.to("cpu")
         torch.cuda.empty_cache()
 
-        name = f'{context.graph_execution_state_id}__{self.id}'
+        name = f"{context.graph_execution_state_id}__{self.id}"
         context.services.latents.save(name, latents)
         return build_latents_output(latents_name=name, latents=latents)
 
+
 class SDXLLatentsToLatentsInvocation(BaseInvocation):
     """Generates latents from conditionings."""
 
@@ -466,9 +466,9 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
     denoising_start: float = Field(default=0.0, ge=0, le=1, description="")
     denoising_end: float = Field(default=1.0, ge=0, le=1, description="")
 
-    #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
-    #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    #seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    # control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
+    # seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
     # fmt: on
 
     @validator("cfg_scale")
@@ -477,10 +477,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
         if isinstance(v, list):
             for i in v:
                 if i < 1:
-                    raise ValueError('cfg_scale must be greater than 1')
+                    raise ValueError("cfg_scale must be greater than 1")
         else:
             if v < 1:
-                raise ValueError('cfg_scale must be greater than 1')
+                raise ValueError("cfg_scale must be greater than 1")
         return v
 
     # Schema customisation
@@ -490,10 +490,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                 "title": "SDXL Latents to Latents",
                 "tags": ["latents"],
                 "type_hints": {
-                  "model": "model",
-                  # "cfg_scale": "float",
-                  "cfg_scale": "number"
-                }
+                    "model": "model",
+                    # "cfg_scale": "float",
+                    "cfg_scale": "number",
+                },
             },
         }
 
@@ -518,9 +518,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
     # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
         source_node_id = graph_execution_state.prepared_source_mapping[self.id]
         latents = context.services.latents.get(self.latents.latents_name)
 
@@ -540,26 +538,27 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
             scheduler_name=self.scheduler,
         )
 
-        # apply denoising_start
-        num_inference_steps = self.steps
-        scheduler.set_timesteps(num_inference_steps)
-
-        t_start = int(round(self.denoising_start * num_inference_steps))
-        timesteps = scheduler.timesteps[t_start * scheduler.order:]
-        num_inference_steps = num_inference_steps - t_start
-
-        # apply noise(if provided)
-        if self.noise is not None and timesteps.shape[0] > 0:
-            noise = context.services.latents.get(self.noise.latents_name)
-            latents = scheduler.add_noise(latents, noise, timesteps[:1])
-            del noise
-
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict(), context=context,
+            **self.unet.unet.dict(),
+            context=context,
         )
+
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
         with unet_info as unet:
+            # apply denoising_start
+            num_inference_steps = self.steps
+            scheduler.set_timesteps(num_inference_steps, device=unet.device)
+
+            t_start = int(round(self.denoising_start * num_inference_steps))
+            timesteps = scheduler.timesteps[t_start * scheduler.order :]
+            num_inference_steps = num_inference_steps - t_start
+
+            # apply noise(if provided)
+            if self.noise is not None and timesteps.shape[0] > 0:
+                noise = context.services.latents.get(self.noise.latents_name)
+                latents = scheduler.add_noise(latents, noise, timesteps[:1])
+                del noise
 
             # apply scheduler extra args
             extra_step_kwargs = dict()
@@ -611,10 +610,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                         if do_classifier_free_guidance:
                             noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                             noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
-                            #del noise_pred_uncond
-                            #del noise_pred_text
+                            # del noise_pred_uncond
+                            # del noise_pred_text
 
-                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        # if do_classifier_free_guidance and guidance_rescale > 0.0:
                         #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                         #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
 
@@ -625,7 +624,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
                             self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
-                            #if callback is not None and i % callback_steps == 0:
+                            # if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
             else:
                 negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
@@ -639,13 +638,13 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                 with tqdm(total=num_inference_steps) as progress_bar:
                     for i, t in enumerate(timesteps):
                         # expand the latents if we are doing classifier free guidance
-                        #latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                        # latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
 
                         latent_model_input = scheduler.scale_model_input(latents, t)
 
-                        #import gc
-                        #gc.collect()
-                        #torch.cuda.empty_cache()
+                        # import gc
+                        # gc.collect()
+                        # torch.cuda.empty_cache()
 
                         # predict the noise residual
 
@@ -672,38 +671,36 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                         # perform guidance
                         noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
 
-                        #del noise_pred_text
-                        #del noise_pred_uncond
-                        #import gc
-                        #gc.collect()
-                        #torch.cuda.empty_cache()
+                        # del noise_pred_text
+                        # del noise_pred_uncond
+                        # import gc
+                        # gc.collect()
+                        # torch.cuda.empty_cache()
 
-                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        # if do_classifier_free_guidance and guidance_rescale > 0.0:
                         #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                         #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
 
                         # compute the previous noisy sample x_t -> x_t-1
                         latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
 
-                        #del noise_pred
-                        #import gc
-                        #gc.collect()
-                        #torch.cuda.empty_cache()
+                        # del noise_pred
+                        # import gc
+                        # gc.collect()
+                        # torch.cuda.empty_cache()
 
                         # call the callback, if provided
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
                             self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
-                            #if callback is not None and i % callback_steps == 0:
+                            # if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
 
-
-
         #################
 
         latents = latents.to("cpu")
         torch.cuda.empty_cache()
 
-        name = f'{context.graph_execution_state_id}__{self.id}'
+        name = f"{context.graph_execution_state_id}__{self.id}"
         context.services.latents.save(name, latents)
         return build_latents_output(latents_name=name, latents=latents)
diff --git a/invokeai/app/invocations/upscale.py b/invokeai/app/invocations/upscale.py
index 1a45a631df..fd220223db 100644
--- a/invokeai/app/invocations/upscale.py
+++ b/invokeai/app/invocations/upscale.py
@@ -29,16 +29,11 @@ class ESRGANInvocation(BaseInvocation):
 
     type: Literal["esrgan"] = "esrgan"
     image: Union[ImageField, None] = Field(default=None, description="The input image")
-    model_name: ESRGAN_MODELS = Field(
-        default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use"
-    )
+    model_name: ESRGAN_MODELS = Field(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
 
     class Config(InvocationConfig):
         schema_extra = {
-            "ui": {
-                "title": "Upscale (RealESRGAN)",
-                "tags": ["image", "upscale", "realesrgan"]
-            },
+            "ui": {"title": "Upscale (RealESRGAN)", "tags": ["image", "upscale", "realesrgan"]},
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -108,9 +103,7 @@ class ESRGANInvocation(BaseInvocation):
         upscaled_image, img_mode = upsampler.enhance(cv_image)
 
         # back to PIL
-        pil_image = Image.fromarray(
-            cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)
-        ).convert("RGBA")
+        pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
 
         image_dto = context.services.images.create(
             image=pil_image,
diff --git a/invokeai/app/models/exceptions.py b/invokeai/app/models/exceptions.py
index 32ad3b8f03..662e1948ce 100644
--- a/invokeai/app/models/exceptions.py
+++ b/invokeai/app/models/exceptions.py
@@ -1,3 +1,4 @@
 class CanceledException(Exception):
     """Execution canceled by user."""
+
     pass
diff --git a/invokeai/app/models/image.py b/invokeai/app/models/image.py
index 988a3e1447..2a5a0f9d3b 100644
--- a/invokeai/app/models/image.py
+++ b/invokeai/app/models/image.py
@@ -1,8 +1,83 @@
 from enum import Enum
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Literal
 from pydantic import BaseModel, Field
 
 from invokeai.app.util.metaenum import MetaEnum
+from ..invocations.baseinvocation import (
+    BaseInvocationOutput,
+    InvocationConfig,
+)
+
+
+class ImageField(BaseModel):
+    """An image field used for passing image objects between invocations"""
+
+    image_name: Optional[str] = Field(default=None, description="The name of the image")
+
+    class Config:
+        schema_extra = {"required": ["image_name"]}
+
+
+class ColorField(BaseModel):
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)
+
+
+class ProgressImage(BaseModel):
+    """The progress image sent intermittently during processing"""
+
+    width: int = Field(description="The effective width of the image in pixels")
+    height: int = Field(description="The effective height of the image in pixels")
+    dataURL: str = Field(description="The image data as a b64 data URL")
+
+
+class PILInvocationConfig(BaseModel):
+    """Helper class to provide all PIL invocations with additional config"""
+
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["PIL", "image"],
+            },
+        }
+
+
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    # fmt: off
+    type: Literal["image_output"] = "image_output"
+    image:      ImageField = Field(default=None, description="The output image")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
+    # fmt: on
+
+    class Config:
+        schema_extra = {"required": ["type", "image", "width", "height"]}
+
+
+class MaskOutput(BaseInvocationOutput):
+    """Base class for invocations that output a mask"""
+
+    # fmt: off
+    type: Literal["mask"] = "mask"
+    mask:      ImageField = Field(default=None, description="The output mask")
+    width:            int = Field(description="The width of the mask in pixels")
+    height:           int = Field(description="The height of the mask in pixels")
+    # fmt: on
+
+    class Config:
+        schema_extra = {
+            "required": [
+                "type",
+                "mask",
+            ]
+        }
 
 
 class ResourceOrigin(str, Enum, metaclass=MetaEnum):
@@ -61,30 +136,3 @@ class InvalidImageCategoryException(ValueError):
 
     def __init__(self, message="Invalid image category."):
         super().__init__(message)
-
-
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
-
-    image_name: Optional[str] = Field(default=None, description="The name of the image")
-
-    class Config:
-        schema_extra = {"required": ["image_name"]}
-
-
-class ColorField(BaseModel):
-    r: int = Field(ge=0, le=255, description="The red component")
-    g: int = Field(ge=0, le=255, description="The green component")
-    b: int = Field(ge=0, le=255, description="The blue component")
-    a: int = Field(ge=0, le=255, description="The alpha component")
-
-    def tuple(self) -> Tuple[int, int, int, int]:
-        return (self.r, self.g, self.b, self.a)
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
diff --git a/invokeai/app/services/board_image_record_storage.py b/invokeai/app/services/board_image_record_storage.py
index 491972bd32..f0007c8cef 100644
--- a/invokeai/app/services/board_image_record_storage.py
+++ b/invokeai/app/services/board_image_record_storage.py
@@ -207,9 +207,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
             raise e
         finally:
             self._lock.release()
-        return OffsetPaginatedResults(
-            items=images, offset=offset, limit=limit, total=count
-        )
+        return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
 
     def get_all_board_image_names_for_board(self, board_id: str) -> list[str]:
         try:
diff --git a/invokeai/app/services/board_images.py b/invokeai/app/services/board_images.py
index b9f9663603..22332d6c29 100644
--- a/invokeai/app/services/board_images.py
+++ b/invokeai/app/services/board_images.py
@@ -102,9 +102,7 @@ class BoardImagesService(BoardImagesServiceABC):
         self,
         board_id: str,
     ) -> list[str]:
-        return self._services.board_image_records.get_all_board_image_names_for_board(
-            board_id
-        )
+        return self._services.board_image_records.get_all_board_image_names_for_board(board_id)
 
     def get_board_for_image(
         self,
@@ -114,9 +112,7 @@ class BoardImagesService(BoardImagesServiceABC):
         return board_id
 
 
-def board_record_to_dto(
-    board_record: BoardRecord, cover_image_name: Optional[str], image_count: int
-) -> BoardDTO:
+def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
     """Converts a board record to a board DTO."""
     return BoardDTO(
         **board_record.dict(exclude={"cover_image_name"}),
diff --git a/invokeai/app/services/board_record_storage.py b/invokeai/app/services/board_record_storage.py
index 15ea9cc5a7..2fad7b0ab3 100644
--- a/invokeai/app/services/board_record_storage.py
+++ b/invokeai/app/services/board_record_storage.py
@@ -15,9 +15,7 @@ from pydantic import BaseModel, Field, Extra
 
 class BoardChanges(BaseModel, extra=Extra.forbid):
     board_name: Optional[str] = Field(description="The board's new name.")
-    cover_image_name: Optional[str] = Field(
-        description="The name of the board's new cover image."
-    )
+    cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
 
 
 class BoardRecordNotFoundException(Exception):
@@ -292,9 +290,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
 
             count = cast(int, self._cursor.fetchone()[0])
 
-            return OffsetPaginatedResults[BoardRecord](
-                items=boards, offset=offset, limit=limit, total=count
-            )
+            return OffsetPaginatedResults[BoardRecord](items=boards, offset=offset, limit=limit, total=count)
 
         except sqlite3.Error as e:
             self._conn.rollback()
diff --git a/invokeai/app/services/boards.py b/invokeai/app/services/boards.py
index 9361322e6c..53d30b2e85 100644
--- a/invokeai/app/services/boards.py
+++ b/invokeai/app/services/boards.py
@@ -108,16 +108,12 @@ class BoardService(BoardServiceABC):
 
     def get_dto(self, board_id: str) -> BoardDTO:
         board_record = self._services.board_records.get(board_id)
-        cover_image = self._services.image_records.get_most_recent_image_for_board(
-            board_record.board_id
-        )
+        cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
         if cover_image:
             cover_image_name = cover_image.image_name
         else:
             cover_image_name = None
-        image_count = self._services.board_image_records.get_image_count_for_board(
-            board_id
-        )
+        image_count = self._services.board_image_records.get_image_count_for_board(board_id)
         return board_record_to_dto(board_record, cover_image_name, image_count)
 
     def update(
@@ -126,60 +122,44 @@ class BoardService(BoardServiceABC):
         changes: BoardChanges,
     ) -> BoardDTO:
         board_record = self._services.board_records.update(board_id, changes)
-        cover_image = self._services.image_records.get_most_recent_image_for_board(
-            board_record.board_id
-        )
+        cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
         if cover_image:
             cover_image_name = cover_image.image_name
         else:
             cover_image_name = None
 
-        image_count = self._services.board_image_records.get_image_count_for_board(
-            board_id
-        )
+        image_count = self._services.board_image_records.get_image_count_for_board(board_id)
         return board_record_to_dto(board_record, cover_image_name, image_count)
 
     def delete(self, board_id: str) -> None:
         self._services.board_records.delete(board_id)
 
-    def get_many(
-        self, offset: int = 0, limit: int = 10
-    ) -> OffsetPaginatedResults[BoardDTO]:
+    def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
         board_records = self._services.board_records.get_many(offset, limit)
         board_dtos = []
         for r in board_records.items:
-            cover_image = self._services.image_records.get_most_recent_image_for_board(
-                r.board_id
-            )
+            cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
             if cover_image:
                 cover_image_name = cover_image.image_name
             else:
                 cover_image_name = None
 
-            image_count = self._services.board_image_records.get_image_count_for_board(
-                r.board_id
-            )
+            image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
             board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
 
-        return OffsetPaginatedResults[BoardDTO](
-            items=board_dtos, offset=offset, limit=limit, total=len(board_dtos)
-        )
+        return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
     def get_all(self) -> list[BoardDTO]:
         board_records = self._services.board_records.get_all()
         board_dtos = []
         for r in board_records:
-            cover_image = self._services.image_records.get_most_recent_image_for_board(
-                r.board_id
-            )
+            cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
             if cover_image:
                 cover_image_name = cover_image.image_name
             else:
                 cover_image_name = None
 
-            image_count = self._services.board_image_records.get_image_count_for_board(
-                r.board_id
-            )
+            image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
             board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
 
-        return board_dtos
\ No newline at end of file
+        return board_dtos
diff --git a/invokeai/app/services/config.py b/invokeai/app/services/config.py
index b1e4e7ed5d..98855fe879 100644
--- a/invokeai/app/services/config.py
+++ b/invokeai/app/services/config.py
@@ -1,6 +1,6 @@
 # Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
 
-'''Invokeai configuration system.
+"""Invokeai configuration system.
 
 Arguments and fields are taken from the pydantic definition of the
 model.  Defaults can be set by creating a yaml configuration file that
@@ -28,7 +28,6 @@ InvokeAI:
     always_use_cpu: false
     free_gpu_mem: false
   Features:
-    nsfw_checker: true
     restore: true
     esrgan: true
     patchmatch: true
@@ -92,18 +91,18 @@ Typical usage at the top level file:
 
  from invokeai.app.services.config import InvokeAIAppConfig
 
- # get global configuration and print its nsfw_checker value
+ # get global configuration and print its cache size
  conf = InvokeAIAppConfig.get_config()
  conf.parse_args()
- print(conf.nsfw_checker)
+ print(conf.max_cache_size)
 
 Typical usage in a backend module:
 
  from invokeai.app.services.config import InvokeAIAppConfig
 
- # get global configuration and print its nsfw_checker value
+ # get global configuration and print its cache size value
  conf = InvokeAIAppConfig.get_config()
- print(conf.nsfw_checker)
+ print(conf.max_cache_size)
 
 
 Computed properties:
@@ -159,7 +158,7 @@ two configs are kept in separate sections of the config file:
         outdir: outputs
      ...
 
-'''
+"""
 from __future__ import annotations
 import argparse
 import pydoc
@@ -171,64 +170,68 @@ from pathlib import Path
 from pydantic import BaseSettings, Field, parse_obj_as
 from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
 
-INIT_FILE = Path('invokeai.yaml')
-MODEL_CORE = Path('models/core')
-DB_FILE   = Path('invokeai.db')
-LEGACY_INIT_FILE = Path('invokeai.init')
+INIT_FILE = Path("invokeai.yaml")
+MODEL_CORE = Path("models/core")
+DB_FILE = Path("invokeai.db")
+LEGACY_INIT_FILE = Path("invokeai.init")
+
 
 class InvokeAISettings(BaseSettings):
-    '''
+    """
     Runtime configuration settings in which default values are
     read from an omegaconf .yaml file.
-    '''
-    initconf             : ClassVar[DictConfig] = None
-    argparse_groups      : ClassVar[Dict] = {}
+    """
 
-    def parse_args(self, argv: list=sys.argv[1:]):
+    initconf: ClassVar[DictConfig] = None
+    argparse_groups: ClassVar[Dict] = {}
+
+    def parse_args(self, argv: list = sys.argv[1:]):
         parser = self.get_parser()
         opt = parser.parse_args(argv)
         for name in self.__fields__:
             if name not in self._excluded():
-                setattr(self, name, getattr(opt,name))
+                setattr(self, name, getattr(opt, name))
 
-    def to_yaml(self)->str:
+    def to_yaml(self) -> str:
         """
         Return a YAML string representing our settings. This can be used
         as the contents of `invokeai.yaml` to restore settings later.
         """
         cls = self.__class__
-        type = get_args(get_type_hints(cls)['type'])[0]
-        field_dict = dict({type:dict()})
-        for name,field in self.__fields__.items():
+        type = get_args(get_type_hints(cls)["type"])[0]
+        field_dict = dict({type: dict()})
+        for name, field in self.__fields__.items():
             if name in cls._excluded_from_yaml():
                 continue
             category = field.field_info.extra.get("category") or "Uncategorized"
-            value = getattr(self,name)
+            value = getattr(self, name)
             if category not in field_dict[type]:
                 field_dict[type][category] = dict()
             # keep paths as strings to make it easier to read
-            field_dict[type][category][name] = str(value) if isinstance(value,Path) else value
+            field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
         conf = OmegaConf.create(field_dict)
         return OmegaConf.to_yaml(conf)
 
     @classmethod
     def add_parser_arguments(cls, parser):
-        if 'type' in get_type_hints(cls):
-            settings_stanza = get_args(get_type_hints(cls)['type'])[0]
+        if "type" in get_type_hints(cls):
+            settings_stanza = get_args(get_type_hints(cls)["type"])[0]
         else:
             settings_stanza = "Uncategorized"
 
-        env_prefix = cls.Config.env_prefix if hasattr(cls.Config,'env_prefix') else settings_stanza.upper()
+        env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
 
-        initconf = cls.initconf.get(settings_stanza) \
-            if cls.initconf and settings_stanza in cls.initconf \
-               else OmegaConf.create()
+        initconf = (
+            cls.initconf.get(settings_stanza)
+            if cls.initconf and settings_stanza in cls.initconf
+            else OmegaConf.create()
+        )
 
         # create an upcase version of the environment in
         # order to achieve case-insensitive environment
         # variables (the way Windows does)
         upcase_environ = dict()
-        for key,value in os.environ.items():
+        for key, value in os.environ.items():
             upcase_environ[key.upper()] = value
 
         fields = cls.__fields__
@@ -238,8 +241,8 @@ class InvokeAISettings(BaseSettings):
             if name not in cls._excluded():
                 current_default = field.default
 
-                category = field.field_info.extra.get("category","Uncategorized")
-                env_name = env_prefix + '_' + name
+                category = field.field_info.extra.get("category", "Uncategorized")
+                env_name = env_prefix + "_" + name
                 if category in initconf and name in initconf.get(category):
                     field.default = initconf.get(category).get(name)
                 if env_name.upper() in upcase_environ:
@@ -249,15 +252,15 @@ class InvokeAISettings(BaseSettings):
                 field.default = current_default
 
     @classmethod
-    def cmd_name(self, command_field: str='type')->str:
+    def cmd_name(self, command_field: str = "type") -> str:
         hints = get_type_hints(self)
         if command_field in hints:
             return get_args(hints[command_field])[0]
         else:
-            return 'Uncategorized'
+            return "Uncategorized"
 
     @classmethod
-    def get_parser(cls)->ArgumentParser:
+    def get_parser(cls) -> ArgumentParser:
         parser = PagingArgumentParser(
             prog=cls.cmd_name(),
             description=cls.__doc__,
@@ -270,24 +273,41 @@ class InvokeAISettings(BaseSettings):
         parser.add_parser(cls.cmd_name(), help=cls.__doc__)
 
     @classmethod
-    def _excluded(self)->List[str]:
+    def _excluded(self) -> List[str]:
         # internal fields that shouldn't be exposed as command line options
-        return ['type','initconf']
-    
+        return ["type", "initconf"]
+
     @classmethod
-    def _excluded_from_yaml(self)->List[str]:
+    def _excluded_from_yaml(self) -> List[str]:
         # combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
-        return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root']
+        return [
+            "type",
+            "initconf",
+            "gpu_mem_reserved",
+            "max_loaded_models",
+            "version",
+            "from_file",
+            "model",
+            "restore",
+            "root",
+            "nsfw_checker",
+        ]
 
     class Config:
-        env_file_encoding = 'utf-8'
+        env_file_encoding = "utf-8"
         arbitrary_types_allowed = True
         case_sensitive = True
 
     @classmethod
-    def add_field_argument(cls, command_parser, name: str, field, default_override = None):
+    def add_field_argument(cls, command_parser, name: str, field, default_override=None):
         field_type = get_type_hints(cls).get(name)
-        default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
+        default = (
+            default_override
+            if default_override is not None
+            else field.default
+            if field.default_factory is None
+            else field.default_factory()
+        )
         if category := field.field_info.extra.get("category"):
             if category not in cls.argparse_groups:
                 cls.argparse_groups[category] = command_parser.add_argument_group(category)
@@ -316,10 +336,10 @@ class InvokeAISettings(BaseSettings):
             argparse_group.add_argument(
                 f"--{name}",
                 dest=name,
-                nargs='*',
+                nargs="*",
                 type=field.type_,
                 default=default,
-                action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
+                action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
                 help=field.field_info.description,
             )
         else:
@@ -328,31 +348,35 @@ class InvokeAISettings(BaseSettings):
                 dest=name,
                 type=field.type_,
                 default=default,
-                action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
+                action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
                 help=field.field_info.description,
             )
-def _find_root()->Path:
+
+
+def _find_root() -> Path:
     venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
     if os.environ.get("INVOKEAI_ROOT"):
         root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
-    elif any([(venv.parent/x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
+    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
         root = (venv.parent).resolve()
     else:
         root = Path("~/invokeai").expanduser().resolve()
     return root
 
+
 class InvokeAIAppConfig(InvokeAISettings):
-    '''
-Generate images using Stable Diffusion. Use "invokeai" to launch
-the command-line client (recommended for experts only), or
-"invokeai-web" to launch the web server. Global options
-can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
-setting environment variables INVOKEAI_<setting>.
-     '''
+    """
+    Generate images using Stable Diffusion. Use "invokeai" to launch
+    the command-line client (recommended for experts only), or
+    "invokeai-web" to launch the web server. Global options
+    can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
+    setting environment variables INVOKEAI_<setting>.
+    """
+
     singleton_config: ClassVar[InvokeAIAppConfig] = None
     singleton_init: ClassVar[Dict] = None
 
-    #fmt: off
+    # fmt: off
     type: Literal["InvokeAI"] = "InvokeAI"
     host                : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
     port                : int = Field(default=9090, description="Port to bind to", category='Web Server')
@@ -364,7 +388,6 @@ setting environment variables INVOKEAI_<setting>.
     esrgan              : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
     internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
     log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
-    nsfw_checker        : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
     patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
     restore             : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
 
@@ -374,6 +397,7 @@ setting environment variables INVOKEAI_<setting>.
     max_cache_size      : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
     max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
     gpu_mem_reserved    : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED')
+    nsfw_checker        : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED')
     precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
@@ -400,16 +424,16 @@ setting environment variables INVOKEAI_<setting>.
     log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or  higher", category="Logging")
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
-    #fmt: on
+    # fmt: on
 
-    def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
-        '''
+    def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
+        """
         Update settings with contents of init file, environment, and
         command-line settings.
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
         :param clobber: ovewrite any initialization parameters passed during initialization
-        '''
+        """
         # Set the runtime root directory. We parse command-line switches here
         # in order to pick up the --root_dir option.
         super().parse_args(argv)
@@ -426,125 +450,139 @@ setting environment variables INVOKEAI_<setting>.
         if self.singleton_init and not clobber:
             hints = get_type_hints(self.__class__)
             for k in self.singleton_init:
-                setattr(self,k,parse_obj_as(hints[k],self.singleton_init[k]))
+                setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k]))
 
     @classmethod
-    def get_config(cls,**kwargs)->InvokeAIAppConfig:
-        '''
+    def get_config(cls, **kwargs) -> InvokeAIAppConfig:
+        """
         This returns a singleton InvokeAIAppConfig configuration object.
-        '''
-        if cls.singleton_config is None \
-           or type(cls.singleton_config)!=cls \
-           or (kwargs and cls.singleton_init != kwargs):
+        """
+        if (
+            cls.singleton_config is None
+            or type(cls.singleton_config) != cls
+            or (kwargs and cls.singleton_init != kwargs)
+        ):
             cls.singleton_config = cls(**kwargs)
             cls.singleton_init = kwargs
         return cls.singleton_config
 
     @property
-    def root_path(self)->Path:
-        '''
+    def root_path(self) -> Path:
+        """
         Path to the runtime root directory
-        '''
+        """
         if self.root:
             return Path(self.root).expanduser().absolute()
         else:
             return self.find_root()
 
     @property
-    def root_dir(self)->Path:
-        '''
+    def root_dir(self) -> Path:
+        """
         Alias for above.
-        '''
+        """
         return self.root_path
 
-    def _resolve(self,partial_path:Path)->Path:
+    def _resolve(self, partial_path: Path) -> Path:
         return (self.root_path / partial_path).resolve()
 
     @property
-    def init_file_path(self)->Path:
-        '''
+    def init_file_path(self) -> Path:
+        """
         Path to invokeai.yaml
-        '''
+        """
         return self._resolve(INIT_FILE)
 
     @property
-    def output_path(self)->Path:
-        '''
+    def output_path(self) -> Path:
+        """
         Path to defaults outputs directory.
-        '''
+        """
         return self._resolve(self.outdir)
 
     @property
-    def db_path(self)->Path:
-        '''
+    def db_path(self) -> Path:
+        """
         Path to the invokeai.db file.
-        '''
+        """
         return self._resolve(self.db_dir) / DB_FILE
 
     @property
-    def model_conf_path(self)->Path:
-        '''
+    def model_conf_path(self) -> Path:
+        """
         Path to models configuration file.
-        '''
+        """
         return self._resolve(self.conf_path)
 
     @property
-    def legacy_conf_path(self)->Path:
-        '''
+    def legacy_conf_path(self) -> Path:
+        """
         Path to directory of legacy configuration files (e.g. v1-inference.yaml)
-        '''
+        """
         return self._resolve(self.legacy_conf_dir)
 
     @property
-    def models_path(self)->Path:
-        '''
+    def models_path(self) -> Path:
+        """
         Path to the models directory
-        '''
+        """
         return self._resolve(self.models_dir)
 
     @property
-    def autoconvert_path(self)->Path:
-        '''
+    def autoconvert_path(self) -> Path:
+        """
         Path to the directory containing models to be imported automatically at startup.
-        '''
+        """
         return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
 
     # the following methods support legacy calls leftover from the Globals era
     @property
-    def full_precision(self)->bool:
+    def full_precision(self) -> bool:
         """Return true if precision set to float32"""
-        return self.precision=='float32'
+        return self.precision == "float32"
 
     @property
-    def disable_xformers(self)->bool:
+    def disable_xformers(self) -> bool:
         """Return true if xformers_enabled is false"""
         return not self.xformers_enabled
 
     @property
-    def try_patchmatch(self)->bool:
+    def try_patchmatch(self) -> bool:
         """Return true if patchmatch true"""
         return self.patchmatch
 
+    @property
+    def nsfw_checker(self) -> bool:
+        """NSFW node is always active and disabled from Web UIe"""
+        return True
+
+    @property
+    def invisible_watermark(self) -> bool:
+        """invisible watermark node is always active and disabled from Web UIe"""
+        return True
+
     @staticmethod
-    def find_root()->Path:
-        '''
+    def find_root() -> Path:
+        """
         Choose the runtime root directory when not specified on command line or
         init file.
-        '''
+        """
         return _find_root()
 
 
 class PagingArgumentParser(argparse.ArgumentParser):
-    '''
+    """
     A custom ArgumentParser that uses pydoc to page its output.
     It also supports reading defaults from an init file.
-    '''
+    """
+
     def print_help(self, file=None):
         text = self.format_help()
         pydoc.pager(text)
 
-def get_invokeai_config(**kwargs)->InvokeAIAppConfig:
-    '''
+
+def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
+    """
     Legacy function which returns InvokeAIAppConfig.get_config()
-    '''
+    """
     return InvokeAIAppConfig.get_config(**kwargs)
diff --git a/invokeai/app/services/default_graphs.py b/invokeai/app/services/default_graphs.py
index 92263751b7..cafb6f0339 100644
--- a/invokeai/app/services/default_graphs.py
+++ b/invokeai/app/services/default_graphs.py
@@ -1,4 +1,5 @@
 from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation
+from ..invocations.image import ImageNSFWBlurInvocation
 from ..invocations.noise import NoiseInvocation
 from ..invocations.compel import CompelInvocation
 from ..invocations.params import ParamIntInvocation
@@ -6,55 +7,80 @@ from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Gr
 from .item_storage import ItemStorageABC
 
 
-default_text_to_image_graph_id = '539b2af5-2b4d-4d8c-8071-e54a3255fc74'
+default_text_to_image_graph_id = "539b2af5-2b4d-4d8c-8071-e54a3255fc74"
 
 
 def create_text_to_image() -> LibraryGraph:
     return LibraryGraph(
         id=default_text_to_image_graph_id,
-        name='t2i',
-        description='Converts text to an image',
+        name="t2i",
+        description="Converts text to an image",
         graph=Graph(
             nodes={
-                'width': ParamIntInvocation(id='width', a=512),
-                'height': ParamIntInvocation(id='height', a=512),
-                'seed': ParamIntInvocation(id='seed', a=-1),
-                '3': NoiseInvocation(id='3'),
-                '4': CompelInvocation(id='4'),
-                '5': CompelInvocation(id='5'),
-                '6': TextToLatentsInvocation(id='6'),
-                '7': LatentsToImageInvocation(id='7'),
+                "width": ParamIntInvocation(id="width", a=512),
+                "height": ParamIntInvocation(id="height", a=512),
+                "seed": ParamIntInvocation(id="seed", a=-1),
+                "3": NoiseInvocation(id="3"),
+                "4": CompelInvocation(id="4"),
+                "5": CompelInvocation(id="5"),
+                "6": TextToLatentsInvocation(id="6"),
+                "7": LatentsToImageInvocation(id="7"),
+                "8": ImageNSFWBlurInvocation(id="8"),
             },
             edges=[
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
-                Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
-                Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
-                Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
-            ]
+                Edge(
+                    source=EdgeConnection(node_id="width", field="a"),
+                    destination=EdgeConnection(node_id="3", field="width"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="height", field="a"),
+                    destination=EdgeConnection(node_id="3", field="height"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="seed", field="a"),
+                    destination=EdgeConnection(node_id="3", field="seed"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="3", field="noise"),
+                    destination=EdgeConnection(node_id="6", field="noise"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="6", field="latents"),
+                    destination=EdgeConnection(node_id="7", field="latents"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="4", field="conditioning"),
+                    destination=EdgeConnection(node_id="6", field="positive_conditioning"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="5", field="conditioning"),
+                    destination=EdgeConnection(node_id="6", field="negative_conditioning"),
+                ),
+                Edge(
+                    source=EdgeConnection(node_id="7", field="image"),
+                    destination=EdgeConnection(node_id="8", field="image"),
+                ),
+            ],
         ),
         exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
-            ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
-            ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height'),
-            ExposedNodeInput(node_path='seed', field='a', alias='seed'),
+            ExposedNodeInput(node_path="4", field="prompt", alias="positive_prompt"),
+            ExposedNodeInput(node_path="5", field="prompt", alias="negative_prompt"),
+            ExposedNodeInput(node_path="width", field="a", alias="width"),
+            ExposedNodeInput(node_path="height", field="a", alias="height"),
+            ExposedNodeInput(node_path="seed", field="a", alias="seed"),
         ],
-        exposed_outputs=[
-            ExposedNodeOutput(node_path='7', field='image', alias='image')
-        ])
+        exposed_outputs=[ExposedNodeOutput(node_path="8", field="image", alias="image")],
+    )
 
 
 def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
     """Creates the default system graphs, or adds new versions if the old ones don't match"""
-    
+
     # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
     graphs: list[LibraryGraph] = list()
 
     # text_to_image = graph_library.get(default_text_to_image_graph_id)
-    
+
     # # TODO: Check if the graph is the same as the default one, and if not, update it
     # #if text_to_image is None:
     text_to_image = create_text_to_image()
diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py
index 73d74de2d9..30fa89bd29 100644
--- a/invokeai/app/services/events.py
+++ b/invokeai/app/services/events.py
@@ -44,9 +44,7 @@ class EventServiceBase:
                 graph_execution_state_id=graph_execution_state_id,
                 node=node,
                 source_node_id=source_node_id,
-                progress_image=progress_image.dict()
-                if progress_image is not None
-                else None,
+                progress_image=progress_image.dict() if progress_image is not None else None,
                 step=step,
                 total_steps=total_steps,
             ),
@@ -90,9 +88,7 @@ class EventServiceBase:
             ),
         )
 
-    def emit_invocation_started(
-        self, graph_execution_state_id: str, node: dict, source_node_id: str
-    ) -> None:
+    def emit_invocation_started(self, graph_execution_state_id: str, node: dict, source_node_id: str) -> None:
         """Emitted when an invocation has started"""
         self.__emit_session_event(
             event_name="invocation_started",
diff --git a/invokeai/app/services/graph.py b/invokeai/app/services/graph.py
index 24096da29b..d7f021df14 100644
--- a/invokeai/app/services/graph.py
+++ b/invokeai/app/services/graph.py
@@ -28,6 +28,7 @@ from ..invocations.baseinvocation import (
 # in 3.10 this would be "from types import NoneType"
 NoneType = type(None)
 
+
 class EdgeConnection(BaseModel):
     node_id: str = Field(description="The id of the node for this edge connection")
     field: str = Field(description="The field for this connection")
@@ -61,6 +62,7 @@ def get_input_field(node: BaseInvocation, field: str) -> Any:
     node_input_field = node_inputs.get(field) or None
     return node_input_field
 
+
 def is_union_subtype(t1, t2):
     t1_args = get_args(t1)
     t2_args = get_args(t2)
@@ -71,6 +73,7 @@ def is_union_subtype(t1, t2):
         # t1 is a Union, check that all of its types are in t2_args
         return all(arg in t2_args for arg in t1_args)
 
+
 def is_list_or_contains_list(t):
     t_args = get_args(t)
 
@@ -154,15 +157,17 @@ class GraphInvocationOutput(BaseInvocationOutput):
 
     class Config:
         schema_extra = {
-            'required': [
-                'type',
-                'image',
+            "required": [
+                "type",
+                "image",
             ]
         }
 
+
 # TODO: Fill this out and move to invocations
 class GraphInvocation(BaseInvocation):
     """Execute a graph"""
+
     type: Literal["graph"] = "graph"
 
     # TODO: figure out how to create a default here
@@ -182,23 +187,21 @@ class IterateInvocationOutput(BaseInvocationOutput):
 
     class Config:
         schema_extra = {
-            'required': [
-                'type',
-                'item',
+            "required": [
+                "type",
+                "item",
             ]
         }
 
+
 # TODO: Fill this out and move to invocations
 class IterateInvocation(BaseInvocation):
     """Iterates over a list of items"""
+
     type: Literal["iterate"] = "iterate"
 
-    collection: list[Any] = Field(
-        description="The list of items to iterate over", default_factory=list
-    )
-    index: int = Field(
-        description="The index, will be provided on executed iterators", default=0
-    )
+    collection: list[Any] = Field(description="The list of items to iterate over", default_factory=list)
+    index: int = Field(description="The index, will be provided on executed iterators", default=0)
 
     def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
         """Produces the outputs as values"""
@@ -212,12 +215,13 @@ class CollectInvocationOutput(BaseInvocationOutput):
 
     class Config:
         schema_extra = {
-            'required': [
-                'type',
-                'collection',
+            "required": [
+                "type",
+                "collection",
             ]
         }
 
+
 class CollectInvocation(BaseInvocation):
     """Collects values into a collection"""
 
@@ -269,9 +273,7 @@ class Graph(BaseModel):
         if node_path in self.nodes:
             return (self, node_path)
 
-        node_id = (
-            node_path if "." not in node_path else node_path[: node_path.index(".")]
-        )
+        node_id = node_path if "." not in node_path else node_path[: node_path.index(".")]
         if node_id not in self.nodes:
             raise NodeNotFoundError(f"Node {node_path} not found in graph")
 
@@ -333,9 +335,7 @@ class Graph(BaseModel):
                 return False
 
         # Validate all edges reference nodes in the graph
-        node_ids = set(
-            [e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges]
-        )
+        node_ids = set([e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges])
         if not all((self.has_node(node_id) for node_id in node_ids)):
             return False
 
@@ -361,22 +361,14 @@ class Graph(BaseModel):
         # Validate all iterators
         # TODO: may need to validate all iterators in subgraphs so edge connections in parent graphs will be available
         if not all(
-            (
-                self._is_iterator_connection_valid(n.id)
-                for n in self.nodes.values()
-                if isinstance(n, IterateInvocation)
-            )
+            (self._is_iterator_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, IterateInvocation))
         ):
             return False
 
         # Validate all collectors
         # TODO: may need to validate all collectors in subgraphs so edge connections in parent graphs will be available
         if not all(
-            (
-                self._is_collector_connection_valid(n.id)
-                for n in self.nodes.values()
-                if isinstance(n, CollectInvocation)
-            )
+            (self._is_collector_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, CollectInvocation))
         ):
             return False
 
@@ -395,48 +387,51 @@ class Graph(BaseModel):
         # Validate that an edge to this node+field doesn't already exist
         input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
         if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
-            raise InvalidEdgeError(f'Edge to node {edge.destination.node_id} field {edge.destination.field} already exists')
+            raise InvalidEdgeError(
+                f"Edge to node {edge.destination.node_id} field {edge.destination.field} already exists"
+            )
 
         # Validate that no cycles would be created
         g = self.nx_graph_flat()
         g.add_edge(edge.source.node_id, edge.destination.node_id)
         if not nx.is_directed_acyclic_graph(g):
-            raise InvalidEdgeError(f'Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}')
+            raise InvalidEdgeError(
+                f"Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}"
+            )
 
         # Validate that the field types are compatible
-        if not are_connections_compatible(
-            from_node, edge.source.field, to_node, edge.destination.field
-        ):
-            raise InvalidEdgeError(f'Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+        if not are_connections_compatible(from_node, edge.source.field, to_node, edge.destination.field):
+            raise InvalidEdgeError(
+                f"Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
+            )
 
         # Validate if iterator output type matches iterator input type (if this edge results in both being set)
         if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
-            if not self._is_iterator_connection_valid(
-                edge.destination.node_id, new_input=edge.source
-            ):
-                raise InvalidEdgeError(f'Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+            if not self._is_iterator_connection_valid(edge.destination.node_id, new_input=edge.source):
+                raise InvalidEdgeError(
+                    f"Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
+                )
 
         # Validate if iterator input type matches output type (if this edge results in both being set)
         if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
-            if not self._is_iterator_connection_valid(
-                edge.source.node_id, new_output=edge.destination
-            ):
-                raise InvalidEdgeError(f'Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+            if not self._is_iterator_connection_valid(edge.source.node_id, new_output=edge.destination):
+                raise InvalidEdgeError(
+                    f"Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
+                )
 
         # Validate if collector input type matches output type (if this edge results in both being set)
         if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
-            if not self._is_collector_connection_valid(
-                edge.destination.node_id, new_input=edge.source
-            ):
-                raise InvalidEdgeError(f'Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+            if not self._is_collector_connection_valid(edge.destination.node_id, new_input=edge.source):
+                raise InvalidEdgeError(
+                    f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
+                )
 
         # Validate if collector output type matches input type (if this edge results in both being set)
         if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
-            if not self._is_collector_connection_valid(
-                edge.source.node_id, new_output=edge.destination
-            ):
-                raise InvalidEdgeError(f'Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
-
+            if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
+                raise InvalidEdgeError(
+                    f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
+                )
 
     def has_node(self, node_path: str) -> bool:
         """Determines whether or not a node exists in the graph."""
@@ -465,17 +460,13 @@ class Graph(BaseModel):
 
         # Ensure the node type matches the new node
         if type(node) != type(new_node):
-            raise TypeError(
-                f"Node {node_path} is type {type(node)} but new node is type {type(new_node)}"
-            )
+            raise TypeError(f"Node {node_path} is type {type(node)} but new node is type {type(new_node)}")
 
         # Ensure the new id is either the same or is not in the graph
         prefix = None if "." not in node_path else node_path[: node_path.rindex(".")]
         new_path = self._get_node_path(new_node.id, prefix=prefix)
         if new_node.id != node.id and self.has_node(new_path):
-            raise NodeAlreadyInGraphError(
-                "Node with id {new_node.id} already exists in graph"
-            )
+            raise NodeAlreadyInGraphError("Node with id {new_node.id} already exists in graph")
 
         # Set the new node in the graph
         graph.nodes[new_node.id] = new_node
@@ -497,9 +488,7 @@ class Graph(BaseModel):
                 graph.add_edge(
                     Edge(
                         source=edge.source,
-                        destination=EdgeConnection(
-                            node_id=new_graph_node_path, field=edge.destination.field
-                        )
+                        destination=EdgeConnection(node_id=new_graph_node_path, field=edge.destination.field),
                     )
                 )
 
@@ -512,16 +501,12 @@ class Graph(BaseModel):
                 )
                 graph.add_edge(
                     Edge(
-                        source=EdgeConnection(
-                            node_id=new_graph_node_path, field=edge.source.field
-                        ),
-                        destination=edge.destination
+                        source=EdgeConnection(node_id=new_graph_node_path, field=edge.source.field),
+                        destination=edge.destination,
                     )
                 )
 
-    def _get_input_edges(
-        self, node_path: str, field: Optional[str] = None
-    ) -> list[Edge]:
+    def _get_input_edges(self, node_path: str, field: Optional[str] = None) -> list[Edge]:
         """Gets all input edges for a node"""
         edges = self._get_input_edges_and_graphs(node_path)
 
@@ -538,7 +523,7 @@ class Graph(BaseModel):
                 destination=EdgeConnection(
                     node_id=self._get_node_path(e.destination.node_id, prefix=prefix),
                     field=e.destination.field,
-                )
+                ),
             )
             for _, prefix, e in filtered_edges
         ]
@@ -550,32 +535,20 @@ class Graph(BaseModel):
         edges = list()
 
         # Return any input edges that appear in this graph
-        edges.extend(
-            [(self, prefix, e) for e in self.edges if e.destination.node_id == node_path]
-        )
+        edges.extend([(self, prefix, e) for e in self.edges if e.destination.node_id == node_path])
 
-        node_id = (
-            node_path if "." not in node_path else node_path[: node_path.index(".")]
-        )
+        node_id = node_path if "." not in node_path else node_path[: node_path.index(".")]
         node = self.nodes[node_id]
 
         if isinstance(node, GraphInvocation):
             graph = node.graph
-            graph_path = (
-                node.id
-                if prefix is None or prefix == ""
-                else self._get_node_path(node.id, prefix=prefix)
-            )
-            graph_edges = graph._get_input_edges_and_graphs(
-                node_path[(len(node_id) + 1) :], prefix=graph_path
-            )
+            graph_path = node.id if prefix is None or prefix == "" else self._get_node_path(node.id, prefix=prefix)
+            graph_edges = graph._get_input_edges_and_graphs(node_path[(len(node_id) + 1) :], prefix=graph_path)
             edges.extend(graph_edges)
 
         return edges
 
-    def _get_output_edges(
-        self, node_path: str, field: str
-    ) -> list[Edge]:
+    def _get_output_edges(self, node_path: str, field: str) -> list[Edge]:
         """Gets all output edges for a node"""
         edges = self._get_output_edges_and_graphs(node_path)
 
@@ -592,7 +565,7 @@ class Graph(BaseModel):
                 destination=EdgeConnection(
                     node_id=self._get_node_path(e.destination.node_id, prefix=prefix),
                     field=e.destination.field,
-                )
+                ),
             )
             for _, prefix, e in filtered_edges
         ]
@@ -604,25 +577,15 @@ class Graph(BaseModel):
         edges = list()
 
         # Return any input edges that appear in this graph
-        edges.extend(
-            [(self, prefix, e) for e in self.edges if e.source.node_id == node_path]
-        )
+        edges.extend([(self, prefix, e) for e in self.edges if e.source.node_id == node_path])
 
-        node_id = (
-            node_path if "." not in node_path else node_path[: node_path.index(".")]
-        )
+        node_id = node_path if "." not in node_path else node_path[: node_path.index(".")]
         node = self.nodes[node_id]
 
         if isinstance(node, GraphInvocation):
             graph = node.graph
-            graph_path = (
-                node.id
-                if prefix is None or prefix == ""
-                else self._get_node_path(node.id, prefix=prefix)
-            )
-            graph_edges = graph._get_output_edges_and_graphs(
-                node_path[(len(node_id) + 1) :], prefix=graph_path
-            )
+            graph_path = node.id if prefix is None or prefix == "" else self._get_node_path(node.id, prefix=prefix)
+            graph_edges = graph._get_output_edges_and_graphs(node_path[(len(node_id) + 1) :], prefix=graph_path)
             edges.extend(graph_edges)
 
         return edges
@@ -646,12 +609,8 @@ class Graph(BaseModel):
             return False
 
         # Get input and output fields (the fields linked to the iterator's input/output)
-        input_field = get_output_field(
-            self.get_node(inputs[0].node_id), inputs[0].field
-        )
-        output_fields = list(
-            [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
-        )
+        input_field = get_output_field(self.get_node(inputs[0].node_id), inputs[0].field)
+        output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
 
         # Input type must be a list
         if get_origin(input_field) != list:
@@ -659,12 +618,7 @@ class Graph(BaseModel):
 
         # Validate that all outputs match the input type
         input_field_item_type = get_args(input_field)[0]
-        if not all(
-            (
-                are_connection_types_compatible(input_field_item_type, f)
-                for f in output_fields
-            )
-        ):
+        if not all((are_connection_types_compatible(input_field_item_type, f) for f in output_fields)):
             return False
 
         return True
@@ -684,35 +638,21 @@ class Graph(BaseModel):
             outputs.append(new_output)
 
         # Get input and output fields (the fields linked to the iterator's input/output)
-        input_fields = list(
-            [get_output_field(self.get_node(e.node_id), e.field) for e in inputs]
-        )
-        output_fields = list(
-            [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
-        )
+        input_fields = list([get_output_field(self.get_node(e.node_id), e.field) for e in inputs])
+        output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
 
         # Validate that all inputs are derived from or match a single type
         input_field_types = set(
             [
                 t
                 for input_field in input_fields
-                for t in (
-                    [input_field]
-                    if get_origin(input_field) == None
-                    else get_args(input_field)
-                )
+                for t in ([input_field] if get_origin(input_field) == None else get_args(input_field))
                 if t != NoneType
             ]
         )  # Get unique types
         type_tree = nx.DiGraph()
         type_tree.add_nodes_from(input_field_types)
-        type_tree.add_edges_from(
-            [
-                e
-                for e in itertools.permutations(input_field_types, 2)
-                if issubclass(e[1], e[0])
-            ]
-        )
+        type_tree.add_edges_from([e for e in itertools.permutations(input_field_types, 2) if issubclass(e[1], e[0])])
         type_degrees = type_tree.in_degree(type_tree.nodes)
         if sum((t[1] == 0 for t in type_degrees)) != 1:  # type: ignore
             return False  # There is more than one root type
@@ -729,9 +669,7 @@ class Graph(BaseModel):
             return False
 
         # Verify that all outputs match the input type (are a base class or the same class)
-        if not all(
-            (issubclass(input_root_type, get_args(f)[0]) for f in output_fields)
-        ):
+        if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
             return False
 
         return True
@@ -751,9 +689,7 @@ class Graph(BaseModel):
         g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
         return g
 
-    def nx_graph_flat(
-        self, nx_graph: Optional[nx.DiGraph] = None, prefix: Optional[str] = None
-    ) -> nx.DiGraph:
+    def nx_graph_flat(self, nx_graph: Optional[nx.DiGraph] = None, prefix: Optional[str] = None) -> nx.DiGraph:
         """Returns a flattened NetworkX DiGraph, including all subgraphs (but not with iterations expanded)"""
         g = nx_graph or nx.DiGraph()
 
@@ -762,26 +698,18 @@ class Graph(BaseModel):
             [
                 self._get_node_path(n.id, prefix)
                 for n in self.nodes.values()
-                if not isinstance(n, GraphInvocation)
-                and not isinstance(n, IterateInvocation)
+                if not isinstance(n, GraphInvocation) and not isinstance(n, IterateInvocation)
             ]
         )
 
         # Expand graph nodes
-        for sgn in (
-            gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)
-        ):
+        for sgn in (gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)):
             g = sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
 
         # TODO: figure out if iteration nodes need to be expanded
 
         unique_edges = set([(e.source.node_id, e.destination.node_id) for e in self.edges])
-        g.add_edges_from(
-            [
-                (self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix))
-                for e in unique_edges
-            ]
-        )
+        g.add_edges_from([(self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix)) for e in unique_edges])
         return g
 
 
@@ -800,23 +728,19 @@ class GraphExecutionState(BaseModel):
     )
 
     # Nodes that have been executed
-    executed: set[str] = Field(
-        description="The set of node ids that have been executed", default_factory=set
-    )
+    executed: set[str] = Field(description="The set of node ids that have been executed", default_factory=set)
     executed_history: list[str] = Field(
         description="The list of node ids that have been executed, in order of execution",
         default_factory=list,
     )
 
     # The results of executed nodes
-    results: dict[
-        str, Annotated[InvocationOutputsUnion, Field(discriminator="type")]
-    ] = Field(description="The results of node executions", default_factory=dict)
+    results: dict[str, Annotated[InvocationOutputsUnion, Field(discriminator="type")]] = Field(
+        description="The results of node executions", default_factory=dict
+    )
 
     # Errors raised when executing nodes
-    errors: dict[str, str] = Field(
-        description="Errors raised when executing nodes", default_factory=dict
-    )
+    errors: dict[str, str] = Field(description="Errors raised when executing nodes", default_factory=dict)
 
     # Map of prepared/executed nodes to their original nodes
     prepared_source_mapping: dict[str, str] = Field(
@@ -832,16 +756,16 @@ class GraphExecutionState(BaseModel):
 
     class Config:
         schema_extra = {
-            'required': [
-                'id',
-                'graph',
-                'execution_graph',
-                'executed',
-                'executed_history',
-                'results',
-                'errors',
-                'prepared_source_mapping',
-                'source_prepared_mapping',
+            "required": [
+                "id",
+                "graph",
+                "execution_graph",
+                "executed",
+                "executed_history",
+                "results",
+                "errors",
+                "prepared_source_mapping",
+                "source_prepared_mapping",
             ]
         }
 
@@ -899,9 +823,7 @@ class GraphExecutionState(BaseModel):
         """Returns true if the graph has any errors"""
         return len(self.errors) > 0
 
-    def _create_execution_node(
-        self, node_path: str, iteration_node_map: list[tuple[str, str]]
-    ) -> list[str]:
+    def _create_execution_node(self, node_path: str, iteration_node_map: list[tuple[str, str]]) -> list[str]:
         """Prepares an iteration node and connects all edges, returning the new node id"""
 
         node = self.graph.get_node(node_path)
@@ -911,20 +833,12 @@ class GraphExecutionState(BaseModel):
         # If this is an iterator node, we must create a copy for each iteration
         if isinstance(node, IterateInvocation):
             # Get input collection edge (should error if there are no inputs)
-            input_collection_edge = next(
-                iter(self.graph._get_input_edges(node_path, "collection"))
-            )
+            input_collection_edge = next(iter(self.graph._get_input_edges(node_path, "collection")))
             input_collection_prepared_node_id = next(
-                n[1]
-                for n in iteration_node_map
-                if n[0] == input_collection_edge.source.node_id
-            )
-            input_collection_prepared_node_output = self.results[
-                input_collection_prepared_node_id
-            ]
-            input_collection = getattr(
-                input_collection_prepared_node_output, input_collection_edge.source.field
+                n[1] for n in iteration_node_map if n[0] == input_collection_edge.source.node_id
             )
+            input_collection_prepared_node_output = self.results[input_collection_prepared_node_id]
+            input_collection = getattr(input_collection_prepared_node_output, input_collection_edge.source.field)
             self_iteration_count = len(input_collection)
 
         new_nodes = list()
@@ -939,9 +853,7 @@ class GraphExecutionState(BaseModel):
         # For collect nodes, this may contain multiple inputs to the same field
         new_edges = list()
         for edge in input_edges:
-            for input_node_id in (
-                n[1] for n in iteration_node_map if n[0] == edge.source.node_id
-            ):
+            for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge.source.node_id):
                 new_edge = Edge(
                     source=EdgeConnection(node_id=input_node_id, field=edge.source.field),
                     destination=EdgeConnection(node_id="", field=edge.destination.field),
@@ -982,11 +894,7 @@ class GraphExecutionState(BaseModel):
     def _iterator_graph(self) -> nx.DiGraph:
         """Gets a DiGraph with edges to collectors removed so an ancestor search produces all active iterators for any node"""
         g = self.graph.nx_graph_flat()
-        collectors = (
-            n
-            for n in self.graph.nodes
-            if isinstance(self.graph.get_node(n), CollectInvocation)
-        )
+        collectors = (n for n in self.graph.nodes if isinstance(self.graph.get_node(n), CollectInvocation))
         for c in collectors:
             g.remove_edges_from(list(g.in_edges(c)))
         return g
@@ -994,11 +902,7 @@ class GraphExecutionState(BaseModel):
     def _get_node_iterators(self, node_id: str) -> list[str]:
         """Gets iterators for a node"""
         g = self._iterator_graph()
-        iterators = [
-            n
-            for n in nx.ancestors(g, node_id)
-            if isinstance(self.graph.get_node(n), IterateInvocation)
-        ]
+        iterators = [n for n in nx.ancestors(g, node_id) if isinstance(self.graph.get_node(n), IterateInvocation)]
         return iterators
 
     def _prepare(self) -> Optional[str]:
@@ -1045,29 +949,18 @@ class GraphExecutionState(BaseModel):
         if isinstance(next_node, CollectInvocation):
             # Collapse all iterator input mappings and create a single execution node for the collect invocation
             all_iteration_mappings = list(
-                itertools.chain(
-                    *(
-                        ((s, p) for p in self.source_prepared_mapping[s])
-                        for s in next_node_parents
-                    )
-                )
+                itertools.chain(*(((s, p) for p in self.source_prepared_mapping[s]) for s in next_node_parents))
             )
             # all_iteration_mappings = list(set(itertools.chain(*prepared_parent_mappings)))
-            create_results = self._create_execution_node(
-                next_node_id, all_iteration_mappings
-            )
+            create_results = self._create_execution_node(next_node_id, all_iteration_mappings)
             if create_results is not None:
                 new_node_ids.extend(create_results)
         else:  # Iterators or normal nodes
             # Get all iterator combinations for this node
             # Will produce a list of lists of prepared iterator nodes, from which results can be iterated
             iterator_nodes = self._get_node_iterators(next_node_id)
-            iterator_nodes_prepared = [
-                list(self.source_prepared_mapping[n]) for n in iterator_nodes
-            ]
-            iterator_node_prepared_combinations = list(
-                itertools.product(*iterator_nodes_prepared)
-            )
+            iterator_nodes_prepared = [list(self.source_prepared_mapping[n]) for n in iterator_nodes]
+            iterator_node_prepared_combinations = list(itertools.product(*iterator_nodes_prepared))
 
             # Select the correct prepared parents for each iteration
             # For every iterator, the parent must either not be a child of that iterator, or must match the prepared iteration for that iterator
@@ -1096,31 +989,16 @@ class GraphExecutionState(BaseModel):
             return next(iter(prepared_nodes))
 
         # Check if the requested node is an iterator
-        prepared_iterator = next(
-            (n for n in prepared_nodes if n in prepared_iterator_nodes), None
-        )
+        prepared_iterator = next((n for n in prepared_nodes if n in prepared_iterator_nodes), None)
         if prepared_iterator is not None:
             return prepared_iterator
 
         # Filter to only iterator nodes that are a parent of the specified node, in tuple format (prepared, source)
-        iterator_source_node_mapping = [
-            (n, self.prepared_source_mapping[n]) for n in prepared_iterator_nodes
-        ]
-        parent_iterators = [
-            itn
-            for itn in iterator_source_node_mapping
-            if nx.has_path(graph, itn[1], source_node_path)
-        ]
+        iterator_source_node_mapping = [(n, self.prepared_source_mapping[n]) for n in prepared_iterator_nodes]
+        parent_iterators = [itn for itn in iterator_source_node_mapping if nx.has_path(graph, itn[1], source_node_path)]
 
         return next(
-            (
-                n
-                for n in prepared_nodes
-                if all(
-                    nx.has_path(execution_graph, pit[0], n)
-                    for pit in parent_iterators
-                )
-            ),
+            (n for n in prepared_nodes if all(nx.has_path(execution_graph, pit[0], n) for pit in parent_iterators)),
             None,
         )
 
@@ -1130,13 +1008,13 @@ class GraphExecutionState(BaseModel):
 
         # Depth-first search with pre-order traversal is a depth-first topological sort
         sorted_nodes = nx.dfs_preorder_nodes(g)
-        
+
         next_node = next(
             (
                 n
                 for n in sorted_nodes
-                if n not in self.executed # the node must not already be executed...
-                and all((e[0] in self.executed for e in g.in_edges(n))) # ...and all its inputs must be executed
+                if n not in self.executed  # the node must not already be executed...
+                and all((e[0] in self.executed for e in g.in_edges(n)))  # ...and all its inputs must be executed
             ),
             None,
         )
@@ -1221,15 +1099,18 @@ class ExposedNodeOutput(BaseModel):
     field: str = Field(description="The field name of the output")
     alias: str = Field(description="The alias of the output")
 
+
 class LibraryGraph(BaseModel):
     id: str = Field(description="The unique identifier for this library graph", default_factory=uuid.uuid4)
     graph: Graph = Field(description="The graph")
     name: str = Field(description="The name of the graph")
     description: str = Field(description="The description of the graph")
     exposed_inputs: list[ExposedNodeInput] = Field(description="The inputs exposed by this graph", default_factory=list)
-    exposed_outputs: list[ExposedNodeOutput] = Field(description="The outputs exposed by this graph", default_factory=list)
+    exposed_outputs: list[ExposedNodeOutput] = Field(
+        description="The outputs exposed by this graph", default_factory=list
+    )
 
-    @validator('exposed_inputs', 'exposed_outputs')
+    @validator("exposed_inputs", "exposed_outputs")
     def validate_exposed_aliases(cls, v):
         if len(v) != len(set(i.alias for i in v)):
             raise ValueError("Duplicate exposed alias")
@@ -1237,23 +1118,27 @@ class LibraryGraph(BaseModel):
 
     @root_validator
     def validate_exposed_nodes(cls, values):
-        graph = values['graph']
+        graph = values["graph"]
 
         # Validate exposed inputs
-        for exposed_input in values['exposed_inputs']:
+        for exposed_input in values["exposed_inputs"]:
             if not graph.has_node(exposed_input.node_path):
                 raise ValueError(f"Exposed input node {exposed_input.node_path} does not exist")
             node = graph.get_node(exposed_input.node_path)
             if get_input_field(node, exposed_input.field) is None:
-                raise ValueError(f"Exposed input field {exposed_input.field} does not exist on node {exposed_input.node_path}")
+                raise ValueError(
+                    f"Exposed input field {exposed_input.field} does not exist on node {exposed_input.node_path}"
+                )
 
         # Validate exposed outputs
-        for exposed_output in values['exposed_outputs']:
+        for exposed_output in values["exposed_outputs"]:
             if not graph.has_node(exposed_output.node_path):
                 raise ValueError(f"Exposed output node {exposed_output.node_path} does not exist")
             node = graph.get_node(exposed_output.node_path)
             if get_output_field(node, exposed_output.field) is None:
-                raise ValueError(f"Exposed output field {exposed_output.field} does not exist on node {exposed_output.node_path}")
+                raise ValueError(
+                    f"Exposed output field {exposed_output.field} does not exist on node {exposed_output.node_path}"
+                )
 
         return values
 
diff --git a/invokeai/app/services/image_file_storage.py b/invokeai/app/services/image_file_storage.py
index 60ae613748..fb8563a3e4 100644
--- a/invokeai/app/services/image_file_storage.py
+++ b/invokeai/app/services/image_file_storage.py
@@ -85,9 +85,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self.__cache_ids = Queue()
         self.__max_cache_size = 10  # TODO: get this from config
 
-        self.__output_folder: Path = (
-            output_folder if isinstance(output_folder, Path) else Path(output_folder)
-        )
+        self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__thumbnails_folder = self.__output_folder / "thumbnails"
 
         # Validate required output folders at launch
@@ -120,7 +118,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
             image_path = self.get_path(image_name)
 
             pnginfo = PngImagePlugin.PngInfo()
-            
+
             if metadata is not None:
                 pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
             if graph is not None:
@@ -183,9 +181,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
     def __set_cache(self, image_name: Path, image: PILImageType):
         if not image_name in self.__cache:
             self.__cache[image_name] = image
-            self.__cache_ids.put(
-                image_name
-            )  # TODO: this should refresh position for LRU cache
+            self.__cache_ids.put(image_name)  # TODO: this should refresh position for LRU cache
             if len(self.__cache) > self.__max_cache_size:
                 cache_id = self.__cache_ids.get()
                 if cache_id in self.__cache:
diff --git a/invokeai/app/services/image_record_storage.py b/invokeai/app/services/image_record_storage.py
index eb69679a35..8c274ab8f9 100644
--- a/invokeai/app/services/image_record_storage.py
+++ b/invokeai/app/services/image_record_storage.py
@@ -426,9 +426,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         finally:
             self._lock.release()
 
-        return OffsetPaginatedResults(
-            items=images, offset=offset, limit=limit, total=count
-        )
+        return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
 
     def delete(self, image_name: str) -> None:
         try:
@@ -466,7 +464,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         finally:
             self._lock.release()
 
-
     def delete_intermediates(self) -> list[str]:
         try:
             self._lock.acquire()
@@ -505,9 +502,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         is_intermediate: bool = False,
     ) -> datetime:
         try:
-            metadata_json = (
-                None if metadata is None else json.dumps(metadata)
-            )
+            metadata_json = None if metadata is None else json.dumps(metadata)
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
diff --git a/invokeai/app/services/images.py b/invokeai/app/services/images.py
index dc1b18babe..f8376eb626 100644
--- a/invokeai/app/services/images.py
+++ b/invokeai/app/services/images.py
@@ -216,16 +216,9 @@ class ImageService(ImageServiceABC):
                 metadata=metadata,
                 session_id=session_id,
             )
-
             if board_id is not None:
-                self._services.board_image_records.add_image_to_board(
-                    board_id=board_id, image_name=image_name
-                )
-
-            self._services.image_files.save(
-                image_name=image_name, image=image, metadata=metadata, graph=graph
-            )
-
+                self._services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
+            self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, graph=graph)
             image_dto = self.get_dto(image_name)
 
             return image_dto
@@ -236,7 +229,7 @@ class ImageService(ImageServiceABC):
             self._services.logger.error("Failed to save image file")
             raise
         except Exception as e:
-            self._services.logger.error("Problem saving image record and file")
+            self._services.logger.error(f"Problem saving image record and file: {str(e)}")
             raise e
 
     def update(
@@ -300,9 +293,7 @@ class ImageService(ImageServiceABC):
             if not image_record.session_id:
                 return ImageMetadata()
 
-            session_raw = self._services.graph_execution_manager.get_raw(
-                image_record.session_id
-            )
+            session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
             graph = None
 
             if session_raw:
@@ -367,9 +358,7 @@ class ImageService(ImageServiceABC):
                         r,
                         self._services.urls.get_image_url(r.image_name),
                         self._services.urls.get_image_url(r.image_name, True),
-                        self._services.board_image_records.get_board_for_image(
-                            r.image_name
-                        ),
+                        self._services.board_image_records.get_board_for_image(r.image_name),
                     ),
                     results.items,
                 )
@@ -401,11 +390,7 @@ class ImageService(ImageServiceABC):
 
     def delete_images_on_board(self, board_id: str):
         try:
-            image_names = (
-                self._services.board_image_records.get_all_board_image_names_for_board(
-                    board_id
-                )
-            )
+            image_names = self._services.board_image_records.get_all_board_image_names_for_board(board_id)
             for image_name in image_names:
                 self._services.image_files.delete(image_name)
             self._services.image_records.delete_many(image_names)
diff --git a/invokeai/app/services/invocation_queue.py b/invokeai/app/services/invocation_queue.py
index eb78a542a6..963d500aa8 100644
--- a/invokeai/app/services/invocation_queue.py
+++ b/invokeai/app/services/invocation_queue.py
@@ -7,6 +7,7 @@ from queue import Queue
 from pydantic import BaseModel, Field
 from typing import Optional
 
+
 class InvocationQueueItem(BaseModel):
     graph_execution_state_id: str = Field(description="The ID of the graph execution state")
     invocation_id: str = Field(description="The ID of the node being invoked")
@@ -45,9 +46,11 @@ class MemoryInvocationQueue(InvocationQueueABC):
     def get(self) -> InvocationQueueItem:
         item = self.__queue.get()
 
-        while isinstance(item, InvocationQueueItem) \
-            and item.graph_execution_state_id in self.__cancellations \
-            and self.__cancellations[item.graph_execution_state_id] > item.timestamp:
+        while (
+            isinstance(item, InvocationQueueItem)
+            and item.graph_execution_state_id in self.__cancellations
+            and self.__cancellations[item.graph_execution_state_id] > item.timestamp
+        ):
             item = self.__queue.get()
 
         # Clear old items
diff --git a/invokeai/app/services/invoker.py b/invokeai/app/services/invoker.py
index 951d3b17c4..1a7b0de27e 100644
--- a/invokeai/app/services/invoker.py
+++ b/invokeai/app/services/invoker.py
@@ -7,6 +7,7 @@ from .graph import Graph, GraphExecutionState
 from .invocation_queue import InvocationQueueItem
 from .invocation_services import InvocationServices
 
+
 class Invoker:
     """The invoker, used to execute invocations"""
 
@@ -16,9 +17,7 @@ class Invoker:
         self.services = services
         self._start()
 
-    def invoke(
-        self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
-    ) -> Optional[str]:
+    def invoke(self, graph_execution_state: GraphExecutionState, invoke_all: bool = False) -> Optional[str]:
         """Determines the next node to invoke and enqueues it, preparing if needed.
         Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
 
diff --git a/invokeai/app/services/item_storage.py b/invokeai/app/services/item_storage.py
index 709d88bf97..5fe4eb7456 100644
--- a/invokeai/app/services/item_storage.py
+++ b/invokeai/app/services/item_storage.py
@@ -9,13 +9,15 @@ T = TypeVar("T", bound=BaseModel)
 
 class PaginatedResults(GenericModel, Generic[T]):
     """Paginated results"""
-    #fmt: off
+
+    # fmt: off
     items: list[T] = Field(description="Items")
     page: int = Field(description="Current Page")
     pages: int = Field(description="Total number of pages")
     per_page: int = Field(description="Number of items per page")
     total: int = Field(description="Total number of items in result")
-    #fmt: on
+    # fmt: on
+
 
 class ItemStorageABC(ABC, Generic[T]):
     _on_changed_callbacks: list[Callable[[T], None]]
@@ -48,9 +50,7 @@ class ItemStorageABC(ABC, Generic[T]):
         pass
 
     @abstractmethod
-    def search(
-        self, query: str, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[T]:
+    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
         pass
 
     def on_changed(self, on_changed: Callable[[T], None]) -> None:
diff --git a/invokeai/app/services/latent_storage.py b/invokeai/app/services/latent_storage.py
index 0e23d6d018..7e781c49ec 100644
--- a/invokeai/app/services/latent_storage.py
+++ b/invokeai/app/services/latent_storage.py
@@ -7,6 +7,7 @@ from typing import Dict, Union, Optional
 
 import torch
 
+
 class LatentsStorageBase(ABC):
     """Responsible for storing and retrieving latents."""
 
@@ -25,7 +26,7 @@ class LatentsStorageBase(ABC):
 
 class ForwardCacheLatentsStorage(LatentsStorageBase):
     """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
-    
+
     __cache: Dict[str, torch.Tensor]
     __cache_ids: Queue
     __max_cache_size: int
@@ -87,8 +88,6 @@ class DiskLatentsStorage(LatentsStorageBase):
     def delete(self, name: str) -> None:
         latent_path = self.get_path(name)
         latent_path.unlink()
-        
 
     def get_path(self, name: str) -> Path:
         return self.__output_folder / name
-    
diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py
index f7d3b3a7a7..c84cc3d189 100644
--- a/invokeai/app/services/model_manager_service.py
+++ b/invokeai/app/services/model_manager_service.py
@@ -103,7 +103,7 @@ class ModelManagerServiceBase(ABC):
         }
         """
         pass
-    
+
     @abstractmethod
     def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
         """
@@ -125,7 +125,7 @@ class ModelManagerServiceBase(ABC):
         base_model: BaseModelType,
         model_type: ModelType,
         model_attributes: dict,
-        clobber: bool = False
+        clobber: bool = False,
     ) -> AddModelResult:
         """
         Update the named model with a dictionary of attributes. Will fail with an
@@ -148,12 +148,12 @@ class ModelManagerServiceBase(ABC):
         Update the named model with a dictionary of attributes. Will fail with a
         ModelNotFoundException if the name does not already exist.
 
-        On a successful update, the config will be changed in memory. Will fail 
-        with an assertion error if provided attributes are incorrect or 
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
         the model name is missing. Call commit() to write changes to disk.
         """
         pass
-    
+
     @abstractmethod
     def del_model(
         self,
@@ -169,21 +169,20 @@ class ModelManagerServiceBase(ABC):
         pass
 
     @abstractmethod
-    def rename_model(self,
-                     model_name: str,
-                     base_model: BaseModelType,
-                     model_type: ModelType,
-                     new_name: str,
-                     ):
+    def rename_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        new_name: str,
+    ):
         """
         Rename the indicated model.
         """
         pass
 
     @abstractmethod
-    def list_checkpoint_configs(
-        self
-    )->List[Path]:
+    def list_checkpoint_configs(self) -> List[Path]:
         """
         List the checkpoint config paths from ROOT/configs/stable-diffusion.
         """
@@ -194,7 +193,7 @@ class ModelManagerServiceBase(ABC):
         self,
         model_name: str,
         base_model: BaseModelType,
-        model_type: Union[ModelType.Main,ModelType.Vae],
+        model_type: Union[ModelType.Main, ModelType.Vae],
     ) -> AddModelResult:
         """
         Convert a checkpoint file into a diffusers folder, deleting the cached
@@ -211,11 +210,12 @@ class ModelManagerServiceBase(ABC):
         pass
 
     @abstractmethod
-    def heuristic_import(self,
-                         items_to_import: set[str],
-                         prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
-                         )->dict[str, AddModelResult]:
-        '''Import a list of paths, repo_ids or URLs. Returns the set of
+    def heuristic_import(
+        self,
+        items_to_import: set[str],
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+    ) -> dict[str, AddModelResult]:
+        """Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
         :param items_to_import: Set of strings corresponding to models to be imported.
         :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
@@ -230,19 +230,23 @@ class ModelManagerServiceBase(ABC):
         The result is a set of successfully installed models. Each element
         of the set is a dict corresponding to the newly-created OmegaConf stanza for
         that model.
-        '''
+        """
         pass
 
     @abstractmethod
     def merge_models(
-            self,
-            model_names: List[str] = Field(default=None, min_items=2, max_items=3, description="List of model names to merge"),
-            base_model: Union[BaseModelType,str] = Field(default=None, description="Base model shared by all models to be merged"),
-            merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
-            alpha: Optional[float] = 0.5,
-            interp: Optional[MergeInterpolationMethod] = None,
-            force: Optional[bool] = False,
-            merge_dest_directory: Optional[Path] = None
+        self,
+        model_names: List[str] = Field(
+            default=None, min_items=2, max_items=3, description="List of model names to merge"
+        ),
+        base_model: Union[BaseModelType, str] = Field(
+            default=None, description="Base model shared by all models to be merged"
+        ),
+        merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
+        alpha: Optional[float] = 0.5,
+        interp: Optional[MergeInterpolationMethod] = None,
+        force: Optional[bool] = False,
+        merge_dest_directory: Optional[Path] = None,
     ) -> AddModelResult:
         """
         Merge two to three diffusrs pipeline models and save as a new model.
@@ -250,27 +254,27 @@ class ModelManagerServiceBase(ABC):
         :param base_model: Base model to use for all models
         :param merged_model_name: Name of destination merged model
         :param alpha: Alpha strength to apply to 2d and 3d model
-        :param interp: Interpolation method. None (default) 
+        :param interp: Interpolation method. None (default)
         :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
         """
         pass
 
     @abstractmethod
-    def search_for_models(self, directory: Path)->List[Path]:
+    def search_for_models(self, directory: Path) -> List[Path]:
         """
         Return list of all models found in the designated directory.
         """
         pass
-        
+
     @abstractmethod
     def sync_to_config(self):
         """
-        Re-read models.yaml, rescan the models directory, and reimport models 
+        Re-read models.yaml, rescan the models directory, and reimport models
         in the autoimport directories. Call after making changes outside the
         model manager API.
         """
         pass
-        
+
     @abstractmethod
     def commit(self, conf_file: Optional[Path] = None) -> None:
         """
@@ -280,9 +284,11 @@ class ModelManagerServiceBase(ABC):
         """
         pass
 
+
 # simple implementation
 class ModelManagerService(ModelManagerServiceBase):
     """Responsible for managing models on disk and in memory"""
+
     def __init__(
         self,
         config: InvokeAIAppConfig,
@@ -298,17 +304,17 @@ class ModelManagerService(ModelManagerServiceBase):
             config_file = config.model_conf_path
         else:
             config_file = config.root_dir / "configs/models.yaml"
-            
-        logger.debug(f'Config file={config_file}')
+
+        logger.debug(f"Config file={config_file}")
 
         device = torch.device(choose_torch_device())
-        device_name =  torch.cuda.get_device_name() if device==torch.device('cuda') else ''
-        logger.info(f'GPU device = {device} {device_name}')
+        device_name = torch.cuda.get_device_name() if device == torch.device("cuda") else ""
+        logger.info(f"GPU device = {device} {device_name}")
 
         precision = config.precision
         if precision == "auto":
             precision = choose_precision(device)
-        dtype = torch.float32 if precision == 'float32' else torch.float16
+        dtype = torch.float32 if precision == "float32" else torch.float16
 
         # this is transitional backward compatibility
         # support for the deprecated `max_loaded_models`
@@ -316,9 +322,7 @@ class ModelManagerService(ModelManagerServiceBase):
         # cache size is set to 2.5 GB times
         # the number of max_loaded_models. Otherwise
         # use new `max_cache_size` config setting
-        max_cache_size = config.max_cache_size \
-            if hasattr(config,'max_cache_size') \
-               else config.max_loaded_models * 2.5
+        max_cache_size = config.max_cache_size if hasattr(config, "max_cache_size") else config.max_loaded_models * 2.5
 
         logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
 
@@ -332,7 +336,7 @@ class ModelManagerService(ModelManagerServiceBase):
             sequential_offload=sequential_offload,
             logger=logger,
         )
-        logger.info('Model manager service initialized')
+        logger.info("Model manager service initialized")
 
     def get_model(
         self,
@@ -371,7 +375,7 @@ class ModelManagerService(ModelManagerServiceBase):
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
-                model_info=model_info
+                model_info=model_info,
             )
 
         return model_info
@@ -405,9 +409,7 @@ class ModelManagerService(ModelManagerServiceBase):
         return self.mgr.model_names()
 
     def list_models(
-        self,
-        base_model: Optional[BaseModelType] = None,
-        model_type: Optional[ModelType] = None
+        self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None
     ) -> list[dict]:
         """
         Return a list of models.
@@ -418,9 +420,7 @@ class ModelManagerService(ModelManagerServiceBase):
         """
         Return information about the model using the same format as list_models()
         """
-        return self.mgr.list_model(model_name=model_name,
-                                   base_model=base_model,
-                                   model_type=model_type)
+        return self.mgr.list_model(model_name=model_name, base_model=base_model, model_type=model_type)
 
     def add_model(
         self,
@@ -429,7 +429,7 @@ class ModelManagerService(ModelManagerServiceBase):
         model_type: ModelType,
         model_attributes: dict,
         clobber: bool = False,
-    )->None:
+    ) -> None:
         """
         Update the named model with a dictionary of attributes. Will fail with an
         assertion error if the name already exists. Pass clobber=True to overwrite.
@@ -437,7 +437,7 @@ class ModelManagerService(ModelManagerServiceBase):
         with an assertion error if provided attributes are incorrect or
         the model name is missing. Call commit() to write changes to disk.
         """
-        self.logger.debug(f'add/update model {model_name}')        
+        self.logger.debug(f"add/update model {model_name}")
         return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber)
 
     def update_model(
@@ -450,15 +450,15 @@ class ModelManagerService(ModelManagerServiceBase):
         """
         Update the named model with a dictionary of attributes. Will fail with a
         ModelNotFoundException exception if the name does not already exist.
-        On a successful update, the config will be changed in memory. Will fail 
-        with an assertion error if provided attributes are incorrect or 
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
         the model name is missing. Call commit() to write changes to disk.
         """
-        self.logger.debug(f'update model {model_name}')
+        self.logger.debug(f"update model {model_name}")
         if not self.model_exists(model_name, base_model, model_type):
             raise ModelNotFoundException(f"Unknown model {model_name}")
         return self.add_model(model_name, base_model, model_type, model_attributes, clobber=True)
-    
+
     def del_model(
         self,
         model_name: str,
@@ -470,7 +470,7 @@ class ModelManagerService(ModelManagerServiceBase):
         then the underlying weight file or diffusers directory will be deleted
         as well.
         """
-        self.logger.debug(f'delete model {model_name}')
+        self.logger.debug(f"delete model {model_name}")
         self.mgr.del_model(model_name, base_model, model_type)
         self.mgr.commit()
 
@@ -478,8 +478,10 @@ class ModelManagerService(ModelManagerServiceBase):
         self,
         model_name: str,
         base_model: BaseModelType,
-        model_type: Union[ModelType.Main,ModelType.Vae],
-        convert_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),        
+        model_type: Union[ModelType.Main, ModelType.Vae],
+        convert_dest_directory: Optional[Path] = Field(
+            default=None, description="Optional directory location for merged model"
+        ),
     ) -> AddModelResult:
         """
         Convert a checkpoint file into a diffusers folder, deleting the cached
@@ -494,10 +496,10 @@ class ModelManagerService(ModelManagerServiceBase):
         also raise a ValueError in the event that there is a similarly-named diffusers
         directory already in place.
         """
-        self.logger.debug(f'convert model {model_name}')        
+        self.logger.debug(f"convert model {model_name}")
         return self.mgr.convert_model(model_name, base_model, model_type, convert_dest_directory)
 
-    def commit(self, conf_file: Optional[Path]=None):
+    def commit(self, conf_file: Optional[Path] = None):
         """
         Write current configuration out to the indicated file.
         If no conf_file is provided, then replaces the
@@ -524,7 +526,7 @@ class ModelManagerService(ModelManagerServiceBase):
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
-                model_info=model_info
+                model_info=model_info,
             )
         else:
             context.services.events.emit_model_load_started(
@@ -535,16 +537,16 @@ class ModelManagerService(ModelManagerServiceBase):
                 submodel=submodel,
             )
 
-
     @property
     def logger(self):
         return self.mgr.logger
 
-    def heuristic_import(self,
-                         items_to_import: set[str],
-                         prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
-                         )->dict[str, AddModelResult]:
-        '''Import a list of paths, repo_ids or URLs. Returns the set of
+    def heuristic_import(
+        self,
+        items_to_import: set[str],
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+    ) -> dict[str, AddModelResult]:
+        """Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
         :param items_to_import: Set of strings corresponding to models to be imported.
         :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
@@ -559,18 +561,24 @@ class ModelManagerService(ModelManagerServiceBase):
         The result is a set of successfully installed models. Each element
         of the set is a dict corresponding to the newly-created OmegaConf stanza for
         that model.
-        '''
-        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)        
+        """
+        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)
 
     def merge_models(
-            self,
-            model_names: List[str] = Field(default=None, min_items=2, max_items=3, description="List of model names to merge"),
-            base_model: Union[BaseModelType,str] = Field(default=None, description="Base model shared by all models to be merged"),
-            merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
-            alpha: Optional[float] = 0.5,
-            interp: Optional[MergeInterpolationMethod] = None,
-            force: Optional[bool] = False,
-            merge_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
+        self,
+        model_names: List[str] = Field(
+            default=None, min_items=2, max_items=3, description="List of model names to merge"
+        ),
+        base_model: Union[BaseModelType, str] = Field(
+            default=None, description="Base model shared by all models to be merged"
+        ),
+        merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
+        alpha: Optional[float] = 0.5,
+        interp: Optional[MergeInterpolationMethod] = None,
+        force: Optional[bool] = False,
+        merge_dest_directory: Optional[Path] = Field(
+            default=None, description="Optional directory location for merged model"
+        ),
     ) -> AddModelResult:
         """
         Merge two to three diffusrs pipeline models and save as a new model.
@@ -578,25 +586,25 @@ class ModelManagerService(ModelManagerServiceBase):
         :param base_model: Base model to use for all models
         :param merged_model_name: Name of destination merged model
         :param alpha: Alpha strength to apply to 2d and 3d model
-        :param interp: Interpolation method. None (default) 
+        :param interp: Interpolation method. None (default)
         :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
         """
         merger = ModelMerger(self.mgr)
         try:
             result = merger.merge_diffusion_models_and_save(
-                model_names = model_names,
-                base_model = base_model,
-                merged_model_name = merged_model_name,
-                alpha = alpha,
-                interp = interp,
-                force = force,
+                model_names=model_names,
+                base_model=base_model,
+                merged_model_name=merged_model_name,
+                alpha=alpha,
+                interp=interp,
+                force=force,
                 merge_dest_directory=merge_dest_directory,
             )
         except AssertionError as e:
             raise ValueError(e)
         return result
 
-    def search_for_models(self, directory: Path)->List[Path]:
+    def search_for_models(self, directory: Path) -> List[Path]:
         """
         Return list of all models found in the designated directory.
         """
@@ -605,28 +613,29 @@ class ModelManagerService(ModelManagerServiceBase):
 
     def sync_to_config(self):
         """
-        Re-read models.yaml, rescan the models directory, and reimport models 
+        Re-read models.yaml, rescan the models directory, and reimport models
         in the autoimport directories. Call after making changes outside the
         model manager API.
         """
         return self.mgr.sync_to_config()
 
-    def list_checkpoint_configs(self)->List[Path]:
+    def list_checkpoint_configs(self) -> List[Path]:
         """
         List the checkpoint config paths from ROOT/configs/stable-diffusion.
         """
         config = self.mgr.app_config
         conf_path = config.legacy_conf_path
         root_path = config.root_path
-        return [(conf_path / x).relative_to(root_path) for x in conf_path.glob('**/*.yaml')]
+        return [(conf_path / x).relative_to(root_path) for x in conf_path.glob("**/*.yaml")]
 
-    def rename_model(self,
-                     model_name: str,
-                     base_model: BaseModelType,
-                     model_type: ModelType,
-                     new_name: str = None,
-                     new_base: BaseModelType = None,
-                     ):
+    def rename_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        new_name: str = None,
+        new_base: BaseModelType = None,
+    ):
         """
         Rename the indicated model. Can provide a new name and/or a new base.
         :param model_name: Current name of the model
@@ -635,10 +644,10 @@ class ModelManagerService(ModelManagerServiceBase):
         :param new_name: New name for the model
         :param new_base: New base for the model
         """
-        self.mgr.rename_model(base_model = base_model,
-                              model_type = model_type,
-                              model_name = model_name,
-                              new_name = new_name,
-                              new_base = new_base,
-                              )
-        
+        self.mgr.rename_model(
+            base_model=base_model,
+            model_type=model_type,
+            model_name=model_name,
+            new_name=new_name,
+            new_base=new_base,
+        )
diff --git a/invokeai/app/services/models/board_record.py b/invokeai/app/services/models/board_record.py
index bf5401b209..658698e794 100644
--- a/invokeai/app/services/models/board_record.py
+++ b/invokeai/app/services/models/board_record.py
@@ -11,30 +11,20 @@ class BoardRecord(BaseModel):
     """The unique ID of the board."""
     board_name: str = Field(description="The name of the board.")
     """The name of the board."""
-    created_at: Union[datetime, str] = Field(
-        description="The created timestamp of the board."
-    )
+    created_at: Union[datetime, str] = Field(description="The created timestamp of the board.")
     """The created timestamp of the image."""
-    updated_at: Union[datetime, str] = Field(
-        description="The updated timestamp of the board."
-    )
+    updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.")
     """The updated timestamp of the image."""
-    deleted_at: Union[datetime, str, None] = Field(
-        description="The deleted timestamp of the board."
-    )
+    deleted_at: Union[datetime, str, None] = Field(description="The deleted timestamp of the board.")
     """The updated timestamp of the image."""
-    cover_image_name: Optional[str] = Field(
-        description="The name of the cover image of the board."
-    )
+    cover_image_name: Optional[str] = Field(description="The name of the cover image of the board.")
     """The name of the cover image of the board."""
 
 
 class BoardDTO(BoardRecord):
     """Deserialized board record with cover image URL and image count."""
 
-    cover_image_name: Optional[str] = Field(
-        description="The name of the board's cover image."
-    )
+    cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
     """The URL of the thumbnail of the most recent image in the board."""
     image_count: int = Field(description="The number of images in the board.")
     """The number of images in the board."""
diff --git a/invokeai/app/services/models/image_record.py b/invokeai/app/services/models/image_record.py
index cf10f6e8b2..a105d03ba8 100644
--- a/invokeai/app/services/models/image_record.py
+++ b/invokeai/app/services/models/image_record.py
@@ -20,17 +20,11 @@ class ImageRecord(BaseModel):
     """The actual width of the image in px. This may be different from the width in metadata."""
     height: int = Field(description="The height of the image in px.")
     """The actual height of the image in px. This may be different from the height in metadata."""
-    created_at: Union[datetime.datetime, str] = Field(
-        description="The created timestamp of the image."
-    )
+    created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the image.")
     """The created timestamp of the image."""
-    updated_at: Union[datetime.datetime, str] = Field(
-        description="The updated timestamp of the image."
-    )
+    updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.")
     """The updated timestamp of the image."""
-    deleted_at: Union[datetime.datetime, str, None] = Field(
-        description="The deleted timestamp of the image."
-    )
+    deleted_at: Union[datetime.datetime, str, None] = Field(description="The deleted timestamp of the image.")
     """The deleted timestamp of the image."""
     is_intermediate: bool = Field(description="Whether this is an intermediate image.")
     """Whether this is an intermediate image."""
@@ -55,18 +49,14 @@ class ImageRecordChanges(BaseModel, extra=Extra.forbid):
       - `is_intermediate`: change the image's `is_intermediate` flag
     """
 
-    image_category: Optional[ImageCategory] = Field(
-        description="The image's new category."
-    )
+    image_category: Optional[ImageCategory] = Field(description="The image's new category.")
     """The image's new category."""
     session_id: Optional[StrictStr] = Field(
         default=None,
         description="The image's new session ID.",
     )
     """The image's new session ID."""
-    is_intermediate: Optional[StrictBool] = Field(
-        default=None, description="The image's new `is_intermediate` flag."
-    )
+    is_intermediate: Optional[StrictBool] = Field(default=None, description="The image's new `is_intermediate` flag.")
     """The image's new `is_intermediate` flag."""
 
 
@@ -84,9 +74,7 @@ class ImageUrlsDTO(BaseModel):
 class ImageDTO(ImageRecord, ImageUrlsDTO):
     """Deserialized image record, enriched for the frontend."""
 
-    board_id: Optional[str] = Field(
-        description="The id of the board the image belongs to, if one exists."
-    )
+    board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
     """The id of the board the image belongs to, if one exists."""
     pass
 
@@ -110,12 +98,8 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
 
     # TODO: do we really need to handle default values here? ideally the data is the correct shape...
     image_name = image_dict.get("image_name", "unknown")
-    image_origin = ResourceOrigin(
-        image_dict.get("image_origin", ResourceOrigin.INTERNAL.value)
-    )
-    image_category = ImageCategory(
-        image_dict.get("image_category", ImageCategory.GENERAL.value)
-    )
+    image_origin = ResourceOrigin(image_dict.get("image_origin", ResourceOrigin.INTERNAL.value))
+    image_category = ImageCategory(image_dict.get("image_category", ImageCategory.GENERAL.value))
     width = image_dict.get("width", 0)
     height = image_dict.get("height", 0)
     session_id = image_dict.get("session_id", None)
diff --git a/invokeai/app/services/processor.py b/invokeai/app/services/processor.py
index 5995e4ffc3..50fe217e05 100644
--- a/invokeai/app/services/processor.py
+++ b/invokeai/app/services/processor.py
@@ -8,6 +8,8 @@ from .invoker import InvocationProcessorABC, Invoker
 from ..models.exceptions import CanceledException
 
 import invokeai.backend.util.logging as logger
+
+
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
@@ -24,9 +26,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             target=self.__process,
             kwargs=dict(stop_event=self.__stop_event),
         )
-        self.__invoker_thread.daemon = (
-            True  # TODO: make async and do not use threads
-        )
+        self.__invoker_thread.daemon = True  # TODO: make async and do not use threads
         self.__invoker_thread.start()
 
     def stop(self, *args, **kwargs) -> None:
@@ -47,10 +47,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     continue
 
                 try:
-                    graph_execution_state = (
-                        self.__invoker.services.graph_execution_manager.get(
-                            queue_item.graph_execution_state_id
-                        )
+                    graph_execution_state = self.__invoker.services.graph_execution_manager.get(
+                        queue_item.graph_execution_state_id
                     )
                 except Exception as e:
                     self.__invoker.services.logger.error("Exception while retrieving session:\n%s" % e)
@@ -60,11 +58,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         error=traceback.format_exc(),
                     )
                     continue
-                
+
                 try:
-                    invocation = graph_execution_state.execution_graph.get_node(
-                        queue_item.invocation_id
-                    )
+                    invocation = graph_execution_state.execution_graph.get_node(queue_item.invocation_id)
                 except Exception as e:
                     self.__invoker.services.logger.error("Exception while retrieving invocation:\n%s" % e)
                     self.__invoker.services.events.emit_invocation_retrieval_error(
@@ -82,7 +78,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 self.__invoker.services.events.emit_invocation_started(
                     graph_execution_state_id=graph_execution_state.id,
                     node=invocation.dict(),
-                    source_node_id=source_node_id
+                    source_node_id=source_node_id,
                 )
 
                 # Invoke
@@ -95,18 +91,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     )
 
                     # Check queue to see if this is canceled, and skip if so
-                    if self.__invoker.services.queue.is_canceled(
-                        graph_execution_state.id
-                    ):
+                    if self.__invoker.services.queue.is_canceled(graph_execution_state.id):
                         continue
 
                     # Save outputs and history
                     graph_execution_state.complete(invocation.id, outputs)
 
                     # Save the state changes
-                    self.__invoker.services.graph_execution_manager.set(
-                        graph_execution_state
-                    )
+                    self.__invoker.services.graph_execution_manager.set(graph_execution_state)
 
                     # Send complete event
                     self.__invoker.services.events.emit_invocation_complete(
@@ -130,9 +122,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     graph_execution_state.set_node_error(invocation.id, error)
 
                     # Save the state changes
-                    self.__invoker.services.graph_execution_manager.set(
-                        graph_execution_state
-                    )
+                    self.__invoker.services.graph_execution_manager.set(graph_execution_state)
 
                     self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
                     # Send error event
@@ -147,9 +137,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     pass
 
                 # Check queue to see if this is canceled, and skip if so
-                if self.__invoker.services.queue.is_canceled(
-                    graph_execution_state.id
-                ):
+                if self.__invoker.services.queue.is_canceled(graph_execution_state.id):
                     continue
 
                 # Queue any further commands if invoking all
@@ -164,12 +152,10 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             node=invocation.dict(),
                             source_node_id=source_node_id,
                             error_type=e.__class__.__name__,
-                            error=traceback.format_exc()
+                            error=traceback.format_exc(),
                         )
                 elif is_complete:
-                    self.__invoker.services.events.emit_graph_execution_complete(
-                        graph_execution_state.id
-                    )
+                    self.__invoker.services.events.emit_graph_execution_complete(graph_execution_state.id)
 
         except KeyboardInterrupt:
             pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor
diff --git a/invokeai/app/services/sqlite.py b/invokeai/app/services/sqlite.py
index 8902415096..855f3f1939 100644
--- a/invokeai/app/services/sqlite.py
+++ b/invokeai/app/services/sqlite.py
@@ -66,9 +66,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def get(self, id: str) -> Optional[T]:
         try:
             self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
+            self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
             result = self._cursor.fetchone()
         finally:
             self._lock.release()
@@ -81,9 +79,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def get_raw(self, id: str) -> Optional[str]:
         try:
             self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
+            self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
             result = self._cursor.fetchone()
         finally:
             self._lock.release()
@@ -96,9 +92,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def delete(self, id: str):
         try:
             self._lock.acquire()
-            self._cursor.execute(
-                f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
+            self._cursor.execute(f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),))
             self._conn.commit()
         finally:
             self._lock.release()
@@ -122,13 +116,9 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
 
         pageCount = int(count / per_page) + 1
 
-        return PaginatedResults[T](
-            items=items, page=page, pages=pageCount, per_page=per_page, total=count
-        )
+        return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)
 
-    def search(
-        self, query: str, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[T]:
+    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
         try:
             self._lock.acquire()
             self._cursor.execute(
@@ -149,6 +139,4 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
 
         pageCount = int(count / per_page) + 1
 
-        return PaginatedResults[T](
-            items=items, page=page, pages=pageCount, per_page=per_page, total=count
-        )
+        return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)
diff --git a/invokeai/app/util/controlnet_utils.py b/invokeai/app/util/controlnet_utils.py
index 342fa147c5..6f2c49fec2 100644
--- a/invokeai/app/util/controlnet_utils.py
+++ b/invokeai/app/util/controlnet_utils.py
@@ -17,16 +17,8 @@ from controlnet_aux.util import HWC3, resize_image
 # If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
 
 lvmin_kernels_raw = [
-    np.array([
-        [-1, -1, -1],
-        [0, 1, 0],
-        [1, 1, 1]
-    ], dtype=np.int32),
-    np.array([
-        [0, -1, -1],
-        [1, 1, -1],
-        [0, 1, 0]
-    ], dtype=np.int32)
+    np.array([[-1, -1, -1], [0, 1, 0], [1, 1, 1]], dtype=np.int32),
+    np.array([[0, -1, -1], [1, 1, -1], [0, 1, 0]], dtype=np.int32),
 ]
 
 lvmin_kernels = []
@@ -36,16 +28,8 @@ lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
 lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
 
 lvmin_prunings_raw = [
-    np.array([
-        [-1, -1, -1],
-        [-1, 1, -1],
-        [0, 0, -1]
-    ], dtype=np.int32),
-    np.array([
-        [-1, -1, -1],
-        [-1, 1, -1],
-        [-1, 0, 0]
-    ], dtype=np.int32)
+    np.array([[-1, -1, -1], [-1, 1, -1], [0, 0, -1]], dtype=np.int32),
+    np.array([[-1, -1, -1], [-1, 1, -1], [-1, 0, 0]], dtype=np.int32),
 ]
 
 lvmin_prunings = []
@@ -99,10 +83,10 @@ def nake_nms(x):
 ################################################################################
 # FIXME: not using yet, if used in the future will most likely require modification of preprocessors
 def pixel_perfect_resolution(
-        image: np.ndarray,
-        target_H: int,
-        target_W: int,
-        resize_mode: str,
+    image: np.ndarray,
+    target_H: int,
+    target_W: int,
+    resize_mode: str,
 ) -> int:
     """
     Calculate the estimated resolution for resizing an image while preserving aspect ratio.
@@ -135,7 +119,7 @@ def pixel_perfect_resolution(
 
     if resize_mode == "fill_resize":
         estimation = min(k0, k1) * float(min(raw_H, raw_W))
-    else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
+    else:  # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
         estimation = max(k0, k1) * float(min(raw_H, raw_W))
 
     # print(f"Pixel Perfect Computation:")
@@ -154,13 +138,7 @@ def pixel_perfect_resolution(
 #    modified for InvokeAI
 ###########################################################################
 # def detectmap_proc(detected_map, module, resize_mode, h, w):
-def np_img_resize(
-    np_img: np.ndarray,
-    resize_mode: str,
-    h: int,
-    w: int,
-    device: torch.device = torch.device('cpu')
-):
+def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device: torch.device = torch.device("cpu")):
     # if 'inpaint' in module:
     #     np_img = np_img.astype(np.float32)
     # else:
@@ -184,15 +162,14 @@ def np_img_resize(
         # below is very boring but do not change these. If you change these Apple or Mac may fail.
         y = torch.from_numpy(y)
         y = y.float() / 255.0
-        y = rearrange(y, 'h w c -> 1 c h w')
+        y = rearrange(y, "h w c -> 1 c h w")
         y = y.clone()
         # y = y.to(devices.get_device_for("controlnet"))
         y = y.to(device)
         y = y.clone()
         return y
 
-    def high_quality_resize(x: np.ndarray,
-                            size):
+    def high_quality_resize(x: np.ndarray, size):
         # Written by lvmin
         # Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
         inpaint_mask = None
@@ -244,7 +221,7 @@ def np_img_resize(
         return y
 
     # if resize_mode == external_code.ResizeMode.RESIZE:
-    if resize_mode == "just_resize": # RESIZE
+    if resize_mode == "just_resize":  # RESIZE
         np_img = high_quality_resize(np_img, (w, h))
         np_img = safe_numpy(np_img)
         return get_pytorch_control(np_img), np_img
@@ -270,20 +247,21 @@ def np_img_resize(
         new_h, new_w, _ = np_img.shape
         pad_h = max(0, (h - new_h) // 2)
         pad_w = max(0, (w - new_w) // 2)
-        high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = np_img
+        high_quality_background[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = np_img
         np_img = high_quality_background
         np_img = safe_numpy(np_img)
         return get_pytorch_control(np_img), np_img
-    else: # resize_mode == "crop_resize"  (INNER_FIT)
+    else:  # resize_mode == "crop_resize"  (INNER_FIT)
         k = max(k0, k1)
         np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
         new_h, new_w, _ = np_img.shape
         pad_h = max(0, (new_h - h) // 2)
         pad_w = max(0, (new_w - w) // 2)
-        np_img = np_img[pad_h:pad_h + h, pad_w:pad_w + w]
+        np_img = np_img[pad_h : pad_h + h, pad_w : pad_w + w]
         np_img = safe_numpy(np_img)
         return get_pytorch_control(np_img), np_img
 
+
 def prepare_control_image(
     # image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
     # but now should be able to assume that image is a single PIL.Image, which simplifies things
@@ -301,15 +279,17 @@ def prepare_control_image(
     resize_mode="just_resize_simple",
 ):
     # FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
-    if (resize_mode == "just_resize_simple" or
-        resize_mode == "crop_resize_simple" or
-        resize_mode == "fill_resize_simple"):
+    if (
+        resize_mode == "just_resize_simple"
+        or resize_mode == "crop_resize_simple"
+        or resize_mode == "fill_resize_simple"
+    ):
         image = image.convert("RGB")
-        if (resize_mode == "just_resize_simple"):
+        if resize_mode == "just_resize_simple":
             image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-        elif (resize_mode == "crop_resize_simple"): # not yet implemented
+        elif resize_mode == "crop_resize_simple":  # not yet implemented
             pass
-        elif (resize_mode == "fill_resize_simple"): # not yet implemented
+        elif resize_mode == "fill_resize_simple":  # not yet implemented
             pass
         nimage = np.array(image)
         nimage = nimage[None, :]
@@ -320,7 +300,7 @@ def prepare_control_image(
         timage = torch.from_numpy(nimage)
 
     # use fancy lvmin controlnet resizing
-    elif (resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize"):
+    elif resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize":
         nimage = np.array(image)
         timage, nimage = np_img_resize(
             np_img=nimage,
@@ -336,7 +316,7 @@ def prepare_control_image(
         exit(1)
 
     timage = timage.to(device=device, dtype=dtype)
-    cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
+    cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
     if do_classifier_free_guidance and not cfg_injection:
         timage = torch.cat([timage] * 2)
     return timage
diff --git a/invokeai/app/util/step_callback.py b/invokeai/app/util/step_callback.py
index 1e8939b0bf..994d83e705 100644
--- a/invokeai/app/util/step_callback.py
+++ b/invokeai/app/util/step_callback.py
@@ -9,19 +9,16 @@ from ...backend.stable_diffusion import PipelineIntermediateState
 from invokeai.app.services.config import InvokeAIAppConfig
 
 
-def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix = None):
+def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
     latent_image = samples[0].permute(1, 2, 0) @ latent_rgb_factors
 
     if smooth_matrix is not None:
         latent_image = latent_image.unsqueeze(0).permute(3, 0, 1, 2)
-        latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1,1,3,3)), padding=1)
+        latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1, 1, 3, 3)), padding=1)
         latent_image = latent_image.permute(1, 2, 3, 0).squeeze(0)
 
     latents_ubyte = (
-        ((latent_image + 1) / 2)
-        .clamp(0, 1)  # change scale from -1..1 to 0..1
-        .mul(0xFF)  # to 0..255
-        .byte()
+        ((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte()  # change scale from -1..1 to 0..1  # to 0..255
     ).cpu()
 
     return Image.fromarray(latents_ubyte.numpy())
@@ -92,6 +89,7 @@ def stable_diffusion_step_callback(
         total_steps=node["steps"],
     )
 
+
 def stable_diffusion_xl_step_callback(
     context: InvocationContext,
     node: dict,
@@ -106,9 +104,9 @@ def stable_diffusion_xl_step_callback(
     sdxl_latent_rgb_factors = torch.tensor(
         [
             #   R        G        B
-            [ 0.3816,  0.4930,  0.5320],
-            [-0.3753,  0.1631,  0.1739],
-            [ 0.1770,  0.3588, -0.2048],
+            [0.3816, 0.4930, 0.5320],
+            [-0.3753, 0.1631, 0.1739],
+            [0.1770, 0.3588, -0.2048],
             [-0.4350, -0.2644, -0.4289],
         ],
         dtype=sample.dtype,
@@ -117,9 +115,9 @@ def stable_diffusion_xl_step_callback(
 
     sdxl_smooth_matrix = torch.tensor(
         [
-            #[ 0.0478,  0.1285,  0.0478],
-            #[ 0.1285,  0.2948,  0.1285],
-            #[ 0.0478,  0.1285,  0.0478],
+            # [ 0.0478,  0.1285,  0.0478],
+            # [ 0.1285,  0.2948,  0.1285],
+            # [ 0.0478,  0.1285,  0.0478],
             [0.0358, 0.0964, 0.0358],
             [0.0964, 0.4711, 0.0964],
             [0.0358, 0.0964, 0.0358],
@@ -143,4 +141,4 @@ def stable_diffusion_xl_step_callback(
         progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
         step=step,
         total_steps=total_steps,
-    )
\ No newline at end of file
+    )
diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py
index ff8b4bc8c5..aa2a1f1ca6 100644
--- a/invokeai/backend/__init__.py
+++ b/invokeai/backend/__init__.py
@@ -1,15 +1,6 @@
 """
 Initialization file for invokeai.backend
 """
-from .generator import (
-    InvokeAIGeneratorBasicParams,
-    InvokeAIGenerator,
-    InvokeAIGeneratorOutput,
-    Img2Img,
-    Inpaint
-)
-from .model_management import (
-    ModelManager, ModelCache, BaseModelType,
-    ModelType, SubModelType, ModelInfo
-    )
-from .safety_checker import SafetyChecker
+from .generator import InvokeAIGeneratorBasicParams, InvokeAIGenerator, InvokeAIGeneratorOutput, Img2Img, Inpaint
+from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo
+from .model_management.models import SilenceWarnings
diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py
index 99a63d56a3..af3231a7d1 100644
--- a/invokeai/backend/generator/base.py
+++ b/invokeai/backend/generator/base.py
@@ -28,68 +28,71 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 import invokeai.backend.util.logging as logger
 from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
-from ..safety_checker import SafetyChecker
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
 downsampling = 8
 
+
 @dataclass
 class InvokeAIGeneratorBasicParams:
-    seed: Optional[int]=None
-    width: int=512
-    height: int=512
-    cfg_scale: float=7.5
-    steps: int=20
-    ddim_eta: float=0.0
-    scheduler: str='ddim'
-    precision: str='float16'
-    perlin: float=0.0
-    threshold: float=0.0
-    seamless: bool=False
-    seamless_axes: List[str]=field(default_factory=lambda: ['x', 'y'])
-    h_symmetry_time_pct: Optional[float]=None
-    v_symmetry_time_pct: Optional[float]=None
+    seed: Optional[int] = None
+    width: int = 512
+    height: int = 512
+    cfg_scale: float = 7.5
+    steps: int = 20
+    ddim_eta: float = 0.0
+    scheduler: str = "ddim"
+    precision: str = "float16"
+    perlin: float = 0.0
+    threshold: float = 0.0
+    seamless: bool = False
+    seamless_axes: List[str] = field(default_factory=lambda: ["x", "y"])
+    h_symmetry_time_pct: Optional[float] = None
+    v_symmetry_time_pct: Optional[float] = None
     variation_amount: float = 0.0
-    with_variations: list=field(default_factory=list)
-    safety_checker: Optional[SafetyChecker]=None
+    with_variations: list = field(default_factory=list)
+
 
 @dataclass
 class InvokeAIGeneratorOutput:
-    '''
+    """
     InvokeAIGeneratorOutput is a dataclass that contains the outputs of a generation
     operation, including the image, its seed, the model name used to generate the image
     and the model hash, as well as all the generate() parameters that went into
     generating the image (in .params, also available as attributes)
-    '''
+    """
+
     image: Image.Image
     seed: int
     model_hash: str
     attention_maps_images: List[Image.Image]
     params: Namespace
 
+
 # we are interposing a wrapper around the original Generator classes so that
 # old code that calls Generate will continue to work.
 class InvokeAIGenerator(metaclass=ABCMeta):
-    def __init__(self,
-                 model_info: dict,
-                 params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
-                 **kwargs,
-                 ):
-        self.model_info=model_info
-        self.params=params
+    def __init__(
+        self,
+        model_info: dict,
+        params: InvokeAIGeneratorBasicParams = InvokeAIGeneratorBasicParams(),
+        **kwargs,
+    ):
+        self.model_info = model_info
+        self.params = params
         self.kwargs = kwargs
 
     def generate(
         self,
         conditioning: tuple,
         scheduler,
-        callback: Optional[Callable]=None,
-        step_callback: Optional[Callable]=None,
-        iterations: int=1,
+        callback: Optional[Callable] = None,
+        step_callback: Optional[Callable] = None,
+        iterations: int = 1,
         **keyword_args,
-    )->Iterator[InvokeAIGeneratorOutput]:
-        '''
+    ) -> Iterator[InvokeAIGeneratorOutput]:
+        """
         Return an iterator across the indicated number of generations.
         Each time the iterator is called it will return an InvokeAIGeneratorOutput
         object. Use like this:
@@ -109,7 +112,7 @@ class InvokeAIGenerator(metaclass=ABCMeta):
            for o in outputs:
                print(o.image, o.seed)
 
-        '''
+        """
         generator_args = dataclasses.asdict(self.params)
         generator_args.update(keyword_args)
 
@@ -120,22 +123,21 @@ class InvokeAIGenerator(metaclass=ABCMeta):
             gen_class = self._generator_class()
             generator = gen_class(model, self.params.precision, **self.kwargs)
             if self.params.variation_amount > 0:
-                generator.set_variation(generator_args.get('seed'),
-                                        generator_args.get('variation_amount'),
-                                        generator_args.get('with_variations')
-                                        )
+                generator.set_variation(
+                    generator_args.get("seed"),
+                    generator_args.get("variation_amount"),
+                    generator_args.get("with_variations"),
+                )
 
             if isinstance(model, DiffusionPipeline):
                 for component in [model.unet, model.vae]:
-                    configure_model_padding(component,
-                                            generator_args.get('seamless',False),
-                                            generator_args.get('seamless_axes')
-                                            )
+                    configure_model_padding(
+                        component, generator_args.get("seamless", False), generator_args.get("seamless_axes")
+                    )
             else:
-                configure_model_padding(model,
-                                        generator_args.get('seamless',False),
-                                        generator_args.get('seamless_axes')
-                                        )
+                configure_model_padding(
+                    model, generator_args.get("seamless", False), generator_args.get("seamless_axes")
+                )
 
             iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
             for i in iteration_count:
@@ -149,66 +151,66 @@ class InvokeAIGenerator(metaclass=ABCMeta):
                     image=results[0][0],
                     seed=results[0][1],
                     attention_maps_images=results[0][2],
-                    model_hash = model_hash,
-                    params=Namespace(model_name=model_name,**generator_args),
+                    model_hash=model_hash,
+                    params=Namespace(model_name=model_name, **generator_args),
                 )
                 if callback:
                     callback(output)
             yield output
 
     @classmethod
-    def schedulers(self)->List[str]:
-        '''
+    def schedulers(self) -> List[str]:
+        """
         Return list of all the schedulers that we currently handle.
-        '''
+        """
         return list(SCHEDULER_MAP.keys())
 
     def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
         return generator_class(model, self.params.precision)
 
     @classmethod
-    def _generator_class(cls)->Type[Generator]:
-        '''
+    def _generator_class(cls) -> Type[Generator]:
+        """
         In derived classes return the name of the generator to apply.
         If you don't override will return the name of the derived
         class, which nicely parallels the generator class names.
-        '''
+        """
         return Generator
 
+
 # ------------------------------------
 class Img2Img(InvokeAIGenerator):
-    def generate(self,
-               init_image: Union[Image.Image, torch.FloatTensor],
-               strength: float=0.75,
-               **keyword_args
-               )->Iterator[InvokeAIGeneratorOutput]:
-        return super().generate(init_image=init_image,
-                                strength=strength,
-                                **keyword_args
-                                )
+    def generate(
+        self, init_image: Union[Image.Image, torch.FloatTensor], strength: float = 0.75, **keyword_args
+    ) -> Iterator[InvokeAIGeneratorOutput]:
+        return super().generate(init_image=init_image, strength=strength, **keyword_args)
+
     @classmethod
     def _generator_class(cls):
         from .img2img import Img2Img
+
         return Img2Img
 
+
 # ------------------------------------
 # Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
 class Inpaint(Img2Img):
-    def generate(self,
-                 mask_image: Union[Image.Image, torch.FloatTensor],
-                 # Seam settings - when 0, doesn't fill seam
-                 seam_size: int = 96,
-                 seam_blur: int = 16,
-                 seam_strength: float = 0.7,
-                 seam_steps: int = 30,
-                 tile_size: int = 32,
-                 inpaint_replace=False,
-                 infill_method=None,
-                 inpaint_width=None,
-                 inpaint_height=None,
-                 inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
-                 **keyword_args
-                 )->Iterator[InvokeAIGeneratorOutput]:
+    def generate(
+        self,
+        mask_image: Union[Image.Image, torch.FloatTensor],
+        # Seam settings - when 0, doesn't fill seam
+        seam_size: int = 96,
+        seam_blur: int = 16,
+        seam_strength: float = 0.7,
+        seam_steps: int = 30,
+        tile_size: int = 32,
+        inpaint_replace=False,
+        infill_method=None,
+        inpaint_width=None,
+        inpaint_height=None,
+        inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
+        **keyword_args,
+    ) -> Iterator[InvokeAIGeneratorOutput]:
         return super().generate(
             mask_image=mask_image,
             seam_size=seam_size,
@@ -221,13 +223,16 @@ class Inpaint(Img2Img):
             inpaint_width=inpaint_width,
             inpaint_height=inpaint_height,
             inpaint_fill=inpaint_fill,
-            **keyword_args
+            **keyword_args,
         )
+
     @classmethod
     def _generator_class(cls):
         from .inpaint import Inpaint
+
         return Inpaint
 
+
 class Generator:
     downsampling_factor: int
     latent_channels: int
@@ -240,7 +245,6 @@ class Generator:
         self.seed = None
         self.latent_channels = model.unet.config.in_channels
         self.downsampling_factor = downsampling  # BUG: should come from model or config
-        self.safety_checker = None
         self.perlin = 0.0
         self.threshold = 0
         self.variation_amount = 0
@@ -254,9 +258,7 @@ class Generator:
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
         """
-        raise NotImplementedError(
-            "image_iterator() must be implemented in a descendent class"
-        )
+        raise NotImplementedError("image_iterator() must be implemented in a descendent class")
 
     def set_variation(self, seed, variation_amount, with_variations):
         self.seed = seed
@@ -277,17 +279,13 @@ class Generator:
         perlin=0.0,
         h_symmetry_time_pct=None,
         v_symmetry_time_pct=None,
-        safety_checker: SafetyChecker=None,
         free_gpu_mem: bool = False,
         **kwargs,
     ):
         scope = nullcontext
-        self.safety_checker = safety_checker
         self.free_gpu_mem = free_gpu_mem
         attention_maps_images = []
-        attention_maps_callback = lambda saver: attention_maps_images.append(
-            saver.get_stacked_maps_image()
-        )
+        attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
         make_image = self.get_make_image(
             sampler=sampler,
             init_image=init_image,
@@ -329,17 +327,10 @@ class Generator:
                 # Pass on the seed in case a layer beneath us needs to generate noise on its own.
                 image = make_image(x_T, seed)
 
-                if self.safety_checker is not None:
-                    image = self.safety_checker.check(image)
-
                 results.append([image, seed, attention_maps_images])
 
                 if image_callback is not None:
-                    attention_maps_image = (
-                        None
-                        if len(attention_maps_images) == 0
-                        else attention_maps_images[-1]
-                    )
+                    attention_maps_image = None if len(attention_maps_images) == 0 else attention_maps_images[-1]
                     image_callback(
                         image,
                         seed,
@@ -350,9 +341,7 @@ class Generator:
                 seed = self.new_seed()
 
                 # Free up memory from the last generation.
-                clear_cuda_cache = (
-                    kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
-                )
+                clear_cuda_cache = kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
                 if clear_cuda_cache is not None:
                     clear_cuda_cache()
 
@@ -379,14 +368,8 @@ class Generator:
 
         # Get the original alpha channel of the mask if there is one.
         # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
-        pil_init_mask = (
-            init_mask.getchannel("A")
-            if init_mask.mode == "RGBA"
-            else init_mask.convert("L")
-        )
-        pil_init_image = init_image.convert(
-            "RGBA"
-        )  # Add an alpha channel if one doesn't exist
+        pil_init_mask = init_mask.getchannel("A") if init_mask.mode == "RGBA" else init_mask.convert("L")
+        pil_init_image = init_image.convert("RGBA")  # Add an alpha channel if one doesn't exist
 
         # Build an image with only visible pixels from source to use as reference for color-matching.
         init_rgb_pixels = np.asarray(init_image.convert("RGB"), dtype=np.uint8)
@@ -412,10 +395,7 @@ class Generator:
             np_matched_result[:, :, :] = (
                 (
                     (
-                        (
-                            np_matched_result[:, :, :].astype(np.float32)
-                            - gen_means[None, None, :]
-                        )
+                        (np_matched_result[:, :, :].astype(np.float32) - gen_means[None, None, :])
                         / gen_std[None, None, :]
                     )
                     * init_std[None, None, :]
@@ -441,9 +421,7 @@ class Generator:
         else:
             blurred_init_mask = pil_init_mask
 
-        multiplied_blurred_init_mask = ImageChops.multiply(
-            blurred_init_mask, self.pil_image.split()[-1]
-        )
+        multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
 
         # Paste original on color-corrected generation (using blurred mask)
         matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
@@ -469,10 +447,7 @@ class Generator:
 
         latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
         latents_ubyte = (
-            ((latent_image + 1) / 2)
-            .clamp(0, 1)  # change scale from -1..1 to 0..1
-            .mul(0xFF)  # to 0..255
-            .byte()
+            ((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte()  # change scale from -1..1 to 0..1  # to 0..255
         ).cpu()
 
         return Image.fromarray(latents_ubyte.numpy())
@@ -502,9 +477,7 @@ class Generator:
         temp_height = int((height + 7) / 8) * 8
         noise = torch.stack(
             [
-                rand_perlin_2d(
-                    (temp_height, temp_width), (8, 8), device=self.model.device
-                ).to(fixdevice)
+                rand_perlin_2d((temp_height, temp_width), (8, 8), device=self.model.device).to(fixdevice)
                 for _ in range(input_channels)
             ],
             dim=0,
@@ -581,8 +554,6 @@ class Generator:
             device=device,
         )
         if self.perlin > 0.0:
-            perlin_noise = self.get_perlin_noise(
-                width // self.downsampling_factor, height // self.downsampling_factor
-            )
+            perlin_noise = self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
             x = (1 - self.perlin) * x + self.perlin * perlin_noise
         return x
diff --git a/invokeai/backend/generator/img2img.py b/invokeai/backend/generator/img2img.py
index b3b0e8f510..5490b2325c 100644
--- a/invokeai/backend/generator/img2img.py
+++ b/invokeai/backend/generator/img2img.py
@@ -77,10 +77,7 @@ class Img2Img(Generator):
                 callback=step_callback,
                 seed=seed,
             )
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
+            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
                 attention_maps_callback(pipeline_output.attention_map_saver)
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
@@ -91,7 +88,5 @@ class Img2Img(Generator):
         x = torch.randn_like(like, device=device)
         if self.perlin > 0.0:
             shape = like.shape
-            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
-                shape[3], shape[2]
-            )
+            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(shape[3], shape[2])
         return x
diff --git a/invokeai/backend/generator/inpaint.py b/invokeai/backend/generator/inpaint.py
index c91fe0c6a7..7aeb3d4809 100644
--- a/invokeai/backend/generator/inpaint.py
+++ b/invokeai/backend/generator/inpaint.py
@@ -68,15 +68,11 @@ class Inpaint(Img2Img):
             return im
 
         # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
-        im_patched_np = PatchMatch.inpaint(
-            im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
-        )
+        im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3)
         im_patched = Image.fromarray(im_patched_np, mode="RGB")
         return im_patched
 
-    def tile_fill_missing(
-        self, im: Image.Image, tile_size: int = 16, seed: Optional[int] = None
-    ) -> Image.Image:
+    def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image:
         # Only fill if there's an alpha layer
         if im.mode != "RGBA":
             return im
@@ -127,15 +123,11 @@ class Inpaint(Img2Img):
 
         return si
 
-    def mask_edge(
-        self, mask: Image.Image, edge_size: int, edge_blur: int
-    ) -> Image.Image:
+    def mask_edge(self, mask: Image.Image, edge_size: int, edge_blur: int) -> Image.Image:
         npimg = np.asarray(mask, dtype=np.uint8)
 
         # Detect any partially transparent regions
-        npgradient = np.uint8(
-            255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0))
-        )
+        npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
 
         # Detect hard edges
         npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
@@ -144,9 +136,7 @@ class Inpaint(Img2Img):
         npmask = npgradient + npedge
 
         # Expand
-        npmask = cv2.dilate(
-            npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2)
-        )
+        npmask = cv2.dilate(npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2))
 
         new_mask = Image.fromarray(npmask)
 
@@ -242,25 +232,19 @@ class Inpaint(Img2Img):
             if infill_method == "patchmatch" and PatchMatch.patchmatch_available():
                 init_filled = self.infill_patchmatch(self.pil_image.copy())
             elif infill_method == "tile":
-                init_filled = self.tile_fill_missing(
-                    self.pil_image.copy(), seed=self.seed, tile_size=tile_size
-                )
+                init_filled = self.tile_fill_missing(self.pil_image.copy(), seed=self.seed, tile_size=tile_size)
             elif infill_method == "solid":
                 solid_bg = Image.new("RGBA", init_image.size, inpaint_fill)
                 init_filled = Image.alpha_composite(solid_bg, init_image)
             else:
-                raise ValueError(
-                    f"Non-supported infill type {infill_method}", infill_method
-                )
+                raise ValueError(f"Non-supported infill type {infill_method}", infill_method)
             init_filled.paste(init_image, (0, 0), init_image.split()[-1])
 
             # Resize if requested for inpainting
             if inpaint_width and inpaint_height:
                 init_filled = init_filled.resize((inpaint_width, inpaint_height))
 
-            debug_image(
-                init_filled, "init_filled", debug_status=self.enable_image_debugging
-            )
+            debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
 
             # Create init tensor
             init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
@@ -289,9 +273,7 @@ class Inpaint(Img2Img):
                 "mask_image AFTER multiply with pil_image",
                 debug_status=self.enable_image_debugging,
             )
-            mask: torch.FloatTensor = image_resized_to_grid_as_tensor(
-                mask_image, normalize=False
-            )
+            mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
         else:
             mask: torch.FloatTensor = mask_image
 
@@ -302,9 +284,9 @@ class Inpaint(Img2Img):
 
         # todo: support cross-attention control
         uc, c, _ = conditioning
-        conditioning_data = ConditioningData(
-            uc, c, cfg_scale
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
+        conditioning_data = ConditioningData(uc, c, cfg_scale).add_scheduler_args_if_applicable(
+            pipeline.scheduler, eta=ddim_eta
+        )
 
         def make_image(x_T: torch.Tensor, seed: int):
             pipeline_output = pipeline.inpaint_from_embeddings(
@@ -318,15 +300,10 @@ class Inpaint(Img2Img):
                 seed=seed,
             )
 
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
+            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
                 attention_maps_callback(pipeline_output.attention_map_saver)
 
-            result = self.postprocess_size_and_mask(
-                pipeline.numpy_to_pil(pipeline_output.images)[0]
-            )
+            result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0])
 
             # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
             if seam_size > 0:
diff --git a/invokeai/backend/image_util/__init__.py b/invokeai/backend/image_util/__init__.py
index 410f003f6a..9fa635b389 100644
--- a/invokeai/backend/image_util/__init__.py
+++ b/invokeai/backend/image_util/__init__.py
@@ -8,9 +8,7 @@ from .txt2mask import Txt2Mask
 from .util import InitImageResizer, make_grid
 
 
-def debug_image(
-    debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False
-):
+def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False):
     if not debug_status:
         return
 
diff --git a/invokeai/backend/image_util/invisible_watermark.py b/invokeai/backend/image_util/invisible_watermark.py
new file mode 100644
index 0000000000..4605daea43
--- /dev/null
+++ b/invokeai/backend/image_util/invisible_watermark.py
@@ -0,0 +1,34 @@
+"""
+This module defines a singleton object, "invisible_watermark" that
+wraps the invisible watermark model. It respects the global "invisible_watermark"
+configuration variable, that allows the watermarking to be supressed.
+"""
+import numpy as np
+import cv2
+from PIL import Image
+from imwatermark import WatermarkEncoder
+from invokeai.app.services.config import InvokeAIAppConfig
+import invokeai.backend.util.logging as logger
+
+config = InvokeAIAppConfig.get_config()
+
+
+class InvisibleWatermark:
+    """
+    Wrapper around InvisibleWatermark module.
+    """
+
+    @classmethod
+    def invisible_watermark_available(self) -> bool:
+        return config.invisible_watermark
+
+    @classmethod
+    def add_watermark(self, image: Image, watermark_text: str) -> Image:
+        if not self.invisible_watermark_available():
+            return image
+        logger.debug(f'Applying invisible watermark "{watermark_text}"')
+        bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
+        encoder = WatermarkEncoder()
+        encoder.set_watermark("bytes", watermark_text.encode("utf-8"))
+        bgr_encoded = encoder.encode(bgr, "dwtDct")
+        return Image.fromarray(cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB)).convert("RGBA")
diff --git a/invokeai/backend/image_util/patchmatch.py b/invokeai/backend/image_util/patchmatch.py
index 2e65f08d9f..98055f60c8 100644
--- a/invokeai/backend/image_util/patchmatch.py
+++ b/invokeai/backend/image_util/patchmatch.py
@@ -7,8 +7,10 @@ be suppressed or deferred
 import numpy as np
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
+
 config = InvokeAIAppConfig.get_config()
 
+
 class PatchMatch:
     """
     Thin class wrapper around the patchmatch function.
diff --git a/invokeai/backend/image_util/pngwriter.py b/invokeai/backend/image_util/pngwriter.py
index 452bbfc783..d3f53d7e72 100644
--- a/invokeai/backend/image_util/pngwriter.py
+++ b/invokeai/backend/image_util/pngwriter.py
@@ -34,9 +34,7 @@ class PngWriter:
 
     # saves image named _image_ to outdir/name, writing metadata from prompt
     # returns full path of output
-    def save_image_and_prompt_to_png(
-        self, image, dream_prompt, name, metadata=None, compress_level=6
-    ):
+    def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None, compress_level=6):
         path = os.path.join(self.outdir, name)
         info = PngImagePlugin.PngInfo()
         info.add_text("Dream", dream_prompt)
@@ -114,8 +112,6 @@ class PromptFormatter:
         if opt.variation_amount > 0:
             switches.append(f"-v{opt.variation_amount}")
         if opt.with_variations:
-            formatted_variations = ",".join(
-                f"{seed}:{weight}" for seed, weight in opt.with_variations
-            )
+            formatted_variations = ",".join(f"{seed}:{weight}" for seed, weight in opt.with_variations)
             switches.append(f"-V{formatted_variations}")
         return " ".join(switches)
diff --git a/invokeai/backend/image_util/safety_checker.py b/invokeai/backend/image_util/safety_checker.py
new file mode 100644
index 0000000000..483e563b82
--- /dev/null
+++ b/invokeai/backend/image_util/safety_checker.py
@@ -0,0 +1,64 @@
+"""
+This module defines a singleton object, "safety_checker" that
+wraps the safety_checker model. It respects the global "nsfw_checker"
+configuration variable, that allows the checker to be supressed.
+"""
+import numpy as np
+from PIL import Image
+from invokeai.backend import SilenceWarnings
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.util.devices import choose_torch_device
+import invokeai.backend.util.logging as logger
+
+config = InvokeAIAppConfig.get_config()
+
+CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
+
+
+class SafetyChecker:
+    """
+    Wrapper around SafetyChecker model.
+    """
+
+    safety_checker = None
+    feature_extractor = None
+    tried_load: bool = False
+
+    @classmethod
+    def _load_safety_checker(self):
+        if self.tried_load:
+            return
+
+        if config.nsfw_checker:
+            try:
+                from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+                from transformers import AutoFeatureExtractor
+
+                self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
+                self.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
+                logger.info("NSFW checker initialized")
+            except Exception as e:
+                logger.warning(f"Could not load NSFW checker: {str(e)}")
+        else:
+            logger.info("NSFW checker loading disabled")
+        self.tried_load = True
+
+    @classmethod
+    def safety_checker_available(self) -> bool:
+        self._load_safety_checker()
+        return self.safety_checker is not None
+
+    @classmethod
+    def has_nsfw_concept(self, image: Image) -> bool:
+        if not self.safety_checker_available():
+            return False
+
+        device = choose_torch_device()
+        features = self.feature_extractor([image], return_tensors="pt")
+        features.to(device)
+        self.safety_checker.to(device)
+        x_image = np.array(image).astype(np.float32) / 255.0
+        x_image = x_image[None].transpose(0, 3, 1, 2)
+        with SilenceWarnings():
+            checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values)
+        return has_nsfw_concept[0]
diff --git a/invokeai/backend/image_util/seamless.py b/invokeai/backend/image_util/seamless.py
index 4fbc0cd78e..6fb2617901 100644
--- a/invokeai/backend/image_util/seamless.py
+++ b/invokeai/backend/image_util/seamless.py
@@ -5,12 +5,8 @@ def _conv_forward_asymmetric(self, input, weight, bias):
     """
     Patch for Conv2d._conv_forward that supports asymmetric padding
     """
-    working = nn.functional.pad(
-        input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"]
-    )
-    working = nn.functional.pad(
-        working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"]
-    )
+    working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
+    working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
     return nn.functional.conv2d(
         working,
         weight,
@@ -32,18 +28,14 @@ def configure_model_padding(model, seamless, seamless_axes):
             if seamless:
                 m.asymmetric_padding_mode = {}
                 m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = (
-                    "circular" if ("x" in seamless_axes) else "constant"
-                )
+                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
                 m.asymmetric_padding["x"] = (
                     m._reversed_padding_repeated_twice[0],
                     m._reversed_padding_repeated_twice[1],
                     0,
                     0,
                 )
-                m.asymmetric_padding_mode["y"] = (
-                    "circular" if ("y" in seamless_axes) else "constant"
-                )
+                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
                 m.asymmetric_padding["y"] = (
                     0,
                     0,
diff --git a/invokeai/backend/image_util/txt2mask.py b/invokeai/backend/image_util/txt2mask.py
index 429c9b63fb..12db54b0db 100644
--- a/invokeai/backend/image_util/txt2mask.py
+++ b/invokeai/backend/image_util/txt2mask.py
@@ -39,23 +39,18 @@ CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
 config = InvokeAIAppConfig.get_config()
 
+
 class SegmentedGrayscale(object):
     def __init__(self, image: Image, heatmap: torch.Tensor):
         self.heatmap = heatmap
         self.image = image
 
     def to_grayscale(self, invert: bool = False) -> Image:
-        return self._rescale(
-            Image.fromarray(
-                np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)
-            )
-        )
+        return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
 
     def to_mask(self, threshold: float = 0.5) -> Image:
         discrete_heatmap = self.heatmap.lt(threshold).int()
-        return self._rescale(
-            Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L")
-        )
+        return self._rescale(Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L"))
 
     def to_transparent(self, invert: bool = False) -> Image:
         transparent_image = self.image.copy()
@@ -67,11 +62,7 @@ class SegmentedGrayscale(object):
 
     # unscales and uncrops the 352x352 heatmap so that it matches the image again
     def _rescale(self, heatmap: Image) -> Image:
-        size = (
-            self.image.width
-            if (self.image.width > self.image.height)
-            else self.image.height
-        )
+        size = self.image.width if (self.image.width > self.image.height) else self.image.height
         resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
         return resized_image.crop((0, 0, self.image.width, self.image.height))
 
@@ -87,12 +78,8 @@ class Txt2Mask(object):
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device
-        self.processor = AutoProcessor.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=config.cache_dir
-        )
-        self.model = CLIPSegForImageSegmentation.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=config.cache_dir
-        )
+        self.processor = AutoProcessor.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir)
+        self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir)
 
     @torch.no_grad()
     def segment(self, image, prompt: str) -> SegmentedGrayscale:
@@ -107,9 +94,7 @@ class Txt2Mask(object):
         image = ImageOps.exif_transpose(image)
         img = self._scale_and_crop(image)
 
-        inputs = self.processor(
-            text=[prompt], images=[img], padding=True, return_tensors="pt"
-        )
+        inputs = self.processor(text=[prompt], images=[img], padding=True, return_tensors="pt")
         outputs = self.model(**inputs)
         heatmap = torch.sigmoid(outputs.logits)
         return SegmentedGrayscale(image, heatmap)
diff --git a/invokeai/backend/install/check_root.py b/invokeai/backend/install/check_root.py
new file mode 100644
index 0000000000..ded9e66635
--- /dev/null
+++ b/invokeai/backend/install/check_root.py
@@ -0,0 +1,36 @@
+"""
+Check that the invokeai_root is correctly configured and exit if not.
+"""
+import sys
+from invokeai.app.services.config import (
+    InvokeAIAppConfig,
+)
+
+
+def check_invokeai_root(config: InvokeAIAppConfig):
+    try:
+        assert config.model_conf_path.exists(), f"{config.model_conf_path} not found"
+        assert config.db_path.parent.exists(), f"{config.db_path.parent} not found"
+        assert config.models_path.exists(), f"{config.models_path} not found"
+        for model in [
+            "CLIP-ViT-bigG-14-laion2B-39B-b160k",
+            "bert-base-uncased",
+            "clip-vit-large-patch14",
+            "sd-vae-ft-mse",
+            "stable-diffusion-2-clip",
+            "stable-diffusion-safety-checker",
+        ]:
+            path = config.models_path / f"core/convert/{model}"
+            assert path.exists(), f"{path} is missing"
+    except Exception as e:
+        print()
+        print(f"An exception has occurred: {str(e)}")
+        print("== STARTUP ABORTED ==")
+        print("** One or more necessary files is missing from your InvokeAI root directory **")
+        print("** Please rerun the configuration script to fix this problem. **")
+        print("** From the launcher, selection option [7]. **")
+        print(
+            '** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
+        )
+        input("Press any key to continue...")
+        sys.exit(0)
diff --git a/invokeai/backend/install/invokeai_configure.py b/invokeai/backend/install/invokeai_configure.py
index 1f0e39961b..972e6668c4 100755
--- a/invokeai/backend/install/invokeai_configure.py
+++ b/invokeai/backend/install/invokeai_configure.py
@@ -13,8 +13,8 @@ import os
 import shutil
 import textwrap
 import traceback
-import warnings
 import yaml
+import warnings
 from argparse import Namespace
 from pathlib import Path
 from shutil import get_terminal_size
@@ -32,6 +32,7 @@ from omegaconf import OmegaConf
 from tqdm import tqdm
 from transformers import (
     CLIPTextModel,
+    CLIPTextConfig,
     CLIPTokenizer,
     AutoFeatureExtractor,
     BertTokenizerFast,
@@ -44,6 +45,7 @@ from invokeai.app.services.config import (
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
 from invokeai.frontend.install.widgets import (
+    SingleSelectColumns,
     CenteredButtonPress,
     FileBox,
     IntTitleSlider,
@@ -58,9 +60,7 @@ from invokeai.backend.install.model_install_backend import (
     InstallSelections,
     ModelInstall,
 )
-from invokeai.backend.model_management.model_probe import (
-    ModelType, BaseModelType
-    )
+from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
@@ -75,7 +75,7 @@ Model_dir = "models"
 Default_config_file = config.model_conf_path
 SD_Configs = config.legacy_conf_path
 
-PRECISION_CHOICES = ['auto','float16','float32']
+PRECISION_CHOICES = ["auto", "float16", "float32"]
 
 INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # This is the InvokeAI initialization file, which contains command-line default values.
@@ -83,7 +83,8 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # or renaming it and then running invokeai-configure again.
 """
 
-logger=InvokeAILogger.getLogger()
+logger = InvokeAILogger.getLogger()
+
 
 # --------------------------------------------
 def postscript(errors: None):
@@ -106,7 +107,9 @@ Add the '--help' argument to see all of the command-line switches available for
 """
 
     else:
-        message = "\n** There were errors during installation. It is possible some of the models were not fully downloaded.\n"
+        message = (
+            "\n** There were errors during installation. It is possible some of the models were not fully downloaded.\n"
+        )
         for err in errors:
             message += f"\t - {err}\n"
         message += "Please check the logs above and correct any issues."
@@ -167,9 +170,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
         logger.info(f"Installing {label} model file {model_url}...")
         if not os.path.exists(model_dest):
             os.makedirs(os.path.dirname(model_dest), exist_ok=True)
-            request.urlretrieve(
-                model_url, model_dest, ProgressBar(os.path.basename(model_dest))
-            )
+            request.urlretrieve(model_url, model_dest, ProgressBar(os.path.basename(model_dest)))
             logger.info("...downloaded successfully")
         else:
             logger.info("...exists")
@@ -180,81 +181,93 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
 
 
 def download_conversion_models():
-    target_dir = config.root_path / 'models/core/convert'
+    target_dir = config.root_path / "models/core/convert"
     kwargs = dict()  # for future use
     try:
-        logger.info('Downloading core tokenizers and text encoders')
+        logger.info("Downloading core tokenizers and text encoders")
 
         # bert
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             bert = BertTokenizerFast.from_pretrained("bert-base-uncased", **kwargs)
-            bert.save_pretrained(target_dir / 'bert-base-uncased', safe_serialization=True)
-        
+            bert.save_pretrained(target_dir / "bert-base-uncased", safe_serialization=True)
+
         # sd-1
-        repo_id = 'openai/clip-vit-large-patch14'
-        hf_download_from_pretrained(CLIPTokenizer, repo_id, target_dir / 'clip-vit-large-patch14')
-        hf_download_from_pretrained(CLIPTextModel, repo_id, target_dir / 'clip-vit-large-patch14')
+        repo_id = "openai/clip-vit-large-patch14"
+        hf_download_from_pretrained(CLIPTokenizer, repo_id, target_dir / "clip-vit-large-patch14")
+        hf_download_from_pretrained(CLIPTextModel, repo_id, target_dir / "clip-vit-large-patch14")
 
         # sd-2
         repo_id = "stabilityai/stable-diffusion-2"
         pipeline = CLIPTokenizer.from_pretrained(repo_id, subfolder="tokenizer", **kwargs)
-        pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'tokenizer', safe_serialization=True)
+        pipeline.save_pretrained(target_dir / "stable-diffusion-2-clip" / "tokenizer", safe_serialization=True)
 
         pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs)
-        pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'text_encoder', safe_serialization=True)
+        pipeline.save_pretrained(target_dir / "stable-diffusion-2-clip" / "text_encoder", safe_serialization=True)
+
+        # sd-xl - tokenizer_2
+        repo_id = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+        _, model_name = repo_id.split("/")
+        pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs)
+        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
+
+        pipeline = CLIPTextConfig.from_pretrained(repo_id, **kwargs)
+        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
 
         # VAE
-        logger.info('Downloading stable diffusion VAE')
-        vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse', **kwargs)
-        vae.save_pretrained(target_dir / 'sd-vae-ft-mse', safe_serialization=True)
+        logger.info("Downloading stable diffusion VAE")
+        vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", **kwargs)
+        vae.save_pretrained(target_dir / "sd-vae-ft-mse", safe_serialization=True)
 
         # safety checking
-        logger.info('Downloading safety checker')
+        logger.info("Downloading safety checker")
         repo_id = "CompVis/stable-diffusion-safety-checker"
-        pipeline = AutoFeatureExtractor.from_pretrained(repo_id,**kwargs)
-        pipeline.save_pretrained(target_dir / 'stable-diffusion-safety-checker', safe_serialization=True)
+        pipeline = AutoFeatureExtractor.from_pretrained(repo_id, **kwargs)
+        pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
 
-        pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id,**kwargs)
-        pipeline.save_pretrained(target_dir / 'stable-diffusion-safety-checker', safe_serialization=True)
+        pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id, **kwargs)
+        pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
     except KeyboardInterrupt:
         raise
     except Exception as e:
         logger.error(str(e))
 
+
 # ---------------------------------------------
 def download_realesrgan():
     logger.info("Installing ESRGAN Upscaling models...")
     URLs = [
         dict(
-            url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-            dest = "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
-            description = "RealESRGAN_x4plus.pth",
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+            dest="core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
+            description="RealESRGAN_x4plus.pth",
         ),
         dict(
-            url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-            dest = "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
-            description = "RealESRGAN_x4plus_anime_6B.pth",
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+            dest="core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
+            description="RealESRGAN_x4plus_anime_6B.pth",
         ),
         dict(
-            url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
-            dest= "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
-            description = "ESRGAN_SRx4_DF2KOST_official.pth",
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
+            dest="core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
+            description="ESRGAN_SRx4_DF2KOST_official.pth",
         ),
         dict(
-            url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-            dest= "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
-            description = "RealESRGAN_x2plus.pth",
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+            dest="core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
+            description="RealESRGAN_x2plus.pth",
         ),
     ]
     for model in URLs:
-        download_with_progress_bar(model['url'], config.models_path / model['dest'], model['description'])
+        download_with_progress_bar(model["url"], config.models_path / model["dest"], model["description"])
+
 
 # ---------------------------------------------
 def download_support_models():
     download_realesrgan()
     download_conversion_models()
 
+
 # -------------------------------------
 def get_root(root: str = None) -> str:
     if root:
@@ -264,6 +277,7 @@ def get_root(root: str = None) -> str:
     else:
         return str(config.root_path)
 
+
 # -------------------------------------
 class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
     # for responsive resizing - disabled
@@ -272,14 +286,14 @@ class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
     def create(self):
         program_opts = self.parentApp.program_opts
         old_opts = self.parentApp.invokeai_opts
-        first_time = not (config.root_path / 'invokeai.yaml').exists()
+        first_time = not (config.root_path / "invokeai.yaml").exists()
         access_token = HfFolder.get_token()
         window_width, window_height = get_terminal_size()
         label = """Configure startup settings. You can come back and change these later. 
 Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.
 Use cursor arrows to make a checkbox selection, and space to toggle.
 """
-        for i in textwrap.wrap(label,width=window_width-6):
+        for i in textwrap.wrap(label, width=window_width - 6):
             self.add_widget_intelligent(
                 npyscreen.FixedText,
                 value=i,
@@ -287,50 +301,9 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
                 color="CONTROL",
             )
 
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="== BASIC OPTIONS ==",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Select an output directory for images:",
-            editable=False,
-            color="CONTROL",
-        )
-        self.outdir = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="(<tab> autocompletes, ctrl-N advances):",
-            value=str(default_output_dir()),
-            select_dir=True,
-            must_exist=False,
-            use_two_lines=False,
-            labelColor="GOOD",
-            begin_entry_at=40,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Activate the NSFW checker to blur images showing potential sexual imagery:",
-            editable=False,
-            color="CONTROL",
-        )
-        self.nsfw_checker = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="NSFW checker",
-            value=old_opts.nsfw_checker,
-            relx=5,
-            scroll_exit=True,
-        )
         self.nextrely += 1
         label = """HuggingFace access token (OPTIONAL) for automatic model downloads. See https://huggingface.co/settings/tokens."""
-        for line in textwrap.wrap(label,width=window_width-6):
+        for line in textwrap.wrap(label, width=window_width - 6):
             self.add_widget_intelligent(
                 npyscreen.FixedText,
                 value=line,
@@ -347,15 +320,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             scroll_exit=True,
         )
         self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="== ADVANCED OPTIONS ==",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
             name="GPU Management",
@@ -369,34 +333,47 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             npyscreen.Checkbox,
             name="Free GPU memory after each generation",
             value=old_opts.free_gpu_mem,
+            max_width=45,
             relx=5,
             scroll_exit=True,
         )
+        self.nextrely -= 1
         self.xformers_enabled = self.add_widget_intelligent(
             npyscreen.Checkbox,
-            name="Enable xformers support if available",
+            name="Enable xformers support",
             value=old_opts.xformers_enabled,
-            relx=5,
+            max_width=30,
+            relx=50,
             scroll_exit=True,
         )
+        self.nextrely -= 1
         self.always_use_cpu = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Force CPU to be used on GPU systems",
             value=old_opts.always_use_cpu,
-            relx=5,
+            relx=80,
             scroll_exit=True,
         )
-        precision = old_opts.precision or (
-            "float32" if program_opts.full_precision else "auto"
+        precision = old_opts.precision or ("float32" if program_opts.full_precision else "auto")
+        self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name="Floating Point Precision",
+            begin_entry_at=0,
+            editable=False,
+            color="CONTROL",
+            scroll_exit=True,
         )
+        self.nextrely -= 1
         self.precision = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            columns = 2,
+            SingleSelectColumns,
+            columns=3,
             name="Precision",
             values=PRECISION_CHOICES,
             value=PRECISION_CHOICES.index(precision),
             begin_entry_at=3,
-            max_height=len(PRECISION_CHOICES) + 1,
+            max_height=2,
+            max_width=80,
             scroll_exit=True,
         )
         self.max_cache_size = self.add_widget_intelligent(
@@ -409,40 +386,38 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             scroll_exit=True,
         )
         self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models (<tab> autocompletes, ctrl-N advances):",
-            editable=False,
-            color="CONTROL",
-        )
-        self.autoimport_dirs = {}
-        self.autoimport_dirs['autoimport_dir'] = self.add_widget_intelligent(
-                FileBox,
-                name=f'Autoimport Folder',
-                value=str(config.root_path / config.autoimport_dir),
-                select_dir=True,
-                must_exist=False,
-                use_two_lines=False,
-                labelColor="GOOD",
-                begin_entry_at=32,
-                max_height = 3,
-                scroll_exit=True
-            )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="== LICENSE ==",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
+        self.outdir = self.add_widget_intelligent(
+            FileBox,
+            name="Output directory for images (<tab> autocompletes, ctrl-N advances):",
+            value=str(default_output_dir()),
+            select_dir=True,
+            must_exist=False,
+            use_two_lines=False,
+            labelColor="GOOD",
+            begin_entry_at=40,
+            max_height=3,
             scroll_exit=True,
         )
-        self.nextrely -= 1
+        self.autoimport_dirs = {}
+        self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
+            FileBox,
+            name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
+            value=str(config.root_path / config.autoimport_dir),
+            select_dir=True,
+            must_exist=False,
+            use_two_lines=False,
+            labelColor="GOOD",
+            begin_entry_at=32,
+            max_height=3,
+            scroll_exit=True,
+        )
+        self.nextrely += 1
         label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
-AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
+AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSES LOCATED AT
+https://huggingface.co/spaces/CompVis/stable-diffusion-license and
+https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md
 """
-        for i in textwrap.wrap(label,width=window_width-6):
+        for i in textwrap.wrap(label, width=window_width - 6):
             self.add_widget_intelligent(
                 npyscreen.FixedText,
                 value=i,
@@ -451,22 +426,17 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
             )
         self.license_acceptance = self.add_widget_intelligent(
             npyscreen.Checkbox,
-            name="I accept the CreativeML Responsible AI License",
+            name="I accept the CreativeML Responsible AI Licenses",
             value=not first_time,
             relx=2,
             scroll_exit=True,
         )
         self.nextrely += 1
-        label = (
-            "DONE"
-            if program_opts.skip_sd_weights or program_opts.default_only
-            else "NEXT"
-        )
+        label = "DONE" if program_opts.skip_sd_weights or program_opts.default_only else "NEXT"
         self.ok_button = self.add_widget_intelligent(
             CenteredButtonPress,
             name=label,
             relx=(window_width - len(label)) // 2,
-            rely=-3,
             when_pressed_function=self.on_ok,
         )
 
@@ -481,13 +451,11 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
             self.editing = False
         else:
             self.editing = True
-            
+
     def validate_field_values(self, opt: Namespace) -> bool:
         bad_fields = []
         if not opt.license_acceptance:
-            bad_fields.append(
-                "Please accept the license terms before proceeding to model downloads"
-            )
+            bad_fields.append("Please accept the license terms before proceeding to model downloads")
         if not Path(opt.outdir).parent.exists():
             bad_fields.append(
                 f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
@@ -505,12 +473,11 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
         new_opts = Namespace()
 
         for attr in [
-                "outdir",
-                "nsfw_checker",
-                "free_gpu_mem",
-                "max_cache_size",
-                "xformers_enabled",
-                "always_use_cpu",
+            "outdir",
+            "free_gpu_mem",
+            "max_cache_size",
+            "xformers_enabled",
+            "always_use_cpu",
         ]:
             setattr(new_opts, attr, getattr(self, attr).value)
 
@@ -523,7 +490,7 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
         new_opts.hf_token = self.hf_token.value
         new_opts.license_acceptance = self.license_acceptance.value
         new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
-        
+
         return new_opts
 
 
@@ -542,7 +509,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
             "MAIN",
             editOptsForm,
             name="InvokeAI Startup Options",
-            cycle_widgets=True,
+            cycle_widgets=False,
         )
         if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
             self.model_select = self.addForm(
@@ -550,7 +517,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
                 addModelsForm,
                 name="Install Stable Diffusion Models",
                 multipage=True,
-                cycle_widgets=True,
+                cycle_widgets=False,
             )
 
     def new_opts(self):
@@ -562,21 +529,20 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
     editApp.run()
     return editApp.new_opts()
 
+
 def default_startup_options(init_file: Path) -> Namespace:
     opts = InvokeAIAppConfig.get_config()
-    if not init_file.exists():
-        opts.nsfw_checker = True
     return opts
 
+
 def default_user_selections(program_opts: Namespace) -> InstallSelections:
-    
     try:
         installer = ModelInstall(config)
     except omegaconf.errors.ConfigKeyError:
-        logger.warning('Your models.yaml file is corrupt or out of date. Reinitializing')
+        logger.warning("Your models.yaml file is corrupt or out of date. Reinitializing")
         initialize_rootdir(config.root_path, True)
         installer = ModelInstall(config)
-        
+
     models = installer.all_models()
     return InstallSelections(
         install_models=[models[installer.default_model()].path or models[installer.default_model()].repo_id]
@@ -586,44 +552,46 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
         else list(),
     )
 
+
 # -------------------------------------
 def initialize_rootdir(root: Path, yes_to_all: bool = False):
-    logger.info("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
-    for name in (
-            "models",
-            "databases",
-            "text-inversion-output",
-            "text-inversion-training-data",
-            "configs"
-    ):
+    logger.info("Initializing InvokeAI runtime directory")
+    for name in ("models", "databases", "text-inversion-output", "text-inversion-training-data", "configs"):
         os.makedirs(os.path.join(root, name), exist_ok=True)
     for model_type in ModelType:
-        Path(root, 'autoimport', model_type.value).mkdir(parents=True, exist_ok=True)
+        Path(root, "autoimport", model_type.value).mkdir(parents=True, exist_ok=True)
 
     configs_src = Path(configs.__path__[0])
     configs_dest = root / "configs"
     if not os.path.samefile(configs_src, configs_dest):
         shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
 
-    dest = root / 'models'
+    dest = root / "models"
     for model_base in BaseModelType:
         for model_type in ModelType:
             path = dest / model_base.value / model_type.value
             path.mkdir(parents=True, exist_ok=True)
-    path = dest / 'core'
+    path = dest / "core"
     path.mkdir(parents=True, exist_ok=True)
 
-    with open(root / 'configs' / 'models.yaml','w') as yaml_file:
-        yaml_file.write(yaml.dump({'__metadata__':
-                                   {'version':'3.0.0'}
-                                   }
-                                  )
-                        )
-        
+    maybe_create_models_yaml(root)
+
+
+def maybe_create_models_yaml(root: Path):
+    models_yaml = root / "configs" / "models.yaml"
+    if models_yaml.exists():
+        if OmegaConf.load(models_yaml).get("__metadata__"):  # up to date
+            return
+        else:
+            logger.info("Creating new models.yaml, original saved as models.yaml.orig")
+            models_yaml.rename(models_yaml.parent / "models.yaml.orig")
+
+    with open(models_yaml, "w") as yaml_file:
+        yaml_file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
+
+
 # -------------------------------------
-def run_console_ui(
-    program_opts: Namespace, initfile: Path = None
-) -> (Namespace, Namespace):
+def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
     # parse_args() will read from init file if present
     invokeai_opts = default_startup_options(initfile)
     invokeai_opts.root = program_opts.root
@@ -635,8 +603,9 @@ def run_console_ui(
     # the install-models application spawns a subprocess to install
     # models, and will crash unless this is set before running.
     import torch
+
     torch.multiprocessing.set_start_method("spawn")
-    
+
     editApp = EditOptApplication(program_opts, invokeai_opts)
     editApp.run()
     if editApp.user_cancelled:
@@ -653,81 +622,86 @@ def write_opts(opts: Namespace, init_file: Path):
     # this will load current settings
     new_config = InvokeAIAppConfig.get_config()
     new_config.root = config.root
-    
-    for key,value in opts.__dict__.items():
-        if hasattr(new_config,key):
-            setattr(new_config,key,value)
 
-    with open(init_file,'w', encoding='utf-8') as file:
+    for key, value in opts.__dict__.items():
+        if hasattr(new_config, key):
+            setattr(new_config, key, value)
+
+    with open(init_file, "w", encoding="utf-8") as file:
         file.write(new_config.to_yaml())
 
-    if hasattr(opts,'hf_token') and opts.hf_token:
+    if hasattr(opts, "hf_token") and opts.hf_token:
         HfLogin(opts.hf_token)
 
+
 # -------------------------------------
 def default_output_dir() -> Path:
     return config.root_path / "outputs"
 
+
 # -------------------------------------
 def write_default_options(program_opts: Namespace, initfile: Path):
     opt = default_startup_options(initfile)
     write_opts(opt, initfile)
 
+
 # -------------------------------------
 # Here we bring in
 # the legacy Args object in order to parse
 # the old init file and write out the new
 # yaml format.
-def migrate_init_file(legacy_format:Path):
-    old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
+def migrate_init_file(legacy_format: Path):
+    old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
     fields = list(get_type_hints(InvokeAIAppConfig).keys())
     for attr in fields:
-        if hasattr(old,attr):
-            setattr(new,attr,getattr(old,attr))
+        if hasattr(old, attr):
+            setattr(new, attr, getattr(old, attr))
 
     # a few places where the field names have changed and we have to
     # manually add in the new names/values
-    new.nsfw_checker = old.safety_checker
     new.xformers_enabled = old.xformers
     new.conf_path = old.conf
     new.root = legacy_format.parent.resolve()
 
-    invokeai_yaml = legacy_format.parent / 'invokeai.yaml'
-    with open(invokeai_yaml,"w", encoding="utf-8") as outfile:
+    invokeai_yaml = legacy_format.parent / "invokeai.yaml"
+    with open(invokeai_yaml, "w", encoding="utf-8") as outfile:
         outfile.write(new.to_yaml())
 
-    legacy_format.replace(legacy_format.parent / 'invokeai.init.orig')
+    legacy_format.replace(legacy_format.parent / "invokeai.init.orig")
+
 
 # -------------------------------------
 def migrate_models(root: Path):
     from invokeai.backend.install.migrate_to_3 import do_migrate
+
     do_migrate(root, root)
 
-def migrate_if_needed(opt: Namespace, root: Path)->bool:
-    # We check for to see if the runtime directory is correctly initialized.
-    old_init_file = root / 'invokeai.init'
-    new_init_file = root / 'invokeai.yaml'
-    old_hub = root / 'models/hub'
-    migration_needed =  (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
-    
-    if migration_needed:
-        if opt.yes_to_all or \
-            yes_or_no(f'{str(config.root_path)} appears to be a 2.3 format root directory. Convert to version 3.0?'):
 
-            logger.info('** Migrating invokeai.init to invokeai.yaml')
+def migrate_if_needed(opt: Namespace, root: Path) -> bool:
+    # We check for to see if the runtime directory is correctly initialized.
+    old_init_file = root / "invokeai.init"
+    new_init_file = root / "invokeai.yaml"
+    old_hub = root / "models/hub"
+    migration_needed = (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
+
+    if migration_needed:
+        if opt.yes_to_all or yes_or_no(
+            f"{str(config.root_path)} appears to be a 2.3 format root directory. Convert to version 3.0?"
+        ):
+            logger.info("** Migrating invokeai.init to invokeai.yaml")
             migrate_init_file(old_init_file)
-            config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
+            config.parse_args(argv=[], conf=OmegaConf.load(new_init_file))
 
             if old_hub.exists():
                 migrate_models(config.root_path)
         else:
-            print('Cannot continue without conversion. Aborting.')
-    
+            print("Cannot continue without conversion. Aborting.")
+
     return migration_needed
 
-    
+
 # -------------------------------------
 def main():
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
@@ -784,9 +758,9 @@ def main():
 
     invoke_args = []
     if opt.root:
-        invoke_args.extend(['--root',opt.root])
+        invoke_args.extend(["--root", opt.root])
     if opt.full_precision:
-        invoke_args.extend(['--precision','float32'])
+        invoke_args.extend(["--precision", "float32"])
     config.parse_args(invoke_args)
     logger = InvokeAILogger().getLogger(config=config)
 
@@ -798,41 +772,36 @@ def main():
         if migrate_if_needed(opt, config.root_path):
             sys.exit(0)
 
-        if not config.model_conf_path.exists():
-            initialize_rootdir(config.root_path, opt.yes_to_all)
+        # run this unconditionally in case new directories need to be added
+        initialize_rootdir(config.root_path, opt.yes_to_all)
 
         models_to_download = default_user_selections(opt)
-        new_init_file = config.root_path / 'invokeai.yaml'
+        new_init_file = config.root_path / "invokeai.yaml"
         if opt.yes_to_all:
             write_default_options(opt, new_init_file)
-            init_options = Namespace(
-                precision="float32" if opt.full_precision else "float16"
-            )
+            init_options = Namespace(precision="float32" if opt.full_precision else "float16")
         else:
             init_options, models_to_download = run_console_ui(opt, new_init_file)
             if init_options:
                 write_opts(init_options, new_init_file)
             else:
-                logger.info(
-                    '\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n'
-                )
+                logger.info('\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n')
                 sys.exit(0)
-                
+
         if opt.skip_support_models:
-            logger.info("SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST")
+            logger.info("Skipping support models at user's request")
         else:
-            logger.info("CHECKING/UPDATING SUPPORT MODELS")
+            logger.info("Installing support models")
             download_support_models()
 
         if opt.skip_sd_weights:
-            logger.warning("SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST")
+            logger.warning("Skipping diffusion weights download per user request")
         elif models_to_download:
-            logger.info("DOWNLOADING DIFFUSION WEIGHTS")
             process_and_execute(opt, models_to_download)
 
         postscript(errors=errors)
         if not opt.yes_to_all:
-            input('Press any key to continue...')
+            input("Press any key to continue...")
     except KeyboardInterrupt:
         print("\nGoodbye! Come back soon.")
 
diff --git a/invokeai/backend/install/legacy_arg_parsing.py b/invokeai/backend/install/legacy_arg_parsing.py
index 684c50c77d..f6a0682f2a 100644
--- a/invokeai/backend/install/legacy_arg_parsing.py
+++ b/invokeai/backend/install/legacy_arg_parsing.py
@@ -47,17 +47,18 @@ PRECISION_CHOICES = [
     "float16",
 ]
 
+
 class FileArgumentParser(ArgumentParser):
     """
     Supports reading defaults from an init file.
     """
+
     def convert_arg_line_to_args(self, arg_line):
         return shlex.split(arg_line, comments=True)
 
 
 legacy_parser = FileArgumentParser(
-    description=
-    """
+    description="""
 Generate images using Stable Diffusion.
     Use --web to launch the web interface.
     Use --from_file to load prompts from a file path or standard input ("-").
@@ -65,304 +66,279 @@ Generate images using Stable Diffusion.
     Other command-line arguments are defaults that can usually be overridden
     prompt the command prompt.
     """,
-    fromfile_prefix_chars='@',
+    fromfile_prefix_chars="@",
 )
-general_group    = legacy_parser.add_argument_group('General')
-model_group      = legacy_parser.add_argument_group('Model selection')
-file_group       = legacy_parser.add_argument_group('Input/output')
-web_server_group = legacy_parser.add_argument_group('Web server')
-render_group     = legacy_parser.add_argument_group('Rendering')
-postprocessing_group     = legacy_parser.add_argument_group('Postprocessing')
-deprecated_group = legacy_parser.add_argument_group('Deprecated options')
+general_group = legacy_parser.add_argument_group("General")
+model_group = legacy_parser.add_argument_group("Model selection")
+file_group = legacy_parser.add_argument_group("Input/output")
+web_server_group = legacy_parser.add_argument_group("Web server")
+render_group = legacy_parser.add_argument_group("Rendering")
+postprocessing_group = legacy_parser.add_argument_group("Postprocessing")
+deprecated_group = legacy_parser.add_argument_group("Deprecated options")
 
-deprecated_group.add_argument('--laion400m')
-deprecated_group.add_argument('--weights') # deprecated
-general_group.add_argument(
-    '--version','-V',
-    action='store_true',
-    help='Print InvokeAI version number'
-)
+deprecated_group.add_argument("--laion400m")
+deprecated_group.add_argument("--weights")  # deprecated
+general_group.add_argument("--version", "-V", action="store_true", help="Print InvokeAI version number")
 model_group.add_argument(
-    '--root_dir',
+    "--root_dir",
     default=None,
     help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai.',
 )
 model_group.add_argument(
-    '--config',
-    '-c',
-    '-config',
-    dest='conf',
-    default='./configs/models.yaml',
-    help='Path to configuration file for alternate models.',
+    "--config",
+    "-c",
+    "-config",
+    dest="conf",
+    default="./configs/models.yaml",
+    help="Path to configuration file for alternate models.",
 )
 model_group.add_argument(
-    '--model',
+    "--model",
     help='Indicates which diffusion model to load (defaults to "default" stanza in configs/models.yaml)',
 )
 model_group.add_argument(
-    '--weight_dirs',
-    nargs='+',
+    "--weight_dirs",
+    nargs="+",
     type=str,
-    help='List of one or more directories that will be auto-scanned for new model weights to import',
+    help="List of one or more directories that will be auto-scanned for new model weights to import",
 )
 model_group.add_argument(
-    '--png_compression','-z',
+    "--png_compression",
+    "-z",
     type=int,
     default=6,
-    choices=range(0,9),
-    dest='png_compression',
-    help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
+    choices=range(0, 9),
+    dest="png_compression",
+    help="level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.",
 )
 model_group.add_argument(
-    '-F',
-    '--full_precision',
-    dest='full_precision',
-    action='store_true',
-    help='Deprecated way to set --precision=float32',
+    "-F",
+    "--full_precision",
+    dest="full_precision",
+    action="store_true",
+    help="Deprecated way to set --precision=float32",
 )
 model_group.add_argument(
-    '--max_loaded_models',
-    dest='max_loaded_models',
+    "--max_loaded_models",
+    dest="max_loaded_models",
     type=int,
     default=2,
-    help='Maximum number of models to keep in memory for fast switching, including the one in GPU',
+    help="Maximum number of models to keep in memory for fast switching, including the one in GPU",
 )
 model_group.add_argument(
-    '--free_gpu_mem',
-    dest='free_gpu_mem',
-    action='store_true',
-    help='Force free gpu memory before final decoding',
+    "--free_gpu_mem",
+    dest="free_gpu_mem",
+    action="store_true",
+    help="Force free gpu memory before final decoding",
 )
 model_group.add_argument(
-    '--sequential_guidance',
-    dest='sequential_guidance',
-    action='store_true',
-    help="Calculate guidance in serial instead of in parallel, lowering memory requirement "
-         "at the expense of speed",
+    "--sequential_guidance",
+    dest="sequential_guidance",
+    action="store_true",
+    help="Calculate guidance in serial instead of in parallel, lowering memory requirement " "at the expense of speed",
 )
 model_group.add_argument(
-    '--xformers',
+    "--xformers",
     action=argparse.BooleanOptionalAction,
     default=True,
-    help='Enable/disable xformers support (default enabled if installed)',
+    help="Enable/disable xformers support (default enabled if installed)",
 )
 model_group.add_argument(
-    "--always_use_cpu",
-    dest="always_use_cpu",
-    action="store_true",
-    help="Force use of CPU even if GPU is available"
+    "--always_use_cpu", dest="always_use_cpu", action="store_true", help="Force use of CPU even if GPU is available"
 )
 model_group.add_argument(
-    '--precision',
-    dest='precision',
+    "--precision",
+    dest="precision",
     type=str,
     choices=PRECISION_CHOICES,
-    metavar='PRECISION',
+    metavar="PRECISION",
     help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
-    default='auto',
+    default="auto",
 )
 model_group.add_argument(
-    '--ckpt_convert',
+    "--ckpt_convert",
     action=argparse.BooleanOptionalAction,
-    dest='ckpt_convert',
+    dest="ckpt_convert",
     default=True,
-    help='Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.'
+    help="Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.",
 )
 model_group.add_argument(
-    '--internet',
+    "--internet",
     action=argparse.BooleanOptionalAction,
-    dest='internet_available',
+    dest="internet_available",
     default=True,
-    help='Indicate whether internet is available for just-in-time model downloading (default: probe automatically).',
+    help="Indicate whether internet is available for just-in-time model downloading (default: probe automatically).",
 )
 model_group.add_argument(
-    '--nsfw_checker',
-    '--safety_checker',
+    "--nsfw_checker",
+    "--safety_checker",
     action=argparse.BooleanOptionalAction,
-    dest='safety_checker',
+    dest="safety_checker",
     default=False,
-    help='Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.',
+    help="Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.",
 )
 model_group.add_argument(
-    '--autoimport',
+    "--autoimport",
     default=None,
     type=str,
-    help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly',
+    help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
 )
 model_group.add_argument(
-    '--autoconvert',
+    "--autoconvert",
     default=None,
     type=str,
-    help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models',
+    help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models",
 )
 model_group.add_argument(
-    '--patchmatch',
+    "--patchmatch",
     action=argparse.BooleanOptionalAction,
     default=True,
-    help='Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.',
+    help="Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.",
 )
 file_group.add_argument(
-    '--from_file',
-    dest='infile',
+    "--from_file",
+    dest="infile",
     type=str,
-    help='If specified, load prompts from this file',
+    help="If specified, load prompts from this file",
 )
 file_group.add_argument(
-    '--outdir',
-    '-o',
+    "--outdir",
+    "-o",
     type=str,
-    help='Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs',
-    default='outputs',
+    help="Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs",
+    default="outputs",
 )
 file_group.add_argument(
-    '--prompt_as_dir',
-    '-p',
-    action='store_true',
-    help='Place images in subdirectories named after the prompt.',
+    "--prompt_as_dir",
+    "-p",
+    action="store_true",
+    help="Place images in subdirectories named after the prompt.",
 )
 render_group.add_argument(
-    '--fnformat',
-    default='{prefix}.{seed}.png',
+    "--fnformat",
+    default="{prefix}.{seed}.png",
     type=str,
-    help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
+    help="Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png",
 )
+render_group.add_argument("-s", "--steps", type=int, default=50, help="Number of steps")
 render_group.add_argument(
-    '-s',
-    '--steps',
+    "-W",
+    "--width",
     type=int,
-    default=50,
-    help='Number of steps'
+    help="Image width, multiple of 64",
 )
 render_group.add_argument(
-    '-W',
-    '--width',
+    "-H",
+    "--height",
     type=int,
-    help='Image width, multiple of 64',
+    help="Image height, multiple of 64",
 )
 render_group.add_argument(
-    '-H',
-    '--height',
-    type=int,
-    help='Image height, multiple of 64',
-)
-render_group.add_argument(
-    '-C',
-    '--cfg_scale',
+    "-C",
+    "--cfg_scale",
     default=7.5,
     type=float,
     help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
 )
 render_group.add_argument(
-    '--sampler',
-    '-A',
-    '-m',
-    dest='sampler_name',
+    "--sampler",
+    "-A",
+    "-m",
+    dest="sampler_name",
     type=str,
     choices=SAMPLER_CHOICES,
-    metavar='SAMPLER_NAME',
+    metavar="SAMPLER_NAME",
     help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
-    default='k_lms',
+    default="k_lms",
 )
 render_group.add_argument(
-    '--log_tokenization',
-    '-t',
-    action='store_true',
-    help='shows how the prompt is split into tokens'
+    "--log_tokenization", "-t", action="store_true", help="shows how the prompt is split into tokens"
 )
 render_group.add_argument(
-    '-f',
-    '--strength',
+    "-f",
+    "--strength",
     type=float,
-    help='img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+    help="img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely",
 )
 render_group.add_argument(
-    '-T',
-    '-fit',
-    '--fit',
+    "-T",
+    "-fit",
+    "--fit",
     action=argparse.BooleanOptionalAction,
-    help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
+    help="If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)",
 )
 
+render_group.add_argument("--grid", "-g", action=argparse.BooleanOptionalAction, help="generate a grid")
 render_group.add_argument(
-    '--grid',
-    '-g',
-    action=argparse.BooleanOptionalAction,
-    help='generate a grid'
-)
-render_group.add_argument(
-    '--embedding_directory',
-    '--embedding_path',
-    dest='embedding_path',
-    default='embeddings',
+    "--embedding_directory",
+    "--embedding_path",
+    dest="embedding_path",
+    default="embeddings",
     type=str,
-    help='Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)'
+    help="Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)",
 )
 render_group.add_argument(
-    '--lora_directory',
-    dest='lora_path',
-    default='loras',
+    "--lora_directory",
+    dest="lora_path",
+    default="loras",
     type=str,
-    help='Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)'
+    help="Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)",
 )
 render_group.add_argument(
-    '--embeddings',
+    "--embeddings",
     action=argparse.BooleanOptionalAction,
     default=True,
-    help='Enable embedding directory (default). Use --no-embeddings to disable.',
+    help="Enable embedding directory (default). Use --no-embeddings to disable.",
 )
+render_group.add_argument("--enable_image_debugging", action="store_true", help="Generates debugging image to display")
 render_group.add_argument(
-    '--enable_image_debugging',
-    action='store_true',
-    help='Generates debugging image to display'
-)
-render_group.add_argument(
-    '--karras_max',
+    "--karras_max",
     type=int,
     default=None,
-    help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29]."
+    help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29].",
 )
 # Restoration related args
 postprocessing_group.add_argument(
-    '--no_restore',
-    dest='restore',
-    action='store_false',
-    help='Disable face restoration with GFPGAN or codeformer',
+    "--no_restore",
+    dest="restore",
+    action="store_false",
+    help="Disable face restoration with GFPGAN or codeformer",
 )
 postprocessing_group.add_argument(
-    '--no_upscale',
-    dest='esrgan',
-    action='store_false',
-    help='Disable upscaling with ESRGAN',
+    "--no_upscale",
+    dest="esrgan",
+    action="store_false",
+    help="Disable upscaling with ESRGAN",
 )
 postprocessing_group.add_argument(
-    '--esrgan_bg_tile',
+    "--esrgan_bg_tile",
     type=int,
     default=400,
-    help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
+    help="Tile size for background sampler, 0 for no tile during testing. Default: 400.",
 )
 postprocessing_group.add_argument(
-    '--esrgan_denoise_str',
+    "--esrgan_denoise_str",
     type=float,
     default=0.75,
-    help='esrgan denoise str. 0 is no denoise, 1 is max denoise.  Default: 0.75',
+    help="esrgan denoise str. 0 is no denoise, 1 is max denoise.  Default: 0.75",
 )
 postprocessing_group.add_argument(
-    '--gfpgan_model_path',
+    "--gfpgan_model_path",
     type=str,
-    default='./models/gfpgan/GFPGANv1.4.pth',
-    help='Indicates the path to the GFPGAN model',
+    default="./models/gfpgan/GFPGANv1.4.pth",
+    help="Indicates the path to the GFPGAN model",
 )
 web_server_group.add_argument(
-    '--web',
-    dest='web',
-    action='store_true',
-    help='Start in web server mode.',
+    "--web",
+    dest="web",
+    action="store_true",
+    help="Start in web server mode.",
 )
 web_server_group.add_argument(
-    '--web_develop',
-    dest='web_develop',
-    action='store_true',
-    help='Start in web server development mode.',
+    "--web_develop",
+    dest="web_develop",
+    action="store_true",
+    help="Start in web server development mode.",
 )
 web_server_group.add_argument(
     "--web_verbose",
@@ -376,32 +352,27 @@ web_server_group.add_argument(
     help="Additional allowed origins, comma-separated",
 )
 web_server_group.add_argument(
-    '--host',
+    "--host",
     type=str,
-    default='127.0.0.1',
-    help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
+    default="127.0.0.1",
+    help="Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.",
 )
+web_server_group.add_argument("--port", type=int, default="9090", help="Web server: Port to listen on")
 web_server_group.add_argument(
-    '--port',
-    type=int,
-    default='9090',
-    help='Web server: Port to listen on'
-)
-web_server_group.add_argument(
-    '--certfile',
+    "--certfile",
     type=str,
     default=None,
-    help='Web server: Path to certificate file to use for SSL. Use together with --keyfile'
+    help="Web server: Path to certificate file to use for SSL. Use together with --keyfile",
 )
 web_server_group.add_argument(
-    '--keyfile',
+    "--keyfile",
     type=str,
     default=None,
-    help='Web server: Path to private key file to use for SSL. Use together with --certfile'
+    help="Web server: Path to private key file to use for SSL. Use together with --certfile",
 )
 web_server_group.add_argument(
-    '--gui',
-    dest='gui',
-    action='store_true',
-    help='Start InvokeAI GUI',
+    "--gui",
+    dest="gui",
+    action="store_true",
+    help="Start InvokeAI GUI",
 )
diff --git a/invokeai/backend/install/migrate_to_3.py b/invokeai/backend/install/migrate_to_3.py
index 85de3f5e69..9152e46951 100644
--- a/invokeai/backend/install/migrate_to_3.py
+++ b/invokeai/backend/install/migrate_to_3.py
@@ -1,7 +1,7 @@
-'''
+"""
 Migrate the models directory and models.yaml file from an existing
 InvokeAI 2.3 installation to 3.0.0.
-'''
+"""
 
 import os
 import argparse
@@ -29,14 +29,13 @@ from transformers import (
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import ModelManager
-from invokeai.backend.model_management.model_probe import (
-    ModelProbe, ModelType, BaseModelType, ModelProbeInfo
-    )
+from invokeai.backend.model_management.model_probe import ModelProbe, ModelType, BaseModelType, ModelProbeInfo
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
 diffusers.logging.set_verbosity_error()
 
+
 # holder for paths that we will migrate
 @dataclass
 class ModelPaths:
@@ -45,81 +44,82 @@ class ModelPaths:
     loras: Path
     controlnets: Path
 
+
 class MigrateTo3(object):
-    def __init__(self,
-                 from_root: Path,
-                 to_models: Path,
-                 model_manager: ModelManager,
-                 src_paths: ModelPaths,
-                 ):
+    def __init__(
+        self,
+        from_root: Path,
+        to_models: Path,
+        model_manager: ModelManager,
+        src_paths: ModelPaths,
+    ):
         self.root_directory = from_root
         self.dest_models = to_models
         self.mgr = model_manager
         self.src_paths = src_paths
-        
+
     @classmethod
     def initialize_yaml(cls, yaml_file: Path):
-        with open(yaml_file, 'w') as file:
-            file.write(
-                yaml.dump(
-                    {
-                        '__metadata__': {'version':'3.0.0'}
-                    }
-                )
-            )
-    
+        with open(yaml_file, "w") as file:
+            file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
+
     def create_directory_structure(self):
-        '''
+        """
         Create the basic directory structure for the models folder.
-        '''
-        for model_base in [BaseModelType.StableDiffusion1,BaseModelType.StableDiffusion2]:
-            for model_type in [ModelType.Main, ModelType.Vae, ModelType.Lora,
-                               ModelType.ControlNet,ModelType.TextualInversion]:
+        """
+        for model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
+            for model_type in [
+                ModelType.Main,
+                ModelType.Vae,
+                ModelType.Lora,
+                ModelType.ControlNet,
+                ModelType.TextualInversion,
+            ]:
                 path = self.dest_models / model_base.value / model_type.value
                 path.mkdir(parents=True, exist_ok=True)
-        path = self.dest_models / 'core'
+        path = self.dest_models / "core"
         path.mkdir(parents=True, exist_ok=True)
 
     @staticmethod
-    def copy_file(src:Path,dest:Path):
-        '''
+    def copy_file(src: Path, dest: Path):
+        """
         copy a single file with logging
-        '''
+        """
         if dest.exists():
-            logger.info(f'Skipping existing {str(dest)}')
+            logger.info(f"Skipping existing {str(dest)}")
             return
-        logger.info(f'Copying {str(src)} to {str(dest)}')
+        logger.info(f"Copying {str(src)} to {str(dest)}")
         try:
             shutil.copy(src, dest)
         except Exception as e:
-            logger.error(f'COPY FAILED: {str(e)}')
+            logger.error(f"COPY FAILED: {str(e)}")
 
     @staticmethod
-    def copy_dir(src:Path,dest:Path):
-        '''
+    def copy_dir(src: Path, dest: Path):
+        """
         Recursively copy a directory with logging
-        '''
+        """
         if dest.exists():
-            logger.info(f'Skipping existing {str(dest)}')
+            logger.info(f"Skipping existing {str(dest)}")
             return
-        
-        logger.info(f'Copying {str(src)} to {str(dest)}')
+
+        logger.info(f"Copying {str(src)} to {str(dest)}")
         try:
             shutil.copytree(src, dest)
         except Exception as e:
-            logger.error(f'COPY FAILED: {str(e)}')
+            logger.error(f"COPY FAILED: {str(e)}")
 
     def migrate_models(self, src_dir: Path):
-        '''
+        """
         Recursively walk through src directory, probe anything
         that looks like a model, and copy the model into the
         appropriate location within the destination models directory.
-        '''
+        """
         directories_scanned = set()
         for root, dirs, files in os.walk(src_dir):
             for d in dirs:
                 try:
-                    model = Path(root,d)
+                    model = Path(root, d)
                     info = ModelProbe().heuristic_probe(model)
                     if not info:
                         continue
@@ -136,9 +136,9 @@ class MigrateTo3(object):
                 # don't copy raw learned_embeds.bin or pytorch_lora_weights.bin
                 # let them be copied as part of a tree copy operation
                 try:
-                    if f in {'learned_embeds.bin','pytorch_lora_weights.bin'}:
+                    if f in {"learned_embeds.bin", "pytorch_lora_weights.bin"}:
                         continue
-                    model = Path(root,f)
+                    model = Path(root, f)
                     if model.parent in directories_scanned:
                         continue
                     info = ModelProbe().heuristic_probe(model)
@@ -154,148 +154,146 @@ class MigrateTo3(object):
                     logger.error(str(e))
 
     def migrate_support_models(self):
-        '''
+        """
         Copy the clipseg, upscaler, and restoration models to their new
         locations.
-        '''
+        """
         dest_directory = self.dest_models
-        if (self.root_directory / 'models/clipseg').exists():
-            self.copy_dir(self.root_directory / 'models/clipseg', dest_directory / 'core/misc/clipseg')
-        if (self.root_directory / 'models/realesrgan').exists():
-            self.copy_dir(self.root_directory / 'models/realesrgan', dest_directory / 'core/upscaling/realesrgan')
-        for d in ['codeformer','gfpgan']:
-            path = self.root_directory / 'models' / d
+        if (self.root_directory / "models/clipseg").exists():
+            self.copy_dir(self.root_directory / "models/clipseg", dest_directory / "core/misc/clipseg")
+        if (self.root_directory / "models/realesrgan").exists():
+            self.copy_dir(self.root_directory / "models/realesrgan", dest_directory / "core/upscaling/realesrgan")
+        for d in ["codeformer", "gfpgan"]:
+            path = self.root_directory / "models" / d
             if path.exists():
-                self.copy_dir(path,dest_directory / f'core/face_restoration/{d}')
+                self.copy_dir(path, dest_directory / f"core/face_restoration/{d}")
 
     def migrate_tuning_models(self):
-        '''
+        """
         Migrate the embeddings, loras and controlnets directories to their new homes.
-        '''
+        """
         for src in [self.src_paths.embeddings, self.src_paths.loras, self.src_paths.controlnets]:
             if not src:
                 continue
             if src.is_dir():
-                logger.info(f'Scanning {src}')
+                logger.info(f"Scanning {src}")
                 self.migrate_models(src)
             else:
-                logger.info(f'{src} directory not found; skipping')
+                logger.info(f"{src} directory not found; skipping")
                 continue
 
     def migrate_conversion_models(self):
-        '''
+        """
         Migrate all the models that are needed by the ckpt_to_diffusers conversion
         script.
-        '''
+        """
 
         dest_directory = self.dest_models
         kwargs = dict(
-            cache_dir = self.root_directory / 'models/hub',
-            #local_files_only = True
+            cache_dir=self.root_directory / "models/hub",
+            # local_files_only = True
         )
         try:
-            logger.info('Migrating core tokenizers and text encoders')
-            target_dir = dest_directory / 'core' / 'convert'
+            logger.info("Migrating core tokenizers and text encoders")
+            target_dir = dest_directory / "core" / "convert"
 
-            self._migrate_pretrained(BertTokenizerFast,
-                                     repo_id='bert-base-uncased',
-                                     dest = target_dir / 'bert-base-uncased',
-                                     **kwargs)
+            self._migrate_pretrained(
+                BertTokenizerFast, repo_id="bert-base-uncased", dest=target_dir / "bert-base-uncased", **kwargs
+            )
 
             # sd-1
-            repo_id = 'openai/clip-vit-large-patch14'
-            self._migrate_pretrained(CLIPTokenizer,
-                                     repo_id= repo_id,
-                                     dest= target_dir / 'clip-vit-large-patch14',
-                                     **kwargs)
-            self._migrate_pretrained(CLIPTextModel,
-                                     repo_id = repo_id,
-                                     dest = target_dir / 'clip-vit-large-patch14',
-                                     force = True,
-                                     **kwargs)
+            repo_id = "openai/clip-vit-large-patch14"
+            self._migrate_pretrained(
+                CLIPTokenizer, repo_id=repo_id, dest=target_dir / "clip-vit-large-patch14", **kwargs
+            )
+            self._migrate_pretrained(
+                CLIPTextModel, repo_id=repo_id, dest=target_dir / "clip-vit-large-patch14", force=True, **kwargs
+            )
 
             # sd-2
             repo_id = "stabilityai/stable-diffusion-2"
-            self._migrate_pretrained(CLIPTokenizer,
-                                     repo_id = repo_id,
-                                     dest = target_dir / 'stable-diffusion-2-clip' / 'tokenizer',
-                                     **{'subfolder':'tokenizer',**kwargs}
-                                     )
-            self._migrate_pretrained(CLIPTextModel,
-                                     repo_id = repo_id,
-                                     dest = target_dir / 'stable-diffusion-2-clip' / 'text_encoder',
-                                     **{'subfolder':'text_encoder',**kwargs}
-                                     )
+            self._migrate_pretrained(
+                CLIPTokenizer,
+                repo_id=repo_id,
+                dest=target_dir / "stable-diffusion-2-clip" / "tokenizer",
+                **{"subfolder": "tokenizer", **kwargs},
+            )
+            self._migrate_pretrained(
+                CLIPTextModel,
+                repo_id=repo_id,
+                dest=target_dir / "stable-diffusion-2-clip" / "text_encoder",
+                **{"subfolder": "text_encoder", **kwargs},
+            )
 
             # VAE
-            logger.info('Migrating stable diffusion VAE')
-            self._migrate_pretrained(AutoencoderKL,
-                                     repo_id = 'stabilityai/sd-vae-ft-mse',
-                                     dest = target_dir / 'sd-vae-ft-mse',
-                                     **kwargs)
-            
+            logger.info("Migrating stable diffusion VAE")
+            self._migrate_pretrained(
+                AutoencoderKL, repo_id="stabilityai/sd-vae-ft-mse", dest=target_dir / "sd-vae-ft-mse", **kwargs
+            )
+
             # safety checking
-            logger.info('Migrating safety checker')
+            logger.info("Migrating safety checker")
             repo_id = "CompVis/stable-diffusion-safety-checker"
-            self._migrate_pretrained(AutoFeatureExtractor,
-                                     repo_id = repo_id,
-                                     dest = target_dir / 'stable-diffusion-safety-checker',
-                                     **kwargs)
-            self._migrate_pretrained(StableDiffusionSafetyChecker,
-                                     repo_id = repo_id,
-                                     dest = target_dir / 'stable-diffusion-safety-checker',
-                                     **kwargs)
+            self._migrate_pretrained(
+                AutoFeatureExtractor, repo_id=repo_id, dest=target_dir / "stable-diffusion-safety-checker", **kwargs
+            )
+            self._migrate_pretrained(
+                StableDiffusionSafetyChecker,
+                repo_id=repo_id,
+                dest=target_dir / "stable-diffusion-safety-checker",
+                **kwargs,
+            )
         except KeyboardInterrupt:
             raise
         except Exception as e:
             logger.error(str(e))
 
-    def _model_probe_to_path(self, info: ModelProbeInfo)->Path:
+    def _model_probe_to_path(self, info: ModelProbeInfo) -> Path:
         return Path(self.dest_models, info.base_type.value, info.model_type.value)
 
-    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force:bool=False, **kwargs):
+    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force: bool = False, **kwargs):
         if dest.exists() and not force:
-            logger.info(f'Skipping existing {dest}')
+            logger.info(f"Skipping existing {dest}")
             return
         model = model_class.from_pretrained(repo_id, **kwargs)
         self._save_pretrained(model, dest, overwrite=force)
 
-    def _save_pretrained(self, model, dest: Path, overwrite: bool=False):
+    def _save_pretrained(self, model, dest: Path, overwrite: bool = False):
         model_name = dest.name
         if overwrite:
             model.save_pretrained(dest, safe_serialization=True)
         else:
-            download_path = dest.with_name(f'{model_name}.downloading')
+            download_path = dest.with_name(f"{model_name}.downloading")
             model.save_pretrained(download_path, safe_serialization=True)
             download_path.replace(dest)
 
-    def _download_vae(self, repo_id: str, subfolder:str=None)->Path:
-        vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / 'models/hub', subfolder=subfolder)
+    def _download_vae(self, repo_id: str, subfolder: str = None) -> Path:
+        vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / "models/hub", subfolder=subfolder)
         info = ModelProbe().heuristic_probe(vae)
-        _, model_name = repo_id.split('/')
+        _, model_name = repo_id.split("/")
         dest = self._model_probe_to_path(info) / self.unique_name(model_name, info)
         vae.save_pretrained(dest, safe_serialization=True)
         return dest
 
-    def _vae_path(self, vae: Union[str,dict])->Path:
-        '''
+    def _vae_path(self, vae: Union[str, dict]) -> Path:
+        """
         Convert 2.3 VAE stanza to a straight path.
-        '''
+        """
         vae_path = None
-        
+
         # First get a path
-        if isinstance(vae,str):
+        if isinstance(vae, str):
             vae_path = vae
 
-        elif isinstance(vae,DictConfig):
-            if p := vae.get('path'):
+        elif isinstance(vae, DictConfig):
+            if p := vae.get("path"):
                 vae_path = p
-            elif repo_id := vae.get('repo_id'):
-                if repo_id=='stabilityai/sd-vae-ft-mse':  # this guy is already downloaded
-                    vae_path = 'models/core/convert/sd-vae-ft-mse'
+            elif repo_id := vae.get("repo_id"):
+                if repo_id == "stabilityai/sd-vae-ft-mse":  # this guy is already downloaded
+                    vae_path = "models/core/convert/sd-vae-ft-mse"
                     return vae_path
                 else:
-                    vae_path = self._download_vae(repo_id, vae.get('subfolder'))
+                    vae_path = self._download_vae(repo_id, vae.get("subfolder"))
 
         assert vae_path is not None, "Couldn't find VAE for this model"
 
@@ -307,152 +305,144 @@ class MigrateTo3(object):
             dest = self._model_probe_to_path(info) / vae_path.name
             if not dest.exists():
                 if vae_path.is_dir():
-                    self.copy_dir(vae_path,dest)
+                    self.copy_dir(vae_path, dest)
                 else:
-                    self.copy_file(vae_path,dest)
+                    self.copy_file(vae_path, dest)
             vae_path = dest
 
         if vae_path.is_relative_to(self.dest_models):
             rel_path = vae_path.relative_to(self.dest_models)
-            return Path('models',rel_path)
+            return Path("models", rel_path)
         else:
             return vae_path
 
-    def migrate_repo_id(self, repo_id: str, model_name: str=None, **extra_config):
-        '''
+    def migrate_repo_id(self, repo_id: str, model_name: str = None, **extra_config):
+        """
         Migrate a locally-cached diffusers pipeline identified with a repo_id
-        '''
+        """
         dest_dir = self.dest_models
-        
-        cache = self.root_directory / 'models/hub'
+
+        cache = self.root_directory / "models/hub"
         kwargs = dict(
-            cache_dir = cache,
-            safety_checker = None,
+            cache_dir=cache,
+            safety_checker=None,
             # local_files_only = True,
         )
 
-        owner,repo_name = repo_id.split('/')
+        owner, repo_name = repo_id.split("/")
         model_name = model_name or repo_name
-        model = cache / '--'.join(['models',owner,repo_name])
-        
-        if len(list(model.glob('snapshots/**/model_index.json')))==0:
+        model = cache / "--".join(["models", owner, repo_name])
+
+        if len(list(model.glob("snapshots/**/model_index.json"))) == 0:
             return
-        revisions = [x.name for x in model.glob('refs/*')]
+        revisions = [x.name for x in model.glob("refs/*")]
 
         # if an fp16 is available we use that
-        revision = 'fp16' if len(revisions) > 1 and 'fp16' in revisions else revisions[0]
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            repo_id,
-            revision=revision,
-            **kwargs)
+        revision = "fp16" if len(revisions) > 1 and "fp16" in revisions else revisions[0]
+        pipeline = StableDiffusionPipeline.from_pretrained(repo_id, revision=revision, **kwargs)
 
         info = ModelProbe().heuristic_probe(pipeline)
         if not info:
             return
 
         if self.mgr.model_exists(model_name, info.base_type, info.model_type):
-            logger.warning(f'A model named {model_name} already exists at the destination. Skipping migration.')
+            logger.warning(f"A model named {model_name} already exists at the destination. Skipping migration.")
             return
 
         dest = self._model_probe_to_path(info) / model_name
         self._save_pretrained(pipeline, dest)
-            
-        rel_path = Path('models',dest.relative_to(dest_dir))
+
+        rel_path = Path("models", dest.relative_to(dest_dir))
         self._add_model(model_name, info, rel_path, **extra_config)
 
-    def migrate_path(self, location: Path, model_name: str=None, **extra_config):
-        '''
+    def migrate_path(self, location: Path, model_name: str = None, **extra_config):
+        """
         Migrate a model referred to using 'weights' or 'path'
-        '''
+        """
 
         # handle relative paths
         dest_dir = self.dest_models
         location = self.root_directory / location
         model_name = model_name or location.stem
-        
+
         info = ModelProbe().heuristic_probe(location)
         if not info:
             return
-        
+
         if self.mgr.model_exists(model_name, info.base_type, info.model_type):
-            logger.warning(f'A model named {model_name} already exists at the destination. Skipping migration.')
+            logger.warning(f"A model named {model_name} already exists at the destination. Skipping migration.")
             return
 
         # uh oh, weights is in the old models directory - move it into the new one
         if Path(location).is_relative_to(self.src_paths.models):
             dest = Path(dest_dir, info.base_type.value, info.model_type.value, location.name)
             if location.is_dir():
-                self.copy_dir(location,dest)
+                self.copy_dir(location, dest)
             else:
-                self.copy_file(location,dest)
-            location = Path('models', info.base_type.value, info.model_type.value, location.name)
+                self.copy_file(location, dest)
+            location = Path("models", info.base_type.value, info.model_type.value, location.name)
 
         self._add_model(model_name, info, location, **extra_config)
 
-    def _add_model(self,
-                   model_name: str,
-                   info: ModelProbeInfo,
-                   location: Path,
-                   **extra_config):
+    def _add_model(self, model_name: str, info: ModelProbeInfo, location: Path, **extra_config):
         if info.model_type != ModelType.Main:
             return
-        
-        self.mgr.add_model(
-            model_name = model_name,
-            base_model = info.base_type,
-            model_type = info.model_type,
-            clobber = True,
-            model_attributes = {
-                'path': str(location),
-                'description': f'A {info.base_type.value} {info.model_type.value} model',
-                'model_format': info.format,
-                'variant': info.variant_type.value,
-                **extra_config,
-            }
-        )
-        
-    def migrate_defined_models(self):
-        '''
-        Migrate models defined in models.yaml
-        '''
-        # find any models referred to in old models.yaml
-        conf = OmegaConf.load(self.root_directory / 'configs/models.yaml')
-        
-        for model_name, stanza in conf.items():
 
+        self.mgr.add_model(
+            model_name=model_name,
+            base_model=info.base_type,
+            model_type=info.model_type,
+            clobber=True,
+            model_attributes={
+                "path": str(location),
+                "description": f"A {info.base_type.value} {info.model_type.value} model",
+                "model_format": info.format,
+                "variant": info.variant_type.value,
+                **extra_config,
+            },
+        )
+
+    def migrate_defined_models(self):
+        """
+        Migrate models defined in models.yaml
+        """
+        # find any models referred to in old models.yaml
+        conf = OmegaConf.load(self.root_directory / "configs/models.yaml")
+
+        for model_name, stanza in conf.items():
             try:
                 passthru_args = {}
-                
-                if vae := stanza.get('vae'):
+
+                if vae := stanza.get("vae"):
                     try:
-                        passthru_args['vae'] = str(self._vae_path(vae))
+                        passthru_args["vae"] = str(self._vae_path(vae))
                     except Exception as e:
                         logger.warning(f'Could not find a VAE matching "{vae}" for model "{model_name}"')
                         logger.warning(str(e))
 
-                if config := stanza.get('config'):
-                    passthru_args['config'] = config
+                if config := stanza.get("config"):
+                    passthru_args["config"] = config
 
-                if description:= stanza.get('description'):
-                    passthru_args['description'] = description
-                
-                if repo_id := stanza.get('repo_id'):
-                    logger.info(f'Migrating diffusers model {model_name}')
+                if description := stanza.get("description"):
+                    passthru_args["description"] = description
+
+                if repo_id := stanza.get("repo_id"):
+                    logger.info(f"Migrating diffusers model {model_name}")
                     self.migrate_repo_id(repo_id, model_name, **passthru_args)
 
-                elif location := stanza.get('weights'):
-                    logger.info(f'Migrating checkpoint model {model_name}')
+                elif location := stanza.get("weights"):
+                    logger.info(f"Migrating checkpoint model {model_name}")
                     self.migrate_path(Path(location), model_name, **passthru_args)
-                    
-                elif location := stanza.get('path'):
-                    logger.info(f'Migrating diffusers model {model_name}')
+
+                elif location := stanza.get("path"):
+                    logger.info(f"Migrating diffusers model {model_name}")
                     self.migrate_path(Path(location), model_name, **passthru_args)
-                    
+
             except KeyboardInterrupt:
                 raise
             except Exception as e:
                 logger.error(str(e))
-                    
+
     def migrate(self):
         self.create_directory_structure()
         # the configure script is doing this
@@ -461,67 +451,71 @@ class MigrateTo3(object):
         self.migrate_tuning_models()
         self.migrate_defined_models()
 
-def _parse_legacy_initfile(root: Path, initfile: Path)->ModelPaths:
-    '''
+
+def _parse_legacy_initfile(root: Path, initfile: Path) -> ModelPaths:
+    """
     Returns tuple of (embedding_path, lora_path, controlnet_path)
-    '''
-    parser = argparse.ArgumentParser(fromfile_prefix_chars='@')
+    """
+    parser = argparse.ArgumentParser(fromfile_prefix_chars="@")
     parser.add_argument(
-        '--embedding_directory',
-        '--embedding_path',
+        "--embedding_directory",
+        "--embedding_path",
         type=Path,
-        dest='embedding_path',
-        default=Path('embeddings'),
+        dest="embedding_path",
+        default=Path("embeddings"),
     )
     parser.add_argument(
-        '--lora_directory',
-        dest='lora_path',
+        "--lora_directory",
+        dest="lora_path",
         type=Path,
-        default=Path('loras'),
+        default=Path("loras"),
     )
-    opt,_ = parser.parse_known_args([f'@{str(initfile)}'])
+    opt, _ = parser.parse_known_args([f"@{str(initfile)}"])
     return ModelPaths(
-        models = root / 'models',
-        embeddings = root / str(opt.embedding_path).strip('"'),
-        loras = root / str(opt.lora_path).strip('"'),
-        controlnets = root / 'controlnets',
+        models=root / "models",
+        embeddings=root / str(opt.embedding_path).strip('"'),
+        loras=root / str(opt.lora_path).strip('"'),
+        controlnets=root / "controlnets",
     )
 
-def _parse_legacy_yamlfile(root: Path, initfile: Path)->ModelPaths:
-    '''
+
+def _parse_legacy_yamlfile(root: Path, initfile: Path) -> ModelPaths:
+    """
     Returns tuple of (embedding_path, lora_path, controlnet_path)
-    '''
+    """
     # Don't use the config object because it is unforgiving of version updates
     # Just use omegaconf directly
     opt = OmegaConf.load(initfile)
     paths = opt.InvokeAI.Paths
-    models = paths.get('models_dir','models')
-    embeddings = paths.get('embedding_dir','embeddings')
-    loras = paths.get('lora_dir','loras')
-    controlnets = paths.get('controlnet_dir','controlnets')
+    models = paths.get("models_dir", "models")
+    embeddings = paths.get("embedding_dir", "embeddings")
+    loras = paths.get("lora_dir", "loras")
+    controlnets = paths.get("controlnet_dir", "controlnets")
     return ModelPaths(
-        models = root / models,
-        embeddings = root / embeddings,
-        loras = root /loras,
-        controlnets = root / controlnets,
+        models=root / models,
+        embeddings=root / embeddings,
+        loras=root / loras,
+        controlnets=root / controlnets,
     )
-    
+
+
 def get_legacy_embeddings(root: Path) -> ModelPaths:
-    path = root / 'invokeai.init'
+    path = root / "invokeai.init"
     if path.exists():
         return _parse_legacy_initfile(root, path)
-    path = root / 'invokeai.yaml'
+    path = root / "invokeai.yaml"
     if path.exists():
         return _parse_legacy_yamlfile(root, path)
 
+
 def do_migrate(src_directory: Path, dest_directory: Path):
     """
     Migrate models from src to dest InvokeAI root directories
     """
-    config_file = dest_directory / 'configs' / 'models.yaml.3'
-    dest_models = dest_directory / 'models.3'
-    
-    version_3 = (dest_directory / 'models' / 'core').exists()
+    config_file = dest_directory / "configs" / "models.yaml.3"
+    dest_models = dest_directory / "models.3"
+
+    version_3 = (dest_directory / "models" / "core").exists()
 
     # Here we create the destination models.yaml file.
     # If we are writing into a version 3 directory and the
@@ -530,80 +524,80 @@ def do_migrate(src_directory: Path, dest_directory: Path):
     # create a new empty one.
     if version_3:  # write into the dest directory
         try:
-            shutil.copy(dest_directory / 'configs' / 'models.yaml', config_file)
+            shutil.copy(dest_directory / "configs" / "models.yaml", config_file)
         except:
             MigrateTo3.initialize_yaml(config_file)
-        mgr = ModelManager(config_file) # important to initialize BEFORE moving the models directory
-        (dest_directory / 'models').replace(dest_models)
+        mgr = ModelManager(config_file)  # important to initialize BEFORE moving the models directory
+        (dest_directory / "models").replace(dest_models)
     else:
         MigrateTo3.initialize_yaml(config_file)
         mgr = ModelManager(config_file)
-    
+
     paths = get_legacy_embeddings(src_directory)
-    migrator = MigrateTo3(
-        from_root = src_directory,
-        to_models = dest_models,
-        model_manager = mgr,
-        src_paths = paths
-    )
+    migrator = MigrateTo3(from_root=src_directory, to_models=dest_models, model_manager=mgr, src_paths=paths)
     migrator.migrate()
     print("Migration successful.")
 
     if not version_3:
-        (dest_directory / 'models').replace(src_directory / 'models.orig')
-        print(f'Original models directory moved to {dest_directory}/models.orig')
-        
-    (dest_directory / 'configs' / 'models.yaml').replace(src_directory / 'configs' / 'models.yaml.orig')
-    print(f'Original models.yaml file moved to {dest_directory}/configs/models.yaml.orig')
-    
-    config_file.replace(config_file.with_suffix(''))
-    dest_models.replace(dest_models.with_suffix(''))
-    
+        (dest_directory / "models").replace(src_directory / "models.orig")
+        print(f"Original models directory moved to {dest_directory}/models.orig")
+
+    (dest_directory / "configs" / "models.yaml").replace(src_directory / "configs" / "models.yaml.orig")
+    print(f"Original models.yaml file moved to {dest_directory}/configs/models.yaml.orig")
+
+    config_file.replace(config_file.with_suffix(""))
+    dest_models.replace(dest_models.with_suffix(""))
+
+
 def main():
-    parser = argparse.ArgumentParser(prog="invokeai-migrate3",
-                                     description="""
+    parser = argparse.ArgumentParser(
+        prog="invokeai-migrate3",
+        description="""
 This will copy and convert the models directory and the configs/models.yaml from the InvokeAI 2.3 format 
 '--from-directory' root to the InvokeAI 3.0 '--to-directory' root. These may be abbreviated '--from' and '--to'.a
 
 The old models directory and config file will be renamed 'models.orig' and 'models.yaml.orig' respectively.
 It is safe to provide the same directory for both arguments, but it is better to use the invokeai_configure
-script, which will perform a full upgrade in place."""
-                                     )
-    parser.add_argument('--from-directory',
-                        dest='src_root',
-                        type=Path,
-                        required=True,
-                        help='Source InvokeAI 2.3 root directory (containing "invokeai.init" or "invokeai.yaml")'
-                        )
-    parser.add_argument('--to-directory',
-                        dest='dest_root',
-                        type=Path,
-                        required=True,
-                        help='Destination InvokeAI 3.0 directory (containing "invokeai.yaml")'
-                        )
+script, which will perform a full upgrade in place.""",
+    )
+    parser.add_argument(
+        "--from-directory",
+        dest="src_root",
+        type=Path,
+        required=True,
+        help='Source InvokeAI 2.3 root directory (containing "invokeai.init" or "invokeai.yaml")',
+    )
+    parser.add_argument(
+        "--to-directory",
+        dest="dest_root",
+        type=Path,
+        required=True,
+        help='Destination InvokeAI 3.0 directory (containing "invokeai.yaml")',
+    )
     args = parser.parse_args()
     src_root = args.src_root
     assert src_root.is_dir(), f"{src_root} is not a valid directory"
-    assert (src_root / 'models').is_dir(), f"{src_root} does not contain a 'models' subdirectory"
-    assert (src_root / 'models' / 'hub').exists(), f"{src_root} does not contain a version 2.3 models directory"
-    assert (src_root / 'invokeai.init').exists() or (src_root / 'invokeai.yaml').exists(), f"{src_root} does not contain an InvokeAI init file."
+    assert (src_root / "models").is_dir(), f"{src_root} does not contain a 'models' subdirectory"
+    assert (src_root / "models" / "hub").exists(), f"{src_root} does not contain a version 2.3 models directory"
+    assert (src_root / "invokeai.init").exists() or (
+        src_root / "invokeai.yaml"
+    ).exists(), f"{src_root} does not contain an InvokeAI init file."
 
     dest_root = args.dest_root
     assert dest_root.is_dir(), f"{dest_root} is not a valid directory"
     config = InvokeAIAppConfig.get_config()
-    config.parse_args(['--root',str(dest_root)])
+    config.parse_args(["--root", str(dest_root)])
 
     # TODO: revisit - don't rely on invokeai.yaml to exist yet!
-    dest_is_setup = (dest_root / 'models/core').exists() and (dest_root / 'databases').exists()
+    dest_is_setup = (dest_root / "models/core").exists() and (dest_root / "databases").exists()
     if not dest_is_setup:
         import invokeai.frontend.install.invokeai_configure
         from invokeai.backend.install.invokeai_configure import initialize_rootdir
+
         initialize_rootdir(dest_root, True)
 
-    do_migrate(src_root,dest_root)
+    do_migrate(src_root, dest_root)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()
-
-
-
diff --git a/invokeai/backend/install/model_install_backend.py b/invokeai/backend/install/model_install_backend.py
index 0537d6069a..b3ab88b5dd 100644
--- a/invokeai/backend/install/model_install_backend.py
+++ b/invokeai/backend/install/model_install_backend.py
@@ -4,7 +4,7 @@ Utility (backend) functions used by model_install.py
 import os
 import shutil
 import warnings
-from dataclasses import dataclass,field
+from dataclasses import dataclass, field
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import List, Dict, Callable, Union, Set
@@ -28,7 +28,7 @@ warnings.filterwarnings("ignore")
 
 # --------------------------globals-----------------------
 config = InvokeAIAppConfig.get_config()
-logger = InvokeAILogger.getLogger(name='InvokeAI')
+logger = InvokeAILogger.getLogger(name="InvokeAI")
 
 # the initial "configs" dir is now bundled in the `invokeai.configs` package
 Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
@@ -45,51 +45,63 @@ Config_preamble = """
 
 LEGACY_CONFIGS = {
     BaseModelType.StableDiffusion1: {
-        ModelVariantType.Normal: 'v1-inference.yaml',
-        ModelVariantType.Inpaint: 'v1-inpainting-inference.yaml',
+        ModelVariantType.Normal: "v1-inference.yaml",
+        ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
     },
-
     BaseModelType.StableDiffusion2: {
         ModelVariantType.Normal: {
-            SchedulerPredictionType.Epsilon: 'v2-inference.yaml',
-            SchedulerPredictionType.VPrediction: 'v2-inference-v.yaml',
+            SchedulerPredictionType.Epsilon: "v2-inference.yaml",
+            SchedulerPredictionType.VPrediction: "v2-inference-v.yaml",
         },
         ModelVariantType.Inpaint: {
-            SchedulerPredictionType.Epsilon: 'v2-inpainting-inference.yaml',
-            SchedulerPredictionType.VPrediction: 'v2-inpainting-inference-v.yaml',
-        }
-    }
+            SchedulerPredictionType.Epsilon: "v2-inpainting-inference.yaml",
+            SchedulerPredictionType.VPrediction: "v2-inpainting-inference-v.yaml",
+        },
+    },
+    BaseModelType.StableDiffusionXL: {
+        ModelVariantType.Normal: "sd_xl_base.yaml",
+    },
+    BaseModelType.StableDiffusionXLRefiner: {
+        ModelVariantType.Normal: "sd_xl_refiner.yaml",
+    },
 }
 
+
 @dataclass
 class ModelInstallList:
-    '''Class for listing models to be installed/removed'''
+    """Class for listing models to be installed/removed"""
+
     install_models: List[str] = field(default_factory=list)
     remove_models: List[str] = field(default_factory=list)
 
-@dataclass
-class InstallSelections():
-    install_models: List[str]= field(default_factory=list)
-    remove_models: List[str]=field(default_factory=list)
 
 @dataclass
-class ModelLoadInfo():
+class InstallSelections:
+    install_models: List[str] = field(default_factory=list)
+    remove_models: List[str] = field(default_factory=list)
+
+
+@dataclass
+class ModelLoadInfo:
     name: str
     model_type: ModelType
     base_type: BaseModelType
     path: Path = None
     repo_id: str = None
-    description: str = ''
+    description: str = ""
     installed: bool = False
     recommended: bool = False
     default: bool = False
 
+
 class ModelInstall(object):
-    def __init__(self,
-                 config:InvokeAIAppConfig,
-                 prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
-                 model_manager: ModelManager = None,
-                 access_token:str = None):
+    def __init__(
+        self,
+        config: InvokeAIAppConfig,
+        prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
+        model_manager: ModelManager = None,
+        access_token: str = None,
+    ):
         self.config = config
         self.mgr = model_manager or ModelManager(config.model_conf_path)
         self.datasets = OmegaConf.load(Dataset_path)
@@ -97,65 +109,66 @@ class ModelInstall(object):
         self.access_token = access_token or HfFolder.get_token()
         self.reverse_paths = self._reverse_paths(self.datasets)
 
-    def all_models(self)->Dict[str,ModelLoadInfo]:
-        '''
+    def all_models(self) -> Dict[str, ModelLoadInfo]:
+        """
         Return dict of model_key=>ModelLoadInfo objects.
         This method consolidates and simplifies the entries in both
         models.yaml and INITIAL_MODELS.yaml so that they can
         be treated uniformly. It also sorts the models alphabetically
         by their name, to improve the display somewhat.
-        '''
+        """
         model_dict = dict()
-        
+
         # first populate with the entries in INITIAL_MODELS.yaml
         for key, value in self.datasets.items():
-            name,base,model_type = ModelManager.parse_key(key)
-            value['name'] = name
-            value['base_type'] = base
-            value['model_type'] = model_type
+            name, base, model_type = ModelManager.parse_key(key)
+            value["name"] = name
+            value["base_type"] = base
+            value["model_type"] = model_type
             model_dict[key] = ModelLoadInfo(**value)
 
         # supplement with entries in models.yaml
         installed_models = self.mgr.list_models()
-        
+
         for md in installed_models:
-            base = md['base_model']
-            model_type = md['model_type']
-            name = md['model_name']
+            base = md["base_model"]
+            model_type = md["model_type"]
+            name = md["model_name"]
             key = ModelManager.create_key(name, base, model_type)
             if key in model_dict:
                 model_dict[key].installed = True
             else:
                 model_dict[key] = ModelLoadInfo(
-                    name = name,
-                    base_type = base,
-                    model_type = model_type,
-                    path = value.get('path'),
-                    installed = True,
+                    name=name,
+                    base_type=base,
+                    model_type=model_type,
+                    path=value.get("path"),
+                    installed=True,
                 )
-        return {x : model_dict[x] for x in sorted(model_dict.keys(),key=lambda y: model_dict[y].name.lower())}
+        return {x: model_dict[x] for x in sorted(model_dict.keys(), key=lambda y: model_dict[y].name.lower())}
 
     def list_models(self, model_type):
         installed = self.mgr.list_models(model_type=model_type)
-        print(f'Installed models of type `{model_type}`:')
+        print(f"Installed models of type `{model_type}`:")
         for i in installed:
             print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}")
 
-    def starter_models(self)->Set[str]:
+    # logic here a little reversed to maintain backward compatibility
+    def starter_models(self, all_models: bool = False) -> Set[str]:
         models = set()
         for key, value in self.datasets.items():
-            name,base,model_type = ModelManager.parse_key(key)
-            if model_type==ModelType.Main:
+            name, base, model_type = ModelManager.parse_key(key)
+            if all_models or model_type in [ModelType.Main, ModelType.Vae]:
                 models.add(key)
         return models
 
-    def recommended_models(self)->Set[str]:
+    def recommended_models(self) -> Set[str]:
+        starters = self.starter_models(all_models=True)
+        return set([x for x in starters if self.datasets[x].get("recommended", False)])
+
+    def default_model(self) -> str:
         starters = self.starter_models()
-        return set([x for x in starters if self.datasets[x].get('recommended',False)])
-    
-    def default_model(self)->str:
-        starters = self.starter_models()
-        defaults = [x for x in starters if self.datasets[x].get('default',False)]
+        defaults = [x for x in starters if self.datasets[x].get("default", False)]
         return defaults[0]
 
     def install(self, selections: InstallSelections):
@@ -164,54 +177,57 @@ class ModelInstall(object):
 
         job = 1
         jobs = len(selections.remove_models) + len(selections.install_models)
-        
+
         # remove requested models
         for key in selections.remove_models:
-            name,base,mtype = self.mgr.parse_key(key)
-            logger.info(f'Deleting {mtype} model {name} [{job}/{jobs}]')
+            name, base, mtype = self.mgr.parse_key(key)
+            logger.info(f"Deleting {mtype} model {name} [{job}/{jobs}]")
             try:
-                self.mgr.del_model(name,base,mtype)
+                self.mgr.del_model(name, base, mtype)
             except FileNotFoundError as e:
                 logger.warning(e)
             job += 1
-            
+
         # add requested models
         for path in selections.install_models:
-            logger.info(f'Installing {path} [{job}/{jobs}]')
+            logger.info(f"Installing {path} [{job}/{jobs}]")
             try:
                 self.heuristic_import(path)
             except (ValueError, KeyError) as e:
                 logger.error(str(e))
             job += 1
-            
+
         dlogging.set_verbosity(verbosity)
         self.mgr.commit()
 
-    def heuristic_import(self,
-                         model_path_id_or_url: Union[str,Path],
-                         models_installed: Set[Path]=None,
-                         )->Dict[str, AddModelResult]:
-        '''
+    def heuristic_import(
+        self,
+        model_path_id_or_url: Union[str, Path],
+        models_installed: Set[Path] = None,
+    ) -> Dict[str, AddModelResult]:
+        """
         :param model_path_id_or_url: A Path to a local model to import, or a string representing its repo_id or URL
         :param models_installed: Set of installed models, used for recursive invocation
         Returns a set of dict objects corresponding to newly-created stanzas in models.yaml.
-        '''
+        """
 
         if not models_installed:
             models_installed = dict()
-            
+
         # A little hack to allow nested routines to retrieve info on the requested ID
         self.current_id = model_path_id_or_url
         path = Path(model_path_id_or_url)
         # checkpoint file, or similar
         if path.is_file():
-            models_installed.update({str(path):self._install_path(path)})
+            models_installed.update({str(path): self._install_path(path)})
 
         # folders style or similar
-        elif path.is_dir() and any([(path/x).exists() for x in \
-                                    {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
-                                    ]
-                                   ):
+        elif path.is_dir() and any(
+            [
+                (path / x).exists()
+                for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
+            ]
+        ):
             models_installed.update({str(model_path_id_or_url): self._install_path(path)})
 
         # recursive scan
@@ -220,7 +236,7 @@ class ModelInstall(object):
                 self.heuristic_import(child, models_installed=models_installed)
 
         # huggingface repo
-        elif len(str(model_path_id_or_url).split('/')) == 2:
+        elif len(str(model_path_id_or_url).split("/")) == 2:
             models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
 
         # a URL
@@ -228,42 +244,43 @@ class ModelInstall(object):
             models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
 
         else:
-            raise KeyError(f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping')
+            raise KeyError(f"{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping")
 
         return models_installed
 
     # install a model from a local path. The optional info parameter is there to prevent
     # the model from being probed twice in the event that it has already been probed.
-    def _install_path(self, path: Path, info: ModelProbeInfo=None)->AddModelResult:
-        info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
+    def _install_path(self, path: Path, info: ModelProbeInfo = None) -> AddModelResult:
+        info = info or ModelProbe().heuristic_probe(path, self.prediction_helper)
         if not info:
-            logger.warning(f'Unable to parse format of {path}')
+            logger.warning(f"Unable to parse format of {path}")
             return None
         model_name = path.stem if path.is_file() else path.name
         if self.mgr.model_exists(model_name, info.base_type, info.model_type):
             raise ValueError(f'A model named "{model_name}" is already installed.')
-        attributes = self._make_attributes(path,info)
-        return self.mgr.add_model(model_name = model_name,
-                                  base_model = info.base_type,
-                                  model_type = info.model_type,
-                                  model_attributes = attributes,
-                                  )
+        attributes = self._make_attributes(path, info)
+        return self.mgr.add_model(
+            model_name=model_name,
+            base_model=info.base_type,
+            model_type=info.model_type,
+            model_attributes=attributes,
+        )
 
-    def _install_url(self, url: str)->AddModelResult:
+    def _install_url(self, url: str) -> AddModelResult:
         with TemporaryDirectory(dir=self.config.models_path) as staging:
-            location = download_with_resume(url,Path(staging))
+            location = download_with_resume(url, Path(staging))
             if not location:
-                logger.error(f'Unable to download {url}. Skipping.')
+                logger.error(f"Unable to download {url}. Skipping.")
             info = ModelProbe().heuristic_probe(location)
             dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
-            models_path = shutil.move(location,dest)
+            models_path = shutil.move(location, dest)
 
         # staged version will be garbage-collected at this time
         return self._install_path(Path(models_path), info)
 
-    def _install_repo(self, repo_id: str)->AddModelResult:
+    def _install_repo(self, repo_id: str) -> AddModelResult:
         hinfo = HfApi().model_info(repo_id)
-        
+
         # we try to figure out how to download this most economically
         # list all the files in the repo
         files = [x.rfilename for x in hinfo.siblings]
@@ -271,42 +288,49 @@ class ModelInstall(object):
 
         with TemporaryDirectory(dir=self.config.models_path) as staging:
             staging = Path(staging)
-            if 'model_index.json' in files:
-                location = self._download_hf_pipeline(repo_id, staging)   # pipeline
+            if "model_index.json" in files:
+                location = self._download_hf_pipeline(repo_id, staging)  # pipeline
             else:
-                for suffix in ['safetensors','bin']:
-                    if f'pytorch_lora_weights.{suffix}' in files:
-                        location = self._download_hf_model(repo_id, ['pytorch_lora_weights.bin'], staging)  # LoRA
+                for suffix in ["safetensors", "bin"]:
+                    if f"pytorch_lora_weights.{suffix}" in files:
+                        location = self._download_hf_model(repo_id, ["pytorch_lora_weights.bin"], staging)  # LoRA
                         break
-                    elif self.config.precision=='float16' and f'diffusion_pytorch_model.fp16.{suffix}' in files: # vae, controlnet or some other standalone
-                        files = ['config.json', f'diffusion_pytorch_model.fp16.{suffix}']
+                    elif (
+                        self.config.precision == "float16" and f"diffusion_pytorch_model.fp16.{suffix}" in files
+                    ):  # vae, controlnet or some other standalone
+                        files = ["config.json", f"diffusion_pytorch_model.fp16.{suffix}"]
                         location = self._download_hf_model(repo_id, files, staging)
                         break
-                    elif f'diffusion_pytorch_model.{suffix}' in files:
-                        files = ['config.json', f'diffusion_pytorch_model.{suffix}']
+                    elif f"diffusion_pytorch_model.{suffix}" in files:
+                        files = ["config.json", f"diffusion_pytorch_model.{suffix}"]
                         location = self._download_hf_model(repo_id, files, staging)
                         break
-                    elif f'learned_embeds.{suffix}' in files:
-                        location = self._download_hf_model(repo_id, [f'learned_embeds.{suffix}'], staging)
+                    elif f"learned_embeds.{suffix}" in files:
+                        location = self._download_hf_model(repo_id, [f"learned_embeds.{suffix}"], staging)
                         break
             if not location:
-                logger.warning(f'Could not determine type of repo {repo_id}. Skipping install.')
+                logger.warning(f"Could not determine type of repo {repo_id}. Skipping install.")
                 return {}
 
             info = ModelProbe().heuristic_probe(location, self.prediction_helper)
             if not info:
-                logger.warning(f'Could not probe {location}. Skipping install.')
+                logger.warning(f"Could not probe {location}. Skipping install.")
                 return {}
-            dest = self.config.models_path / info.base_type.value / info.model_type.value / self._get_model_name(repo_id,location)
+            dest = (
+                self.config.models_path
+                / info.base_type.value
+                / info.model_type.value
+                / self._get_model_name(repo_id, location)
+            )
             if dest.exists():
                 shutil.rmtree(dest)
-            shutil.copytree(location,dest)
+            shutil.copytree(location, dest)
             return self._install_path(dest, info)
 
-    def _get_model_name(self,path_name: str, location: Path)->str:
-        '''
+    def _get_model_name(self, path_name: str, location: Path) -> str:
+        """
         Calculate a name for the model - primitive implementation.
-        '''
+        """
         if key := self.reverse_paths.get(path_name):
             (name, base, mtype) = ModelManager.parse_key(key)
             return name
@@ -315,92 +339,103 @@ class ModelInstall(object):
         else:
             return location.stem
 
-    def _make_attributes(self, path: Path, info: ModelProbeInfo)->dict:
+    def _make_attributes(self, path: Path, info: ModelProbeInfo) -> dict:
         model_name = path.name if path.is_dir() else path.stem
-        description = f'{info.base_type.value} {info.model_type.value} model {model_name}'
+        description = f"{info.base_type.value} {info.model_type.value} model {model_name}"
         if key := self.reverse_paths.get(self.current_id):
             if key in self.datasets:
-                description = self.datasets[key].get('description') or description
+                description = self.datasets[key].get("description") or description
 
         rel_path = self.relative_to_root(path)
 
         attributes = dict(
-            path = str(rel_path),
-            description = str(description),
-            model_format = info.format,
-            )
+            path=str(rel_path),
+            description=str(description),
+            model_format=info.format,
+        )
+        legacy_conf = None
         if info.model_type == ModelType.Main:
-            attributes.update(dict(variant = info.variant_type,))
-            if info.format=="checkpoint":
+            attributes.update(
+                dict(
+                    variant=info.variant_type,
+                )
+            )
+            if info.format == "checkpoint":
                 try:
-                    possible_conf = path.with_suffix('.yaml')
+                    possible_conf = path.with_suffix(".yaml")
                     if possible_conf.exists():
                         legacy_conf = str(self.relative_to_root(possible_conf))
                     elif info.base_type == BaseModelType.StableDiffusion2:
-                        legacy_conf = Path(self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type])
+                        legacy_conf = Path(
+                            self.config.legacy_conf_dir,
+                            LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type],
+                        )
                     else:
-                        legacy_conf = Path(self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type])
+                        legacy_conf = Path(
+                            self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type]
+                        )
                 except KeyError:
-                    legacy_conf = Path(self.config.legacy_conf_dir, 'v1-inference.yaml')  # best guess
-                    
-                attributes.update(
-                    dict(
-                        config = str(legacy_conf)
-                    )
-                )
+                    legacy_conf = Path(self.config.legacy_conf_dir, "v1-inference.yaml")  # best guess
+
+        if info.model_type == ModelType.ControlNet and info.format == "checkpoint":
+            possible_conf = path.with_suffix(".yaml")
+            if possible_conf.exists():
+                legacy_conf = str(self.relative_to_root(possible_conf))
+
+        if legacy_conf:
+            attributes.update(dict(config=str(legacy_conf)))
         return attributes
 
-    def relative_to_root(self, path: Path)->Path:
+    def relative_to_root(self, path: Path) -> Path:
         root = self.config.root_path
         if path.is_relative_to(root):
             return path.relative_to(root)
         else:
             return path
 
-    def _download_hf_pipeline(self, repo_id: str, staging: Path)->Path:
-        '''
+    def _download_hf_pipeline(self, repo_id: str, staging: Path) -> Path:
+        """
         This retrieves a StableDiffusion model from cache or remote and then
         does a save_pretrained() to the indicated staging area.
-        '''
-        _,name = repo_id.split("/")
-        revisions = ['fp16','main'] if self.config.precision=='float16' else ['main']
+        """
+        _, name = repo_id.split("/")
+        revisions = ["fp16", "main"] if self.config.precision == "float16" else ["main"]
         model = None
         for revision in revisions:
             try:
-                model = DiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
+                model = DiffusionPipeline.from_pretrained(repo_id, revision=revision, safety_checker=None)
             except:  # most errors are due to fp16 not being present. Fix this to catch other errors
                 pass
             if model:
                 break
         if not model:
-            logger.error(f'Diffusers model {repo_id} could not be downloaded. Skipping.')
+            logger.error(f"Diffusers model {repo_id} could not be downloaded. Skipping.")
             return None
         model.save_pretrained(staging / name, safe_serialization=True)
         return staging / name
 
-    def _download_hf_model(self, repo_id: str, files: List[str], staging: Path)->Path:
-        _,name = repo_id.split("/")
+    def _download_hf_model(self, repo_id: str, files: List[str], staging: Path) -> Path:
+        _, name = repo_id.split("/")
         location = staging / name
         paths = list()
         for filename in files:
-            p = hf_download_with_resume(repo_id,
-                                        model_dir=location,
-                                        model_name=filename,
-                                        access_token = self.access_token
-                                        )
+            p = hf_download_with_resume(
+                repo_id, model_dir=location, model_name=filename, access_token=self.access_token
+            )
             if p:
                 paths.append(p)
             else:
-                logger.warning(f'Could not download {filename} from {repo_id}.')
-            
-        return location if len(paths)>0 else None
+                logger.warning(f"Could not download {filename} from {repo_id}.")
+
+        return location if len(paths) > 0 else None
 
     @classmethod
-    def _reverse_paths(cls,datasets)->dict:
-        '''
+    def _reverse_paths(cls, datasets) -> dict:
+        """
         Reverse mapping from repo_id/path to destination name.
-        '''
-        return {v.get('path') or v.get('repo_id') : k for k, v in datasets.items()}
+        """
+        return {v.get("path") or v.get("repo_id"): k for k, v in datasets.items()}
+
 
 # -------------------------------------
 def yes_or_no(prompt: str, default_yes=True):
@@ -411,13 +446,12 @@ def yes_or_no(prompt: str, default_yes=True):
     else:
         return response[0] in ("y", "Y")
 
+
 # ---------------------------------------------
-def hf_download_from_pretrained(
-        model_class: object, model_name: str, destination: Path, **kwargs
-):
-    logger = InvokeAILogger.getLogger('InvokeAI')
-    logger.addFilter(lambda x: 'fp16 is not a valid' not in x.getMessage())
-    
+def hf_download_from_pretrained(model_class: object, model_name: str, destination: Path, **kwargs):
+    logger = InvokeAILogger.getLogger("InvokeAI")
+    logger.addFilter(lambda x: "fp16 is not a valid" not in x.getMessage())
+
     model = model_class.from_pretrained(
         model_name,
         resume_download=True,
@@ -426,13 +460,14 @@ def hf_download_from_pretrained(
     model.save_pretrained(destination, safe_serialization=True)
     return destination
 
+
 # ---------------------------------------------
 def hf_download_with_resume(
-        repo_id: str,
-        model_dir: str,
-        model_name: str,
-        model_dest: Path = None,
-        access_token: str = None,
+    repo_id: str,
+    model_dir: str,
+    model_name: str,
+    model_dest: Path = None,
+    access_token: str = None,
 ) -> Path:
     model_dest = model_dest or Path(os.path.join(model_dir, model_name))
     os.makedirs(model_dir, exist_ok=True)
@@ -451,9 +486,7 @@ def hf_download_with_resume(
     resp = requests.get(url, headers=header, stream=True)
     total = int(resp.headers.get("content-length", 0))
 
-    if (
-        resp.status_code == 416
-    ):  # "range not satisfiable", which means nothing to return
+    if resp.status_code == 416:  # "range not satisfiable", which means nothing to return
         logger.info(f"{model_name}: complete file found. Skipping.")
         return model_dest
     elif resp.status_code == 404:
@@ -482,5 +515,3 @@ def hf_download_with_resume(
         logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
         return None
     return model_dest
-
-
diff --git a/invokeai/backend/model_management/__init__.py b/invokeai/backend/model_management/__init__.py
index 91180d4474..a277ecb29c 100644
--- a/invokeai/backend/model_management/__init__.py
+++ b/invokeai/backend/model_management/__init__.py
@@ -3,6 +3,12 @@ Initialization file for invokeai.backend.model_management
 """
 from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
 from .model_cache import ModelCache
-from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException, DuplicateModelException
+from .models import (
+    BaseModelType,
+    ModelType,
+    SubModelType,
+    ModelVariantType,
+    ModelNotFoundException,
+    DuplicateModelException,
+)
 from .model_merge import ModelMerger, MergeInterpolationMethod
-
diff --git a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py
index d9d4262e47..2c62b8b192 100644
--- a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py
+++ b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
+# Copyright 2023 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,23 +13,58 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-# Adapted for use as a module by Lincoln Stein <lstein@gmail.com>
-# Original file at: https://github.com/huggingface/diffusers/blob/main/scripts/convert_ldm_original_checkpoint_to_diffusers.py
-""" Conversion script for the LDM checkpoints. """
+# Adapted for use in InvokeAI by Lincoln Stein, July 2023
+#
+""" Conversion script for the Stable Diffusion checkpoints."""
 
 import re
-import warnings
+from contextlib import nullcontext
+from io import BytesIO
+from typing import Optional, Union
 from pathlib import Path
-from typing import Union
-from packaging import version
 
+import requests
 import torch
-from safetensors.torch import load_file
+from transformers import (
+    AutoFeatureExtractor,
+    BertTokenizerFast,
+    CLIPImageProcessor,
+    CLIPTextConfig,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionConfig,
+    CLIPVisionModelWithProjection,
+)
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from diffusers.models import (
+    AutoencoderKL,
+    ControlNetModel,
+    PriorTransformer,
+    UNet2DConditionModel,
+)
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UnCLIPScheduler,
+)
+from diffusers.utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available
+from diffusers.utils.import_utils import BACKENDS_MAPPING
+from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
+from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
+
+from invokeai.backend.util.logging import InvokeAILogger
+from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE
 
-from .model_manager import ModelManager
 from picklescan.scanner import scan_file_path
 from .models import BaseModelType, ModelVariantType
 
@@ -41,43 +76,13 @@ except ImportError:
         "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
     )
 
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    HeunDiscreteScheduler,
-    LDMTextToImagePipeline,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    UniPCMultistepScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers import logging as dlogging
-from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
-    LDMBertConfig,
-    LDMBertModel,
-)
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from diffusers.utils import is_safetensors_available
-import transformers
-from transformers import (
-    AutoFeatureExtractor,
-    BertTokenizerFast,
-    CLIPTextModel,
-    CLIPTokenizer,
-    CLIPVisionConfig,
-)
+if is_accelerate_available():
+    from accelerate import init_empty_weights
+    from accelerate.utils import set_module_tensor_to_device
 
-from ..stable_diffusion import StableDiffusionGeneratorPipeline
+logger = InvokeAILogger.getLogger(__name__)
+CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
 
-# TODO: redo in future
-#CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core" / "convert"
-CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / "models" / "core" / "convert"
 
 def shave_segments(path, n_shave_prefix_segments=1):
     """
@@ -104,9 +109,7 @@ def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
         new_item = new_item.replace("emb_layers.1", "time_emb_proj")
         new_item = new_item.replace("skip_connection", "conv_shortcut")
 
-        new_item = shave_segments(
-            new_item, n_shave_prefix_segments=n_shave_prefix_segments
-        )
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
 
         mapping.append({"old": old_item, "new": new_item})
 
@@ -122,9 +125,7 @@ def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
         new_item = old_item
 
         new_item = new_item.replace("nin_shortcut", "conv_shortcut")
-        new_item = shave_segments(
-            new_item, n_shave_prefix_segments=n_shave_prefix_segments
-        )
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
 
         mapping.append({"old": old_item, "new": new_item})
 
@@ -175,9 +176,7 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
         new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
         new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
 
-        new_item = shave_segments(
-            new_item, n_shave_prefix_segments=n_shave_prefix_segments
-        )
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
 
         mapping.append({"old": old_item, "new": new_item})
 
@@ -185,26 +184,40 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
 
 
 def assign_to_checkpoint(
-    paths,
-    checkpoint,
-    old_checkpoint,
-    additional_replacements=None,
-    config=None,
+    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
 ):
     """
-    This does the final conversion step: take locally converted weights and apply a global renaming
-    to them. It splits attention layers, and takes into account additional replacements
-    that may arise.
+    This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
+    attention layers, and takes into account additional replacements that may arise.
 
     Assigns the weights to the new checkpoint.
     """
-    assert isinstance(
-        paths, list
-    ), "Paths should be a list of dicts containing 'old' and 'new' keys."
+    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    # Splits the attention layers into three variables.
+    if attention_paths_to_split is not None:
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map["query"]] = query.reshape(target_shape)
+            checkpoint[path_map["key"]] = key.reshape(target_shape)
+            checkpoint[path_map["value"]] = value.reshape(target_shape)
 
     for path in paths:
         new_path = path["new"]
 
+        # These have already been assigned
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
         # Global renaming happens here
         new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
         new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
@@ -215,82 +228,124 @@ def assign_to_checkpoint(
                 new_path = new_path.replace(replacement["old"], replacement["new"])
 
         # proj_attn.weight has to be converted from conv 1D to linear
-        if "proj_attn.weight" in new_path:
+        is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path)
+        shape = old_checkpoint[path["old"]].shape
+        if is_attn_weight and len(shape) == 3:
             checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        elif is_attn_weight and len(shape) == 4:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
         else:
             checkpoint[new_path] = old_checkpoint[path["old"]]
 
 
 def conv_attn_to_linear(checkpoint):
     keys = list(checkpoint.keys())
-    attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"]
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
     for key in keys:
         if ".".join(key.split(".")[-2:]) in attn_keys:
             if checkpoint[key].ndim > 2:
                 checkpoint[key] = checkpoint[key][:, :, 0, 0]
-        elif "to_out.0.weight" in key:
+        elif "proj_attn.weight" in key:
             if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+                checkpoint[key] = checkpoint[key][:, :, 0]
 
 
-def create_unet_diffusers_config(original_config, image_size: int):
+def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
     """
     Creates a config for the diffusers based on the config of the LDM model.
     """
-    unet_params = original_config.model.params.unet_config.params
+    if controlnet:
+        unet_params = original_config.model.params.control_stage_config.params
+    else:
+        if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None:
+            unet_params = original_config.model.params.unet_config.params
+        else:
+            unet_params = original_config.model.params.network_config.params
+
     vae_params = original_config.model.params.first_stage_config.params.ddconfig
 
-    block_out_channels = [
-        unet_params.model_channels * mult for mult in unet_params.channel_mult
-    ]
+    block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
 
     down_block_types = []
     resolution = 1
     for i in range(len(block_out_channels)):
-        block_type = (
-            "CrossAttnDownBlock2D"
-            if resolution in unet_params.attention_resolutions
-            else "DownBlock2D"
-        )
+        block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
         down_block_types.append(block_type)
         if i != len(block_out_channels) - 1:
             resolution *= 2
 
     up_block_types = []
     for i in range(len(block_out_channels)):
-        block_type = (
-            "CrossAttnUpBlock2D"
-            if resolution in unet_params.attention_resolutions
-            else "UpBlock2D"
-        )
+        block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
         up_block_types.append(block_type)
         resolution //= 2
 
+    if unet_params.transformer_depth is not None:
+        transformer_layers_per_block = (
+            unet_params.transformer_depth
+            if isinstance(unet_params.transformer_depth, int)
+            else list(unet_params.transformer_depth)
+        )
+    else:
+        transformer_layers_per_block = 1
+
     vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
 
     head_dim = unet_params.num_heads if "num_heads" in unet_params else None
     use_linear_projection = (
-        unet_params.use_linear_in_transformer
-        if "use_linear_in_transformer" in unet_params
-        else False
+        unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
     )
     if use_linear_projection:
         # stable diffusion 2-base-512 and 2-768
         if head_dim is None:
-            head_dim = [5, 10, 20, 20]
+            head_dim_mult = unet_params.model_channels // unet_params.num_head_channels
+            head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]
 
-    config = dict(
-        sample_size=image_size // vae_scale_factor,
-        in_channels=unet_params.in_channels,
-        out_channels=unet_params.out_channels,
-        down_block_types=tuple(down_block_types),
-        up_block_types=tuple(up_block_types),
-        block_out_channels=tuple(block_out_channels),
-        layers_per_block=unet_params.num_res_blocks,
-        cross_attention_dim=unet_params.context_dim,
-        attention_head_dim=head_dim,
-        use_linear_projection=use_linear_projection,
-    )
+    class_embed_type = None
+    addition_embed_type = None
+    addition_time_embed_dim = None
+    projection_class_embeddings_input_dim = None
+    context_dim = None
+
+    if unet_params.context_dim is not None:
+        context_dim = (
+            unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]
+        )
+
+    if "num_classes" in unet_params:
+        if unet_params.num_classes == "sequential":
+            if context_dim in [2048, 1280]:
+                # SDXL
+                addition_embed_type = "text_time"
+                addition_time_embed_dim = 256
+            else:
+                class_embed_type = "projection"
+            assert "adm_in_channels" in unet_params
+            projection_class_embeddings_input_dim = unet_params.adm_in_channels
+        else:
+            raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}")
+
+    config = {
+        "sample_size": image_size // vae_scale_factor,
+        "in_channels": unet_params.in_channels,
+        "down_block_types": tuple(down_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "layers_per_block": unet_params.num_res_blocks,
+        "cross_attention_dim": context_dim,
+        "attention_head_dim": head_dim,
+        "use_linear_projection": use_linear_projection,
+        "class_embed_type": class_embed_type,
+        "addition_embed_type": addition_embed_type,
+        "addition_time_embed_dim": addition_time_embed_dim,
+        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
+        "transformer_layers_per_block": transformer_layers_per_block,
+    }
+
+    if controlnet:
+        config["conditioning_channels"] = unet_params.hint_channels
+    else:
+        config["out_channels"] = unet_params.out_channels
+        config["up_block_types"] = tuple(up_block_types)
 
     return config
 
@@ -306,16 +361,16 @@ def create_vae_diffusers_config(original_config, image_size: int):
     down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
     up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
 
-    config = dict(
-        sample_size=image_size,
-        in_channels=vae_params.in_channels,
-        out_channels=vae_params.out_ch,
-        down_block_types=tuple(down_block_types),
-        up_block_types=tuple(up_block_types),
-        block_out_channels=tuple(block_out_channels),
-        latent_channels=vae_params.z_channels,
-        layers_per_block=vae_params.num_res_blocks,
-    )
+    config = {
+        "sample_size": image_size,
+        "in_channels": vae_params.in_channels,
+        "out_channels": vae_params.out_ch,
+        "down_block_types": tuple(down_block_types),
+        "up_block_types": tuple(up_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "latent_channels": vae_params.z_channels,
+        "layers_per_block": vae_params.num_res_blocks,
+    }
     return config
 
 
@@ -330,7 +385,7 @@ def create_diffusers_schedular(original_config):
 
 
 def create_ldm_bert_config(original_config):
-    bert_params = original_config.model.params.cond_stage_config.params
+    bert_params = original_config.model.parms.cond_stage_config.params
     config = LDMBertConfig(
         d_model=bert_params.n_embed,
         encoder_layers=bert_params.n_layer,
@@ -339,103 +394,96 @@ def create_ldm_bert_config(original_config):
     return config
 
 
-def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
+def convert_ldm_unet_checkpoint(
+    checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False
+):
     """
     Takes a state dict and a config, and returns a converted checkpoint.
     """
 
-    # extract state_dict for UNet
-    unet_state_dict = {}
-    keys = list(checkpoint.keys())
+    if skip_extract_state_dict:
+        unet_state_dict = checkpoint
+    else:
+        # extract state_dict for UNet
+        unet_state_dict = {}
+        keys = list(checkpoint.keys())
 
-    unet_key = "model.diffusion_model."
-    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
-    if sum(k.startswith("model_ema") for k in keys) > 100:
-        logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.")
-        if extract_ema:
-            logger.debug("Extracting EMA weights (usually better for inference)")
+        if controlnet:
+            unet_key = "control_model."
+        else:
+            unet_key = "model.diffusion_model."
+
+        # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
+        if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
+            logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.")
+            logger.warning(
+                "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
+                " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
+            )
             for key in keys:
                 if key.startswith("model.diffusion_model"):
                     flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
-                    flat_ema_key_alt = "model_ema." + "".join(key.split(".")[2:])
-                    if flat_ema_key in checkpoint:
-                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
-                            flat_ema_key
-                        )
-                    elif flat_ema_key_alt in checkpoint:
-                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
-                            flat_ema_key_alt
-                        )
-                    else:
-                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
-                            key
-                        )
+                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
         else:
-            logger.debug(
-                "Extracting only the non-EMA weights (usually better for fine-tuning)"
-            )
+            if sum(k.startswith("model_ema") for k in keys) > 100:
+                logger.warning(
+                    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
+                    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
+                )
 
-    for key in keys:
-        if key.startswith("model.diffusion_model") and key in checkpoint:
-            unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
+            for key in keys:
+                if key.startswith(unet_key):
+                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
 
     new_checkpoint = {}
 
-    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict[
-        "time_embed.0.weight"
-    ]
-    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict[
-        "time_embed.0.bias"
-    ]
-    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict[
-        "time_embed.2.weight"
-    ]
-    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict[
-        "time_embed.2.bias"
-    ]
+    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
+    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
+    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
+    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
+
+    if config["class_embed_type"] is None:
+        # No parameters to port
+        ...
+    elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
+        new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
+        new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
+        new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
+        new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
+    else:
+        raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
+
+    if config["addition_embed_type"] == "text_time":
+        new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
+        new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
+        new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
+        new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
 
     new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
     new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
 
-    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
-    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
-    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
-    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
+    if not controlnet:
+        new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
+        new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
+        new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
+        new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
 
     # Retrieves the keys for the input blocks only
-    num_input_blocks = len(
-        {
-            ".".join(layer.split(".")[:2])
-            for layer in unet_state_dict
-            if "input_blocks" in layer
-        }
-    )
+    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
     input_blocks = {
         layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
         for layer_id in range(num_input_blocks)
     }
 
     # Retrieves the keys for the middle blocks only
-    num_middle_blocks = len(
-        {
-            ".".join(layer.split(".")[:2])
-            for layer in unet_state_dict
-            if "middle_block" in layer
-        }
-    )
+    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
     middle_blocks = {
         layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
         for layer_id in range(num_middle_blocks)
     }
 
     # Retrieves the keys for the output blocks only
-    num_output_blocks = len(
-        {
-            ".".join(layer.split(".")[:2])
-            for layer in unet_state_dict
-            if "output_blocks" in layer
-        }
-    )
+    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
     output_blocks = {
         layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
         for layer_id in range(num_output_blocks)
@@ -446,45 +494,27 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
         layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
 
         resnets = [
-            key
-            for key in input_blocks[i]
-            if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
         ]
         attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
 
         if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
-            new_checkpoint[
-                f"down_blocks.{block_id}.downsamplers.0.conv.weight"
-            ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.weight")
-            new_checkpoint[
-                f"down_blocks.{block_id}.downsamplers.0.conv.bias"
-            ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
 
         paths = renew_resnet_paths(resnets)
-        meta_path = {
-            "old": f"input_blocks.{i}.0",
-            "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}",
-        }
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            unet_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
+        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
+        assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
 
         if len(attentions):
             paths = renew_attention_paths(attentions)
-            meta_path = {
-                "old": f"input_blocks.{i}.1",
-                "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
-            }
+            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
             assign_to_checkpoint(
-                paths,
-                new_checkpoint,
-                unet_state_dict,
-                additional_replacements=[meta_path],
-                config=config,
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
             )
 
     resnet_0 = middle_blocks[0]
@@ -500,11 +530,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     attentions_paths = renew_attention_paths(attentions)
     meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
     assign_to_checkpoint(
-        attentions_paths,
-        new_checkpoint,
-        unet_state_dict,
-        additional_replacements=[meta_path],
-        config=config,
+        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
     )
 
     for i in range(num_output_blocks):
@@ -522,36 +548,25 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
 
         if len(output_block_list) > 1:
             resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
-            attentions = [
-                key for key in output_blocks[i] if f"output_blocks.{i}.1" in key
-            ]
+            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
 
             resnet_0_paths = renew_resnet_paths(resnets)
             paths = renew_resnet_paths(resnets)
 
-            meta_path = {
-                "old": f"output_blocks.{i}.0",
-                "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}",
-            }
+            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
             assign_to_checkpoint(
-                paths,
-                new_checkpoint,
-                unet_state_dict,
-                additional_replacements=[meta_path],
-                config=config,
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
             )
 
             output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
             if ["conv.bias", "conv.weight"] in output_block_list.values():
-                index = list(output_block_list.values()).index(
-                    ["conv.bias", "conv.weight"]
-                )
-                new_checkpoint[
-                    f"up_blocks.{block_id}.upsamplers.0.conv.weight"
-                ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"]
-                new_checkpoint[
-                    f"up_blocks.{block_id}.upsamplers.0.conv.bias"
-                ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"]
+                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.weight"
+                ]
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.bias"
+                ]
 
                 # Clear attentions as they have been attributed above.
                 if len(attentions) == 2:
@@ -564,82 +579,85 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                     "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                 }
                 assign_to_checkpoint(
-                    paths,
-                    new_checkpoint,
-                    unet_state_dict,
-                    additional_replacements=[meta_path],
-                    config=config,
+                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
                 )
         else:
-            resnet_0_paths = renew_resnet_paths(
-                output_block_layers, n_shave_prefix_segments=1
-            )
+            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
             for path in resnet_0_paths:
                 old_path = ".".join(["output_blocks", str(i), path["old"]])
-                new_path = ".".join(
-                    [
-                        "up_blocks",
-                        str(block_id),
-                        "resnets",
-                        str(layer_in_block_id),
-                        path["new"],
-                    ]
-                )
+                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
 
                 new_checkpoint[new_path] = unet_state_dict[old_path]
 
+    if controlnet:
+        # conditioning embedding
+
+        orig_index = 0
+
+        new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.weight"
+        )
+        new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.bias"
+        )
+
+        orig_index += 2
+
+        diffusers_index = 0
+
+        while diffusers_index < 6:
+            new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
+                f"input_hint_block.{orig_index}.weight"
+            )
+            new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
+                f"input_hint_block.{orig_index}.bias"
+            )
+            diffusers_index += 1
+            orig_index += 2
+
+        new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.weight"
+        )
+        new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.bias"
+        )
+
+        # down blocks
+        for i in range(num_input_blocks):
+            new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
+            new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
+
+        # mid block
+        new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
+        new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
+
     return new_checkpoint
 
+
 def convert_ldm_vae_checkpoint(checkpoint, config):
-    # Extract state dict for VAE. Works both with burnt-in
-    # VAEs, and with standalone VAEs.
+    # extract state dict for VAE
+    vae_state_dict = {}
+    keys = list(checkpoint.keys())
+    vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
+    for key in keys:
+        if key.startswith(vae_key):
+            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
 
-    # checkpoint can either be a all-in-one stable diffusion
-    # model, or an isolated vae .ckpt. This tests for
-    # a key that will be present in the all-in-one model
-    # that isn't present in the isolated ckpt.
-    probe_key = "first_stage_model.encoder.conv_in.weight"
-    if probe_key in checkpoint:
-        vae_state_dict = {}
-        vae_key = "first_stage_model."
-        keys = list(checkpoint.keys())
-        for key in keys:
-            if key.startswith(vae_key):
-                vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
-    else:
-        vae_state_dict = checkpoint
-        
-    new_checkpoint = convert_ldm_vae_state_dict(vae_state_dict, config)
-    return new_checkpoint
-
-def convert_ldm_vae_state_dict(vae_state_dict, config):
     new_checkpoint = {}
 
     new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
     new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
-    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
-        "encoder.conv_out.weight"
-    ]
+    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
     new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
-    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
-        "encoder.norm_out.weight"
-    ]
-    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
-        "encoder.norm_out.bias"
-    ]
+    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
+    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
 
     new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
     new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
-    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
-        "decoder.conv_out.weight"
-    ]
+    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
     new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
-    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
-        "decoder.norm_out.weight"
-    ]
-    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
-        "decoder.norm_out.bias"
-    ]
+    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
+    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
 
     new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
     new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
@@ -647,55 +665,31 @@ def convert_ldm_vae_state_dict(vae_state_dict, config):
     new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
 
     # Retrieves the keys for the encoder down blocks only
-    num_down_blocks = len(
-        {
-            ".".join(layer.split(".")[:3])
-            for layer in vae_state_dict
-            if "encoder.down" in layer
-        }
-    )
+    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
     down_blocks = {
-        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
-        for layer_id in range(num_down_blocks)
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
     }
 
     # Retrieves the keys for the decoder up blocks only
-    num_up_blocks = len(
-        {
-            ".".join(layer.split(".")[:3])
-            for layer in vae_state_dict
-            if "decoder.up" in layer
-        }
-    )
+    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
     up_blocks = {
-        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
-        for layer_id in range(num_up_blocks)
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
     }
 
     for i in range(num_down_blocks):
-        resnets = [
-            key
-            for key in down_blocks[i]
-            if f"down.{i}" in key and f"down.{i}.downsample" not in key
-        ]
+        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
 
         if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
-            new_checkpoint[
-                f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
-            ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
-            new_checkpoint[
-                f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
-            ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.weight"
+            )
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.bias"
+            )
 
         paths = renew_vae_resnet_paths(resnets)
         meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
 
     mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
     num_mid_res_blocks = 2
@@ -704,51 +698,31 @@ def convert_ldm_vae_state_dict(vae_state_dict, config):
 
         paths = renew_vae_resnet_paths(resnets)
         meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
 
     mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
     paths = renew_vae_attention_paths(mid_attentions)
     meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(
-        paths,
-        new_checkpoint,
-        vae_state_dict,
-        additional_replacements=[meta_path],
-        config=config,
-    )
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
     conv_attn_to_linear(new_checkpoint)
 
     for i in range(num_up_blocks):
         block_id = num_up_blocks - 1 - i
         resnets = [
-            key
-            for key in up_blocks[block_id]
-            if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
         ]
 
         if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
-            new_checkpoint[
-                f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
-            ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
-            new_checkpoint[
-                f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
-            ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.weight"
+            ]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.bias"
+            ]
 
         paths = renew_vae_resnet_paths(resnets)
         meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
 
     mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
     num_mid_res_blocks = 2
@@ -757,24 +731,12 @@ def convert_ldm_vae_state_dict(vae_state_dict, config):
 
         paths = renew_vae_resnet_paths(resnets)
         meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
 
     mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
     paths = renew_vae_attention_paths(mid_attentions)
     meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(
-        paths,
-        new_checkpoint,
-        vae_state_dict,
-        additional_replacements=[meta_path],
-        config=config,
-    )
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
     conv_attn_to_linear(new_checkpoint)
     return new_checkpoint
 
@@ -816,9 +778,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
 
     # copy  embeds
     hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
-    hf_model.model.embed_positions.weight.data = (
-        checkpoint.transformer.pos_emb.emb.weight
-    )
+    hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
 
     # copy layer norm
     _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
@@ -831,43 +791,40 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
     return hf_model
 
 
-def convert_ldm_clip_checkpoint(checkpoint):
-    text_model = CLIPTextModel.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14')
+def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
+    if text_encoder is None:
+        config = CLIPTextConfig.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+
+        ctx = init_empty_weights if is_accelerate_available() else nullcontext
+        with ctx():
+            text_model = CLIPTextModel(config)
+
     keys = list(checkpoint.keys())
 
     text_model_dict = {}
 
+    remove_prefixes = ["cond_stage_model.transformer", "conditioner.embedders.0.transformer"]
+
     for key in keys:
-        if key.startswith("cond_stage_model.transformer"):
-            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[
-                key
-            ]
+        for prefix in remove_prefixes:
+            if key.startswith(prefix):
+                text_model_dict[key[len(prefix + ".") :]] = checkpoint[key]
 
-    # transformers 4.31.0 and higher - this key no longer in state dict
-    if version.parse(transformers.__version__) >= version.parse("4.31.0"):
-        position_ids = text_model_dict.pop("text_model.embeddings.position_ids", None)
-        text_model.load_state_dict(text_model_dict)
-        if position_ids is not None:
-            text_model.text_model.embeddings.position_ids.copy_(position_ids)
-
-    # transformers 4.30.2 and lower - position_ids is part of state_dict
+    if is_accelerate_available():
+        for param_name, param in text_model_dict.items():
+            set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
     else:
-        text_model.load_state_dict(text_model_dict)        
+        text_model.load_state_dict(text_model_dict)
 
     return text_model
 
 
 textenc_conversion_lst = [
-    (
-        "cond_stage_model.model.positional_embedding",
-        "text_model.embeddings.position_embedding.weight",
-    ),
-    (
-        "cond_stage_model.model.token_embedding.weight",
-        "text_model.embeddings.token_embedding.weight",
-    ),
-    ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"),
-    ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"),
+    ("positional_embedding", "text_model.embeddings.position_embedding.weight"),
+    ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
+    ("ln_final.weight", "text_model.final_layer_norm.weight"),
+    ("ln_final.bias", "text_model.final_layer_norm.bias"),
+    ("text_projection", "text_projection.weight"),
 ]
 textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
 
@@ -880,129 +837,308 @@ textenc_transformer_conversion_lst = [
     (".c_proj.", ".fc2."),
     (".attn", ".self_attn"),
     ("ln_final.", "transformer.text_model.final_layer_norm."),
-    (
-        "token_embedding.weight",
-        "transformer.text_model.embeddings.token_embedding.weight",
-    ),
-    (
-        "positional_embedding",
-        "transformer.text_model.embeddings.position_embedding.weight",
-    ),
+    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
 ]
 protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
 textenc_pattern = re.compile("|".join(protected.keys()))
 
 
-def convert_open_clip_checkpoint(checkpoint):
-    text_model = CLIPTextModel.from_pretrained(
-        CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip',
-        subfolder='text_encoder',
-    )
+def convert_paint_by_example_checkpoint(checkpoint):
+    config = CLIPVisionConfig.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+    model = PaintByExampleImageEncoder(config)
 
     keys = list(checkpoint.keys())
 
     text_model_dict = {}
 
-    if "cond_stage_model.model.text_projection" in keys:
-        d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0])
-    elif "cond_stage_model.model.ln_final.bias" in keys:
-        d_model = int(checkpoint["cond_stage_model.model.ln_final.bias"].shape[0])
-    else:
-        raise KeyError(
-            'Expected key "cond_stage_model.model.text_projection" not found in model'
-        )
+    for key in keys:
+        if key.startswith("cond_stage_model.transformer"):
+            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
 
-    text_model_dict[
-        "text_model.embeddings.position_ids"
-    ] = text_model.text_model.embeddings.get_buffer("position_ids")
+    # load clip vision
+    model.model.load_state_dict(text_model_dict)
+
+    # load mapper
+    keys_mapper = {
+        k[len("cond_stage_model.mapper.res") :]: v
+        for k, v in checkpoint.items()
+        if k.startswith("cond_stage_model.mapper")
+    }
+
+    MAPPING = {
+        "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
+        "attn.c_proj": ["attn1.to_out.0"],
+        "ln_1": ["norm1"],
+        "ln_2": ["norm3"],
+        "mlp.c_fc": ["ff.net.0.proj"],
+        "mlp.c_proj": ["ff.net.2"],
+    }
+
+    mapped_weights = {}
+    for key, value in keys_mapper.items():
+        prefix = key[: len("blocks.i")]
+        suffix = key.split(prefix)[-1].split(".")[-1]
+        name = key.split(prefix)[-1].split(suffix)[0][1:-1]
+        mapped_names = MAPPING[name]
+
+        num_splits = len(mapped_names)
+        for i, mapped_name in enumerate(mapped_names):
+            new_name = ".".join([prefix, mapped_name, suffix])
+            shape = value.shape[0] // num_splits
+            mapped_weights[new_name] = value[i * shape : (i + 1) * shape]
+
+    model.mapper.load_state_dict(mapped_weights)
+
+    # load final layer norm
+    model.final_layer_norm.load_state_dict(
+        {
+            "bias": checkpoint["cond_stage_model.final_ln.bias"],
+            "weight": checkpoint["cond_stage_model.final_ln.weight"],
+        }
+    )
+
+    # load final proj
+    model.proj_out.load_state_dict(
+        {
+            "bias": checkpoint["proj_out.bias"],
+            "weight": checkpoint["proj_out.weight"],
+        }
+    )
+
+    # load uncond vector
+    model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
+    return model
+
+
+def convert_open_clip_checkpoint(
+    checkpoint, config_name, prefix="cond_stage_model.model.", has_projection=False, **config_kwargs
+):
+    # text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
+    # text_model = CLIPTextModelWithProjection.from_pretrained(
+    #    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
+    # )
+    config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs)
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
+
+    keys = list(checkpoint.keys())
+
+    keys_to_ignore = []
+    if config_name == "stabilityai/stable-diffusion-2" and config.num_hidden_layers == 23:
+        # make sure to remove all keys > 22
+        keys_to_ignore += [k for k in keys if k.startswith("cond_stage_model.model.transformer.resblocks.23")]
+        keys_to_ignore += ["cond_stage_model.model.text_projection"]
+
+    text_model_dict = {}
+
+    if prefix + "text_projection" in checkpoint:
+        d_model = int(checkpoint[prefix + "text_projection"].shape[0])
+    else:
+        d_model = 1024
+
+    text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
 
     for key in keys:
-        if (
-            "resblocks.23" in key
-        ):  # Diffusers drops the final layer and only uses the penultimate layer
+        if key in keys_to_ignore:
             continue
-        if key in textenc_conversion_map:
-            text_model_dict[textenc_conversion_map[key]] = checkpoint[key]
-        if key.startswith("cond_stage_model.model.transformer."):
-            new_key = key[len("cond_stage_model.model.transformer.") :]
+        if key[len(prefix) :] in textenc_conversion_map:
+            if key.endswith("text_projection"):
+                value = checkpoint[key].T.contiguous()
+            else:
+                value = checkpoint[key]
+
+            text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value
+
+        if key.startswith(prefix + "transformer."):
+            new_key = key[len(prefix + "transformer.") :]
             if new_key.endswith(".in_proj_weight"):
                 new_key = new_key[: -len(".in_proj_weight")]
-                new_key = textenc_pattern.sub(
-                    lambda m: protected[re.escape(m.group(0))], new_key
-                )
-                text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][
-                    :d_model, :
-                ]
-                text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][
-                    d_model : d_model * 2, :
-                ]
-                text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][
-                    d_model * 2 :, :
-                ]
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+                text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
+                text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
+                text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
             elif new_key.endswith(".in_proj_bias"):
                 new_key = new_key[: -len(".in_proj_bias")]
-                new_key = textenc_pattern.sub(
-                    lambda m: protected[re.escape(m.group(0))], new_key
-                )
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
                 text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
-                text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][
-                    d_model : d_model * 2
-                ]
-                text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][
-                    d_model * 2 :
-                ]
+                text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
+                text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
             else:
-                new_key = textenc_pattern.sub(
-                    lambda m: protected[re.escape(m.group(0))], new_key
-                )
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
 
                 text_model_dict[new_key] = checkpoint[key]
 
-    # transformers 4.31.0 and higher - this key no longer in state dict
-    if version.parse(transformers.__version__) >= version.parse("4.31.0"):
-        position_ids = text_model_dict.pop("text_model.embeddings.position_ids", None)
-        text_model.load_state_dict(text_model_dict)
-        if position_ids is not None:
-            text_model.text_model.embeddings.position_ids.copy_(position_ids)
-
-    # transformers 4.30.2 and lower - position_ids is part of state_dict
+    if is_accelerate_available():
+        for param_name, param in text_model_dict.items():
+            set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
     else:
-        text_model.load_state_dict(text_model_dict)  
+        text_model.load_state_dict(text_model_dict)
 
     return text_model
 
-def replace_checkpoint_vae(checkpoint, vae_path: str):
-    if vae_path.endswith(".safetensors"):
-        vae_ckpt = load_file(vae_path)
+
+def stable_unclip_image_encoder(original_config):
+    """
+    Returns the image processor and clip image encoder for the img2img unclip pipeline.
+
+    We currently know of two types of stable unclip models which separately use the clip and the openclip image
+    encoders.
+    """
+
+    image_embedder_config = original_config.model.params.embedder_config
+
+    sd_clip_image_embedder_class = image_embedder_config.target
+    sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1]
+
+    if sd_clip_image_embedder_class == "ClipImageEmbedder":
+        clip_model_name = image_embedder_config.params.model
+
+        if clip_model_name == "ViT-L/14":
+            feature_extractor = CLIPImageProcessor()
+            image_encoder = CLIPVisionModelWithProjection.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+        else:
+            raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}")
+
+    elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder":
+        feature_extractor = CLIPImageProcessor()
+        # InvokeAI doesn't use CLIPVisionModelWithProjection so it isn't in the core - if this code is hit a download will occur
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            CONVERT_MODEL_ROOT / "CLIP-ViT-H-14-laion2B-s32B-b79K"
+        )
     else:
-        vae_ckpt = torch.load(vae_path, map_location="cpu")
-    state_dict = vae_ckpt['state_dict'] if "state_dict" in vae_ckpt else vae_ckpt
-    for vae_key in state_dict:
-        new_key = f'first_stage_model.{vae_key}'
-        checkpoint[new_key] = state_dict[vae_key]
+        raise NotImplementedError(
+            f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}"
+        )
 
-def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:
-    vae_config = create_vae_diffusers_config(
-        vae_config, image_size=image_size
+    return feature_extractor, image_encoder
+
+
+def stable_unclip_image_noising_components(
+    original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None
+):
+    """
+    Returns the noising components for the img2img and txt2img unclip pipelines.
+
+    Converts the stability noise augmentor into
+    1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats
+    2. a `DDPMScheduler` for holding the noise schedule
+
+    If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided.
+    """
+    noise_aug_config = original_config.model.params.noise_aug_config
+    noise_aug_class = noise_aug_config.target
+    noise_aug_class = noise_aug_class.split(".")[-1]
+
+    if noise_aug_class == "CLIPEmbeddingNoiseAugmentation":
+        noise_aug_config = noise_aug_config.params
+        embedding_dim = noise_aug_config.timestep_dim
+        max_noise_level = noise_aug_config.noise_schedule_config.timesteps
+        beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule
+
+        image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim)
+        image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)
+
+        if "clip_stats_path" in noise_aug_config:
+            if clip_stats_path is None:
+                raise ValueError("This stable unclip config requires a `clip_stats_path`")
+
+            clip_mean, clip_std = torch.load(clip_stats_path, map_location=device)
+            clip_mean = clip_mean[None, :]
+            clip_std = clip_std[None, :]
+
+            clip_stats_state_dict = {
+                "mean": clip_mean,
+                "std": clip_std,
+            }
+
+            image_normalizer.load_state_dict(clip_stats_state_dict)
+    else:
+        raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}")
+
+    return image_normalizer, image_noising_scheduler
+
+
+def convert_controlnet_checkpoint(
+    checkpoint,
+    original_config,
+    checkpoint_path,
+    image_size,
+    upcast_attention,
+    extract_ema,
+    use_linear_projection=None,
+    cross_attention_dim=None,
+    precision: torch.dtype = torch.float32,
+):
+    ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
+    ctrlnet_config["upcast_attention"] = upcast_attention
+
+    ctrlnet_config.pop("sample_size")
+    original_config = ctrlnet_config.copy()
+
+    ctrlnet_config.pop("addition_embed_type")
+    ctrlnet_config.pop("addition_time_embed_dim")
+    ctrlnet_config.pop("transformer_layers_per_block")
+
+    if use_linear_projection is not None:
+        ctrlnet_config["use_linear_projection"] = use_linear_projection
+
+    if cross_attention_dim is not None:
+        ctrlnet_config["cross_attention_dim"] = cross_attention_dim
+
+    controlnet = ControlNetModel(**ctrlnet_config)
+
+    # Some controlnet ckpt files are distributed independently from the rest of the
+    # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
+    if "time_embed.0.weight" in checkpoint:
+        skip_extract_state_dict = True
+    else:
+        skip_extract_state_dict = False
+
+    converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(
+        checkpoint,
+        original_config,
+        path=checkpoint_path,
+        extract_ema=extract_ema,
+        controlnet=True,
+        skip_extract_state_dict=skip_extract_state_dict,
     )
 
-    converted_vae_checkpoint = convert_ldm_vae_checkpoint(
-        checkpoint, vae_config
-    )
+    controlnet.load_state_dict(converted_ctrl_checkpoint)
 
-    vae = AutoencoderKL(**vae_config)
-    vae.load_state_dict(converted_vae_checkpoint)
-    return vae
+    return controlnet.to(precision)
 
-def load_pipeline_from_original_stable_diffusion_ckpt(
+
+# TO DO - PASS PRECISION
+def download_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,
     model_version: BaseModelType,
     model_variant: ModelVariantType,
-    original_config_file: str,
-    extract_ema: bool = True,
+    original_config_file: str = None,
+    image_size: Optional[int] = None,
+    prediction_type: str = None,
+    model_type: str = None,
+    extract_ema: bool = False,
     precision: torch.dtype = torch.float32,
+    scheduler_type: str = "pndm",
+    num_in_channels: Optional[int] = None,
+    upcast_attention: Optional[bool] = None,
+    device: str = None,
+    from_safetensors: bool = False,
+    stable_unclip: Optional[str] = None,
+    stable_unclip_prior: Optional[str] = None,
+    clip_stats_path: Optional[str] = None,
+    controlnet: Optional[bool] = None,
+    load_safety_checker: bool = True,
+    pipeline_class: DiffusionPipeline = None,
+    local_files_only=False,
+    vae_path=None,
+    text_encoder=None,
+    tokenizer=None,
     scan_needed: bool = True,
-) -> StableDiffusionPipeline:
+) -> DiffusionPipeline:
     """
     Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
     config file.
@@ -1011,158 +1147,601 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
     recommended that you override the default values and/or supply an `original_config_file` wherever possible.
 
-    :param checkpoint_path: Path to `.ckpt` file.
-    :param original_config_file: Path to `.yaml` config file corresponding to the original architecture.
-      If `None`, will be automatically inferred by looking for a key that only exists in SD2.0 models.
-    :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
-     "euler-ancestral", "dpm", "ddim"]`. :param model_type: The pipeline type. `None` to automatically infer, or one of
-     `["FrozenOpenCLIPEmbedder", "FrozenCLIPEmbedder"]`. :param extract_ema: Only relevant for
-     checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights
-     or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher
-     quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
-    :param precision: precision to use - torch.float16, torch.float32 or torch.autocast
+    Args:
+        checkpoint_path (`str`): Path to `.ckpt` file.
+        original_config_file (`str`):
+            Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically
+            inferred by looking for a key that only exists in SD2.0 models.
+        image_size (`int`, *optional*, defaults to 512):
+            The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
+            Base. Use 768 for Stable Diffusion v2.
+        prediction_type (`str`, *optional*):
+            The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable
+            Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
+        num_in_channels (`int`, *optional*, defaults to None):
+            The number of input channels. If `None`, it will be automatically inferred.
+        scheduler_type (`str`, *optional*, defaults to 'pndm'):
+            Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
+            "ddim"]`.
+        model_type (`str`, *optional*, defaults to `None`):
+            The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder",
+            "FrozenCLIPEmbedder", "PaintByExample"]`.
+        is_img2img (`bool`, *optional*, defaults to `False`):
+            Whether the model should be loaded as an img2img pipeline.
+        extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
+            checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
+            `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
+            inference. Non-EMA weights are usually better to continue fine-tuning.
+        upcast_attention (`bool`, *optional*, defaults to `None`):
+            Whether the attention computation should always be upcasted. This is necessary when running stable
+            diffusion 2.1.
+        device (`str`, *optional*, defaults to `None`):
+            The device to use. Pass `None` to determine automatically.
+        from_safetensors (`str`, *optional*, defaults to `False`):
+            If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.
+        load_safety_checker (`bool`, *optional*, defaults to `True`):
+            Whether to load the safety checker or not. Defaults to `True`.
+        pipeline_class (`str`, *optional*, defaults to `None`):
+            The pipeline class to use. Pass `None` to determine automatically.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            Whether or not to only look at local files (i.e., do not try to download the model).
+        text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):
+            An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)
+            to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)
+            variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
+        tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`):
+            An instance of
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
+            to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
+            needed.
+        return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
     """
-    if not isinstance(checkpoint_path, Path):
-        checkpoint_path = Path(checkpoint_path)
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        verbosity = dlogging.get_verbosity()
-        dlogging.set_verbosity_error()
+    # import pipelines here to avoid circular import error when using from_single_file method
+    from diffusers import (
+        LDMTextToImagePipeline,
+        PaintByExamplePipeline,
+        StableDiffusionControlNetPipeline,
+        StableDiffusionInpaintPipeline,
+        StableDiffusionPipeline,
+        StableDiffusionXLPipeline,
+        StableDiffusionXLImg2ImgPipeline,
+        StableUnCLIPImg2ImgPipeline,
+        StableUnCLIPPipeline,
+    )
 
-        if checkpoint_path.suffix == ".safetensors":
-            checkpoint = load_file(checkpoint_path)
+    if pipeline_class is None:
+        pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline
+
+    if prediction_type == "v-prediction":
+        prediction_type = "v_prediction"
+
+    if not is_omegaconf_available():
+        raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
+
+    if from_safetensors:
+        if not is_safetensors_available():
+            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
+
+        from safetensors.torch import load_file as safe_load
+
+        checkpoint = safe_load(checkpoint_path, device="cpu")
+    else:
+        if scan_needed:
+            # scan model
+            scan_result = scan_file_path(checkpoint_path)
+            if scan_result.infected_files != 0:
+                raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            checkpoint = torch.load(checkpoint_path, map_location=device)
         else:
-            if scan_needed:
-                # scan model
-                scan_result = scan_file_path(checkpoint_path)
-                if scan_result.infected_files != 0:
-                    raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
-            checkpoint = torch.load(checkpoint_path)
+            checkpoint = torch.load(checkpoint_path, map_location=device)
 
-        # sometimes there is a state_dict key and sometimes not
-        if "state_dict" in checkpoint:
-            checkpoint = checkpoint["state_dict"]
+    # Sometimes models don't have the global_step item
+    if "global_step" in checkpoint:
+        global_step = checkpoint["global_step"]
+    else:
+        logger.debug("global_step key not found in model")
+        global_step = None
 
-        original_config = OmegaConf.load(original_config_file)
+    # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
+    # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
+    while "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
 
-        if model_version == BaseModelType.StableDiffusion2 and original_config["model"]["params"]["parameterization"] == "v":
-            prediction_type = "v_prediction"
-            upcast_attention = True
-            image_size = 768
+    logger.debug(f"model_type = {model_type}; original_config_file = {original_config_file}")
+
+    if original_config_file is None:
+        key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
+        key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
+        key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
+
+        # model_type = "v1"
+        config_url = (
+            "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+        )
+
+        if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
+            # model_type = "v2"
+            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
+
+            if global_step == 110000:
+                # v2.1 needs to upcast attention
+                upcast_attention = True
+        elif key_name_sd_xl_base in checkpoint:
+            # only base xl has two text embedders
+            config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
+        elif key_name_sd_xl_refiner in checkpoint:
+            # only refiner xl has embedder and one text embedders
+            config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
+
+        original_config_file = BytesIO(requests.get(config_url).content)
+
+    original_config = OmegaConf.load(original_config_file)
+    if (
+        model_version == BaseModelType.StableDiffusion2
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        prediction_type = "v_prediction"
+        upcast_attention = True
+        image_size = 768
+    else:
+        prediction_type = "epsilon"
+        upcast_attention = False
+        image_size = 512
+
+    # Convert the text model.
+    if (
+        model_type is None
+        and "cond_stage_config" in original_config.model.params
+        and original_config.model.params.cond_stage_config is not None
+    ):
+        model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
+        logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}")
+    elif model_type is None and original_config.model.params.network_config is not None:
+        if original_config.model.params.network_config.params.context_dim == 2048:
+            model_type = "SDXL"
         else:
+            model_type = "SDXL-Refiner"
+        if image_size is None:
+            image_size = 1024
+
+    if num_in_channels is None and pipeline_class == StableDiffusionInpaintPipeline:
+        num_in_channels = 9
+    elif num_in_channels is None:
+        num_in_channels = 4
+
+    if "unet_config" in original_config.model.params:
+        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+
+    if (
+        "parameterization" in original_config["model"]["params"]
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        if prediction_type is None:
+            # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
+            # as it relies on a brittle global step parameter here
+            prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
+        if image_size is None:
+            # NOTE: For stable diffusion 2 base one has to pass `image_size==512`
+            # as it relies on a brittle global step parameter here
+            image_size = 512 if global_step == 875000 else 768
+    else:
+        if prediction_type is None:
             prediction_type = "epsilon"
-            upcast_attention = False
+        if image_size is None:
             image_size = 512
 
-        #
-        # convert scheduler
-        #
+    if controlnet is None and "control_stage_config" in original_config.model.params:
+        controlnet = convert_controlnet_checkpoint(
+            checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema
+        )
 
-        num_train_timesteps = original_config.model.params.timesteps
-        beta_start = original_config.model.params.linear_start
-        beta_end = original_config.model.params.linear_end
+    num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000
 
-        scheduler = PNDMScheduler(
+    if model_type in ["SDXL", "SDXL-Refiner"]:
+        scheduler_dict = {
+            "beta_schedule": "scaled_linear",
+            "beta_start": 0.00085,
+            "beta_end": 0.012,
+            "interpolation_type": "linear",
+            "num_train_timesteps": num_train_timesteps,
+            "prediction_type": "epsilon",
+            "sample_max_value": 1.0,
+            "set_alpha_to_one": False,
+            "skip_prk_steps": True,
+            "steps_offset": 1,
+            "timestep_spacing": "leading",
+        }
+        scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)
+        scheduler_type = "euler"
+    else:
+        beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02
+        beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085
+        scheduler = DDIMScheduler(
             beta_end=beta_end,
             beta_schedule="scaled_linear",
             beta_start=beta_start,
             num_train_timesteps=num_train_timesteps,
             steps_offset=1,
+            clip_sample=False,
             set_alpha_to_one=False,
             prediction_type=prediction_type,
-            skip_prk_steps=True
         )
-        # make sure scheduler works correctly with DDIM
-        scheduler.register_to_config(clip_sample=False)
+    # make sure scheduler works correctly with DDIM
+    scheduler.register_to_config(clip_sample=False)
 
-        #
-        # convert unet
-        #
+    if scheduler_type == "pndm":
+        config = dict(scheduler.config)
+        config["skip_prk_steps"] = True
+        scheduler = PNDMScheduler.from_config(config)
+    elif scheduler_type == "lms":
+        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "heun":
+        scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler":
+        scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler-ancestral":
+        scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "dpm":
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+    elif scheduler_type == "ddim":
+        scheduler = scheduler
+    else:
+        raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
 
-        unet_config = create_unet_diffusers_config(
-            original_config, image_size=image_size
-        )
-        unet_config["upcast_attention"] = upcast_attention
+    # Convert the UNet2DConditionModel model.
+    unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
+    unet_config["upcast_attention"] = upcast_attention
+    converted_unet_checkpoint = convert_ldm_unet_checkpoint(
+        checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
+    )
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
         unet = UNet2DConditionModel(**unet_config)
 
-        converted_unet_checkpoint = convert_ldm_unet_checkpoint(
-            checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
-        )
-
+    if is_accelerate_available():
+        for param_name, param in converted_unet_checkpoint.items():
+            set_module_tensor_to_device(unet, param_name, "cpu", value=param)
+    else:
         unet.load_state_dict(converted_unet_checkpoint)
 
-        #
-        # convert vae
-        #
+    # Convert the VAE model.
+    if vae_path is None:
+        vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
+        converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
 
-        vae = convert_ldm_vae_to_diffusers(
-            checkpoint,
-            original_config,
-            image_size,
+        if (
+            "model" in original_config
+            and "params" in original_config.model
+            and "scale_factor" in original_config.model.params
+        ):
+            vae_scaling_factor = original_config.model.params.scale_factor
+        else:
+            vae_scaling_factor = 0.18215  # default SD scaling factor
+
+        vae_config["scaling_factor"] = vae_scaling_factor
+
+        ctx = init_empty_weights if is_accelerate_available() else nullcontext
+        with ctx():
+            vae = AutoencoderKL(**vae_config)
+
+        if is_accelerate_available():
+            for param_name, param in converted_vae_checkpoint.items():
+                set_module_tensor_to_device(vae, param_name, "cpu", value=param)
+        else:
+            vae.load_state_dict(converted_vae_checkpoint)
+    else:
+        vae = AutoencoderKL.from_pretrained(vae_path)
+
+    if model_type == "FrozenOpenCLIPEmbedder":
+        config_name = "stabilityai/stable-diffusion-2"
+        config_kwargs = {"subfolder": "text_encoder"}
+
+        text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
+        tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-2-clip", subfolder="tokenizer")
+
+        if stable_unclip is None:
+            if controlnet:
+                pipe = pipeline_class(
+                    vae=vae.to(precision),
+                    text_encoder=text_model,
+                    tokenizer=tokenizer,
+                    unet=unet.to(precision),
+                    scheduler=scheduler,
+                    controlnet=controlnet,
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                )
+            else:
+                pipe = pipeline_class(
+                    vae=vae.to(precision),
+                    text_encoder=text_model,
+                    tokenizer=tokenizer,
+                    unet=unet.to(precision),
+                    scheduler=scheduler,
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                )
+        else:
+            image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(
+                original_config, clip_stats_path=clip_stats_path, device=device
+            )
+
+            if stable_unclip == "img2img":
+                feature_extractor, image_encoder = stable_unclip_image_encoder(original_config)
+
+                pipe = StableUnCLIPImg2ImgPipeline(
+                    # image encoding components
+                    feature_extractor=feature_extractor,
+                    image_encoder=image_encoder,
+                    # image noising components
+                    image_normalizer=image_normalizer,
+                    image_noising_scheduler=image_noising_scheduler,
+                    # regular denoising components
+                    tokenizer=tokenizer,
+                    text_encoder=text_model,
+                    unet=unet,
+                    scheduler=scheduler,
+                    # vae
+                    vae=vae,
+                )
+            elif stable_unclip == "txt2img":
+                if stable_unclip_prior is None or stable_unclip_prior == "karlo":
+                    karlo_model = "kakaobrain/karlo-v1-alpha"
+                    prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior")
+
+                    prior_tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+                    prior_text_model = CLIPTextModelWithProjection.from_pretrained(
+                        CONVERT_MODEL_ROOT / "clip-vit-large-patch14"
+                    )
+
+                    prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler")
+                    prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
+                else:
+                    raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}")
+
+                pipe = StableUnCLIPPipeline(
+                    # prior components
+                    prior_tokenizer=prior_tokenizer,
+                    prior_text_encoder=prior_text_model,
+                    prior=prior,
+                    prior_scheduler=prior_scheduler,
+                    # image noising components
+                    image_normalizer=image_normalizer,
+                    image_noising_scheduler=image_noising_scheduler,
+                    # regular denoising components
+                    tokenizer=tokenizer,
+                    text_encoder=text_model,
+                    unet=unet,
+                    scheduler=scheduler,
+                    # vae
+                    vae=vae,
+                )
+            else:
+                raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}")
+    elif model_type == "PaintByExample":
+        vision_model = convert_paint_by_example_checkpoint(checkpoint)
+        tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+        feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker")
+        pipe = PaintByExamplePipeline(
+            vae=vae,
+            image_encoder=vision_model,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=None,
+            feature_extractor=feature_extractor,
+        )
+    elif model_type == "FrozenCLIPEmbedder":
+        text_model = convert_ldm_clip_checkpoint(
+            checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
+        )
+        tokenizer = (
+            CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+            if tokenizer is None
+            else tokenizer
         )
 
-        # Convert the text model.
-        model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
-        if model_type == "FrozenOpenCLIPEmbedder":
-            text_model = convert_open_clip_checkpoint(checkpoint)
-            tokenizer = CLIPTokenizer.from_pretrained(
-                CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip',
-                subfolder='tokenizer',
+        if load_safety_checker:
+            safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+                CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker"
             )
-            pipe = StableDiffusionPipeline(
-                vae=vae.to(precision),
-                text_encoder=text_model.to(precision),
-                tokenizer=tokenizer,
-                unet=unet.to(precision),
-                scheduler=scheduler,
-                safety_checker=None,
-                feature_extractor=None,
-                requires_safety_checker=False,
+            feature_extractor = AutoFeatureExtractor.from_pretrained(
+                CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker"
             )
+        else:
+            safety_checker = None
+            feature_extractor = None
 
-        elif model_type in ["FrozenCLIPEmbedder", "WeightedFrozenCLIPEmbedder"]:
-            text_model = convert_ldm_clip_checkpoint(checkpoint)
-            tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14')
-            safety_checker = StableDiffusionSafetyChecker.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-safety-checker')
-            feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-safety-checker')
-            pipe = StableDiffusionPipeline(
+        if controlnet:
+            pipe = pipeline_class(
                 vae=vae.to(precision),
-                text_encoder=text_model.to(precision),
+                text_encoder=text_model,
                 tokenizer=tokenizer,
                 unet=unet.to(precision),
+                controlnet=controlnet,
                 scheduler=scheduler,
-                safety_checker=safety_checker.to(precision),
+                safety_checker=safety_checker,
                 feature_extractor=feature_extractor,
             )
-
         else:
-            text_config = create_ldm_bert_config(original_config)
-            text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
-            tokenizer = BertTokenizerFast.from_pretrained(CONVERT_MODEL_ROOT / "bert-base-uncased")
-            pipe = LDMTextToImagePipeline(
-                vqvae=vae,
-                bert=text_model,
+            pipe = pipeline_class(
+                vae=vae.to(precision),
+                text_encoder=text_model,
                 tokenizer=tokenizer,
-                unet=unet,
+                unet=unet.to(precision),
                 scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
             )
-    dlogging.set_verbosity(verbosity)
+    elif model_type in ["SDXL", "SDXL-Refiner"]:
+        if model_type == "SDXL":
+            tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
+            text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
+
+            tokenizer_name = CONVERT_MODEL_ROOT / "CLIP-ViT-bigG-14-laion2B-39B-b160k"
+            tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_name, pad_token="!")
+
+            config_name = tokenizer_name
+            config_kwargs = {"projection_dim": 1280}
+            text_encoder_2 = convert_open_clip_checkpoint(
+                checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs
+            )
+
+            pipe = StableDiffusionXLPipeline(
+                vae=vae.to(precision),
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                text_encoder_2=text_encoder_2,
+                tokenizer_2=tokenizer_2,
+                unet=unet.to(precision),
+                scheduler=scheduler,
+                force_zeros_for_empty_prompt=True,
+            )
+        else:
+            tokenizer = None
+            text_encoder = None
+            tokenizer_name = CONVERT_MODEL_ROOT / "CLIP-ViT-bigG-14-laion2B-39B-b160k"
+            tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_name, pad_token="!")
+
+            config_name = tokenizer_name
+            config_kwargs = {"projection_dim": 1280}
+            text_encoder_2 = convert_open_clip_checkpoint(
+                checkpoint, config_name, prefix="conditioner.embedders.0.model.", has_projection=True, **config_kwargs
+            )
+
+            pipe = StableDiffusionXLImg2ImgPipeline(
+                vae=vae.to(precision),
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                text_encoder_2=text_encoder_2,
+                tokenizer_2=tokenizer_2,
+                unet=unet.to(precision),
+                scheduler=scheduler,
+                requires_aesthetics_score=True,
+                force_zeros_for_empty_prompt=False,
+            )
+    else:
+        text_config = create_ldm_bert_config(original_config)
+        text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
+        tokenizer = BertTokenizerFast.from_pretrained(CONVERT_MODEL_ROOT / "bert-base-uncased")
+        pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
 
     return pipe
 
 
+def download_controlnet_from_original_ckpt(
+    checkpoint_path: str,
+    original_config_file: str,
+    image_size: int = 512,
+    extract_ema: bool = False,
+    precision: torch.dtype = torch.float32,
+    num_in_channels: Optional[int] = None,
+    upcast_attention: Optional[bool] = None,
+    device: str = None,
+    from_safetensors: bool = False,
+    use_linear_projection: Optional[bool] = None,
+    cross_attention_dim: Optional[bool] = None,
+    scan_needed: bool = False,
+) -> DiffusionPipeline:
+    if not is_omegaconf_available():
+        raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
+
+    from omegaconf import OmegaConf
+
+    if from_safetensors:
+        if not is_safetensors_available():
+            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
+
+        from safetensors import safe_open
+
+        checkpoint = {}
+        with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                checkpoint[key] = f.get_tensor(key)
+    else:
+        if scan_needed:
+            # scan model
+            scan_result = scan_file_path(checkpoint_path)
+            if scan_result.infected_files != 0:
+                raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+        else:
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+
+    # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
+    # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
+    while "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
+
+    original_config = OmegaConf.load(original_config_file)
+
+    if num_in_channels is not None:
+        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+
+    if "control_stage_config" not in original_config.model.params:
+        raise ValueError("`control_stage_config` not present in original config")
+
+    controlnet = convert_controlnet_checkpoint(
+        checkpoint,
+        original_config,
+        checkpoint_path,
+        image_size,
+        upcast_attention,
+        extract_ema,
+        use_linear_projection=use_linear_projection,
+        cross_attention_dim=cross_attention_dim,
+    )
+
+    return controlnet
+
+
+def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:
+    vae_config = create_vae_diffusers_config(vae_config, image_size=image_size)
+
+    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
+
+    vae = AutoencoderKL(**vae_config)
+    vae.load_state_dict(converted_vae_checkpoint)
+    return vae
+
+
 def convert_ckpt_to_diffusers(
-        checkpoint_path: Union[str, Path],
-        dump_path: Union[str, Path],
-        **kwargs,
+    checkpoint_path: Union[str, Path],
+    dump_path: Union[str, Path],
+    use_safetensors: bool = True,
+    **kwargs,
 ):
     """
-    Takes all the arguments of load_pipeline_from_original_stable_diffusion_ckpt(),
+    Takes all the arguments of download_from_original_stable_diffusion_ckpt(),
     and in addition a path-like object indicating the location of the desired diffusers
     model to be written.
     """
-    pipe = load_pipeline_from_original_stable_diffusion_ckpt(checkpoint_path, **kwargs)
+    pipe = download_from_original_stable_diffusion_ckpt(checkpoint_path, **kwargs)
+
+    pipe.save_pretrained(
+        dump_path,
+        safe_serialization=use_safetensors and is_safetensors_available(),
+    )
+
+
+def convert_controlnet_to_diffusers(
+    checkpoint_path: Union[str, Path],
+    dump_path: Union[str, Path],
+    **kwargs,
+):
+    """
+    Takes all the arguments of download_controlnet_from_original_ckpt(),
+    and in addition a path-like object indicating the location of the desired diffusers
+    model to be written.
+    """
+    pipe = download_controlnet_from_original_ckpt(checkpoint_path, **kwargs)
 
     pipe.save_pretrained(
         dump_path,
diff --git a/invokeai/backend/model_management/lora.py b/invokeai/backend/model_management/lora.py
index 222169afbb..06770843ec 100644
--- a/invokeai/backend/model_management/lora.py
+++ b/invokeai/backend/model_management/lora.py
@@ -11,14 +11,15 @@ from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
-class LoRALayerBase:
-    #rank: Optional[int]
-    #alpha: Optional[float]
-    #bias: Optional[torch.Tensor]
-    #layer_key: str
 
-    #@property
-    #def scale(self):
+class LoRALayerBase:
+    # rank: Optional[int]
+    # alpha: Optional[float]
+    # bias: Optional[torch.Tensor]
+    # layer_key: str
+
+    # @property
+    # def scale(self):
     #    return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
 
     def __init__(
@@ -31,11 +32,7 @@ class LoRALayerBase:
         else:
             self.alpha = None
 
-        if (
-            "bias_indices" in values
-            and "bias_values" in values
-            and "bias_size" in values
-        ):
+        if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
             self.bias = torch.sparse_coo_tensor(
                 values["bias_indices"],
                 values["bias_values"],
@@ -45,13 +42,13 @@ class LoRALayerBase:
         else:
             self.bias = None
 
-        self.rank = None # set in layer implementation
+        self.rank = None  # set in layer implementation
         self.layer_key = layer_key
 
     def forward(
         self,
         module: torch.nn.Module,
-        input_h: Any, # for real looks like Tuple[torch.nn.Tensor] but not sure
+        input_h: Any,  # for real looks like Tuple[torch.nn.Tensor] but not sure
         multiplier: float,
     ):
         if type(module) == torch.nn.Conv2d:
@@ -71,12 +68,16 @@ class LoRALayerBase:
 
         bias = self.bias if self.bias is not None else 0
         scale = self.alpha / self.rank if (self.alpha and self.rank) else 1.0
-        return op(
-            *input_h,
-            (weight + bias).view(module.weight.shape),
-            None,
-            **extra_args,
-        ) * multiplier * scale
+        return (
+            op(
+                *input_h,
+                (weight + bias).view(module.weight.shape),
+                None,
+                **extra_args,
+            )
+            * multiplier
+            * scale
+        )
 
     def get_weight(self):
         raise NotImplementedError()
@@ -99,9 +100,9 @@ class LoRALayerBase:
 
 # TODO: find and debug lora/locon with bias
 class LoRALayer(LoRALayerBase):
-    #up: torch.Tensor
-    #mid: Optional[torch.Tensor]
-    #down: torch.Tensor
+    # up: torch.Tensor
+    # mid: Optional[torch.Tensor]
+    # down: torch.Tensor
 
     def __init__(
         self,
@@ -151,12 +152,12 @@ class LoRALayer(LoRALayerBase):
 
 
 class LoHALayer(LoRALayerBase):
-    #w1_a: torch.Tensor
-    #w1_b: torch.Tensor
-    #w2_a: torch.Tensor
-    #w2_b: torch.Tensor
-    #t1: Optional[torch.Tensor] = None
-    #t2: Optional[torch.Tensor] = None
+    # w1_a: torch.Tensor
+    # w1_b: torch.Tensor
+    # w2_a: torch.Tensor
+    # w2_b: torch.Tensor
+    # t1: Optional[torch.Tensor] = None
+    # t2: Optional[torch.Tensor] = None
 
     def __init__(
         self,
@@ -187,12 +188,8 @@ class LoHALayer(LoRALayerBase):
             weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
 
         else:
-            rebuild1 = torch.einsum(
-                "i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a
-            )
-            rebuild2 = torch.einsum(
-                "i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a
-            )
+            rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
+            rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
             weight = rebuild1 * rebuild2
 
         return weight
@@ -223,20 +220,20 @@ class LoHALayer(LoRALayerBase):
 
 
 class LoKRLayer(LoRALayerBase):
-    #w1: Optional[torch.Tensor] = None
-    #w1_a: Optional[torch.Tensor] = None
-    #w1_b: Optional[torch.Tensor] = None
-    #w2: Optional[torch.Tensor] = None
-    #w2_a: Optional[torch.Tensor] = None
-    #w2_b: Optional[torch.Tensor] = None
-    #t2: Optional[torch.Tensor] = None
+    # w1: Optional[torch.Tensor] = None
+    # w1_a: Optional[torch.Tensor] = None
+    # w1_b: Optional[torch.Tensor] = None
+    # w2: Optional[torch.Tensor] = None
+    # w2_a: Optional[torch.Tensor] = None
+    # w2_b: Optional[torch.Tensor] = None
+    # t2: Optional[torch.Tensor] = None
 
     def __init__(
         self,
         layer_key: str,
         values: dict,
     ):
-        super().__init__(layer_key, values)        
+        super().__init__(layer_key, values)
 
         if "lokr_w1" in values:
             self.w1 = values["lokr_w1"]
@@ -266,7 +263,7 @@ class LoKRLayer(LoRALayerBase):
         elif "lokr_w2_b" in values:
             self.rank = values["lokr_w2_b"].shape[0]
         else:
-            self.rank = None # unscaled
+            self.rank = None  # unscaled
 
     def get_weight(self):
         w1 = self.w1
@@ -278,7 +275,7 @@ class LoKRLayer(LoRALayerBase):
             if self.t2 is None:
                 w2 = self.w2_a @ self.w2_b
             else:
-                w2 = torch.einsum('i j k l, i p, j r -> p r k l', self.t2, self.w2_a, self.w2_b)
+                w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
 
         if len(w2.shape) == 4:
             w1 = w1.unsqueeze(2).unsqueeze(2)
@@ -317,7 +314,7 @@ class LoKRLayer(LoRALayerBase):
             self.t2 = self.t2.to(device=device, dtype=dtype)
 
 
-class LoRAModel: #(torch.nn.Module):
+class LoRAModel:  # (torch.nn.Module):
     _name: str
     layers: Dict[str, LoRALayer]
     _device: torch.device
@@ -345,7 +342,7 @@ class LoRAModel: #(torch.nn.Module):
 
     @property
     def dtype(self):
-        return self._dtype    
+        return self._dtype
 
     def to(
         self,
@@ -380,7 +377,7 @@ class LoRAModel: #(torch.nn.Module):
         model = cls(
             device=device,
             dtype=dtype,
-            name=file_path.stem, # TODO:
+            name=file_path.stem,  # TODO:
             layers=dict(),
         )
 
@@ -392,7 +389,6 @@ class LoRAModel: #(torch.nn.Module):
         state_dict = cls._group_state(state_dict)
 
         for layer_key, values in state_dict.items():
-
             # lora and locon
             if "lora_down.weight" in values:
                 layer = LoRALayer(layer_key, values)
@@ -407,9 +403,7 @@ class LoRAModel: #(torch.nn.Module):
 
             else:
                 # TODO: diff/ia3/... format
-                print(
-                    f">> Encountered unknown lora layer module in {model.name}: {layer_key}"
-                )
+                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key}")
                 return
 
             # lower memory consumption by removing already parsed layer values
@@ -443,9 +437,10 @@ with LoRAHelper.apply_lora_unet(unet, loras):
 # unmodified unet
 
 """
+
+
 # TODO: rename smth like ModelPatcher and add TI method?
 class ModelPatcher:
-
     @staticmethod
     def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
         assert "." not in lora_key
@@ -455,10 +450,10 @@ class ModelPatcher:
 
         module = model
         module_key = ""
-        key_parts = lora_key[len(prefix):].split('_')
+        key_parts = lora_key[len(prefix) :].split("_")
 
         submodule_name = key_parts.pop(0)
-        
+
         while len(key_parts) > 0:
             try:
                 module = module.get_submodule(submodule_name)
@@ -477,7 +472,6 @@ class ModelPatcher:
         applied_loras: List[Tuple[LoRAModel, float]],
         layer_name: str,
     ):
-
         def lora_forward(module, input_h, output):
             if len(applied_loras) == 0:
                 return output
@@ -491,7 +485,6 @@ class ModelPatcher:
 
         return lora_forward
 
-
     @classmethod
     @contextmanager
     def apply_lora_unet(
@@ -502,7 +495,6 @@ class ModelPatcher:
         with cls.apply_lora(unet, loras, "lora_unet_"):
             yield
 
-
     @classmethod
     @contextmanager
     def apply_lora_text_encoder(
@@ -513,7 +505,6 @@ class ModelPatcher:
         with cls.apply_lora(text_encoder, loras, "lora_te_"):
             yield
 
-
     @classmethod
     @contextmanager
     def apply_lora(
@@ -526,7 +517,7 @@ class ModelPatcher:
         try:
             with torch.no_grad():
                 for lora, lora_weight in loras:
-                    #assert lora.device.type == "cpu"
+                    # assert lora.device.type == "cpu"
                     for layer_key, layer in lora.layers.items():
                         if not layer_key.startswith(prefix):
                             continue
@@ -536,7 +527,7 @@ class ModelPatcher:
                             original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
 
                         # enable autocast to calc fp16 loras on cpu
-                        #with torch.autocast(device_type="cpu"):
+                        # with torch.autocast(device_type="cpu"):
                         layer.to(dtype=torch.float32)
                         layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
                         layer_weight = layer.get_weight() * lora_weight * layer_scale
@@ -547,14 +538,13 @@ class ModelPatcher:
 
                         module.weight += layer_weight.to(device=module.weight.device, dtype=module.weight.dtype)
 
-            yield # wait for context manager exit
+            yield  # wait for context manager exit
 
         finally:
             with torch.no_grad():
                 for module_key, weight in original_weights.items():
                     model.get_submodule(module_key).weight.copy_(weight)
 
-
     @classmethod
     @contextmanager
     def apply_ti(
@@ -602,7 +592,9 @@ class ModelPatcher:
                             f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {model_embeddings.weight.data[token_id].shape[0]}."
                         )
 
-                    model_embeddings.weight.data[token_id] = embedding.to(device=text_encoder.device, dtype=text_encoder.dtype)
+                    model_embeddings.weight.data[token_id] = embedding.to(
+                        device=text_encoder.device, dtype=text_encoder.dtype
+                    )
                     ti_tokens.append(token_id)
 
                 if len(ti_tokens) > 1:
@@ -614,7 +606,6 @@ class ModelPatcher:
             if init_tokens_count and new_tokens_added:
                 text_encoder.resize_token_embeddings(init_tokens_count)
 
-
     @classmethod
     @contextmanager
     def apply_clip_skip(
@@ -633,9 +624,10 @@ class ModelPatcher:
             while len(skipped_layers) > 0:
                 text_encoder.text_model.encoder.layers.append(skipped_layers.pop())
 
+
 class TextualInversionModel:
     name: str
-    embedding: torch.Tensor # [n, 768]|[n, 1280]
+    embedding: torch.Tensor  # [n, 768]|[n, 1280]
 
     @classmethod
     def from_checkpoint(
@@ -647,8 +639,8 @@ class TextualInversionModel:
         if not isinstance(file_path, Path):
             file_path = Path(file_path)
 
-        result = cls() # TODO:
-        result.name = file_path.stem # TODO:
+        result = cls()  # TODO:
+        result.name = file_path.stem  # TODO:
 
         if file_path.suffix == ".safetensors":
             state_dict = load_file(file_path.absolute().as_posix(), device="cpu")
@@ -659,7 +651,9 @@ class TextualInversionModel:
         # difference mostly in metadata
         if "string_to_param" in state_dict:
             if len(state_dict["string_to_param"]) > 1:
-                print(f"Warn: Embedding \"{file_path.name}\" contains multiple tokens, which is not supported. The first token will be used.")
+                print(
+                    f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first token will be used.'
+                )
 
             result.embedding = next(iter(state_dict["string_to_param"].values()))
 
@@ -688,10 +682,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         self.pad_tokens = dict()
         self.tokenizer = tokenizer
 
-    def expand_textual_inversion_token_ids_if_necessary(
-        self, token_ids: list[int]
-    ) -> list[int]:
-
+    def expand_textual_inversion_token_ids_if_necessary(self, token_ids: list[int]) -> list[int]:
         if len(self.pad_tokens) == 0:
             return token_ids
 
@@ -707,4 +698,3 @@ class TextualInversionManager(BaseTextualInversionManager):
                 new_token_ids.extend(self.pad_tokens[token_id])
 
         return new_token_ids
-
diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py
index 5ca17f00fc..a171c81e3c 100644
--- a/invokeai/backend/model_management/model_cache.py
+++ b/invokeai/backend/model_management/model_cache.py
@@ -37,19 +37,22 @@ from .models import BaseModelType, ModelType, SubModelType, ModelBase
 DEFAULT_MAX_CACHE_SIZE = 6.0
 
 # amount of GPU memory to hold in reserve for use by generations (GB)
-DEFAULT_MAX_VRAM_CACHE_SIZE= 2.75
+DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
 
 # actual size of a gig
 GIG = 1073741824
 
+
 class ModelLocker(object):
     "Forward declaration"
     pass
 
+
 class ModelCache(object):
     "Forward declaration"
     pass
 
+
 class _CacheRecord:
     size: int
     model: Any
@@ -79,22 +82,22 @@ class _CacheRecord:
             return self.model.device != self.cache.storage_device
         else:
             return False
-    
+
 
 class ModelCache(object):
     def __init__(
         self,
-        max_cache_size: float=DEFAULT_MAX_CACHE_SIZE,
-        max_vram_cache_size: float=DEFAULT_MAX_VRAM_CACHE_SIZE,
-        execution_device: torch.device=torch.device('cuda'),
-        storage_device: torch.device=torch.device('cpu'),
-        precision: torch.dtype=torch.float16,
-        sequential_offload: bool=False,
-        lazy_offloading: bool=True,
+        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
+        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
+        execution_device: torch.device = torch.device("cuda"),
+        storage_device: torch.device = torch.device("cpu"),
+        precision: torch.dtype = torch.float16,
+        sequential_offload: bool = False,
+        lazy_offloading: bool = True,
         sha_chunksize: int = 16777216,
-        logger: types.ModuleType = logger
+        logger: types.ModuleType = logger,
     ):
-        '''
+        """
         :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
         :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
@@ -102,16 +105,16 @@ class ModelCache(object):
         :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
         :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :param sha_chunksize: Chunksize to use when calculating sha256 model hash
-        '''
+        """
         self.model_infos: Dict[str, ModelBase] = dict()
         # allow lazy offloading only when vram cache enabled
         self.lazy_offloading = lazy_offloading and max_vram_cache_size > 0
-        self.precision: torch.dtype=precision
-        self.max_cache_size: float=max_cache_size
-        self.max_vram_cache_size: float=max_vram_cache_size
-        self.execution_device: torch.device=execution_device
-        self.storage_device: torch.device=storage_device
-        self.sha_chunksize=sha_chunksize
+        self.precision: torch.dtype = precision
+        self.max_cache_size: float = max_cache_size
+        self.max_vram_cache_size: float = max_vram_cache_size
+        self.execution_device: torch.device = execution_device
+        self.storage_device: torch.device = storage_device
+        self.sha_chunksize = sha_chunksize
         self.logger = logger
 
         self._cached_models = dict()
@@ -124,7 +127,6 @@ class ModelCache(object):
         model_type: ModelType,
         submodel_type: Optional[SubModelType] = None,
     ):
-
         key = f"{model_path}:{base_model}:{model_type}"
         if submodel_type:
             key += f":{submodel_type}"
@@ -163,7 +165,6 @@ class ModelCache(object):
         submodel: Optional[SubModelType] = None,
         gpu_load: bool = True,
     ) -> Any:
-
         if not isinstance(model_path, Path):
             model_path = Path(model_path)
 
@@ -186,7 +187,7 @@ class ModelCache(object):
         # TODO: lock for no copies on simultaneous calls?
         cache_entry = self._cached_models.get(key, None)
         if cache_entry is None:
-            self.logger.info(f'Loading model {model_path}, type {base_model}:{model_type}:{submodel}')
+            self.logger.info(f"Loading model {model_path}, type {base_model}:{model_type}:{submodel}")
 
             # this will remove older cached models until
             # there is sufficient room to load the requested model
@@ -196,7 +197,7 @@ class ModelCache(object):
             gc.collect()
             model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
             if mem_used := model_info.get_size(submodel):
-                self.logger.debug(f'CPU RAM used for load: {(mem_used/GIG):.2f} GB')
+                self.logger.debug(f"CPU RAM used for load: {(mem_used/GIG):.2f} GB")
 
             cache_entry = _CacheRecord(self, model, mem_used)
             self._cached_models[key] = cache_entry
@@ -209,13 +210,13 @@ class ModelCache(object):
 
     class ModelLocker(object):
         def __init__(self, cache, key, model, gpu_load, size_needed):
-            '''
+            """
             :param cache: The model_cache object
             :param key: The key of the model to lock in GPU
             :param model: The model to lock
             :param gpu_load: True if load into gpu
             :param size_needed: Size of the model to load
-            '''
+            """
             self.gpu_load = gpu_load
             self.cache = cache
             self.key = key
@@ -224,7 +225,7 @@ class ModelCache(object):
             self.cache_entry = self.cache._cached_models[self.key]
 
         def __enter__(self) -> Any:
-            if not hasattr(self.model, 'to'):
+            if not hasattr(self.model, "to"):
                 return self.model
 
             # NOTE that the model has to have the to() method in order for this
@@ -234,22 +235,21 @@ class ModelCache(object):
 
                 try:
                     if self.cache.lazy_offloading:
-                       self.cache._offload_unlocked_models(self.size_needed)
-                       
+                        self.cache._offload_unlocked_models(self.size_needed)
+
                     if self.model.device != self.cache.execution_device:
-                        self.cache.logger.debug(f'Moving {self.key} into {self.cache.execution_device}')
+                        self.cache.logger.debug(f"Moving {self.key} into {self.cache.execution_device}")
                         with VRAMUsage() as mem:
                             self.model.to(self.cache.execution_device)  # move into GPU
-                        self.cache.logger.debug(f'GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB')
-                        
-                    self.cache.logger.debug(f'Locking {self.key} in {self.cache.execution_device}')                
+                        self.cache.logger.debug(f"GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB")
+
+                    self.cache.logger.debug(f"Locking {self.key} in {self.cache.execution_device}")
                     self.cache._print_cuda_stats()
 
                 except:
                     self.cache_entry.unlock()
                     raise
 
-            
             # TODO: not fully understand
             # in the event that the caller wants the model in RAM, we
             # move it into CPU if it is in GPU and not locked
@@ -259,7 +259,7 @@ class ModelCache(object):
             return self.model
 
         def __exit__(self, type, value, traceback):
-            if not hasattr(self.model, 'to'):
+            if not hasattr(self.model, "to"):
                 return
 
             self.cache_entry.unlock()
@@ -277,11 +277,11 @@ class ModelCache(object):
         self,
         model_path: Union[str, Path],
     ) -> str:
-        '''
+        """
         Given the HF repo id or path to a model on disk, returns a unique
         hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs
         :param model_path: Path to model file/directory on disk.
-        '''
+        """
         return self._local_model_hash(model_path)
 
     def cache_size(self) -> float:
@@ -290,7 +290,7 @@ class ModelCache(object):
         return current_cache_size / GIG
 
     def _has_cuda(self) -> bool:
-        return self.execution_device.type == 'cuda'
+        return self.execution_device.type == "cuda"
 
     def _print_cuda_stats(self):
         vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
@@ -306,18 +306,21 @@ class ModelCache(object):
             if model_info.locked:
                 locked_models += 1
 
-        self.logger.debug(f"Current VRAM/RAM usage: {vram}/{ram}; cached_models/loaded_models/locked_models/ = {cached_models}/{loaded_models}/{locked_models}")
-
+        self.logger.debug(
+            f"Current VRAM/RAM usage: {vram}/{ram}; cached_models/loaded_models/locked_models/ = {cached_models}/{loaded_models}/{locked_models}"
+        )
 
     def _make_cache_room(self, model_size):
         # calculate how much memory this model will require
-        #multiplier = 2 if self.precision==torch.float32 else 1
+        # multiplier = 2 if self.precision==torch.float32 else 1
         bytes_needed = model_size
         maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
         current_size = sum([m.size for m in self._cached_models.values()])
 
         if current_size + bytes_needed > maximum_size:
-            self.logger.debug(f'Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional {(bytes_needed/GIG):.2f} GB')
+            self.logger.debug(
+                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional {(bytes_needed/GIG):.2f} GB"
+            )
 
         self.logger.debug(f"Before unloading: cached_models={len(self._cached_models)}")
 
@@ -339,7 +342,7 @@ class ModelCache(object):
                             with suppress(RuntimeError):
                                 referrer.clear()
                                 cleared = True
-                                #break
+                                # break
 
                     # repeat if referrers changes(due to frame clear), else exit loop
                     if cleared:
@@ -348,13 +351,17 @@ class ModelCache(object):
                         break
 
             device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
-            self.logger.debug(f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}")
+            self.logger.debug(
+                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}"
+            )
 
             # 2 refs:
             # 1 from cache_entry
             # 1 from getrefcount function
             if not cache_entry.locked and refs <= 2:
-                self.logger.debug(f'Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)')
+                self.logger.debug(
+                    f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
+                )
                 current_size -= cache_entry.size
                 del self._cache_stack[pos]
                 del self._cached_models[model_key]
@@ -368,38 +375,36 @@ class ModelCache(object):
 
         self.logger.debug(f"After unloading: cached_models={len(self._cached_models)}")
 
-    def _offload_unlocked_models(self, size_needed: int=0):
+    def _offload_unlocked_models(self, size_needed: int = 0):
         reserved = self.max_vram_cache_size * GIG
         vram_in_use = torch.cuda.memory_allocated()
-        self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
-        for model_key, cache_entry in sorted(self._cached_models.items(), key=lambda x:x[1].size):
+        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
+        for model_key, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
             if vram_in_use <= reserved:
                 break
             if not cache_entry.locked and cache_entry.loaded:
-                self.logger.debug(f'Offloading {model_key} from {self.execution_device} into {self.storage_device}')
+                self.logger.debug(f"Offloading {model_key} from {self.execution_device} into {self.storage_device}")
                 with VRAMUsage() as mem:
                     cache_entry.model.to(self.storage_device)
-                self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
+                self.logger.debug(f"GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB")
                 vram_in_use += mem.vram_used  # note vram_used is negative
-                self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
+                self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
 
         gc.collect()
         torch.cuda.empty_cache()
-        
+
     def _local_model_hash(self, model_path: Union[str, Path]) -> str:
         sha = hashlib.sha256()
         path = Path(model_path)
-        
+
         hashpath = path / "checksum.sha256"
         if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        
-        self.logger.debug(f'computing hash of model {path.name}')
-        for file in list(path.rglob("*.ckpt")) \
-            + list(path.rglob("*.safetensors")) \
-            + list(path.rglob("*.pth")):
+
+        self.logger.debug(f"computing hash of model {path.name}")
+        for file in list(path.rglob("*.ckpt")) + list(path.rglob("*.safetensors")) + list(path.rglob("*.pth")):
             with open(file, "rb") as f:
                 while chunk := f.read(self.sha_chunksize):
                     sha.update(chunk)
@@ -408,11 +413,12 @@ class ModelCache(object):
             f.write(hash)
         return hash
 
+
 class VRAMUsage(object):
     def __init__(self):
         self.vram = None
         self.vram_used = 0
-        
+
     def __enter__(self):
         self.vram = torch.cuda.memory_allocated()
         return self
diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py
index a0b3e6d625..2a82061a97 100644
--- a/invokeai/backend/model_management/model_manager.py
+++ b/invokeai/backend/model_management/model_manager.py
@@ -249,20 +249,26 @@ from invokeai.backend.util import CUDA_DEVICE, Chdir
 from .model_cache import ModelCache, ModelLocker
 from .model_search import ModelSearch
 from .models import (
-    BaseModelType, ModelType, SubModelType,
-    ModelError, SchedulerPredictionType, MODEL_CLASSES,
+    BaseModelType,
+    ModelType,
+    SubModelType,
+    ModelError,
+    SchedulerPredictionType,
+    MODEL_CLASSES,
     ModelConfigBase,
-    ModelNotFoundException, InvalidModelException,
+    ModelNotFoundException,
+    InvalidModelException,
     DuplicateModelException,
 )
 
 # We are only starting to number the config file with release 3.
 # The config file version doesn't have to start at release version, but it will help
 # reduce confusion.
-CONFIG_FILE_VERSION='3.0.0'
+CONFIG_FILE_VERSION = "3.0.0"
+
 
 @dataclass
-class ModelInfo():
+class ModelInfo:
     context: ModelLocker
     name: str
     base_model: BaseModelType
@@ -275,20 +281,24 @@ class ModelInfo():
     def __enter__(self):
         return self.context.__enter__()
 
-    def __exit__(self,*args, **kwargs):
+    def __exit__(self, *args, **kwargs):
         self.context.__exit__(*args, **kwargs)
 
+
 class AddModelResult(BaseModel):
     name: str = Field(description="The name of the model after installation")
     model_type: ModelType = Field(description="The type of model")
     base_model: BaseModelType = Field(description="The base model")
     config: ModelConfigBase = Field(description="The configuration of the model")
 
+
 MAX_CACHE_SIZE = 6.0  # GB
 
+
 class ConfigMeta(BaseModel):
     version: str
 
+
 class ModelManager(object):
     """
     High-level interface to model management.
@@ -315,12 +325,12 @@ class ModelManager(object):
         if isinstance(config, (str, Path)):
             self.config_path = Path(config)
             if not self.config_path.exists():
-                logger.warning(f'The file {self.config_path} was not found. Initializing a new file')
+                logger.warning(f"The file {self.config_path} was not found. Initializing a new file")
                 self.initialize_model_config(self.config_path)
             config = OmegaConf.load(self.config_path)
 
         elif not isinstance(config, DictConfig):
-            raise ValueError('config argument must be an OmegaConf object, a Path or a string')
+            raise ValueError("config argument must be an OmegaConf object, a Path or a string")
 
         self.config_meta = ConfigMeta(**config.pop("__metadata__"))
         # TODO: metadata not found
@@ -330,11 +340,11 @@ class ModelManager(object):
         self.logger = logger
         self.cache = ModelCache(
             max_cache_size=max_cache_size,
-            max_vram_cache_size = self.app_config.max_vram_cache_size,
-            execution_device = device_type,
-            precision = precision,
-            sequential_offload = sequential_offload,
-            logger = logger,
+            max_vram_cache_size=self.app_config.max_vram_cache_size,
+            execution_device=device_type,
+            precision=precision,
+            sequential_offload=sequential_offload,
+            logger=logger,
         )
 
         self._read_models(config)
@@ -348,7 +358,7 @@ class ModelManager(object):
 
         self.models = dict()
         for model_key, model_config in config.items():
-            if model_key.startswith('_'):
+            if model_key.startswith("_"):
                 continue
             model_name, base_model, model_type = self.parse_key(model_key)
             model_class = MODEL_CLASSES[base_model][model_type]
@@ -391,11 +401,15 @@ class ModelManager(object):
         base_model: BaseModelType,
         model_type: ModelType,
     ) -> str:
-        return f"{base_model}/{model_type}/{model_name}"
+        # In 3.11, the behavior of (str,enum) when interpolated into a
+        # string has changed. The next two lines are defensive.
+        base_model = BaseModelType(base_model)
+        model_type = ModelType(model_type)
+        return f"{base_model.value}/{model_type.value}/{model_name}"
 
     @classmethod
     def parse_key(cls, model_key: str) -> Tuple[str, BaseModelType, ModelType]:
-        base_model_str, model_type_str, model_name = model_key.split('/', 2)
+        base_model_str, model_type_str, model_name = model_key.split("/", 2)
         try:
             model_type = ModelType(model_type_str)
         except:
@@ -414,20 +428,16 @@ class ModelManager(object):
     @classmethod
     def initialize_model_config(cls, config_path: Path):
         """Create empty config file"""
-        with open(config_path,'w') as yaml_file:
-            yaml_file.write(yaml.dump({'__metadata__':
-                                       {'version':'3.0.0'}
-                                       }
-                                      )
-                            )
+        with open(config_path, "w") as yaml_file:
+            yaml_file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
 
     def get_model(
         self,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
-        submodel_type: Optional[SubModelType] = None
-    )->ModelInfo:
+        submodel_type: Optional[SubModelType] = None,
+    ) -> ModelInfo:
         """Given a model named identified in models.yaml, return
         an ModelInfo object describing it.
         :param model_name: symbolic name of the model in models.yaml
@@ -451,7 +461,7 @@ class ModelManager(object):
         if not model_path.exists():
             if model_class.save_to_config:
                 self.models[model_key].error = ModelError.NotFound
-                raise Exception(f"Files for model \"{model_key}\" not found")
+                raise Exception(f'Files for model "{model_key}" not found')
 
             else:
                 self.models.pop(model_key, None)
@@ -473,7 +483,7 @@ class ModelManager(object):
 
         model_path = model_class.convert_if_required(
             base_model=base_model,
-            model_path=str(model_path), # TODO: refactor str/Path types logic
+            model_path=str(model_path),  # TODO: refactor str/Path types logic
             output_path=dst_convert_path,
             config=model_config,
         )
@@ -490,17 +500,17 @@ class ModelManager(object):
             self.cache_keys[model_key] = set()
         self.cache_keys[model_key].add(model_context.key)
 
-        model_hash = "<NO_HASH>" # TODO:
+        model_hash = "<NO_HASH>"  # TODO:
 
         return ModelInfo(
-            context = model_context,
-            name = model_name,
-            base_model = base_model,
-            type = submodel_type or model_type,
-            hash = model_hash,
-            location = model_path, # TODO:
-            precision = self.cache.precision,
-            _cache = self.cache,
+            context=model_context,
+            name=model_name,
+            base_model=base_model,
+            type=submodel_type or model_type,
+            hash=model_hash,
+            location=model_path,  # TODO:
+            precision=self.cache.precision,
+            _cache=self.cache,
         )
 
     def model_info(
@@ -516,7 +526,7 @@ class ModelManager(object):
         if model_key in self.models:
             return self.models[model_key].dict(exclude_defaults=True)
         else:
-            return None # TODO: None or empty dict on not found
+            return None  # TODO: None or empty dict on not found
 
     def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
         """
@@ -526,16 +536,16 @@ class ModelManager(object):
         return [(self.parse_key(x)) for x in self.models.keys()]
 
     def list_model(
-            self,
-            model_name: str,
-            base_model: BaseModelType,
-            model_type: ModelType,
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
     ) -> dict:
         """
         Returns a dict describing one installed model, using
         the combined format of the list_models() method.
         """
-        models = self.list_models(base_model,model_type,model_name)
+        models = self.list_models(base_model, model_type, model_name)
         return models[0] if models else None
 
     def list_models(
@@ -548,13 +558,17 @@ class ModelManager(object):
         Return a list of models.
         """
 
-        model_keys = [self.create_key(model_name, base_model, model_type)] if model_name else sorted(self.models, key=str.casefold)
+        model_keys = (
+            [self.create_key(model_name, base_model, model_type)]
+            if model_name
+            else sorted(self.models, key=str.casefold)
+        )
         models = []
         for model_key in model_keys:
             model_config = self.models.get(model_key)
             if not model_config:
-                self.logger.error(f'Unknown model {model_name}')
-                raise ModelNotFoundException(f'Unknown model {model_name}')
+                self.logger.error(f"Unknown model {model_name}")
+                raise ModelNotFoundException(f"Unknown model {model_name}")
 
             cur_model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
             if base_model is not None and cur_base_model != base_model:
@@ -571,8 +585,8 @@ class ModelManager(object):
             )
 
             # expose paths as absolute to help web UI
-            if path := model_dict.get('path'):
-                model_dict['path'] = str(self.app_config.root_path / path)
+            if path := model_dict.get("path"):
+                model_dict["path"] = str(self.app_config.root_path / path)
             models.append(model_dict)
 
         return models
@@ -641,15 +655,15 @@ class ModelManager(object):
         model_info().
         """
         # relativize paths as they go in - this makes it easier to move the root directory around
-        if path := model_attributes.get('path'):
+        if path := model_attributes.get("path"):
             if Path(path).is_relative_to(self.app_config.root_path):
-                model_attributes['path'] = str(Path(path).relative_to(self.app_config.root_path))
+                model_attributes["path"] = str(Path(path).relative_to(self.app_config.root_path))
 
         model_class = MODEL_CLASSES[base_model][model_type]
         model_config = model_class.create_config(**model_attributes)
         model_key = self.create_key(model_name, base_model, model_type)
 
-        if  model_key in self.models and not clobber:
+        if model_key in self.models and not clobber:
             raise Exception(f'Attempt to overwrite existing model definition "{model_key}"')
 
         old_model = self.models.pop(model_key, None)
@@ -673,24 +687,25 @@ class ModelManager(object):
 
         self.models[model_key] = model_config
         self.commit()
+
         return AddModelResult(
-            name = model_name,
-            model_type = model_type,
-            base_model = base_model,
-            config = model_config,
+            name=model_name,
+            model_type=model_type,
+            base_model=base_model,
+            config=model_config,
         )
 
     def rename_model(
-            self,
-            model_name: str,
-            base_model: BaseModelType,
-            model_type: ModelType,
-            new_name: str = None,
-            new_base: BaseModelType = None,
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        new_name: str = None,
+        new_base: BaseModelType = None,
     ):
-        '''
+        """
         Rename or rebase a model.
-        '''
+        """
         if new_name is None and new_base is None:
             self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
             return
@@ -709,7 +724,13 @@ class ModelManager(object):
 
         # if this is a model file/directory that we manage ourselves, we need to move it
         if old_path.is_relative_to(self.app_config.models_path):
-            new_path = self.app_config.root_path / 'models' / BaseModelType(new_base).value / ModelType(model_type).value / new_name
+            new_path = (
+                self.app_config.root_path
+                / "models"
+                / BaseModelType(new_base).value
+                / ModelType(model_type).value
+                / new_name
+            )
             move(old_path, new_path)
             model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
 
@@ -725,18 +746,18 @@ class ModelManager(object):
         for cache_id in cache_ids:
             self.cache.uncache_model(cache_id)
 
-        self.models.pop(model_key, None) # delete
+        self.models.pop(model_key, None)  # delete
         self.models[new_key] = model_cfg
         self.commit()
 
-    def convert_model (
-            self,
-            model_name: str,
-            base_model: BaseModelType,
-            model_type: Union[ModelType.Main,ModelType.Vae],
-            dest_directory: Optional[Path]=None,
+    def convert_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: Union[ModelType.Main, ModelType.Vae],
+        dest_directory: Optional[Path] = None,
     ) -> AddModelResult:
-        '''
+        """
         Convert a checkpoint file into a diffusers folder, deleting the cached
         version and deleting the original checkpoint file if it is in the models
         directory.
@@ -745,7 +766,7 @@ class ModelManager(object):
         :param model_type: Type of model ['vae' or 'main']
 
         This will raise a ValueError unless the model is a checkpoint.
-        '''
+        """
         info = self.model_info(model_name, base_model, model_type)
         if info["model_format"] != "checkpoint":
             raise ValueError(f"not a checkpoint format model: {model_name}")
@@ -753,27 +774,32 @@ class ModelManager(object):
         # We are taking advantage of a side effect of get_model() that converts check points
         # into cached diffusers directories stored at `location`. It doesn't matter
         # what submodeltype we request here, so we get the smallest.
-        submodel = {"submodel_type": SubModelType.Tokenizer} if model_type==ModelType.Main else {}
-        model = self.get_model(model_name,
-                               base_model,
-                               model_type,
-                               **submodel,
-                               )
+        submodel = {"submodel_type": SubModelType.Scheduler} if model_type == ModelType.Main else {}
+        model = self.get_model(
+            model_name,
+            base_model,
+            model_type,
+            **submodel,
+        )
         checkpoint_path = self.app_config.root_path / info["path"]
         old_diffusers_path = self.app_config.models_path / model.location
-        new_diffusers_path = (dest_directory or self.app_config.models_path / base_model.value / model_type.value) / model_name
+        new_diffusers_path = (
+            dest_directory or self.app_config.models_path / base_model.value / model_type.value
+        ) / model_name
         if new_diffusers_path.exists():
             raise ValueError(f"A diffusers model already exists at {new_diffusers_path}")
 
         try:
-            move(old_diffusers_path,new_diffusers_path)
+            move(old_diffusers_path, new_diffusers_path)
             info["model_format"] = "diffusers"
-            info["path"] = str(new_diffusers_path) if dest_directory else str(new_diffusers_path.relative_to(self.app_config.root_path))
-            info.pop('config')
+            info["path"] = (
+                str(new_diffusers_path)
+                if dest_directory
+                else str(new_diffusers_path.relative_to(self.app_config.root_path))
+            )
+            info.pop("config")
 
-            result = self.add_model(model_name, base_model, model_type,
-                                    model_attributes = info,
-                                    clobber=True)
+            result = self.add_model(model_name, base_model, model_type, model_attributes=info, clobber=True)
         except:
             # something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
             rmtree(new_diffusers_path)
@@ -797,15 +823,12 @@ class ModelManager(object):
         found_models = []
         for file in files:
             location = str(file.resolve()).replace("\\", "/")
-            if (
-                "model.safetensors" not in location
-                and "diffusion_pytorch_model.safetensors" not in location
-            ):
+            if "model.safetensors" not in location and "diffusion_pytorch_model.safetensors" not in location:
                 found_models.append({"name": file.stem, "location": location})
 
         return search_folder, found_models
 
-    def commit(self, conf_file: Path=None) -> None:
+    def commit(self, conf_file: Path = None) -> None:
         """
         Write current configuration out to the indicated file.
         """
@@ -823,7 +846,7 @@ class ModelManager(object):
 
         yaml_str = OmegaConf.to_yaml(data_to_save)
         config_file_path = conf_file or self.config_path
-        assert config_file_path is not None,'no config file path to write to'
+        assert config_file_path is not None, "no config file path to write to"
         config_file_path = self.app_config.root_path / config_file_path
         tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
         try:
@@ -840,7 +863,7 @@ class ModelManager(object):
         Returns the preamble for the config file.
         """
         return textwrap.dedent(
-            """\
+            """
             # This file describes the alternative machine learning models
             # available to InvokeAI script.
             #
@@ -856,11 +879,10 @@ class ModelManager(object):
         base_model: Optional[BaseModelType] = None,
         model_type: Optional[ModelType] = None,
     ):
-
         loaded_files = set()
         new_models_found = False
 
-        self.logger.info(f'Scanning {self.app_config.models_path} for new models')
+        self.logger.info(f"Scanning {self.app_config.models_path} for new models")
         with Chdir(self.app_config.root_path):
             for model_key, model_config in list(self.models.items()):
                 model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
@@ -886,10 +908,10 @@ class ModelManager(object):
                     models_dir = self.app_config.models_path / cur_base_model.value / cur_model_type.value
 
                     if not models_dir.exists():
-                        continue # TODO: or create all folders?
+                        continue  # TODO: or create all folders?
 
                     for model_path in models_dir.iterdir():
-                        if model_path not in loaded_files: # TODO: check
+                        if model_path not in loaded_files:  # TODO: check
                             model_name = model_path.name if model_path.is_dir() else model_path.stem
                             model_key = self.create_key(model_name, cur_base_model, cur_model_type)
 
@@ -899,7 +921,7 @@ class ModelManager(object):
 
                                 if model_path.is_relative_to(self.app_config.root_path):
                                     model_path = model_path.relative_to(self.app_config.root_path)
-                                    
+
                                 model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                 self.models[model_key] = model_config
                                 new_models_found = True
@@ -915,11 +937,10 @@ class ModelManager(object):
         if (new_models_found or imported_models) and self.config_path:
             self.commit()
 
-
-    def autoimport(self)->Dict[str, AddModelResult]:
-        '''
+    def autoimport(self) -> Dict[str, AddModelResult]:
+        """
         Scan the autoimport directory (if defined) and import new models, delete defunct models.
-        '''
+        """
         # avoid circular import
         from invokeai.backend.install.model_install_backend import ModelInstall
         from invokeai.frontend.install.model_install import ask_user_for_prediction_type
@@ -938,7 +959,9 @@ class ModelManager(object):
                     self.new_models_found.update(self.installer.heuristic_import(model))
 
             def on_search_completed(self):
-                self.logger.info(f'Scanned {self._items_scanned} files and directories, imported {len(self.new_models_found)} models')
+                self.logger.info(
+                    f"Scanned {self._items_scanned} files and directories, imported {len(self.new_models_found)} models"
+                )
 
             def models_found(self):
                 return self.new_models_found
@@ -948,31 +971,37 @@ class ModelManager(object):
         # LS: hacky
         # Patch in the SD VAE from core so that it is available for use by the UI
         try:
-            self.heuristic_import({config.root_path / 'models/core/convert/sd-vae-ft-mse'})
+            self.heuristic_import({config.root_path / "models/core/convert/sd-vae-ft-mse"})
         except:
             pass
 
-        installer = ModelInstall(config = self.app_config,
-                                 model_manager = self,
-                                 prediction_type_helper = ask_user_for_prediction_type,
-                                 )
-        known_paths = {config.root_path / x['path'] for x in self.list_models()}
-        directories = {config.root_path / x for x in [config.autoimport_dir,
-                                                      config.lora_dir,
-                                                      config.embedding_dir,
-                                                      config.controlnet_dir,
-                                                      ] if x
-                       }
+        installer = ModelInstall(
+            config=self.app_config,
+            model_manager=self,
+            prediction_type_helper=ask_user_for_prediction_type,
+        )
+        known_paths = {config.root_path / x["path"] for x in self.list_models()}
+        directories = {
+            config.root_path / x
+            for x in [
+                config.autoimport_dir,
+                config.lora_dir,
+                config.embedding_dir,
+                config.controlnet_dir,
+            ]
+            if x
+        }
         scanner = ScanAndImport(directories, self.logger, ignore=known_paths, installer=installer)
         scanner.search()
-        
+
         return scanner.models_found()
 
-    def heuristic_import(self,
-                         items_to_import: Set[str],
-                         prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
-                         )->Dict[str, AddModelResult]:
-        '''Import a list of paths, repo_ids or URLs. Returns the set of
+    def heuristic_import(
+        self,
+        items_to_import: Set[str],
+        prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
+    ) -> Dict[str, AddModelResult]:
+        """Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
         :param items_to_import: Set of strings corresponding to models to be imported.
         :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
@@ -991,14 +1020,15 @@ class ModelManager(object):
         May return the following exceptions:
         - ModelNotFoundException   - one or more of the items to import is not a valid path, repo_id or URL
         - ValueError - a corresponding model already exists
-        '''
+        """
         # avoid circular import here
         from invokeai.backend.install.model_install_backend import ModelInstall
+
         successfully_installed = dict()
 
-        installer = ModelInstall(config = self.app_config,
-                                 prediction_type_helper = prediction_type_helper,
-                                 model_manager = self)
+        installer = ModelInstall(
+            config=self.app_config, prediction_type_helper=prediction_type_helper, model_manager=self
+        )
         for thing in items_to_import:
             installed = installer.heuristic_import(thing)
             successfully_installed.update(installed)
diff --git a/invokeai/backend/model_management/model_merge.py b/invokeai/backend/model_management/model_merge.py
index 6427b9e430..8cf3ce4ad0 100644
--- a/invokeai/backend/model_management/model_merge.py
+++ b/invokeai/backend/model_management/model_merge.py
@@ -17,23 +17,25 @@ import invokeai.backend.util.logging as logger
 
 from ...backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
 
+
 class MergeInterpolationMethod(str, Enum):
     WeightedSum = "weighted_sum"
     Sigmoid = "sigmoid"
     InvSigmoid = "inv_sigmoid"
     AddDifference = "add_difference"
 
+
 class ModelMerger(object):
     def __init__(self, manager: ModelManager):
         self.manager = manager
 
     def merge_diffusion_models(
-            self,
-            model_paths: List[Path],
-            alpha: float = 0.5,
-            interp: MergeInterpolationMethod = None,
-            force: bool = False,
-            **kwargs,
+        self,
+        model_paths: List[Path],
+        alpha: float = 0.5,
+        interp: MergeInterpolationMethod = None,
+        force: bool = False,
+        **kwargs,
     ) -> DiffusionPipeline:
         """
         :param model_paths:  up to three models, designated by their local paths or HuggingFace repo_ids
@@ -58,24 +60,23 @@ class ModelMerger(object):
             merged_pipe = pipe.merge(
                 pretrained_model_name_or_path_list=model_paths,
                 alpha=alpha,
-                interp=interp.value if interp else None, #diffusers API treats None as "weighted sum"
+                interp=interp.value if interp else None,  # diffusers API treats None as "weighted sum"
                 force=force,
                 **kwargs,
             )
             dlogging.set_verbosity(verbosity)
         return merged_pipe
 
-
-    def merge_diffusion_models_and_save (
-            self,
-            model_names: List[str],
-            base_model: Union[BaseModelType,str],
-            merged_model_name: str,
-            alpha: float = 0.5,
-            interp: MergeInterpolationMethod = None,
-            force: bool = False,
-            merge_dest_directory: Optional[Path] = None,
-            **kwargs,
+    def merge_diffusion_models_and_save(
+        self,
+        model_names: List[str],
+        base_model: Union[BaseModelType, str],
+        merged_model_name: str,
+        alpha: float = 0.5,
+        interp: MergeInterpolationMethod = None,
+        force: bool = False,
+        merge_dest_directory: Optional[Path] = None,
+        **kwargs,
     ) -> AddModelResult:
         """
         :param models: up to three models, designated by their InvokeAI models.yaml model name
@@ -94,39 +95,45 @@ class ModelMerger(object):
         config = self.manager.app_config
         base_model = BaseModelType(base_model)
         vae = None
-        
+
         for mod in model_names:
             info = self.manager.list_model(mod, base_model=base_model, model_type=ModelType.Main)
-            assert info,                                f"model {mod}, base_model {base_model}, is unknown"
-            assert info["model_format"] == "diffusers", f"{mod} is not a diffusers model. It must be optimized before merging"
-            assert info["variant"] == "normal",         f"{mod} is a {info['variant']} model, which cannot currently be merged"
-            assert len(model_names) <= 2 or \
-                interp==MergeInterpolationMethod.AddDifference, "When merging three models, only the 'add_difference' merge method is supported"
+            assert info, f"model {mod}, base_model {base_model}, is unknown"
+            assert (
+                info["model_format"] == "diffusers"
+            ), f"{mod} is not a diffusers model. It must be optimized before merging"
+            assert info["variant"] == "normal", f"{mod} is a {info['variant']} model, which cannot currently be merged"
+            assert (
+                len(model_names) <= 2 or interp == MergeInterpolationMethod.AddDifference
+            ), "When merging three models, only the 'add_difference' merge method is supported"
             # pick up the first model's vae
             if mod == model_names[0]:
                 vae = info.get("vae")
             model_paths.extend([config.root_path / info["path"]])
 
-        merge_method = None if interp == 'weighted_sum' else MergeInterpolationMethod(interp)
-        logger.debug(f'interp = {interp}, merge_method={merge_method}')
-        merged_pipe = self.merge_diffusion_models(
-           model_paths, alpha, merge_method, force, **kwargs
+        merge_method = None if interp == "weighted_sum" else MergeInterpolationMethod(interp)
+        logger.debug(f"interp = {interp}, merge_method={merge_method}")
+        merged_pipe = self.merge_diffusion_models(model_paths, alpha, merge_method, force, **kwargs)
+        dump_path = (
+            Path(merge_dest_directory)
+            if merge_dest_directory
+            else config.models_path / base_model.value / ModelType.Main.value
         )
-        dump_path = Path(merge_dest_directory) if merge_dest_directory else config.models_path / base_model.value / ModelType.Main.value
         dump_path.mkdir(parents=True, exist_ok=True)
         dump_path = dump_path / merged_model_name
 
         merged_pipe.save_pretrained(dump_path, safe_serialization=1)
         attributes = dict(
-            path = str(dump_path),
-            description = f"Merge of models {', '.join(model_names)}",
-            model_format = "diffusers",
-            variant = ModelVariantType.Normal.value,
-            vae = vae,
+            path=str(dump_path),
+            description=f"Merge of models {', '.join(model_names)}",
+            model_format="diffusers",
+            variant=ModelVariantType.Normal.value,
+            vae=vae,
+        )
+        return self.manager.add_model(
+            merged_model_name,
+            base_model=base_model,
+            model_type=ModelType.Main,
+            model_attributes=attributes,
+            clobber=True,
         )
-        return self.manager.add_model(merged_model_name,
-                                      base_model = base_model,
-                                      model_type = ModelType.Main,
-                                      model_attributes = attributes,
-                                      clobber = True
-                                      )
diff --git a/invokeai/backend/model_management/model_probe.py b/invokeai/backend/model_management/model_probe.py
index 6a65401675..0e06f9b32d 100644
--- a/invokeai/backend/model_management/model_probe.py
+++ b/invokeai/backend/model_management/model_probe.py
@@ -10,12 +10,16 @@ from typing import Callable, Literal, Union, Dict, Optional
 from picklescan.scanner import scan_file_path
 
 from .models import (
-    BaseModelType, ModelType, ModelVariantType,
-    SchedulerPredictionType, SilenceWarnings,
-    InvalidModelException
+    BaseModelType,
+    ModelType,
+    ModelVariantType,
+    SchedulerPredictionType,
+    SilenceWarnings,
+    InvalidModelException,
 )
 from .models.base import read_checkpoint_meta
 
+
 @dataclass
 class ModelProbeInfo(object):
     model_type: ModelType
@@ -23,70 +27,74 @@ class ModelProbeInfo(object):
     variant_type: ModelVariantType
     prediction_type: SchedulerPredictionType
     upcast_attention: bool
-    format: Literal['diffusers','checkpoint', 'lycoris']
+    format: Literal["diffusers", "checkpoint", "lycoris"]
     image_size: int
 
+
 class ProbeBase(object):
-    '''forward declaration'''
+    """forward declaration"""
+
     pass
 
+
 class ModelProbe(object):
-    
     PROBES = {
-        'diffusers': { },
-        'checkpoint': { },
+        "diffusers": {},
+        "checkpoint": {},
     }
 
     CLASS2TYPE = {
-        'StableDiffusionPipeline' : ModelType.Main,
-        'StableDiffusionInpaintPipeline' : ModelType.Main,
-        'StableDiffusionXLPipeline' : ModelType.Main,
-        'StableDiffusionXLImg2ImgPipeline' : ModelType.Main,
-        'AutoencoderKL' : ModelType.Vae,
-        'ControlNetModel' : ModelType.ControlNet,
+        "StableDiffusionPipeline": ModelType.Main,
+        "StableDiffusionInpaintPipeline": ModelType.Main,
+        "StableDiffusionXLPipeline": ModelType.Main,
+        "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
+        "AutoencoderKL": ModelType.Vae,
+        "ControlNetModel": ModelType.ControlNet,
     }
-    
+
     @classmethod
-    def register_probe(cls,
-                       format: Literal['diffusers','checkpoint'],
-                       model_type: ModelType,
-                       probe_class: ProbeBase):
+    def register_probe(cls, format: Literal["diffusers", "checkpoint"], model_type: ModelType, probe_class: ProbeBase):
         cls.PROBES[format][model_type] = probe_class
 
     @classmethod
-    def heuristic_probe(cls,
-                        model: Union[Dict, ModelMixin, Path],
-                        prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
-                        )->ModelProbeInfo:
-        if isinstance(model,Path):
-            return cls.probe(model_path=model,prediction_type_helper=prediction_type_helper)
-        elif isinstance(model,(dict,ModelMixin,ConfigMixin)):
+    def heuristic_probe(
+        cls,
+        model: Union[Dict, ModelMixin, Path],
+        prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
+    ) -> ModelProbeInfo:
+        if isinstance(model, Path):
+            return cls.probe(model_path=model, prediction_type_helper=prediction_type_helper)
+        elif isinstance(model, (dict, ModelMixin, ConfigMixin)):
             return cls.probe(model_path=None, model=model, prediction_type_helper=prediction_type_helper)
         else:
             raise InvalidModelException("model parameter {model} is neither a Path, nor a model")
 
     @classmethod
-    def probe(cls,
-              model_path: Path,
-              model: Optional[Union[Dict, ModelMixin]] = None,
-              prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]] = None)->ModelProbeInfo:
-        '''
+    def probe(
+        cls,
+        model_path: Path,
+        model: Optional[Union[Dict, ModelMixin]] = None,
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+    ) -> ModelProbeInfo:
+        """
         Probe the model at model_path and return sufficient information about it
         to place it somewhere in the models directory hierarchy. If the model is
         already loaded into memory, you may provide it as model in order to avoid
         opening it a second time. The prediction_type_helper callable is a function that receives
         the path to the model and returns the BaseModelType. It is called to distinguish
         between V2-Base and V2-768 SD models.
-        '''
+        """
         if model_path:
-            format_type = 'diffusers' if model_path.is_dir() else 'checkpoint'
+            format_type = "diffusers" if model_path.is_dir() else "checkpoint"
         else:
-            format_type = 'diffusers' if isinstance(model,(ConfigMixin,ModelMixin)) else 'checkpoint'
+            format_type = "diffusers" if isinstance(model, (ConfigMixin, ModelMixin)) else "checkpoint"
         model_info = None
         try:
-            model_type = cls.get_model_type_from_folder(model_path, model) \
-                if format_type == 'diffusers' \
-                   else cls.get_model_type_from_checkpoint(model_path, model)
+            model_type = (
+                cls.get_model_type_from_folder(model_path, model)
+                if format_type == "diffusers"
+                else cls.get_model_type_from_checkpoint(model_path, model)
+            )
             probe_class = cls.PROBES[format_type].get(model_type)
             if not probe_class:
                 return None
@@ -96,17 +104,23 @@ class ModelProbe(object):
             prediction_type = probe.get_scheduler_prediction_type()
             format = probe.get_format()
             model_info = ModelProbeInfo(
-                model_type = model_type,
-                base_type = base_type,
-                variant_type = variant_type,
-                prediction_type = prediction_type,
-                upcast_attention = (base_type==BaseModelType.StableDiffusion2 \
-                                     and prediction_type==SchedulerPredictionType.VPrediction),
-                format = format,
-                image_size = 1024 if (base_type in {BaseModelType.StableDiffusionXL,BaseModelType.StableDiffusionXLRefiner}) else \
-                              768 if (base_type==BaseModelType.StableDiffusion2 \
-                                     and prediction_type==SchedulerPredictionType.VPrediction ) else \
-                              512
+                model_type=model_type,
+                base_type=base_type,
+                variant_type=variant_type,
+                prediction_type=prediction_type,
+                upcast_attention=(
+                    base_type == BaseModelType.StableDiffusion2
+                    and prediction_type == SchedulerPredictionType.VPrediction
+                ),
+                format=format,
+                image_size=1024
+                if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
+                else 768
+                if (
+                    base_type == BaseModelType.StableDiffusion2
+                    and prediction_type == SchedulerPredictionType.VPrediction
+                )
+                else 512,
             )
         except Exception:
             raise
@@ -115,7 +129,7 @@ class ModelProbe(object):
 
     @classmethod
     def get_model_type_from_checkpoint(cls, model_path: Path, checkpoint: dict) -> ModelType:
-        if model_path.suffix not in ('.bin','.pt','.ckpt','.safetensors','.pth'):
+        if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth"):
             return None
 
         if model_path.name == "learned_embeds.bin":
@@ -142,32 +156,32 @@ class ModelProbe(object):
             # diffusers-ti
             if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
                 return ModelType.TextualInversion
-        
+
         raise InvalidModelException(f"Unable to determine model type for {model_path}")
 
     @classmethod
-    def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
-        '''
+    def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin) -> ModelType:
+        """
         Get the model type of a hugging-face style folder.
-        '''
+        """
         class_name = None
         if model:
             class_name = model.__class__.__name__
         else:
-            if (folder_path / 'learned_embeds.bin').exists():
+            if (folder_path / "learned_embeds.bin").exists():
                 return ModelType.TextualInversion
 
-            if (folder_path / 'pytorch_lora_weights.bin').exists():
+            if (folder_path / "pytorch_lora_weights.bin").exists():
                 return ModelType.Lora
 
-            i  = folder_path / 'model_index.json'
-            c = folder_path / 'config.json'
+            i = folder_path / "model_index.json"
+            c = folder_path / "config.json"
             config_path = i if i.exists() else c if c.exists() else None
 
             if config_path:
-                with open(config_path,'r') as file:
+                with open(config_path, "r") as file:
                     conf = json.load(file)
-                class_name = conf['_class_name']
+                class_name = conf["_class_name"]
 
         if class_name and (type := cls.CLASS2TYPE.get(class_name)):
             return type
@@ -176,7 +190,7 @@ class ModelProbe(object):
         raise InvalidModelException(f"Unable to determine model type for {folder_path}")
 
     @classmethod
-    def _scan_and_load_checkpoint(cls,model_path: Path)->dict:
+    def _scan_and_load_checkpoint(cls, model_path: Path) -> dict:
         with SilenceWarnings():
             if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
                 cls._scan_model(model_path, model_path)
@@ -186,55 +200,53 @@ class ModelProbe(object):
 
     @classmethod
     def _scan_model(cls, model_name, checkpoint):
-            """
-            Apply picklescanner to the indicated checkpoint and issue a warning
-            and option to exit if an infected file is identified.
-            """
-            # scan model
-            scan_result = scan_file_path(checkpoint)
-            if scan_result.infected_files != 0:
-                raise "The model {model_name} is potentially infected by malware. Aborting import."
+        """
+        Apply picklescanner to the indicated checkpoint and issue a warning
+        and option to exit if an infected file is identified.
+        """
+        # scan model
+        scan_result = scan_file_path(checkpoint)
+        if scan_result.infected_files != 0:
+            raise "The model {model_name} is potentially infected by malware. Aborting import."
+
 
 ###################################################3
 # Checkpoint probing
 ###################################################3
 class ProbeBase(object):
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         pass
 
-    def get_variant_type(self)->ModelVariantType:
-        pass
-    
-    def get_scheduler_prediction_type(self)->SchedulerPredictionType:
+    def get_variant_type(self) -> ModelVariantType:
         pass
 
-    def get_format(self)->str:
+    def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         pass
 
+    def get_format(self) -> str:
+        pass
+
+
 class CheckpointProbeBase(ProbeBase):
-    def __init__(self,
-                 checkpoint_path: Path,
-                 checkpoint: dict,
-                 helper: Callable[[Path],SchedulerPredictionType] = None
-                 )->BaseModelType:
+    def __init__(
+        self, checkpoint_path: Path, checkpoint: dict, helper: Callable[[Path], SchedulerPredictionType] = None
+    ) -> BaseModelType:
         self.checkpoint = checkpoint or ModelProbe._scan_and_load_checkpoint(checkpoint_path)
         self.checkpoint_path = checkpoint_path
         self.helper = helper
 
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         pass
 
-    def get_format(self)->str:
-        return 'checkpoint'
+    def get_format(self) -> str:
+        return "checkpoint"
 
-    def get_variant_type(self)-> ModelVariantType:
-        model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path,self.checkpoint)
+    def get_variant_type(self) -> ModelVariantType:
+        model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path, self.checkpoint)
         if model_type != ModelType.Main:
             return ModelVariantType.Normal
-        state_dict = self.checkpoint.get('state_dict') or self.checkpoint
-        in_channels = state_dict[
-            "model.diffusion_model.input_blocks.0.0.weight"
-        ].shape[1]
+        state_dict = self.checkpoint.get("state_dict") or self.checkpoint
+        in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
         if in_channels == 9:
             return ModelVariantType.Inpaint
         elif in_channels == 5:
@@ -242,51 +254,60 @@ class CheckpointProbeBase(ProbeBase):
         elif in_channels == 4:
             return ModelVariantType.Normal
         else:
-            raise InvalidModelException(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
+            raise InvalidModelException(
+                f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}"
+            )
+
 
 class PipelineCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-        state_dict = self.checkpoint.get('state_dict') or checkpoint
+        state_dict = self.checkpoint.get("state_dict") or checkpoint
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
             return BaseModelType.StableDiffusion1
         if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
             return BaseModelType.StableDiffusion2
-        # TODO: Verify that this is correct! Need an XL checkpoint file for this.
+        key_name = "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
             return BaseModelType.StableDiffusionXL
-        raise InvalidModelException("Cannot determine base type")
+        elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
+            return BaseModelType.StableDiffusionXLRefiner
+        else:
+            raise InvalidModelException("Cannot determine base type")
 
-    def get_scheduler_prediction_type(self)->SchedulerPredictionType:
+    def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         type = self.get_base_type()
         if type == BaseModelType.StableDiffusion1:
             return SchedulerPredictionType.Epsilon
         checkpoint = self.checkpoint
-        state_dict = self.checkpoint.get('state_dict') or checkpoint
+        state_dict = self.checkpoint.get("state_dict") or checkpoint
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
-            if 'global_step' in checkpoint:
-                if checkpoint['global_step'] == 220000:
+            if "global_step" in checkpoint:
+                if checkpoint["global_step"] == 220000:
                     return SchedulerPredictionType.Epsilon
                 elif checkpoint["global_step"] == 110000:
                     return SchedulerPredictionType.VPrediction
-            if self.checkpoint_path and self.helper \
-               and not self.checkpoint_path.with_suffix('.yaml').exists():  # if a .yaml config file exists, then this step not needed
+            if (
+                self.checkpoint_path and self.helper and not self.checkpoint_path.with_suffix(".yaml").exists()
+            ):  # if a .yaml config file exists, then this step not needed
                 return self.helper(self.checkpoint_path)
             else:
                 return None
 
+
 class VaeCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         # I can't find any standalone 2.X VAEs to test with!
         return BaseModelType.StableDiffusion1
 
-class LoRACheckpointProbe(CheckpointProbeBase):
-    def get_format(self)->str:
-        return 'lycoris'
 
-    def get_base_type(self)->BaseModelType:
+class LoRACheckpointProbe(CheckpointProbeBase):
+    def get_format(self) -> str:
+        return "lycoris"
+
+    def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
         key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
         key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a"
@@ -304,16 +325,17 @@ class LoRACheckpointProbe(CheckpointProbeBase):
         else:
             return None
 
+
 class TextualInversionCheckpointProbe(CheckpointProbeBase):
-    def get_format(self)->str:
+    def get_format(self) -> str:
         return None
 
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-        if 'string_to_token' in checkpoint:
-            token_dim = list(checkpoint['string_to_param'].values())[0].shape[-1]
-        elif 'emb_params' in checkpoint:
-            token_dim = checkpoint['emb_params'].shape[-1]
+        if "string_to_token" in checkpoint:
+            token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
+        elif "emb_params" in checkpoint:
+            token_dim = checkpoint["emb_params"].shape[-1]
         else:
             token_dim = list(checkpoint.values())[0].shape[0]
         if token_dim == 768:
@@ -323,12 +345,14 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
         else:
             return None
 
+
 class ControlNetCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-        for key_name in ('control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight',
-                         'input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight'
-                         ):
+        for key_name in (
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
+            "input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
+        ):
             if key_name not in checkpoint:
                 continue
             if checkpoint[key_name].shape[-1] == 768:
@@ -339,56 +363,54 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
                 return self.helper(self.checkpoint_path)
         raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
 
+
 ########################################################
 # classes for probing folders
 #######################################################
 class FolderProbeBase(ProbeBase):
-    def __init__(self,
-                 folder_path: Path,
-                 model: ModelMixin = None,
-                 helper: Callable=None  # not used
-                 ):
+    def __init__(self, folder_path: Path, model: ModelMixin = None, helper: Callable = None):  # not used
         self.model = model
         self.folder_path = folder_path
 
-    def get_variant_type(self)->ModelVariantType:
+    def get_variant_type(self) -> ModelVariantType:
         return ModelVariantType.Normal
 
-    def get_format(self)->str:
-        return 'diffusers'
-    
+    def get_format(self) -> str:
+        return "diffusers"
+
+
 class PipelineFolderProbe(FolderProbeBase):
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         if self.model:
             unet_conf = self.model.unet.config
         else:
-            with open(self.folder_path / 'unet' / 'config.json','r') as file:
+            with open(self.folder_path / "unet" / "config.json", "r") as file:
                 unet_conf = json.load(file)
-        if unet_conf['cross_attention_dim'] == 768:
-            return BaseModelType.StableDiffusion1  
-        elif unet_conf['cross_attention_dim'] == 1024:
+        if unet_conf["cross_attention_dim"] == 768:
+            return BaseModelType.StableDiffusion1
+        elif unet_conf["cross_attention_dim"] == 1024:
             return BaseModelType.StableDiffusion2
-        elif unet_conf['cross_attention_dim'] == 1280:
+        elif unet_conf["cross_attention_dim"] == 1280:
             return BaseModelType.StableDiffusionXLRefiner
-        elif unet_conf['cross_attention_dim'] == 2048:
+        elif unet_conf["cross_attention_dim"] == 2048:
             return BaseModelType.StableDiffusionXL
         else:
-            raise InvalidModelException(f'Unknown base model for {self.folder_path}')
+            raise InvalidModelException(f"Unknown base model for {self.folder_path}")
 
-    def get_scheduler_prediction_type(self)->SchedulerPredictionType:
+    def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         if self.model:
             scheduler_conf = self.model.scheduler.config
         else:
-            with open(self.folder_path / 'scheduler' / 'scheduler_config.json','r') as file:
+            with open(self.folder_path / "scheduler" / "scheduler_config.json", "r") as file:
                 scheduler_conf = json.load(file)
-        if scheduler_conf['prediction_type'] == "v_prediction":
+        if scheduler_conf["prediction_type"] == "v_prediction":
             return SchedulerPredictionType.VPrediction
-        elif scheduler_conf['prediction_type'] == 'epsilon':
+        elif scheduler_conf["prediction_type"] == "epsilon":
             return SchedulerPredictionType.Epsilon
         else:
             return None
-        
-    def get_variant_type(self)->ModelVariantType:
+
+    def get_variant_type(self) -> ModelVariantType:
         # This only works for pipelines! Any kind of
         # exception results in our returning the
         # "normal" variant type
@@ -396,11 +418,11 @@ class PipelineFolderProbe(FolderProbeBase):
             if self.model:
                 conf = self.model.unet.config
             else:
-                config_file = self.folder_path / 'unet' / 'config.json'
-                with open(config_file,'r') as file:
+                config_file = self.folder_path / "unet" / "config.json"
+                with open(config_file, "r") as file:
                     conf = json.load(file)
-                
-            in_channels = conf['in_channels']
+
+            in_channels = conf["in_channels"]
             if in_channels == 9:
                 return ModelVariantType.Inpaint
             elif in_channels == 5:
@@ -411,53 +433,67 @@ class PipelineFolderProbe(FolderProbeBase):
             pass
         return ModelVariantType.Normal
 
+
 class VaeFolderProbe(FolderProbeBase):
-    def get_base_type(self)->BaseModelType:
-        return BaseModelType.StableDiffusion1
+    def get_base_type(self) -> BaseModelType:
+        config_file = self.folder_path / "config.json"
+        if not config_file.exists():
+            raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
+        with open(config_file, "r") as file:
+            config = json.load(file)
+        return (
+            BaseModelType.StableDiffusionXL
+            if config.get("scaling_factor", 0) == 0.13025 and config.get("sample_size") in [512, 1024]
+            else BaseModelType.StableDiffusion1
+        )
+
 
 class TextualInversionFolderProbe(FolderProbeBase):
-    def get_format(self)->str:
+    def get_format(self) -> str:
         return None
-    
-    def get_base_type(self)->BaseModelType:
-        path = self.folder_path / 'learned_embeds.bin'
+
+    def get_base_type(self) -> BaseModelType:
+        path = self.folder_path / "learned_embeds.bin"
         if not path.exists():
             return None
         checkpoint = ModelProbe._scan_and_load_checkpoint(path)
-        return TextualInversionCheckpointProbe(None,checkpoint=checkpoint).get_base_type()
+        return TextualInversionCheckpointProbe(None, checkpoint=checkpoint).get_base_type()
+
 
 class ControlNetFolderProbe(FolderProbeBase):
-    def get_base_type(self)->BaseModelType:
-        config_file = self.folder_path / 'config.json'
+    def get_base_type(self) -> BaseModelType:
+        config_file = self.folder_path / "config.json"
         if not config_file.exists():
             raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
-        with open(config_file,'r') as file:
+        with open(config_file, "r") as file:
             config = json.load(file)
         # no obvious way to distinguish between sd2-base and sd2-768
-        return BaseModelType.StableDiffusion1 \
-            if config['cross_attention_dim']==768 \
-               else BaseModelType.StableDiffusion2
+        return (
+            BaseModelType.StableDiffusion1 if config["cross_attention_dim"] == 768 else BaseModelType.StableDiffusion2
+        )
+
 
 class LoRAFolderProbe(FolderProbeBase):
-    def get_base_type(self)->BaseModelType:
+    def get_base_type(self) -> BaseModelType:
         model_file = None
-        for suffix in ['safetensors','bin']:
-            base_file = self.folder_path / f'pytorch_lora_weights.{suffix}'
+        for suffix in ["safetensors", "bin"]:
+            base_file = self.folder_path / f"pytorch_lora_weights.{suffix}"
             if base_file.exists():
                 model_file = base_file
                 break
         if not model_file:
-            raise InvalidModelException('Unknown LoRA format encountered')
-        return LoRACheckpointProbe(model_file,None).get_base_type()
+            raise InvalidModelException("Unknown LoRA format encountered")
+        return LoRACheckpointProbe(model_file, None).get_base_type()
+
 
 ############## register probe classes ######
-ModelProbe.register_probe('diffusers', ModelType.Main,  PipelineFolderProbe)
-ModelProbe.register_probe('diffusers', ModelType.Vae, VaeFolderProbe)
-ModelProbe.register_probe('diffusers', ModelType.Lora, LoRAFolderProbe)
-ModelProbe.register_probe('diffusers', ModelType.TextualInversion, TextualInversionFolderProbe)
-ModelProbe.register_probe('diffusers', ModelType.ControlNet, ControlNetFolderProbe)
-ModelProbe.register_probe('checkpoint', ModelType.Main, PipelineCheckpointProbe)
-ModelProbe.register_probe('checkpoint', ModelType.Vae, VaeCheckpointProbe)
-ModelProbe.register_probe('checkpoint', ModelType.Lora, LoRACheckpointProbe)
-ModelProbe.register_probe('checkpoint', ModelType.TextualInversion, TextualInversionCheckpointProbe)
-ModelProbe.register_probe('checkpoint', ModelType.ControlNet, ControlNetCheckpointProbe)
+ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
+ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
diff --git a/invokeai/backend/model_management/model_search.py b/invokeai/backend/model_management/model_search.py
index 5657bd9549..9c87d6c408 100644
--- a/invokeai/backend/model_management/model_search.py
+++ b/invokeai/backend/model_management/model_search.py
@@ -10,8 +10,9 @@ from pathlib import Path
 
 import invokeai.backend.util.logging as logger
 
+
 class ModelSearch(ABC):
-    def __init__(self, directories: List[Path], logger: types.ModuleType=logger):
+    def __init__(self, directories: List[Path], logger: types.ModuleType = logger):
         """
         Initialize a recursive model directory search.
         :param directories: List of directory Paths to recurse through
@@ -56,18 +57,23 @@ class ModelSearch(ABC):
 
     def walk_directory(self, path: Path):
         for root, dirs, files in os.walk(path):
-            if str(Path(root).name).startswith('.'):
+            if str(Path(root).name).startswith("."):
                 self._pruned_paths.add(root)
             if any([Path(root).is_relative_to(x) for x in self._pruned_paths]):
                 continue
-            
+
             self._items_scanned += len(dirs) + len(files)
             for d in dirs:
                 path = Path(root) / d
                 if path in self._scanned_paths or path.parent in self._scanned_dirs:
                     self._scanned_dirs.add(path)
                     continue
-                if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
+                if any(
+                    [
+                        (path / x).exists()
+                        for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
+                    ]
+                ):
                     try:
                         self.on_model_found(path)
                         self._models_found += 1
@@ -79,18 +85,19 @@ class ModelSearch(ABC):
                 path = Path(root) / f
                 if path.parent in self._scanned_dirs:
                     continue
-                if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
+                if path.suffix in {".ckpt", ".bin", ".pth", ".safetensors", ".pt"}:
                     try:
                         self.on_model_found(path)
                         self._models_found += 1
                     except Exception as e:
                         self.logger.warning(str(e))
 
+
 class FindModels(ModelSearch):
     def on_search_started(self):
         self.models_found: Set[Path] = set()
 
-    def on_model_found(self,model: Path):
+    def on_model_found(self, model: Path):
         self.models_found.add(model)
 
     def on_search_completed(self):
@@ -99,5 +106,3 @@ class FindModels(ModelSearch):
     def list_models(self) -> List[Path]:
         self.search()
         return list(self.models_found)
-
-    
diff --git a/invokeai/backend/model_management/models/__init__.py b/invokeai/backend/model_management/models/__init__.py
index 2eb9028d6e..b15e1d309a 100644
--- a/invokeai/backend/model_management/models/__init__.py
+++ b/invokeai/backend/model_management/models/__init__.py
@@ -3,15 +3,24 @@ from enum import Enum
 from pydantic import BaseModel
 from typing import Literal, get_origin
 from .base import (
-    BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase,
-    ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings,
-    ModelNotFoundException, InvalidModelException, DuplicateModelException
-    )
+    BaseModelType,
+    ModelType,
+    SubModelType,
+    ModelBase,
+    ModelConfigBase,
+    ModelVariantType,
+    SchedulerPredictionType,
+    ModelError,
+    SilenceWarnings,
+    ModelNotFoundException,
+    InvalidModelException,
+    DuplicateModelException,
+)
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
 from .sdxl import StableDiffusionXLModel
 from .vae import VaeModel
 from .lora import LoRAModel
-from .controlnet import ControlNetModel # TODO:
+from .controlnet import ControlNetModel  # TODO:
 from .textual_inversion import TextualInversionModel
 
 MODEL_CLASSES = {
@@ -45,18 +54,19 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
     },
-    #BaseModelType.Kandinsky2_1: {
+    # BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,
     #    ModelType.MoVQ: MoVQModel,
     #    ModelType.Lora: LoRAModel,
     #    ModelType.ControlNet: ControlNetModel,
     #    ModelType.TextualInversion: TextualInversionModel,
-    #},
+    # },
 }
 
 MODEL_CONFIGS = list()
 OPENAPI_MODEL_CONFIGS = list()
 
+
 class OpenAPIModelInfoBase(BaseModel):
     model_name: str
     base_model: BaseModelType
@@ -72,27 +82,31 @@ for base_model, models in MODEL_CLASSES.items():
         # LS: sort to get the checkpoint configs first, which makes
         # for a better template in the Swagger docs
         for cfg in sorted(model_configs, key=lambda x: str(x)):
-            model_name, cfg_name = cfg.__qualname__.split('.')[-2:]
+            model_name, cfg_name = cfg.__qualname__.split(".")[-2:]
             openapi_cfg_name = model_name + cfg_name
             if openapi_cfg_name in vars():
                 continue
 
-            api_wrapper = type(openapi_cfg_name, (cfg, OpenAPIModelInfoBase), dict(
-                __annotations__ = dict(
-                    model_type=Literal[model_type.value],
+            api_wrapper = type(
+                openapi_cfg_name,
+                (cfg, OpenAPIModelInfoBase),
+                dict(
+                    __annotations__=dict(
+                        model_type=Literal[model_type.value],
+                    ),
                 ),
-            ))
+            )
 
-            #globals()[openapi_cfg_name] = api_wrapper
+            # globals()[openapi_cfg_name] = api_wrapper
             vars()[openapi_cfg_name] = api_wrapper
             OPENAPI_MODEL_CONFIGS.append(api_wrapper)
 
+
 def get_model_config_enums():
     enums = list()
 
     for model_config in MODEL_CONFIGS:
-
-        if hasattr(inspect,'get_annotations'):
+        if hasattr(inspect, "get_annotations"):
             fields = inspect.get_annotations(model_config)
         else:
             fields = model_config.__annotations__
@@ -109,7 +123,9 @@ def get_model_config_enums():
         if isinstance(field, type) and issubclass(field, str) and issubclass(field, Enum):
             enums.append(field)
 
-        elif get_origin(field) is Literal and all(isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__):
+        elif get_origin(field) is Literal and all(
+            isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__
+        ):
             enums.append(type(field.__args__[0]))
 
         elif field is None:
@@ -119,4 +135,3 @@ def get_model_config_enums():
             raise Exception(f"Unsupported format definition in {model_configs.__qualname__}")
 
     return enums
-
diff --git a/invokeai/backend/model_management/models/base.py b/invokeai/backend/model_management/models/base.py
index 912c3270cd..f2142f772f 100644
--- a/invokeai/backend/model_management/models/base.py
+++ b/invokeai/backend/model_management/models/base.py
@@ -15,29 +15,35 @@ from contextlib import suppress
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 
+
 class DuplicateModelException(Exception):
     pass
 
+
 class InvalidModelException(Exception):
     pass
 
+
 class ModelNotFoundException(Exception):
     pass
 
+
 class BaseModelType(str, Enum):
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
-    #Kandinsky2_1 = "kandinsky-2.1"
+    # Kandinsky2_1 = "kandinsky-2.1"
+
 
 class ModelType(str, Enum):
     Main = "main"
     Vae = "vae"
     Lora = "lora"
-    ControlNet = "controlnet" # used by model_probe
+    ControlNet = "controlnet"  # used by model_probe
     TextualInversion = "embedding"
 
+
 class SubModelType(str, Enum):
     UNet = "unet"
     TextEncoder = "text_encoder"
@@ -47,23 +53,27 @@ class SubModelType(str, Enum):
     Vae = "vae"
     Scheduler = "scheduler"
     SafetyChecker = "safety_checker"
-    #MoVQ = "movq"
+    # MoVQ = "movq"
+
 
 class ModelVariantType(str, Enum):
     Normal = "normal"
     Inpaint = "inpaint"
     Depth = "depth"
 
+
 class SchedulerPredictionType(str, Enum):
     Epsilon = "epsilon"
     VPrediction = "v_prediction"
     Sample = "sample"
-    
+
+
 class ModelError(str, Enum):
     NotFound = "not_found"
 
+
 class ModelConfigBase(BaseModel):
-    path: str # or Path
+    path: str  # or Path
     description: Optional[str] = Field(None)
     model_format: Optional[str] = Field(None)
     error: Optional[ModelError] = Field(None)
@@ -71,13 +81,17 @@ class ModelConfigBase(BaseModel):
     class Config:
         use_enum_values = True
 
+
 class EmptyConfigLoader(ConfigMixin):
     @classmethod
     def load_config(cls, *args, **kwargs):
         cls.config_name = kwargs.pop("config_name")
         return super().load_config(*args, **kwargs)
 
-T_co = TypeVar('T_co', covariant=True)
+
+T_co = TypeVar("T_co", covariant=True)
+
+
 class classproperty(Generic[T_co]):
     def __init__(self, fget: Callable[[Any], T_co]) -> None:
         self.fget = fget
@@ -86,12 +100,13 @@ class classproperty(Generic[T_co]):
         return self.fget(owner)
 
     def __set__(self, instance: Optional[Any], value: Any) -> None:
-        raise AttributeError('cannot set attribute')
+        raise AttributeError("cannot set attribute")
+
 
 class ModelBase(metaclass=ABCMeta):
-    #model_path: str
-    #base_model: BaseModelType
-    #model_type: ModelType
+    # model_path: str
+    # base_model: BaseModelType
+    # model_type: ModelType
 
     def __init__(
         self,
@@ -110,7 +125,7 @@ class ModelBase(metaclass=ABCMeta):
             return None
         elif any(t is None for t in subtypes):
             raise Exception(f"Unsupported definition: {subtypes}")
-        
+
         if subtypes[0] in ["diffusers", "transformers"]:
             res_type = sys.modules[subtypes[0]]
             subtypes = subtypes[1:]
@@ -119,7 +134,6 @@ class ModelBase(metaclass=ABCMeta):
             res_type = sys.modules["diffusers"]
             res_type = getattr(res_type, "pipelines")
 
-
         for subtype in subtypes:
             res_type = getattr(res_type, subtype)
         return res_type
@@ -128,7 +142,7 @@ class ModelBase(metaclass=ABCMeta):
     def _get_configs(cls):
         with suppress(Exception):
             return cls.__configs
-        
+
         configs = dict()
         for name in dir(cls):
             if name.startswith("__"):
@@ -138,7 +152,7 @@ class ModelBase(metaclass=ABCMeta):
             if not isinstance(value, type) or not issubclass(value, ModelConfigBase):
                 continue
 
-            if hasattr(inspect,'get_annotations'):
+            if hasattr(inspect, "get_annotations"):
                 fields = inspect.get_annotations(value)
             else:
                 fields = value.__annotations__
@@ -151,7 +165,9 @@ class ModelBase(metaclass=ABCMeta):
                 for model_format in field:
                     configs[model_format.value] = value
 
-            elif typing.get_origin(field) is Literal and all(isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__):
+            elif typing.get_origin(field) is Literal and all(
+                isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__
+            ):
                 for model_format in field.__args__:
                     configs[model_format.value] = value
 
@@ -203,8 +219,8 @@ class ModelBase(metaclass=ABCMeta):
 
 
 class DiffusersModel(ModelBase):
-    #child_types: Dict[str, Type]
-    #child_sizes: Dict[str, int]
+    # child_types: Dict[str, Type]
+    # child_sizes: Dict[str, int]
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         super().__init__(model_path, base_model, model_type)
@@ -214,7 +230,7 @@ class DiffusersModel(ModelBase):
 
         try:
             config_data = DiffusionPipeline.load_config(self.model_path)
-            #config_data = json.loads(os.path.join(self.model_path, "model_index.json"))
+            # config_data = json.loads(os.path.join(self.model_path, "model_index.json"))
         except:
             raise Exception("Invalid diffusers model! (model_index.json not found or invalid)")
 
@@ -228,14 +244,12 @@ class DiffusersModel(ModelBase):
             self.child_types[child_name] = child_type
             self.child_sizes[child_name] = calc_model_size_by_fs(self.model_path, subfolder=child_name)
 
-
     def get_size(self, child_type: Optional[SubModelType] = None):
         if child_type is None:
             return sum(self.child_sizes.values())
         else:
             return self.child_sizes[child_type]
 
-
     def get_model(
         self,
         torch_dtype: Optional[torch.dtype],
@@ -245,7 +259,7 @@ class DiffusersModel(ModelBase):
         if child_type is None:
             raise Exception("Child model type can't be null on diffusers model")
         if child_type not in self.child_types:
-            return None # TODO: or raise
+            return None  # TODO: or raise
 
         if torch_dtype == torch.float16:
             variants = ["fp16", None]
@@ -265,8 +279,8 @@ class DiffusersModel(ModelBase):
                 )
                 break
             except Exception as e:
-                #print("====ERR LOAD====")
-                #print(f"{variant}: {e}")
+                # print("====ERR LOAD====")
+                # print(f"{variant}: {e}")
                 pass
         else:
             raise Exception(f"Failed to load {self.base_model}:{self.model_type}:{child_type} model")
@@ -275,15 +289,10 @@ class DiffusersModel(ModelBase):
         self.child_sizes[child_type] = calc_model_size_by_data(model)
         return model
 
-    #def convert_if_required(model_path: str, cache_path: str, config: Optional[dict]) -> str:
+    # def convert_if_required(model_path: str, cache_path: str, config: Optional[dict]) -> str:
 
 
-
-def calc_model_size_by_fs(
-    model_path: str,
-    subfolder: Optional[str] = None,
-    variant: Optional[str] = None
-):
+def calc_model_size_by_fs(model_path: str, subfolder: Optional[str] = None, variant: Optional[str] = None):
     if subfolder is not None:
         model_path = os.path.join(model_path, subfolder)
 
@@ -325,12 +334,12 @@ def calc_model_size_by_fs(
 
     # calculate files size if there is no index file
     formats = [
-        (".safetensors",), # safetensors
-        (".bin",), # torch
-        (".onnx", ".pb"), # onnx
-        (".msgpack",), # flax
-        (".ckpt",), # tf
-        (".h5",), # tf2
+        (".safetensors",),  # safetensors
+        (".bin",),  # torch
+        (".onnx", ".pb"),  # onnx
+        (".msgpack",),  # flax
+        (".ckpt",),  # tf
+        (".h5",),  # tf2
     ]
 
     for file_format in formats:
@@ -343,9 +352,9 @@ def calc_model_size_by_fs(
             file_stats = os.stat(os.path.join(model_path, model_file))
             model_size += file_stats.st_size
         return model_size
-    
-    #raise NotImplementedError(f"Unknown model structure! Files: {all_files}")
-    return 0 # scheduler/feature_extractor/tokenizer - models without loading to gpu
+
+    # raise NotImplementedError(f"Unknown model structure! Files: {all_files}")
+    return 0  # scheduler/feature_extractor/tokenizer - models without loading to gpu
 
 
 def calc_model_size_by_data(model) -> int:
@@ -364,12 +373,12 @@ def _calc_pipeline_by_data(pipeline) -> int:
         if submodel is not None and isinstance(submodel, torch.nn.Module):
             res += _calc_model_by_data(submodel)
     return res
-    
+
 
 def _calc_model_by_data(model) -> int:
-    mem_params = sum([param.nelement()*param.element_size() for param in model.parameters()])
-    mem_bufs = sum([buf.nelement()*buf.element_size() for buf in model.buffers()])
-    mem = mem_params + mem_bufs # in bytes
+    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
+    mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
+    mem = mem_params + mem_bufs  # in bytes
     return mem
 
 
@@ -377,11 +386,15 @@ def _fast_safetensors_reader(path: str):
     checkpoint = dict()
     device = torch.device("meta")
     with open(path, "rb") as f:
-        definition_len = int.from_bytes(f.read(8), 'little')
+        definition_len = int.from_bytes(f.read(8), "little")
         definition_json = f.read(definition_len)
         definition = json.loads(definition_json)
 
-        if "__metadata__" in definition and definition["__metadata__"].get("format", "pt") not in {"pt", "torch", "pytorch"}:
+        if "__metadata__" in definition and definition["__metadata__"].get("format", "pt") not in {
+            "pt",
+            "torch",
+            "pytorch",
+        }:
             raise Exception("Supported only pytorch safetensors files")
         definition.pop("__metadata__", None)
 
@@ -400,6 +413,7 @@ def _fast_safetensors_reader(path: str):
 
     return checkpoint
 
+
 def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
     if str(path).endswith(".safetensors"):
         try:
@@ -411,25 +425,27 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
         if scan:
             scan_result = scan_file_path(path)
             if scan_result.infected_files != 0:
-                raise Exception(f"The model file \"{path}\" is potentially infected by malware. Aborting import.")
+                raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
         checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint
 
+
 import warnings
 from diffusers import logging as diffusers_logging
 from transformers import logging as transformers_logging
 
+
 class SilenceWarnings(object):
     def __init__(self):
         self.transformers_verbosity = transformers_logging.get_verbosity()
         self.diffusers_verbosity = diffusers_logging.get_verbosity()
-        
+
     def __enter__(self):
         transformers_logging.set_verbosity_error()
         diffusers_logging.set_verbosity_error()
-        warnings.simplefilter('ignore')
+        warnings.simplefilter("ignore")
 
     def __exit__(self, type, value, traceback):
         transformers_logging.set_verbosity(self.transformers_verbosity)
         diffusers_logging.set_verbosity(self.diffusers_verbosity)
-        warnings.simplefilter('default')
+        warnings.simplefilter("default")
diff --git a/invokeai/backend/model_management/models/controlnet.py b/invokeai/backend/model_management/models/controlnet.py
index 178fea4d13..e075843a56 100644
--- a/invokeai/backend/model_management/models/controlnet.py
+++ b/invokeai/backend/model_management/models/controlnet.py
@@ -1,7 +1,8 @@
 import os
 import torch
 from enum import Enum
-from typing import Optional
+from pathlib import Path
+from typing import Optional, Literal
 from .base import (
     ModelBase,
     ModelConfigBase,
@@ -15,17 +16,24 @@ from .base import (
     InvalidModelException,
     ModelNotFoundException,
 )
+from invokeai.app.services.config import InvokeAIAppConfig
+
 
 class ControlNetModelFormat(str, Enum):
     Checkpoint = "checkpoint"
     Diffusers = "diffusers"
 
-class ControlNetModel(ModelBase):
-    #model_class: Type
-    #model_size: int
 
-    class Config(ModelConfigBase):
-        model_format: ControlNetModelFormat
+class ControlNetModel(ModelBase):
+    # model_class: Type
+    # model_size: int
+
+    class DiffusersConfig(ModelConfigBase):
+        model_format: Literal[ControlNetModelFormat.Diffusers]
+
+    class CheckpointConfig(ModelConfigBase):
+        model_format: Literal[ControlNetModelFormat.Checkpoint]
+        config: str
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.ControlNet
@@ -33,7 +41,7 @@ class ControlNetModel(ModelBase):
 
         try:
             config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
-            #config = json.loads(os.path.join(self.model_path, "config.json"))
+            # config = json.loads(os.path.join(self.model_path, "config.json"))
         except:
             raise Exception("Invalid controlnet model! (config.json not found or invalid)")
 
@@ -61,7 +69,7 @@ class ControlNetModel(ModelBase):
             raise Exception("There is no child models in controlnet model")
 
         model = None
-        for variant in ['fp16',None]:
+        for variant in ["fp16", None]:
             try:
                 model = self.model_class.from_pretrained(
                     self.model_path,
@@ -73,7 +81,7 @@ class ControlNetModel(ModelBase):
                 pass
         if not model:
             raise ModelNotFoundException()
-        
+
         # calc more accurate size
         self.model_size = calc_model_size_by_data(model)
         return model
@@ -102,10 +110,50 @@ class ControlNetModel(ModelBase):
         cls,
         model_path: str,
         output_path: str,
-        config: ModelConfigBase, # empty config or config of parent model
+        config: ModelConfigBase,
         base_model: BaseModelType,
     ) -> str:
-        if cls.detect_format(model_path) != ControlNetModelFormat.Diffusers:
-            raise NotImplementedError("Checkpoint controlnet models currently unsupported")
+        if cls.detect_format(model_path) == ControlNetModelFormat.Checkpoint:
+            return _convert_controlnet_ckpt_and_cache(
+                model_path=model_path,
+                model_config=config.config,
+                output_path=output_path,
+                base_model=base_model,
+            )
         else:
             return model_path
+
+
+@classmethod
+def _convert_controlnet_ckpt_and_cache(
+    cls,
+    model_path: str,
+    output_path: str,
+    base_model: BaseModelType,
+    model_config: ControlNetModel.CheckpointConfig,
+) -> str:
+    """
+    Convert the controlnet from checkpoint format to diffusers format,
+    cache it to disk, and return Path to converted
+    file. If already on disk then just returns Path.
+    """
+    app_config = InvokeAIAppConfig.get_config()
+    weights = app_config.root_path / model_path
+    output_path = Path(output_path)
+
+    # return cached version if it exists
+    if output_path.exists():
+        return output_path
+
+    # to avoid circular import errors
+    from ..convert_ckpt_to_diffusers import convert_controlnet_to_diffusers
+
+    convert_controlnet_to_diffusers(
+        weights,
+        output_path,
+        original_config_file=app_config.root_path / model_config,
+        image_size=512,
+        scan_needed=True,
+        from_safetensors=weights.suffix == ".safetensors",
+    )
+    return output_path
diff --git a/invokeai/backend/model_management/models/lora.py b/invokeai/backend/model_management/models/lora.py
index eb771841ec..642f8bbeec 100644
--- a/invokeai/backend/model_management/models/lora.py
+++ b/invokeai/backend/model_management/models/lora.py
@@ -12,18 +12,21 @@ from .base import (
     InvalidModelException,
     ModelNotFoundException,
 )
+
 # TODO: naming
 from ..lora import LoRAModel as LoRAModelRaw
 
+
 class LoRAModelFormat(str, Enum):
     LyCORIS = "lycoris"
     Diffusers = "diffusers"
 
+
 class LoRAModel(ModelBase):
-    #model_size: int
+    # model_size: int
 
     class Config(ModelConfigBase):
-        model_format: LoRAModelFormat # TODO:
+        model_format: LoRAModelFormat  # TODO:
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.Lora
@@ -54,7 +57,7 @@ class LoRAModel(ModelBase):
 
     @classproperty
     def save_to_config(cls) -> bool:
-        return False
+        return True
 
     @classmethod
     def detect_format(cls, path: str):
diff --git a/invokeai/backend/model_management/models/sdxl.py b/invokeai/backend/model_management/models/sdxl.py
index 76cabcdc62..7fc3efb77c 100644
--- a/invokeai/backend/model_management/models/sdxl.py
+++ b/invokeai/backend/model_management/models/sdxl.py
@@ -1,5 +1,6 @@
 import os
 import json
+import invokeai.backend.util.logging as logger
 from enum import Enum
 from pydantic import Field
 from typing import Literal, Optional
@@ -14,12 +15,13 @@ from .base import (
 )
 from omegaconf import OmegaConf
 
+
 class StableDiffusionXLModelFormat(str, Enum):
     Checkpoint = "checkpoint"
     Diffusers = "diffusers"
-    
-class StableDiffusionXLModel(DiffusersModel):
 
+
+class StableDiffusionXLModel(DiffusersModel):
     # TODO: check that configs overwriten properly
     class DiffusersConfig(ModelConfigBase):
         model_format: Literal[StableDiffusionXLModelFormat.Diffusers]
@@ -48,11 +50,11 @@ class StableDiffusionXLModel(DiffusersModel):
         if model_format == StableDiffusionXLModelFormat.Checkpoint:
             if ckpt_config_path:
                 ckpt_config = OmegaConf.load(ckpt_config_path)
-                ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
+                in_channels = ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
 
             else:
                 checkpoint = read_checkpoint_meta(path)
-                checkpoint = checkpoint.get('state_dict', checkpoint)
+                checkpoint = checkpoint.get("state_dict", checkpoint)
                 in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
 
         elif model_format == StableDiffusionXLModelFormat.Diffusers:
@@ -60,7 +62,7 @@ class StableDiffusionXLModel(DiffusersModel):
             if os.path.exists(unet_config_path):
                 with open(unet_config_path, "r") as f:
                     unet_config = json.loads(f.read())
-                in_channels = unet_config['in_channels']
+                in_channels = unet_config["in_channels"]
 
             else:
                 raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
@@ -80,11 +82,10 @@ class StableDiffusionXLModel(DiffusersModel):
         if ckpt_config_path is None:
             # TO DO: implement picking
             pass
-        
+
         return cls.create_config(
             path=path,
             model_format=model_format,
-
             config=ckpt_config_path,
             variant=variant,
         )
@@ -108,7 +109,17 @@ class StableDiffusionXLModel(DiffusersModel):
         config: ModelConfigBase,
         base_model: BaseModelType,
     ) -> str:
+        # The convert script adapted from the diffusers package uses
+        # strings for the base model type. To avoid making too many
+        # source code changes, we simply translate here
         if isinstance(config, cls.CheckpointConfig):
-            raise NotImplementedError('conversion of SDXL checkpoint models to diffusers format is not yet supported')
+            from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
+
+            return _convert_ckpt_and_cache(
+                version=base_model,
+                model_config=config,
+                output_path=output_path,
+                use_safetensors=False,  # corrupts sdxl models for some reason
+            )
         else:
             return model_path
diff --git a/invokeai/backend/model_management/models/stable_diffusion.py b/invokeai/backend/model_management/models/stable_diffusion.py
index a90a72ba30..76b4833f9c 100644
--- a/invokeai/backend/model_management/models/stable_diffusion.py
+++ b/invokeai/backend/model_management/models/stable_diffusion.py
@@ -14,16 +14,20 @@ from .base import (
     read_checkpoint_meta,
     classproperty,
     InvalidModelException,
+    ModelNotFoundException,
 )
+from .sdxl import StableDiffusionXLModel
+import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from omegaconf import OmegaConf
 
+
 class StableDiffusion1ModelFormat(str, Enum):
     Checkpoint = "checkpoint"
     Diffusers = "diffusers"
 
-class StableDiffusion1Model(DiffusersModel):
 
+class StableDiffusion1Model(DiffusersModel):
     class DiffusersConfig(ModelConfigBase):
         model_format: Literal[StableDiffusion1ModelFormat.Diffusers]
         vae: Optional[str] = Field(None)
@@ -34,7 +38,7 @@ class StableDiffusion1Model(DiffusersModel):
         vae: Optional[str] = Field(None)
         config: str
         variant: ModelVariantType
-        
+
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert base_model == BaseModelType.StableDiffusion1
         assert model_type == ModelType.Main
@@ -55,7 +59,7 @@ class StableDiffusion1Model(DiffusersModel):
 
             else:
                 checkpoint = read_checkpoint_meta(path)
-                checkpoint = checkpoint.get('state_dict', checkpoint)
+                checkpoint = checkpoint.get("state_dict", checkpoint)
                 in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
 
         elif model_format == StableDiffusion1ModelFormat.Diffusers:
@@ -63,7 +67,7 @@ class StableDiffusion1Model(DiffusersModel):
             if os.path.exists(unet_config_path):
                 with open(unet_config_path, "r") as f:
                     unet_config = json.loads(f.read())
-                in_channels = unet_config['in_channels']
+                in_channels = unet_config["in_channels"]
 
             else:
                 raise NotImplementedError(f"{path} is not a supported stable diffusion diffusers format")
@@ -84,7 +88,6 @@ class StableDiffusion1Model(DiffusersModel):
         return cls.create_config(
             path=path,
             model_format=model_format,
-
             config=ckpt_config_path,
             variant=variant,
         )
@@ -121,16 +124,17 @@ class StableDiffusion1Model(DiffusersModel):
                 version=BaseModelType.StableDiffusion1,
                 model_config=config,
                 output_path=output_path,
-           )
+            )
         else:
             return model_path
 
+
 class StableDiffusion2ModelFormat(str, Enum):
     Checkpoint = "checkpoint"
     Diffusers = "diffusers"
 
-class StableDiffusion2Model(DiffusersModel):
 
+class StableDiffusion2Model(DiffusersModel):
     # TODO: check that configs overwriten properly
     class DiffusersConfig(ModelConfigBase):
         model_format: Literal[StableDiffusion2ModelFormat.Diffusers]
@@ -163,7 +167,7 @@ class StableDiffusion2Model(DiffusersModel):
 
             else:
                 checkpoint = read_checkpoint_meta(path)
-                checkpoint = checkpoint.get('state_dict', checkpoint)
+                checkpoint = checkpoint.get("state_dict", checkpoint)
                 in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
 
         elif model_format == StableDiffusion2ModelFormat.Diffusers:
@@ -171,7 +175,7 @@ class StableDiffusion2Model(DiffusersModel):
             if os.path.exists(unet_config_path):
                 with open(unet_config_path, "r") as f:
                     unet_config = json.loads(f.read())
-                in_channels = unet_config['in_channels']
+                in_channels = unet_config["in_channels"]
 
             else:
                 raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
@@ -194,7 +198,6 @@ class StableDiffusion2Model(DiffusersModel):
         return cls.create_config(
             path=path,
             model_format=model_format,
-
             config=ckpt_config_path,
             variant=variant,
         )
@@ -235,42 +238,19 @@ class StableDiffusion2Model(DiffusersModel):
         else:
             return model_path
 
-def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
-    ckpt_configs = {
-        BaseModelType.StableDiffusion1: {
-            ModelVariantType.Normal: "v1-inference.yaml",
-            ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
-        },
-        BaseModelType.StableDiffusion2: {
-            ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
-            ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
-            ModelVariantType.Depth: "v2-midas-inference.yaml",
-        },
-        # note that these .yaml files don't yet exist!
-        BaseModelType.StableDiffusionXL: {
-            ModelVariantType.Normal: "xl-inference-v.yaml",
-            ModelVariantType.Inpaint: "xl-inpainting-inference.yaml",
-            ModelVariantType.Depth: "xl-midas-inference.yaml",
-        }
-    }
-
-    app_config = InvokeAIAppConfig.get_config()
-    try:
-        config_path = app_config.legacy_conf_path / ckpt_configs[version][variant]
-        if config_path.is_relative_to(app_config.root_path):
-            config_path = config_path.relative_to(app_config.root_path)
-        return str(config_path)
-            
-    except:
-        return None
-
 
 # TODO: rework
-# Note that convert_ckpt_to_diffuses does not currently support conversion of SDXL models
+# pass precision - currently defaulting to fp16
 def _convert_ckpt_and_cache(
     version: BaseModelType,
-    model_config: Union[StableDiffusion1Model.CheckpointConfig, StableDiffusion2Model.CheckpointConfig],
+    model_config: Union[
+        StableDiffusion1Model.CheckpointConfig,
+        StableDiffusion2Model.CheckpointConfig,
+        StableDiffusionXLModel.CheckpointConfig,
+    ],
     output_path: str,
+    use_save_model: bool = False,
+    **kwargs,
 ) -> str:
     """
     Convert the checkpoint model indicated in mconfig into a
@@ -289,14 +269,61 @@ def _convert_ckpt_and_cache(
 
     # to avoid circular import errors
     from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
-    with SilenceWarnings():        
+    from ...util.devices import choose_torch_device, torch_dtype
+
+    model_base_to_model_type = {
+        BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
+        BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
+        BaseModelType.StableDiffusionXL: "SDXL",
+        BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
+    }
+    logger.info(f"Converting {weights} to diffusers format")
+    with SilenceWarnings():
         convert_ckpt_to_diffusers(
             weights,
             output_path,
+            model_type=model_base_to_model_type[version],
             model_version=version,
             model_variant=model_config.variant,
             original_config_file=config_file,
             extract_ema=True,
             scan_needed=True,
+            from_safetensors=weights.suffix == ".safetensors",
+            precision=torch_dtype(choose_torch_device()),
+            **kwargs,
         )
     return output_path
+
+
+def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
+    ckpt_configs = {
+        BaseModelType.StableDiffusion1: {
+            ModelVariantType.Normal: "v1-inference.yaml",
+            ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
+        },
+        BaseModelType.StableDiffusion2: {
+            ModelVariantType.Normal: "v2-inference-v.yaml",  # best guess, as we can't differentiate with base(512)
+            ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
+            ModelVariantType.Depth: "v2-midas-inference.yaml",
+        },
+        BaseModelType.StableDiffusionXL: {
+            ModelVariantType.Normal: "sd_xl_base.yaml",
+            ModelVariantType.Inpaint: None,
+            ModelVariantType.Depth: None,
+        },
+        BaseModelType.StableDiffusionXLRefiner: {
+            ModelVariantType.Normal: "sd_xl_refiner.yaml",
+            ModelVariantType.Inpaint: None,
+            ModelVariantType.Depth: None,
+        },
+    }
+
+    app_config = InvokeAIAppConfig.get_config()
+    try:
+        config_path = app_config.legacy_conf_path / ckpt_configs[version][variant]
+        if config_path.is_relative_to(app_config.root_path):
+            config_path = config_path.relative_to(app_config.root_path)
+        return str(config_path)
+
+    except:
+        return None
diff --git a/invokeai/backend/model_management/models/textual_inversion.py b/invokeai/backend/model_management/models/textual_inversion.py
index eea0e85245..a949a15be1 100644
--- a/invokeai/backend/model_management/models/textual_inversion.py
+++ b/invokeai/backend/model_management/models/textual_inversion.py
@@ -11,11 +11,13 @@ from .base import (
     ModelNotFoundException,
     InvalidModelException,
 )
+
 # TODO: naming
 from ..lora import TextualInversionModel as TextualInversionModelRaw
 
+
 class TextualInversionModel(ModelBase):
-    #model_size: int
+    # model_size: int
 
     class Config(ModelConfigBase):
         model_format: None
@@ -65,7 +67,7 @@ class TextualInversionModel(ModelBase):
 
         if os.path.isdir(path):
             if os.path.exists(os.path.join(path, "learned_embeds.bin")):
-                return None # diffusers-ti
+                return None  # diffusers-ti
 
         if os.path.isfile(path):
             if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]]):
diff --git a/invokeai/backend/model_management/models/vae.py b/invokeai/backend/model_management/models/vae.py
index f740615509..b15844bcf8 100644
--- a/invokeai/backend/model_management/models/vae.py
+++ b/invokeai/backend/model_management/models/vae.py
@@ -22,13 +22,15 @@ from invokeai.app.services.config import InvokeAIAppConfig
 from diffusers.utils import is_safetensors_available
 from omegaconf import OmegaConf
 
+
 class VaeModelFormat(str, Enum):
     Checkpoint = "checkpoint"
     Diffusers = "diffusers"
 
+
 class VaeModel(ModelBase):
-    #vae_class: Type
-    #model_size: int
+    # vae_class: Type
+    # model_size: int
 
     class Config(ModelConfigBase):
         model_format: VaeModelFormat
@@ -39,7 +41,7 @@ class VaeModel(ModelBase):
 
         try:
             config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
-            #config = json.loads(os.path.join(self.model_path, "config.json"))
+            # config = json.loads(os.path.join(self.model_path, "config.json"))
         except:
             raise Exception("Invalid vae model! (config.json not found or invalid)")
 
@@ -95,7 +97,7 @@ class VaeModel(ModelBase):
         cls,
         model_path: str,
         output_path: str,
-        config: ModelConfigBase, # empty config or config of parent model
+        config: ModelConfigBase,  # empty config or config of parent model
         base_model: BaseModelType,
     ) -> str:
         if cls.detect_format(model_path) == VaeModelFormat.Checkpoint:
@@ -108,6 +110,7 @@ class VaeModel(ModelBase):
         else:
             return model_path
 
+
 # TODO: rework
 def _convert_vae_ckpt_and_cache(
     weights_path: str,
@@ -138,13 +141,14 @@ def _convert_vae_ckpt_and_cache(
     2.1 - 768
     """
     image_size = 512
-        
+
     # return cached version if it exists
     if output_path.exists():
         return output_path
 
     if base_model in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
         from .stable_diffusion import _select_ckpt_config
+
         # all sd models use same vae settings
         config_file = _select_ckpt_config(base_model, ModelVariantType.Normal)
     else:
@@ -152,7 +156,8 @@ def _convert_vae_ckpt_and_cache(
 
     # this avoids circular import error
     from ..convert_ckpt_to_diffusers import convert_ldm_vae_to_diffusers
-    if weights_path.suffix == '.safetensors':
+
+    if weights_path.suffix == ".safetensors":
         checkpoint = safetensors.torch.load_file(weights_path, device="cpu")
     else:
         checkpoint = torch.load(weights_path, map_location="cpu")
@@ -161,15 +166,12 @@ def _convert_vae_ckpt_and_cache(
     if "state_dict" in checkpoint:
         checkpoint = checkpoint["state_dict"]
 
-    config = OmegaConf.load(app_config.root_path/config_file)
+    config = OmegaConf.load(app_config.root_path / config_file)
 
     vae_model = convert_ldm_vae_to_diffusers(
-        checkpoint = checkpoint,
-        vae_config = config,
-        image_size = image_size,
-    )
-    vae_model.save_pretrained(
-        output_path,
-        safe_serialization=is_safetensors_available()
+        checkpoint=checkpoint,
+        vae_config=config,
+        image_size=image_size,
     )
+    vae_model.save_pretrained(output_path, safe_serialization=is_safetensors_available())
     return output_path
diff --git a/invokeai/backend/safety_checker.py b/invokeai/backend/safety_checker.py
deleted file mode 100644
index 0a943ad7b6..0000000000
--- a/invokeai/backend/safety_checker.py
+++ /dev/null
@@ -1,77 +0,0 @@
-'''
-SafetyChecker class - checks images against the StabilityAI NSFW filter
-and blurs images that contain potential NSFW content.
-'''
-import diffusers
-import numpy as np
-import torch
-import traceback
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from pathlib import Path
-from PIL import Image, ImageFilter
-from transformers import AutoFeatureExtractor
-
-import invokeai.assets.web as web_assets
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
-from .util import CPU_DEVICE
-
-config = InvokeAIAppConfig.get_config()
-
-class SafetyChecker(object):
-    CAUTION_IMG = "caution.png"
-    
-    def __init__(self, device: torch.device):
-        path = Path(web_assets.__path__[0]) / self.CAUTION_IMG
-        caution = Image.open(path)
-        self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
-        self.device = device
-
-        try:
-            safety_model_id = config.models_path / 'core/convert/stable-diffusion-safety-checker'
-            feature_extractor_id = config.models_path / 'core/convert/stable-diffusion-safety-checker-extractor'
-            self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-            self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_id)
-        except Exception:
-            logger.error(
-                "An error was encountered while installing the safety checker:"
-            )
-            print(traceback.format_exc())
-
-    def check(self, image: Image.Image):
-        """
-        Check provided image against the StabilityAI safety checker and return
-
-        """
-
-        self.safety_checker.to(self.device)
-        features = self.safety_feature_extractor([image], return_tensors="pt")
-        features.to(self.device)
-        
-        # unfortunately checker requires the numpy version, so we have to convert back
-        x_image = np.array(image).astype(np.float32) / 255.0
-        x_image = x_image[None].transpose(0, 3, 1, 2)
-
-        diffusers.logging.set_verbosity_error()
-        checked_image, has_nsfw_concept = self.safety_checker(
-            images=x_image, clip_input=features.pixel_values
-        )
-        self.safety_checker.to(CPU_DEVICE) # offload
-        if has_nsfw_concept[0]:
-            logger.warning(
-                "An image with potential non-safe content has been detected. A blurred image will be returned."
-            )
-            return self.blur(image)
-        else:
-            return image
-
-    def blur(self, input):
-        blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
-        try:
-            if caution := self.caution_img:
-                blurry.paste(caution, (0, 0), caution)
-        except FileNotFoundError:
-            pass
-        return blurry
diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index 8acfb100a6..624d47ff64 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -47,6 +47,7 @@ from .diffusion import (
 )
 from .offloading import FullyLoadedModelGroup, ModelGroup
 
+
 @dataclass
 class PipelineIntermediateState:
     run_id: str
@@ -72,7 +73,11 @@ class AddsMaskLatents:
     initial_image_latents: torch.Tensor
 
     def __call__(
-        self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor, **kwargs,
+        self,
+        latents: torch.Tensor,
+        t: torch.Tensor,
+        text_embeddings: torch.Tensor,
+        **kwargs,
     ) -> torch.Tensor:
         model_input = self.add_mask_channels(latents)
         return self.forward(model_input, t, text_embeddings, **kwargs)
@@ -80,12 +85,8 @@ class AddsMaskLatents:
     def add_mask_channels(self, latents):
         batch_size = latents.size(0)
         # duplicate mask and latents for each batch
-        mask = einops.repeat(
-            self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size
-        )
-        image_latents = einops.repeat(
-            self.initial_image_latents, "b c h w -> (repeat b) c h w", repeat=batch_size
-        )
+        mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)
+        image_latents = einops.repeat(self.initial_image_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
         # add mask and image as additional channels
         model_input, _ = einops.pack([latents, mask, image_latents], "b * h w")
         return model_input
@@ -103,9 +104,7 @@ class AddsMaskGuidance:
     noise: torch.Tensor
     _debug: Optional[Callable] = None
 
-    def __call__(
-        self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning
-    ) -> BaseOutput:
+    def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput:
         output_class = step_output.__class__  # We'll create a new one with masked data.
 
         # The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
@@ -116,11 +115,7 @@ class AddsMaskGuidance:
         # Mask anything that has the same shape as prev_sample, return others as-is.
         return output_class(
             {
-                k: (
-                    self.apply_mask(v, self._t_for_field(k, t))
-                    if are_like_tensors(prev_sample, v)
-                    else v
-                )
+                k: (self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v)
                 for k, v in step_output.items()
             }
         )
@@ -132,9 +127,7 @@ class AddsMaskGuidance:
 
     def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
         batch_size = latents.size(0)
-        mask = einops.repeat(
-            self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size
-        )
+        mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)
         if t.dim() == 0:
             # some schedulers expect t to be one-dimensional.
             # TODO: file diffusers bug about inconsistency?
@@ -144,12 +137,8 @@ class AddsMaskGuidance:
         mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t)
         # TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
         # mask_latents = self.scheduler.scale_model_input(mask_latents, t)
-        mask_latents = einops.repeat(
-            mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size
-        )
-        masked_input = torch.lerp(
-            mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype)
-        )
+        mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
+        masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
         if self._debug:
             self._debug(masked_input, f"t={t} lerped")
         return masked_input
@@ -159,9 +148,7 @@ def trim_to_multiple_of(*args, multiple_of=8):
     return tuple((x - x % multiple_of) for x in args)
 
 
-def image_resized_to_grid_as_tensor(
-    image: PIL.Image.Image, normalize: bool = True, multiple_of=8
-) -> torch.FloatTensor:
+def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool = True, multiple_of=8) -> torch.FloatTensor:
     """
 
     :param image: input image
@@ -211,6 +198,7 @@ class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
             raise AssertionError("why was that an empty generator?")
         return result
 
+
 @dataclass
 class ControlNetData:
     model: ControlNetModel = Field(default=None)
@@ -341,9 +329,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             # FIXME: can't currently register control module
             # control_model=control_model,
         )
-        self.invokeai_diffuser = InvokeAIDiffuserComponent(
-            self.unet, self._unet_forward
-        )
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
 
         self._model_group = FullyLoadedModelGroup(execution_device or self.unet.device)
         self._model_group.install(*self._submodels)
@@ -354,11 +340,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if xformers is available, use it, otherwise use sliced attention.
         """
         config = InvokeAIAppConfig.get_config()
-        if (
-            torch.cuda.is_available()
-            and is_xformers_available()
-            and not config.disable_xformers
-        ):
+        if torch.cuda.is_available() and is_xformers_available() and not config.disable_xformers:
             self.enable_xformers_memory_efficient_attention()
         else:
             if self.device.type == "cpu" or self.device.type == "mps":
@@ -369,9 +351,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 raise ValueError(f"unrecognized device {self.device}")
             # input tensor of [1, 4, h/8, w/8]
             # output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
-            bytes_per_element_needed_for_baddbmm_duplication = (
-                latents.element_size() + 4
-            )
+            bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
             max_size_required_for_baddbmm = (
                 16
                 * latents.size(dim=2)
@@ -380,9 +360,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 * latents.size(dim=3)
                 * bytes_per_element_needed_for_baddbmm_duplication
             )
-            if max_size_required_for_baddbmm > (
-                mem_free * 3.0 / 4.0
-            ):  # 3.3 / 4.0 is from old Invoke code
+            if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0):  # 3.3 / 4.0 is from old Invoke code
                 self.enable_attention_slicing(slice_size="max")
             elif torch.backends.mps.is_available():
                 # diffusers recommends always enabling for mps
@@ -470,7 +448,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         control_data: List[ControlNetData] = None,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if self.scheduler.config.get("cpu_only", False):
-            scheduler_device = torch.device('cpu')
+            scheduler_device = torch.device("cpu")
         else:
             scheduler_device = self._model_group.device_for(self.unet)
 
@@ -488,7 +466,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             run_id=run_id,
             additional_guidance=additional_guidance,
             control_data=control_data,
-
             callback=callback,
         )
         return result.latents, result.attention_map_saver
@@ -511,9 +488,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance = []
         extra_conditioning_info = conditioning_data.extra
         with self.invokeai_diffuser.custom_attention_context(
-                self.invokeai_diffuser.model,
-                extra_conditioning_info=extra_conditioning_info,
-                step_count=len(self.scheduler.timesteps),
+            self.invokeai_diffuser.model,
+            extra_conditioning_info=extra_conditioning_info,
+            step_count=len(self.scheduler.timesteps),
         ):
             yield PipelineIntermediateState(
                 run_id=run_id,
@@ -607,16 +584,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 #     that are combined at higher level to make control_mode enum
                 #  soft_injection determines whether to do per-layer re-weighting adjustment (if True)
                 #     or default weighting (if False)
-                soft_injection = (control_mode == "more_prompt" or control_mode == "more_control")
+                soft_injection = control_mode == "more_prompt" or control_mode == "more_control"
                 #  cfg_injection = determines whether to apply ControlNet to only the conditional (if True)
                 #      or the default both conditional and unconditional (if False)
-                cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
+                cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
 
                 first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
                 last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
                 # only apply controlnet if current step is within the controlnet's begin/end step range
                 if step_index >= first_control_step and step_index <= last_control_step:
-
                     if cfg_injection:
                         control_latent_input = unet_latent_input
                     else:
@@ -629,7 +605,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                         encoder_hidden_states = conditioning_data.text_embeddings
                         encoder_attention_mask = None
                     else:
-                        encoder_hidden_states, encoder_attention_mask = self.invokeai_diffuser._concat_conditionings_for_batch(
+                        (
+                            encoder_hidden_states,
+                            encoder_attention_mask,
+                        ) = self.invokeai_diffuser._concat_conditionings_for_batch(
                             conditioning_data.unconditioned_embeddings,
                             conditioning_data.text_embeddings,
                         )
@@ -646,9 +625,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                         timestep=timestep,
                         encoder_hidden_states=encoder_hidden_states,
                         controlnet_cond=control_datum.image_tensor,
-                        conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
+                        conditioning_scale=controlnet_weight,  # controlnet specific, NOT the guidance scale
                         encoder_attention_mask=encoder_attention_mask,
-                        guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
+                        guess_mode=soft_injection,  # this is still called guess_mode in diffusers ControlNetModel
                         return_dict=False,
                     )
                     if cfg_injection:
@@ -678,13 +657,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             step_index=step_index,
             total_step_count=total_step_count,
             down_block_additional_residuals=down_block_res_samples,  # from controlnet(s)
-            mid_block_additional_residual=mid_block_res_sample,      # from controlnet(s)
+            mid_block_additional_residual=mid_block_res_sample,  # from controlnet(s)
         )
 
         # compute the previous noisy sample x_t -> x_t-1
-        step_output = self.scheduler.step(
-            noise_pred, timestep, latents, **conditioning_data.scheduler_args
-        )
+        step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
 
         # TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
         #    But the way things are now, scheduler runs _after_ that, so there was
@@ -710,17 +687,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             #     use of AddsMaskLatents.
             latents = AddsMaskLatents(
                 self._unet_forward,
-                mask=torch.ones_like(
-                    latents[:1, :1], device=latents.device, dtype=latents.dtype
-                ),
-                initial_image_latents=torch.zeros_like(
-                    latents[:1], device=latents.device, dtype=latents.dtype
-                ),
+                mask=torch.ones_like(latents[:1, :1], device=latents.device, dtype=latents.dtype),
+                initial_image_latents=torch.zeros_like(latents[:1], device=latents.device, dtype=latents.dtype),
             ).add_mask_channels(latents)
 
         # First three args should be positional, not keywords, so torch hooks can see them.
         return self.unet(
-            latents, t, text_embeddings, cross_attention_kwargs=cross_attention_kwargs,
+            latents,
+            t,
+            text_embeddings,
+            cross_attention_kwargs=cross_attention_kwargs,
             **kwargs,
         ).sample
 
@@ -774,9 +750,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     ) -> InvokeAIStableDiffusionPipelineOutput:
         timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
         result_latents, result_attention_maps = self.latents_from_embeddings(
-            latents=initial_latents if strength < 1.0 else torch.zeros_like(
-                initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
-            ),
+            latents=initial_latents
+            if strength < 1.0
+            else torch.zeros_like(initial_latents, device=initial_latents.device, dtype=initial_latents.dtype),
             num_inference_steps=num_inference_steps,
             conditioning_data=conditioning_data,
             timesteps=timesteps,
@@ -797,14 +773,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             )
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
-    def get_img2img_timesteps(
-        self, num_inference_steps: int, strength: float, device=None
-    ) -> (torch.Tensor, int):
+    def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device=None) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
 
         if self.scheduler.config.get("cpu_only", False):
-            scheduler_device = torch.device('cpu')
+            scheduler_device = torch.device("cpu")
         else:
             scheduler_device = self._model_group.device_for(self.unet)
 
@@ -849,18 +823,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         # 6. Prepare latent variables
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
         # because we have our own noise function
-        init_image_latents = self.non_noised_latents_from_image(
-            init_image, device=device, dtype=latents_dtype
-        )
+        init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
         if seed is not None:
             set_seed(seed)
         noise = noise_func(init_image_latents)
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
-        latent_mask = tv_resize(
-            mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR
-        ).to(device=device, dtype=latents_dtype)
+        latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR).to(
+            device=device, dtype=latents_dtype
+        )
 
         guidance: List[Callable] = []
 
@@ -868,22 +840,20 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
             # (that's why there's a mask!) but it seems to really want that blanked out.
             masked_init_image = init_image * torch.where(mask < 0.5, 1, 0)
-            masked_latents = self.non_noised_latents_from_image(
-                masked_init_image, device=device, dtype=latents_dtype
-            )
+            masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype)
 
             # TODO: we should probably pass this in so we don't have to try/finally around setting it.
             self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
                 self._unet_forward, latent_mask, masked_latents
             )
         else:
-            guidance.append(
-                AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise)
-            )
+            guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise))
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(
-                latents=init_image_latents if strength < 1.0 else torch.zeros_like(
+                latents=init_image_latents
+                if strength < 1.0
+                else torch.zeros_like(
                     init_image_latents, device=init_image_latents.device, dtype=init_image_latents.dtype
                 ),
                 num_inference_steps=num_inference_steps,
@@ -914,18 +884,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         with torch.inference_mode():
             self._model_group.load(self.vae)
             init_latent_dist = self.vae.encode(init_image).latent_dist
-            init_latents = init_latent_dist.sample().to(
-                dtype=dtype
-            )  # FIXME: uses torch.randn. make reproducible!
+            init_latents = init_latent_dist.sample().to(dtype=dtype)  # FIXME: uses torch.randn. make reproducible!
 
         init_latents = 0.18215 * init_latents
         return init_latents
 
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
-            screened_images, has_nsfw_concept = self.run_safety_checker(
-                output.images, dtype=dtype
-            )
+            screened_images, has_nsfw_concept = self.run_safety_checker(output.images, dtype=dtype)
         screened_attention_map_saver = None
         if has_nsfw_concept is None or not has_nsfw_concept:
             screened_attention_map_saver = output.attention_map_saver
@@ -949,9 +915,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
     def debug_latents(self, latents, msg):
         from invokeai.backend.image_util import debug_image
+
         with torch.inference_mode():
             decoded = self.numpy_to_pil(self.decode_latents(latents))
         for i, img in enumerate(decoded):
-            debug_image(
-                img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
-            )
+            debug_image(img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True)
diff --git a/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py b/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
index 79a0982cfe..38763ebbee 100644
--- a/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
+++ b/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
@@ -17,6 +17,7 @@ from torch import nn
 import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
+
 class CrossAttentionType(enum.Enum):
     SELF = 1
     TOKENS = 2
@@ -55,9 +56,7 @@ class Context:
             if name in self.self_cross_attention_module_identifiers:
                 assert False, f"name {name} cannot appear more than once"
             self.self_cross_attention_module_identifiers.append(name)
-        for name, module in get_cross_attention_modules(
-            model, CrossAttentionType.TOKENS
-        ):
+        for name, module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
             if name in self.tokens_cross_attention_module_identifiers:
                 assert False, f"name {name} cannot appear more than once"
             self.tokens_cross_attention_module_identifiers.append(name)
@@ -68,9 +67,7 @@ class Context:
         else:
             self.tokens_cross_attention_action = Context.Action.SAVE
 
-    def request_apply_saved_attention_maps(
-        self, cross_attention_type: CrossAttentionType
-    ):
+    def request_apply_saved_attention_maps(self, cross_attention_type: CrossAttentionType):
         if cross_attention_type == CrossAttentionType.SELF:
             self.self_cross_attention_action = Context.Action.APPLY
         else:
@@ -139,9 +136,7 @@ class Context:
         saved_attention_dict = self.saved_cross_attention_maps[identifier]
         if requested_dim is None:
             if saved_attention_dict["dim"] is not None:
-                raise RuntimeError(
-                    f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}"
-                )
+                raise RuntimeError(f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}")
             return saved_attention_dict["slices"][0]
 
         if saved_attention_dict["dim"] == requested_dim:
@@ -154,21 +149,13 @@ class Context:
         if saved_attention_dict["dim"] is None:
             whole_saved_attention = saved_attention_dict["slices"][0]
             if requested_dim == 0:
-                return whole_saved_attention[
-                    requested_offset : requested_offset + slice_size
-                ]
+                return whole_saved_attention[requested_offset : requested_offset + slice_size]
             elif requested_dim == 1:
-                return whole_saved_attention[
-                    :, requested_offset : requested_offset + slice_size
-                ]
+                return whole_saved_attention[:, requested_offset : requested_offset + slice_size]
 
-        raise RuntimeError(
-            f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}"
-        )
+        raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
 
-    def get_slicing_strategy(
-        self, identifier: str
-    ) -> tuple[Optional[int], Optional[int]]:
+    def get_slicing_strategy(self, identifier: str) -> tuple[Optional[int], Optional[int]]:
         saved_attention = self.saved_cross_attention_maps.get(identifier, None)
         if saved_attention is None:
             return None, None
@@ -201,9 +188,7 @@ class InvokeAICrossAttentionMixin:
 
     def set_attention_slice_wrangler(
         self,
-        wrangler: Optional[
-            Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]
-        ],
+        wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]],
     ):
         """
         Set custom attention calculator to be called when attention is calculated
@@ -219,14 +204,10 @@ class InvokeAICrossAttentionMixin:
         """
         self.attention_slice_wrangler = wrangler
 
-    def set_slicing_strategy_getter(
-        self, getter: Optional[Callable[[nn.Module], tuple[int, int]]]
-    ):
+    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int, int]]]):
         self.slicing_strategy_getter = getter
 
-    def set_attention_slice_calculated_callback(
-        self, callback: Optional[Callable[[torch.Tensor], None]]
-    ):
+    def set_attention_slice_calculated_callback(self, callback: Optional[Callable[[torch.Tensor], None]]):
         self.attention_slice_calculated_callback = callback
 
     def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
@@ -247,45 +228,31 @@ class InvokeAICrossAttentionMixin:
         )
 
         # calculate attention slice by taking the best scores for each latent pixel
-        default_attention_slice = attention_scores.softmax(
-            dim=-1, dtype=attention_scores.dtype
-        )
+        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
         attention_slice_wrangler = self.attention_slice_wrangler
         if attention_slice_wrangler is not None:
-            attention_slice = attention_slice_wrangler(
-                self, default_attention_slice, dim, offset, slice_size
-            )
+            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
         else:
             attention_slice = default_attention_slice
 
         if self.attention_slice_calculated_callback is not None:
-            self.attention_slice_calculated_callback(
-                attention_slice, dim, offset, slice_size
-            )
+            self.attention_slice_calculated_callback(attention_slice, dim, offset, slice_size)
 
         hidden_states = torch.bmm(attention_slice, value)
         return hidden_states
 
     def einsum_op_slice_dim0(self, q, k, v, slice_size):
-        r = torch.zeros(
-            q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
-        )
+        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
         for i in range(0, q.shape[0], slice_size):
             end = i + slice_size
-            r[i:end] = self.einsum_lowest_level(
-                q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size
-            )
+            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
         return r
 
     def einsum_op_slice_dim1(self, q, k, v, slice_size):
-        r = torch.zeros(
-            q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
-        )
+        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
         for i in range(0, q.shape[1], slice_size):
             end = i + slice_size
-            r[:, i:end] = self.einsum_lowest_level(
-                q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size
-            )
+            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
         return r
 
     def einsum_op_mps_v1(self, q, k, v):
@@ -353,6 +320,7 @@ def restore_default_cross_attention(
     else:
         remove_attention_function(model)
 
+
 def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
@@ -372,7 +340,7 @@ def setup_cross_attention_control_attention_processors(unet: UNet2DConditionMode
     indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
-            if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
+            if name == "equal":  # or (name == "replace" and a1 - a0 == b1 - b0):
                 # these tokens have not been edited
                 indices[b0:b1] = indices_target[a0:a1]
                 mask[b0:b1] = 1
@@ -386,16 +354,14 @@ def setup_cross_attention_control_attention_processors(unet: UNet2DConditionMode
     else:
         # try to re-use an existing slice size
         default_slice_size = 4
-        slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
+        slice_size = next(
+            (p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size
+        )
         unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
 
-def get_cross_attention_modules(
-    model, which: CrossAttentionType
-) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
 
-    cross_attention_class: type = (
-        InvokeAIDiffusersCrossAttention
-    )
+def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
+    cross_attention_class: type = InvokeAIDiffusersCrossAttention
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
     attention_module_tuples = [
         (name, module)
@@ -420,9 +386,7 @@ def get_cross_attention_modules(
 def inject_attention_function(unet, context: Context):
     # ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
 
-    def attention_slice_wrangler(
-        module, suggested_attention_slice: torch.Tensor, dim, offset, slice_size
-    ):
+    def attention_slice_wrangler(module, suggested_attention_slice: torch.Tensor, dim, offset, slice_size):
         # memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
 
         attention_slice = suggested_attention_slice
@@ -430,9 +394,7 @@ def inject_attention_function(unet, context: Context):
         if context.get_should_save_maps(module.identifier):
             # print(module.identifier, "saving suggested_attention_slice of shape",
             #      suggested_attention_slice.shape, "dim", dim, "offset", offset)
-            slice_to_save = (
-                attention_slice.to("cpu") if dim is not None else attention_slice
-            )
+            slice_to_save = attention_slice.to("cpu") if dim is not None else attention_slice
             context.save_slice(
                 module.identifier,
                 slice_to_save,
@@ -442,31 +404,20 @@ def inject_attention_function(unet, context: Context):
             )
         elif context.get_should_apply_saved_maps(module.identifier):
             # print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
-            saved_attention_slice = context.get_slice(
-                module.identifier, dim, offset, slice_size
-            )
+            saved_attention_slice = context.get_slice(module.identifier, dim, offset, slice_size)
 
             # slice may have been offloaded to CPU
-            saved_attention_slice = saved_attention_slice.to(
-                suggested_attention_slice.device
-            )
+            saved_attention_slice = saved_attention_slice.to(suggested_attention_slice.device)
 
             if context.is_tokens_cross_attention(module.identifier):
                 index_map = context.cross_attention_index_map
-                remapped_saved_attention_slice = torch.index_select(
-                    saved_attention_slice, -1, index_map
-                )
+                remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
                 this_attention_slice = suggested_attention_slice
 
-                mask = context.cross_attention_mask.to(
-                    torch_dtype(suggested_attention_slice.device)
-                )
+                mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
                 saved_mask = mask
                 this_mask = 1 - mask
-                attention_slice = (
-                    remapped_saved_attention_slice * saved_mask
-                    + this_attention_slice * this_mask
-                )
+                attention_slice = remapped_saved_attention_slice * saved_mask + this_attention_slice * this_mask
             else:
                 # just use everything
                 attention_slice = saved_attention_slice
@@ -480,14 +431,10 @@ def inject_attention_function(unet, context: Context):
         module.identifier = identifier
         try:
             module.set_attention_slice_wrangler(attention_slice_wrangler)
-            module.set_slicing_strategy_getter(
-                lambda module: context.get_slicing_strategy(identifier)
-            )
+            module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier))
         except AttributeError as e:
             if is_attribute_error_about(e, "set_attention_slice_wrangler"):
-                print(
-                    f"TODO: implement set_attention_slice_wrangler for {type(module)}"
-                )  # TODO
+                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO
             else:
                 raise
 
@@ -503,9 +450,7 @@ def remove_attention_function(unet):
             module.set_slicing_strategy_getter(None)
         except AttributeError as e:
             if is_attribute_error_about(e, "set_attention_slice_wrangler"):
-                print(
-                    f"TODO: implement set_attention_slice_wrangler for {type(module)}"
-                )
+                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
             else:
                 raise
 
@@ -530,9 +475,7 @@ def get_mem_free_total(device):
     return mem_free_total
 
 
-class InvokeAIDiffusersCrossAttention(
-    diffusers.models.attention.Attention, InvokeAICrossAttentionMixin
-):
+class InvokeAIDiffusersCrossAttention(diffusers.models.attention.Attention, InvokeAICrossAttentionMixin):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         InvokeAICrossAttentionMixin.__init__(self)
@@ -641,11 +584,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
         # kwargs
         swap_cross_attn_context: SwapCrossAttnContext = None,
     ):
-        attention_type = (
-            CrossAttentionType.SELF
-            if encoder_hidden_states is None
-            else CrossAttentionType.TOKENS
-        )
+        attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
 
         # if cross-attention control is not in play, just call through to the base implementation.
         if (
@@ -654,9 +593,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
             or not swap_cross_attn_context.wants_cross_attention_control(attention_type)
         ):
             # print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
-            return super().__call__(
-                attn, hidden_states, encoder_hidden_states, attention_mask
-            )
+            return super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask)
         # else:
         #    print(f"SwapCrossAttnContext for {attention_type} active")
 
@@ -699,18 +636,10 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
             query_slice = query[start_idx:end_idx]
             original_key_slice = original_text_key[start_idx:end_idx]
             modified_key_slice = modified_text_key[start_idx:end_idx]
-            attn_mask_slice = (
-                attention_mask[start_idx:end_idx]
-                if attention_mask is not None
-                else None
-            )
+            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
 
-            original_attn_slice = attn.get_attention_scores(
-                query_slice, original_key_slice, attn_mask_slice
-            )
-            modified_attn_slice = attn.get_attention_scores(
-                query_slice, modified_key_slice, attn_mask_slice
-            )
+            original_attn_slice = attn.get_attention_scores(query_slice, original_key_slice, attn_mask_slice)
+            modified_attn_slice = attn.get_attention_scores(query_slice, modified_key_slice, attn_mask_slice)
 
             # because the prompt modifications may result in token sequences shifted forwards or backwards,
             # the original attention probabilities must be remapped to account for token index changes in the
@@ -722,9 +651,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
             # only some tokens taken from the original attention probabilities. this is controlled by the mask.
             mask = swap_cross_attn_context.mask
             inverse_mask = 1 - mask
-            attn_slice = (
-                remapped_original_attn_slice * mask + modified_attn_slice * inverse_mask
-            )
+            attn_slice = remapped_original_attn_slice * mask + modified_attn_slice * inverse_mask
 
             del remapped_original_attn_slice, modified_attn_slice
 
@@ -744,6 +671,4 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 
 class SwapCrossAttnProcessor(SlicedSwapCrossAttnProcesser):
     def __init__(self):
-        super(SwapCrossAttnProcessor, self).__init__(
-            slice_size=int(1e9)
-        )  # massive slice size = don't slice
+        super(SwapCrossAttnProcessor, self).__init__(slice_size=int(1e9))  # massive slice size = don't slice
diff --git a/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py b/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py
index c489c2f0a9..b0174a455e 100644
--- a/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py
+++ b/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py
@@ -59,9 +59,7 @@ class AttentionMapSaver:
         for key, maps in self.collated_maps.items():
             # maps has shape [(H*W), N] for N tokens
             # but we want [N, H, W]
-            this_scale_factor = math.sqrt(
-                maps.shape[0] / (latents_width * latents_height)
-            )
+            this_scale_factor = math.sqrt(maps.shape[0] / (latents_width * latents_height))
             this_maps_height = int(float(latents_height) * this_scale_factor)
             this_maps_width = int(float(latents_width) * this_scale_factor)
             # and we need to do some dimension juggling
@@ -72,9 +70,7 @@ class AttentionMapSaver:
 
             # scale to output size if necessary
             if this_scale_factor != 1:
-                maps = tv_resize(
-                    maps, [latents_height, latents_width], InterpolationMode.BICUBIC
-                )
+                maps = tv_resize(maps, [latents_height, latents_width], InterpolationMode.BICUBIC)
 
             # normalize
             maps_min = torch.min(maps)
@@ -83,9 +79,7 @@ class AttentionMapSaver:
             maps_normalized = (maps - maps_min) / maps_range
             # expand to (-0.1, 1.1) and clamp
             maps_normalized_expanded = maps_normalized * 1.1 - 0.05
-            maps_normalized_expanded_clamped = torch.clamp(
-                maps_normalized_expanded, 0, 1
-            )
+            maps_normalized_expanded_clamped = torch.clamp(maps_normalized_expanded, 0, 1)
 
             # merge together, producing a vertical stack
             maps_stacked = torch.reshape(
diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index f44578cd47..272518e928 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -31,6 +31,7 @@ ModelForwardCallback: TypeAlias = Union[
     Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
 ]
 
+
 @dataclass(frozen=True)
 class PostprocessingSettings:
     threshold: float
@@ -81,14 +82,12 @@ class InvokeAIDiffuserComponent:
     @contextmanager
     def custom_attention_context(
         cls,
-        unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
+        unet: UNet2DConditionModel,  # note: also may futz with the text encoder depending on requested LoRAs
         extra_conditioning_info: Optional[ExtraConditioningInfo],
-        step_count: int
+        step_count: int,
     ):
         old_attn_processors = None
-        if extra_conditioning_info and (
-            extra_conditioning_info.wants_cross_attention_control
-        ):
+        if extra_conditioning_info and (extra_conditioning_info.wants_cross_attention_control):
             old_attn_processors = unet.attn_processors
             # Load lora conditions into the model
             if extra_conditioning_info.wants_cross_attention_control:
@@ -116,27 +115,15 @@ class InvokeAIDiffuserComponent:
                 return
             saver.add_attention_maps(slice, key)
 
-        tokens_cross_attention_modules = get_cross_attention_modules(
-            self.model, CrossAttentionType.TOKENS
-        )
+        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
         for identifier, module in tokens_cross_attention_modules:
-            key = (
-                "down"
-                if identifier.startswith("down")
-                else "up"
-                if identifier.startswith("up")
-                else "mid"
-            )
+            key = "down" if identifier.startswith("down") else "up" if identifier.startswith("up") else "mid"
             module.set_attention_slice_calculated_callback(
-                lambda slice, dim, offset, slice_size, key=key: callback(
-                    slice, dim, offset, slice_size, key
-                )
+                lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key)
             )
 
     def remove_attention_map_saving(self):
-        tokens_cross_attention_modules = get_cross_attention_modules(
-            self.model, CrossAttentionType.TOKENS
-        )
+        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
         for _, module in tokens_cross_attention_modules:
             module.set_attention_slice_calculated_callback(None)
 
@@ -171,10 +158,8 @@ class InvokeAIDiffuserComponent:
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
             percent_through = step_index / total_step_count
-            cross_attention_control_types_to_do = (
-                context.get_active_cross_attention_control_types_for_step(
-                    percent_through
-                )
+            cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(
+                percent_through
             )
 
         wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
@@ -182,7 +167,11 @@ class InvokeAIDiffuserComponent:
 
         if wants_hybrid_conditioning:
             unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(
-                x, sigma, unconditioning, conditioning, **kwargs,
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                **kwargs,
             )
         elif wants_cross_attention_control:
             (
@@ -201,7 +190,11 @@ class InvokeAIDiffuserComponent:
                 unconditioned_next_x,
                 conditioned_next_x,
             ) = self._apply_standard_conditioning_sequentially(
-                x, sigma, unconditioning, conditioning, **kwargs,
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                **kwargs,
             )
 
         else:
@@ -209,12 +202,18 @@ class InvokeAIDiffuserComponent:
                 unconditioned_next_x,
                 conditioned_next_x,
             ) = self._apply_standard_conditioning(
-                x, sigma, unconditioning, conditioning, **kwargs,
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                **kwargs,
             )
 
         combined_next_x = self._combine(
             # unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
-            unconditioned_next_x, conditioned_next_x, guidance_scale
+            unconditioned_next_x,
+            conditioned_next_x,
+            guidance_scale,
         )
 
         return combined_next_x
@@ -229,37 +228,47 @@ class InvokeAIDiffuserComponent:
     ) -> torch.Tensor:
         if postprocessing_settings is not None:
             percent_through = step_index / total_step_count
-            latents = self.apply_threshold(
-                postprocessing_settings, latents, percent_through
-            )
-            latents = self.apply_symmetry(
-                postprocessing_settings, latents, percent_through
-            )
+            latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
+            latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
         return latents
 
     def _concat_conditionings_for_batch(self, unconditioning, conditioning):
         def _pad_conditioning(cond, target_len, encoder_attention_mask):
-            conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
+            conditioning_attention_mask = torch.ones(
+                (cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype
+            )
 
             if cond.shape[1] < max_len:
-                conditioning_attention_mask = torch.cat([
-                    conditioning_attention_mask,
-                    torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
-                ], dim=1)
+                conditioning_attention_mask = torch.cat(
+                    [
+                        conditioning_attention_mask,
+                        torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
+                    ],
+                    dim=1,
+                )
 
-                cond = torch.cat([
-                    cond,
-                    torch.zeros((cond.shape[0], max_len - cond.shape[1], cond.shape[2]), device=cond.device, dtype=cond.dtype),
-                ], dim=1)
+                cond = torch.cat(
+                    [
+                        cond,
+                        torch.zeros(
+                            (cond.shape[0], max_len - cond.shape[1], cond.shape[2]),
+                            device=cond.device,
+                            dtype=cond.dtype,
+                        ),
+                    ],
+                    dim=1,
+                )
 
             if encoder_attention_mask is None:
                 encoder_attention_mask = conditioning_attention_mask
             else:
-                encoder_attention_mask = torch.cat([
-                    encoder_attention_mask,
-                    conditioning_attention_mask,
-                ])
-            
+                encoder_attention_mask = torch.cat(
+                    [
+                        encoder_attention_mask,
+                        conditioning_attention_mask,
+                    ]
+                )
+
             return cond, encoder_attention_mask
 
         encoder_attention_mask = None
@@ -277,11 +286,11 @@ class InvokeAIDiffuserComponent:
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
 
-        both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
-            unconditioning, conditioning
-        )
+        both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(unconditioning, conditioning)
         both_results = self.model_forward_callback(
-            x_twice, sigma_twice, both_conditionings,
+            x_twice,
+            sigma_twice,
+            both_conditionings,
             encoder_attention_mask=encoder_attention_mask,
             **kwargs,
         )
@@ -312,13 +321,17 @@ class InvokeAIDiffuserComponent:
             uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
 
         unconditioned_next_x = self.model_forward_callback(
-            x, sigma, unconditioning,
+            x,
+            sigma,
+            unconditioning,
             down_block_additional_residuals=uncond_down_block,
             mid_block_additional_residual=uncond_mid_block,
             **kwargs,
         )
         conditioned_next_x = self.model_forward_callback(
-            x, sigma, conditioning,
+            x,
+            sigma,
+            conditioning,
             down_block_additional_residuals=cond_down_block,
             mid_block_additional_residual=cond_mid_block,
             **kwargs,
@@ -335,13 +348,15 @@ class InvokeAIDiffuserComponent:
         for k in conditioning:
             if isinstance(conditioning[k], list):
                 both_conditionings[k] = [
-                    torch.cat([unconditioning[k][i], conditioning[k][i]])
-                    for i in range(len(conditioning[k]))
+                    torch.cat([unconditioning[k][i], conditioning[k][i]]) for i in range(len(conditioning[k]))
                 ]
             else:
                 both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
         unconditioned_next_x, conditioned_next_x = self.model_forward_callback(
-            x_twice, sigma_twice, both_conditionings, **kwargs,
+            x_twice,
+            sigma_twice,
+            both_conditionings,
+            **kwargs,
         ).chunk(2)
         return unconditioned_next_x, conditioned_next_x
 
@@ -388,9 +403,7 @@ class InvokeAIDiffuserComponent:
         )
 
         # do requested cross attention types for conditioning (positive prompt)
-        cross_attn_processor_context.cross_attention_types_to_do = (
-            cross_attention_control_types_to_do
-        )
+        cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
         conditioned_next_x = self.model_forward_callback(
             x,
             sigma,
@@ -414,19 +427,14 @@ class InvokeAIDiffuserComponent:
         latents: torch.Tensor,
         percent_through: float,
     ) -> torch.Tensor:
-        if (
-            postprocessing_settings.threshold is None
-            or postprocessing_settings.threshold == 0.0
-        ):
+        if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
             return latents
 
         threshold = postprocessing_settings.threshold
         warmup = postprocessing_settings.warmup
 
         if percent_through < warmup:
-            current_threshold = threshold + threshold * 5 * (
-                1 - (percent_through / warmup)
-            )
+            current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
         else:
             current_threshold = threshold
 
@@ -440,18 +448,10 @@ class InvokeAIDiffuserComponent:
 
         if self.debug_thresholding:
             std, mean = [i.item() for i in torch.std_mean(latents)]
-            outside = torch.count_nonzero(
-                (latents < -current_threshold) | (latents > current_threshold)
-            )
-            logger.info(
-                f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
-                )
-            logger.debug(
-                f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
-            )
-            logger.debug(
-                f"{outside / latents.numel() * 100:.2f}% values outside threshold"
-            )
+            outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
+            logger.info(f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})")
+            logger.debug(f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}")
+            logger.debug(f"{outside / latents.numel() * 100:.2f}% values outside threshold")
 
         if maxval < current_threshold and minval > -current_threshold:
             return latents
@@ -464,25 +464,17 @@ class InvokeAIDiffuserComponent:
             latents = torch.clone(latents)
             maxval = np.clip(maxval * scale, 1, current_threshold)
             num_altered += torch.count_nonzero(latents > maxval)
-            latents[latents > maxval] = (
-                torch.rand_like(latents[latents > maxval]) * maxval
-            )
+            latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
 
         if minval < -current_threshold:
             latents = torch.clone(latents)
             minval = np.clip(minval * scale, -current_threshold, -1)
             num_altered += torch.count_nonzero(latents < minval)
-            latents[latents < minval] = (
-                torch.rand_like(latents[latents < minval]) * minval
-            )
+            latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
 
         if self.debug_thresholding:
-            logger.debug(
-                f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})"
-            )
-            logger.debug(
-                f"{num_altered / latents.numel() * 100:.2f}% values altered"
-            )
+            logger.debug(f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})")
+            logger.debug(f"{num_altered / latents.numel() * 100:.2f}% values altered")
 
         return latents
 
@@ -501,15 +493,11 @@ class InvokeAIDiffuserComponent:
 
         # Check for out of bounds
         h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
-        if h_symmetry_time_pct is not None and (
-            h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0
-        ):
+        if h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0):
             h_symmetry_time_pct = None
 
         v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
-        if v_symmetry_time_pct is not None and (
-            v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0
-        ):
+        if v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0):
             v_symmetry_time_pct = None
 
         dev = latents.device.type
@@ -554,9 +542,7 @@ class InvokeAIDiffuserComponent:
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)
-            return float(step_index) / float(
-                self.cross_attention_control_context.step_count
-            )
+            return float(step_index) / float(self.cross_attention_control_context.step_count)
         # find the best possible index of the current sigma in the sigma sequence
         smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
         sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
@@ -567,19 +553,13 @@ class InvokeAIDiffuserComponent:
 
     # todo: make this work
     @classmethod
-    def apply_conjunction(
-        cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale
-    ):
+    def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
         x_in = torch.cat([x] * 2)
         t_in = torch.cat([t] * 2)  # aka sigmas
 
         deltas = None
         uncond_latents = None
-        weighted_cond_list = (
-            c_or_weighted_c_list
-            if type(c_or_weighted_c_list) is list
-            else [(c_or_weighted_c_list, 1)]
-        )
+        weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
 
         # below is fugly omg
         conditionings = [uc] + [c for c, weight in weighted_cond_list]
@@ -608,15 +588,11 @@ class InvokeAIDiffuserComponent:
                     deltas = torch.cat((deltas, latents_b - uncond_latents))
 
         # merge the weighted deltas together into a single merged delta
-        per_delta_weights = torch.tensor(
-            weights[1:], dtype=deltas.dtype, device=deltas.device
-        )
+        per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
         normalize = False
         if normalize:
             per_delta_weights /= torch.sum(per_delta_weights)
-        reshaped_weights = per_delta_weights.reshape(
-            per_delta_weights.shape + (1, 1, 1)
-        )
+        reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
         deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
 
         # old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)
diff --git a/invokeai/backend/stable_diffusion/image_degradation/bsrgan.py b/invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
index 1760206073..493c8be781 100644
--- a/invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
+++ b/invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
@@ -261,9 +261,7 @@ def srmd_degradation(x, k, sf=3):
           year={2018}
         }
     """
-    x = ndimage.filters.convolve(
-        x, np.expand_dims(k, axis=2), mode="wrap"
-    )  # 'nearest' | 'mirror'
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")  # 'nearest' | 'mirror'
     x = bicubic_degradation(x, sf=sf)
     return x
 
@@ -389,21 +387,15 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
     noise_level = random.randint(noise_level1, noise_level2)
     rnum = np.random.rand()
     if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
-            np.float32
-        )
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
     elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(
-            0, noise_level / 255.0, (*img.shape[:2], 1)
-        ).astype(np.float32)
+        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
     else:  # add  noise
         L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
-        ).astype(np.float32)
+        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
@@ -413,21 +405,15 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
     img = np.clip(img, 0.0, 1.0)
     rnum = random.random()
     if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
-            np.float32
-        )
+        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
     elif rnum < 0.4:
-        img += img * np.random.normal(
-            0, noise_level / 255.0, (*img.shape[:2], 1)
-        ).astype(np.float32)
+        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
     else:
         L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
-        ).astype(np.float32)
+        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
@@ -440,9 +426,7 @@ def add_Poisson_noise(img):
     else:
         img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
         img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
-        noise_gray = (
-            np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        )
+        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
         img += noise_gray[:, :, np.newaxis]
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -451,9 +435,7 @@ def add_Poisson_noise(img):
 def add_JPEG_noise(img):
     quality_factor = random.randint(30, 95)
     img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode(
-        ".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
-    )
+    result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
     img = cv2.imdecode(encimg, 1)
     img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
     return img
@@ -540,9 +522,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
                 k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
                 k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(
-                    img, np.expand_dims(k_shifted, axis=2), mode="mirror"
-                )
+                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
                 img = img[0::sf, 0::sf, ...]  # nearest downsampling
             img = np.clip(img, 0.0, 1.0)
 
@@ -646,9 +626,7 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
                 k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
                 k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(
-                    image, np.expand_dims(k_shifted, axis=2), mode="mirror"
-                )
+                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
                 image = image[0::sf, 0::sf, ...]  # nearest downsampling
             image = np.clip(image, 0.0, 1.0)
 
@@ -796,9 +774,7 @@ if __name__ == "__main__":
         print(i)
         img_lq = deg_fn(img)
         print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(
-            max_size=h, interpolation=cv2.INTER_CUBIC
-        )(image=img)["image"]
+        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
         print(img_lq.shape)
         print("bicubic", img_lq_bicubic.shape)
         print(img_hq.shape)
@@ -812,7 +788,5 @@ if __name__ == "__main__":
             (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
             interpolation=0,
         )
-        img_concat = np.concatenate(
-            [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
-        )
+        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
         util.imsave(img_concat, str(i) + ".png")
diff --git a/invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py b/invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
index 1e8eee82b5..d0e0abadbc 100644
--- a/invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
+++ b/invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
@@ -261,9 +261,7 @@ def srmd_degradation(x, k, sf=3):
           year={2018}
         }
     """
-    x = ndimage.filters.convolve(
-        x, np.expand_dims(k, axis=2), mode="wrap"
-    )  # 'nearest' | 'mirror'
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")  # 'nearest' | 'mirror'
     x = bicubic_degradation(x, sf=sf)
     return x
 
@@ -393,21 +391,15 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
     noise_level = random.randint(noise_level1, noise_level2)
     rnum = np.random.rand()
     if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
-            np.float32
-        )
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
     elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(
-            0, noise_level / 255.0, (*img.shape[:2], 1)
-        ).astype(np.float32)
+        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
     else:  # add  noise
         L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
-        ).astype(np.float32)
+        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
@@ -417,21 +409,15 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
     img = np.clip(img, 0.0, 1.0)
     rnum = random.random()
     if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
-            np.float32
-        )
+        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
     elif rnum < 0.4:
-        img += img * np.random.normal(
-            0, noise_level / 255.0, (*img.shape[:2], 1)
-        ).astype(np.float32)
+        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
     else:
         L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
-        ).astype(np.float32)
+        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
@@ -444,9 +430,7 @@ def add_Poisson_noise(img):
     else:
         img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
         img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
-        noise_gray = (
-            np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        )
+        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
         img += noise_gray[:, :, np.newaxis]
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -455,9 +439,7 @@ def add_Poisson_noise(img):
 def add_JPEG_noise(img):
     quality_factor = random.randint(80, 95)
     img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode(
-        ".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
-    )
+    result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
     img = cv2.imdecode(encimg, 1)
     img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
     return img
@@ -544,9 +526,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
                 k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
                 k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(
-                    img, np.expand_dims(k_shifted, axis=2), mode="mirror"
-                )
+                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
                 img = img[0::sf, 0::sf, ...]  # nearest downsampling
             img = np.clip(img, 0.0, 1.0)
 
@@ -653,9 +633,7 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
                 k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
                 k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(
-                    image, np.expand_dims(k_shifted, axis=2), mode="mirror"
-                )
+                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
                 image = image[0::sf, 0::sf, ...]  # nearest downsampling
 
             image = np.clip(image, 0.0, 1.0)
@@ -705,9 +683,9 @@ if __name__ == "__main__":
         img_lq = deg_fn(img)["image"]
         img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
         print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(
-            max_size=h, interpolation=cv2.INTER_CUBIC
-        )(image=img_hq)["image"]
+        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)[
+            "image"
+        ]
         print(img_lq.shape)
         print("bicubic", img_lq_bicubic.shape)
         print(img_hq.shape)
@@ -721,7 +699,5 @@ if __name__ == "__main__":
             (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
             interpolation=0,
         )
-        img_concat = np.concatenate(
-            [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
-        )
+        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
         util.imsave(img_concat, str(i) + ".png")
diff --git a/invokeai/backend/stable_diffusion/image_degradation/utils_image.py b/invokeai/backend/stable_diffusion/image_degradation/utils_image.py
index c4d37a24bf..d45ca602e6 100644
--- a/invokeai/backend/stable_diffusion/image_degradation/utils_image.py
+++ b/invokeai/backend/stable_diffusion/image_degradation/utils_image.py
@@ -11,6 +11,7 @@ from torchvision.utils import make_grid
 # import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
 
 import invokeai.backend.util.logging as logger
+
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 
@@ -296,22 +297,14 @@ def single2uint16(img):
 def uint2tensor4(img):
     if img.ndim == 2:
         img = np.expand_dims(img, axis=2)
-    return (
-        torch.from_numpy(np.ascontiguousarray(img))
-        .permute(2, 0, 1)
-        .float()
-        .div(255.0)
-        .unsqueeze(0)
-    )
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0).unsqueeze(0)
 
 
 # convert uint to 3-dimensional torch tensor
 def uint2tensor3(img):
     if img.ndim == 2:
         img = np.expand_dims(img, axis=2)
-    return (
-        torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0)
-    )
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0)
 
 
 # convert 2/3/4-dimensional torch tensor to uint
@@ -334,12 +327,7 @@ def single2tensor3(img):
 
 # convert single (HxWxC) to 4-dimensional torch tensor
 def single2tensor4(img):
-    return (
-        torch.from_numpy(np.ascontiguousarray(img))
-        .permute(2, 0, 1)
-        .float()
-        .unsqueeze(0)
-    )
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
 
 
 # convert torch tensor to single
@@ -362,12 +350,7 @@ def tensor2single3(img):
 
 
 def single2tensor5(img):
-    return (
-        torch.from_numpy(np.ascontiguousarray(img))
-        .permute(2, 0, 1, 3)
-        .float()
-        .unsqueeze(0)
-    )
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
 
 
 def single32tensor5(img):
@@ -385,9 +368,7 @@ def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
     Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
     Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
     """
-    tensor = (
-        tensor.squeeze().float().cpu().clamp_(*min_max)
-    )  # squeeze first, then clamp
+    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
     tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
     n_dim = tensor.dim()
     if n_dim == 4:
@@ -400,11 +381,7 @@ def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
     elif n_dim == 2:
         img_np = tensor.numpy()
     else:
-        raise TypeError(
-            "Only support 4D, 3D and 2D tensor. But received with dimension: {:d}".format(
-                n_dim
-            )
-        )
+        raise TypeError("Only support 4D, 3D and 2D tensor. But received with dimension: {:d}".format(n_dim))
     if out_type == np.uint8:
         img_np = (img_np * 255.0).round()
         # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
@@ -744,9 +721,7 @@ def ssim(img1, img2):
     sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
     sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
 
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
-        (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
-    )
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
     return ssim_map.mean()
 
 
@@ -767,9 +742,7 @@ def cubic(x):
     ) * (((absx > 1) * (absx <= 2)).type_as(absx))
 
 
-def calculate_weights_indices(
-    in_length, out_length, scale, kernel, kernel_width, antialiasing
-):
+def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
     if (scale < 1) and (antialiasing):
         # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
         kernel_width = kernel_width / scale
@@ -793,9 +766,9 @@ def calculate_weights_indices(
 
     # The indices of the input pixels involved in computing the k-th output
     # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(
-        0, P - 1, P
-    ).view(1, P).expand(out_length, P)
+    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(1, P).expand(
+        out_length, P
+    )
 
     # The weights used to compute the k-th output pixel are in row k of the
     # weights matrix.
@@ -876,9 +849,7 @@ def imresize(img, scale, antialiasing=True):
     for i in range(out_H):
         idx = int(indices_H[i][0])
         for j in range(out_C):
-            out_1[j, i, :] = (
-                img_aug[j, idx : idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-            )
+            out_1[j, i, :] = img_aug[j, idx : idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
 
     # process W dimension
     # symmetric copying
@@ -959,9 +930,7 @@ def imresize_np(img, scale, antialiasing=True):
     for i in range(out_H):
         idx = int(indices_H[i][0])
         for j in range(out_C):
-            out_1[i, :, j] = (
-                img_aug[idx : idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-            )
+            out_1[i, :, j] = img_aug[idx : idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
 
     # process W dimension
     # symmetric copying
diff --git a/invokeai/backend/stable_diffusion/offloading.py b/invokeai/backend/stable_diffusion/offloading.py
index d36b65872a..aa2426d514 100644
--- a/invokeai/backend/stable_diffusion/offloading.py
+++ b/invokeai/backend/stable_diffusion/offloading.py
@@ -95,10 +95,7 @@ class ModelGroup(metaclass=ABCMeta):
         pass
 
     def __repr__(self) -> str:
-        return (
-            f"<{self.__class__.__name__} object at {id(self):x}: "
-            f"device={self.execution_device} >"
-        )
+        return f"<{self.__class__.__name__} object at {id(self):x}: " f"device={self.execution_device} >"
 
 
 class LazilyLoadedModelGroup(ModelGroup):
@@ -143,8 +140,7 @@ class LazilyLoadedModelGroup(ModelGroup):
         self.load(module)
         if len(forward_input) == 0:
             warnings.warn(
-                f"Hook for {module.__class__.__name__} got no input. "
-                f"Inputs must be positional, not keywords.",
+                f"Hook for {module.__class__.__name__} got no input. " f"Inputs must be positional, not keywords.",
                 stacklevel=3,
             )
         return send_to_device(forward_input, self.execution_device)
@@ -161,9 +157,7 @@ class LazilyLoadedModelGroup(ModelGroup):
         self.clear_current_model()
 
     def _load(self, module: torch.nn.Module) -> torch.nn.Module:
-        assert (
-            self.is_empty()
-        ), f"A model is already loaded: {self._current_model_ref()}"
+        assert self.is_empty(), f"A model is already loaded: {self._current_model_ref()}"
         module = module.to(self.execution_device)
         self.set_current_model(module)
         return module
@@ -192,12 +186,8 @@ class LazilyLoadedModelGroup(ModelGroup):
 
     def device_for(self, model):
         if model not in self:
-            raise KeyError(
-                f"This does not manage this model {type(model).__name__}", model
-            )
-        return (
-            self.execution_device
-        )  # this implementation only dispatches to one device
+            raise KeyError(f"This does not manage this model {type(model).__name__}", model)
+        return self.execution_device  # this implementation only dispatches to one device
 
     def ready(self):
         pass  # always ready to load on-demand
@@ -256,12 +246,8 @@ class FullyLoadedModelGroup(ModelGroup):
 
     def device_for(self, model):
         if model not in self:
-            raise KeyError(
-                "This does not manage this model f{type(model).__name__}", model
-            )
-        return (
-            self.execution_device
-        )  # this implementation only dispatches to one device
+            raise KeyError("This does not manage this model f{type(model).__name__}", model)
+        return self.execution_device  # this implementation only dispatches to one device
 
     def __contains__(self, model):
         return model in self._models
diff --git a/invokeai/backend/stable_diffusion/schedulers/__init__.py b/invokeai/backend/stable_diffusion/schedulers/__init__.py
index b2df0df231..29a96eb3a5 100644
--- a/invokeai/backend/stable_diffusion/schedulers/__init__.py
+++ b/invokeai/backend/stable_diffusion/schedulers/__init__.py
@@ -1 +1 @@
-from .schedulers import SCHEDULER_MAP
\ No newline at end of file
+from .schedulers import SCHEDULER_MAP
diff --git a/invokeai/backend/stable_diffusion/schedulers/schedulers.py b/invokeai/backend/stable_diffusion/schedulers/schedulers.py
index 77c45d5eb8..2f62f8c477 100644
--- a/invokeai/backend/stable_diffusion/schedulers/schedulers.py
+++ b/invokeai/backend/stable_diffusion/schedulers/schedulers.py
@@ -1,7 +1,19 @@
-from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, KDPM2DiscreteScheduler, \
-    KDPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, \
-    HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UniPCMultistepScheduler, \
-    DPMSolverSinglestepScheduler, DEISMultistepScheduler, DDPMScheduler, DPMSolverSDEScheduler
+from diffusers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    KDPM2DiscreteScheduler,
+    KDPM2AncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UniPCMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    DEISMultistepScheduler,
+    DDPMScheduler,
+    DPMSolverSDEScheduler,
+)
 
 SCHEDULER_MAP = dict(
     ddim=(DDIMScheduler, dict()),
@@ -21,9 +33,9 @@ SCHEDULER_MAP = dict(
     dpmpp_2s_k=(DPMSolverSinglestepScheduler, dict(use_karras_sigmas=True)),
     dpmpp_2m=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False)),
     dpmpp_2m_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)),
-    dpmpp_2m_sde=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False, algorithm_type='sde-dpmsolver++')),
-    dpmpp_2m_sde_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True, algorithm_type='sde-dpmsolver++')),
+    dpmpp_2m_sde=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False, algorithm_type="sde-dpmsolver++")),
+    dpmpp_2m_sde_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")),
     dpmpp_sde=(DPMSolverSDEScheduler, dict(use_karras_sigmas=False, noise_sampler_seed=0)),
     dpmpp_sde_k=(DPMSolverSDEScheduler, dict(use_karras_sigmas=True, noise_sampler_seed=0)),
-    unipc=(UniPCMultistepScheduler, dict(cpu_only=True))
+    unipc=(UniPCMultistepScheduler, dict(cpu_only=True)),
 )
diff --git a/invokeai/backend/training/textual_inversion_training.py b/invokeai/backend/training/textual_inversion_training.py
index aadf8c76a3..d92aa80b38 100644
--- a/invokeai/backend/training/textual_inversion_training.py
+++ b/invokeai/backend/training/textual_inversion_training.py
@@ -45,7 +45,7 @@ from tqdm.auto import tqdm
 from transformers import CLIPTextModel, CLIPTokenizer
 
 # invokeai stuff
-from invokeai.app.services.config import InvokeAIAppConfig,PagingArgumentParser
+from invokeai.app.services.config import InvokeAIAppConfig, PagingArgumentParser
 from invokeai.app.services.model_manager_service import ModelManagerService
 from invokeai.backend.model_management.models import SubModelType
 
@@ -75,24 +75,16 @@ check_min_version("0.10.0.dev0")
 logger = get_logger(__name__)
 
 
-def save_progress(
-    text_encoder, placeholder_token_id, accelerator, placeholder_token, save_path
-):
+def save_progress(text_encoder, placeholder_token_id, accelerator, placeholder_token, save_path):
     logger.info("Saving embeddings")
-    learned_embeds = (
-        accelerator.unwrap_model(text_encoder)
-        .get_input_embeddings()
-        .weight[placeholder_token_id]
-    )
+    learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
     learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
     torch.save(learned_embeds_dict, save_path)
 
 
 def parse_args():
     config = InvokeAIAppConfig.get_config()
-    parser = PagingArgumentParser(
-        description="Textual inversion training"
-    )
+    parser = PagingArgumentParser(description="Textual inversion training")
     general_group = parser.add_argument_group("General")
     model_group = parser.add_argument_group("Models and Paths")
     image_group = parser.add_argument_group("Training Image Location and Options")
@@ -221,9 +213,7 @@ def parse_args():
         default=100,
         help="How many times to repeat the training data.",
     )
-    training_group.add_argument(
-        "--seed", type=int, default=None, help="A seed for reproducible training."
-    )
+    training_group.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
     training_group.add_argument(
         "--train_batch_size",
         type=int,
@@ -287,9 +277,7 @@ def parse_args():
         default=0.999,
         help="The beta2 parameter for the Adam optimizer.",
     )
-    training_group.add_argument(
-        "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use."
-    )
+    training_group.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
     training_group.add_argument(
         "--adam_epsilon",
         type=float,
@@ -442,9 +430,7 @@ class TextualInversionDataset(Dataset):
             self.data_root / file_path
             for file_path in self.data_root.iterdir()
             if file_path.is_file()
-            and file_path.name.endswith(
-                (".png", ".PNG", ".jpg", ".JPG", ".jpeg", ".JPEG", ".gif", ".GIF")
-            )
+            and file_path.name.endswith((".png", ".PNG", ".jpg", ".JPG", ".jpeg", ".JPEG", ".gif", ".GIF"))
         ]
 
         self.num_images = len(self.image_paths)
@@ -460,11 +446,7 @@ class TextualInversionDataset(Dataset):
             "lanczos": PIL_INTERPOLATION["lanczos"],
         }[interpolation]
 
-        self.templates = (
-            imagenet_style_templates_small
-            if learnable_property == "style"
-            else imagenet_templates_small
-        )
+        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
         self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
 
     def __len__(self):
@@ -500,9 +482,7 @@ class TextualInversionDataset(Dataset):
                 img.shape[0],
                 img.shape[1],
             )
-            img = img[
-                (h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2
-            ]
+            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
 
         image = Image.fromarray(img)
         image = image.resize((self.size, self.size), resample=self.interpolation)
@@ -515,9 +495,7 @@ class TextualInversionDataset(Dataset):
         return example
 
 
-def get_full_repo_name(
-    model_id: str, organization: Optional[str] = None, token: Optional[str] = None
-):
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
     if token is None:
         token = HfFolder.get_token()
     if organization is None:
@@ -570,9 +548,7 @@ def do_textual_inversion_training(
     **kwargs,
 ):
     assert model, "Please specify a base model with --model"
-    assert (
-        train_data_dir
-    ), "Please specify a directory containing the training images using --train_data_dir"
+    assert train_data_dir, "Please specify a directory containing the training images using --train_data_dir"
     assert placeholder_token, "Please specify a trigger term using --placeholder_token"
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
     if env_local_rank != -1 and env_local_rank != local_rank:
@@ -593,7 +569,7 @@ def do_textual_inversion_training(
         project_config=accelerator_config,
     )
 
-    model_manager = ModelManagerService(config,logger)
+    model_manager = ModelManagerService(config, logger)
 
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
@@ -633,13 +609,11 @@ def do_textual_inversion_training(
             os.makedirs(output_dir, exist_ok=True)
 
     known_models = model_manager.model_names()
-    model_name = model.split('/')[-1]
+    model_name = model.split("/")[-1]
     model_meta = next((mm for mm in known_models if mm[0].endswith(model_name)), None)
     assert model_meta is not None, f"Unknown model: {model}"
     model_info = model_manager.model_info(*model_meta)
-    assert (
-        model_info['model_format'] == "diffusers"
-    ), "This script only works with models of type 'diffusers'"
+    assert model_info["model_format"] == "diffusers", "This script only works with models of type 'diffusers'"
     tokenizer_info = model_manager.get_model(*model_meta, submodel=SubModelType.Tokenizer)
     noise_scheduler_info = model_manager.get_model(*model_meta, submodel=SubModelType.Scheduler)
     text_encoder_info = model_manager.get_model(*model_meta, submodel=SubModelType.TextEncoder)
@@ -650,9 +624,7 @@ def do_textual_inversion_training(
     if tokenizer_name:
         tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name, **pipeline_args)
     else:
-        tokenizer = CLIPTokenizer.from_pretrained(
-            tokenizer_info.location, subfolder='tokenizer', **pipeline_args
-        )
+        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_info.location, subfolder="tokenizer", **pipeline_args)
 
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(
@@ -722,9 +694,7 @@ def do_textual_inversion_training(
         if is_xformers_available():
             unet.enable_xformers_memory_efficient_attention()
         else:
-            raise ValueError(
-                "xformers is not available. Make sure it is installed correctly"
-            )
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
 
     # Enable TF32 for faster training on Ampere GPUs,
     # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
@@ -732,12 +702,7 @@ def do_textual_inversion_training(
         torch.backends.cuda.matmul.allow_tf32 = True
 
     if scale_lr:
-        learning_rate = (
-            learning_rate
-            * gradient_accumulation_steps
-            * train_batch_size
-            * accelerator.num_processes
-        )
+        learning_rate = learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
 
     # Initialize the optimizer
     optimizer = torch.optim.AdamW(
@@ -759,15 +724,11 @@ def do_textual_inversion_training(
         center_crop=center_crop,
         set="train",
     )
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=train_batch_size, shuffle=True
-    )
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader) / gradient_accumulation_steps
-    )
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
     if max_train_steps is None:
         max_train_steps = num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
@@ -797,9 +758,7 @@ def do_textual_inversion_training(
     vae.to(accelerator.device, dtype=weight_dtype)
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader) / gradient_accumulation_steps
-    )
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
     if overrode_max_train_steps:
         max_train_steps = num_train_epochs * num_update_steps_per_epoch
     # Afterwards we recalculate our number of training epochs
@@ -814,17 +773,13 @@ def do_textual_inversion_training(
         accelerator.init_trackers("textual_inversion", config=params)
 
     # Train!
-    total_batch_size = (
-        train_batch_size * accelerator.num_processes * gradient_accumulation_steps
-    )
+    total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
 
     logger.info("***** Running training *****")
     logger.info(f"  Num examples = {len(train_dataset)}")
     logger.info(f"  Num Epochs = {num_train_epochs}")
     logger.info(f"  Instantaneous batch size per device = {train_batch_size}")
-    logger.info(
-        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
-    )
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
     logger.info(f"  Gradient Accumulation steps = {gradient_accumulation_steps}")
     logger.info(f"  Total optimization steps = {max_train_steps}")
     global_step = 0
@@ -843,9 +798,7 @@ def do_textual_inversion_training(
             path = dirs[-1] if len(dirs) > 0 else None
 
         if path is None:
-            accelerator.print(
-                f"Checkpoint '{resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
+            accelerator.print(f"Checkpoint '{resume_from_checkpoint}' does not exist. Starting a new training run.")
             resume_from_checkpoint = None
         else:
             accelerator.print(f"Resuming from checkpoint {path}")
@@ -854,9 +807,7 @@ def do_textual_inversion_training(
 
             resume_global_step = global_step * gradient_accumulation_steps
             first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (
-                num_update_steps_per_epoch * gradient_accumulation_steps
-            )
+            resume_step = resume_global_step % (num_update_steps_per_epoch * gradient_accumulation_steps)
 
     # Only show the progress bar once on each machine.
     progress_bar = tqdm(
@@ -866,33 +817,20 @@ def do_textual_inversion_training(
     progress_bar.set_description("Steps")
 
     # keep original embeddings as reference
-    orig_embeds_params = (
-        accelerator.unwrap_model(text_encoder)
-        .get_input_embeddings()
-        .weight.data.clone()
-    )
+    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
 
     for epoch in range(first_epoch, num_train_epochs):
         text_encoder.train()
         for step, batch in enumerate(train_dataloader):
             # Skip steps until we reach the resumed step
-            if (
-                resume_step
-                and resume_from_checkpoint
-                and epoch == first_epoch
-                and step < resume_step
-            ):
+            if resume_step and resume_from_checkpoint and epoch == first_epoch and step < resume_step:
                 if step % gradient_accumulation_steps == 0:
                     progress_bar.update(1)
                 continue
 
             with accelerator.accumulate(text_encoder):
                 # Convert images to latent space
-                latents = (
-                    vae.encode(batch["pixel_values"].to(dtype=weight_dtype))
-                    .latent_dist.sample()
-                    .detach()
-                )
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
                 latents = latents * 0.18215
 
                 # Sample noise that we'll add to the latents
@@ -912,14 +850,10 @@ def do_textual_inversion_training(
                 noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
                 # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(
-                    dtype=weight_dtype
-                )
+                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
 
                 # Predict the noise residual
-                model_pred = unet(
-                    noisy_latents, timesteps, encoder_hidden_states
-                ).sample
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
                 # Get the target for loss depending on the prediction type
                 if noise_scheduler.config.prediction_type == "epsilon":
@@ -927,9 +861,7 @@ def do_textual_inversion_training(
                 elif noise_scheduler.config.prediction_type == "v_prediction":
                     target = noise_scheduler.get_velocity(latents, noise, timesteps)
                 else:
-                    raise ValueError(
-                        f"Unknown prediction type {noise_scheduler.config.prediction_type}"
-                    )
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
 
                 loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
 
@@ -942,22 +874,16 @@ def do_textual_inversion_training(
                 # Let's make sure we don't update any embedding weights besides the newly added token
                 index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
                 with torch.no_grad():
-                    accelerator.unwrap_model(
-                        text_encoder
-                    ).get_input_embeddings().weight[
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
                         index_no_updates
-                    ] = orig_embeds_params[
-                        index_no_updates
-                    ]
+                    ] = orig_embeds_params[index_no_updates]
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)
                 global_step += 1
                 if global_step % save_steps == 0:
-                    save_path = os.path.join(
-                        output_dir, f"learned_embeds-steps-{global_step}.bin"
-                    )
+                    save_path = os.path.join(output_dir, f"learned_embeds-steps-{global_step}.bin")
                     save_progress(
                         text_encoder,
                         placeholder_token_id,
@@ -968,9 +894,7 @@ def do_textual_inversion_training(
 
                 if global_step % checkpointing_steps == 0:
                     if accelerator.is_main_process:
-                        save_path = os.path.join(
-                            output_dir, f"checkpoint-{global_step}"
-                        )
+                        save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
                         accelerator.save_state(save_path)
                         logger.info(f"Saved state to {save_path}")
 
@@ -985,9 +909,7 @@ def do_textual_inversion_training(
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
         if push_to_hub and only_save_embeds:
-            logger.warn(
-                "Enabling full model saving because --push_to_hub=True was specified."
-            )
+            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
             save_full_model = True
         else:
             save_full_model = not only_save_embeds
@@ -1012,8 +934,6 @@ def do_textual_inversion_training(
         )
 
         if push_to_hub:
-            repo.push_to_hub(
-                commit_message="End of training", blocking=False, auto_lfs_prune=True
-            )
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
 
     accelerator.end_training()
diff --git a/invokeai/backend/util/__init__.py b/invokeai/backend/util/__init__.py
index fadeff4d75..2e69af5382 100644
--- a/invokeai/backend/util/__init__.py
+++ b/invokeai/backend/util/__init__.py
@@ -11,12 +11,4 @@ from .devices import (
     torch_dtype,
 )
 from .log import write_log
-from .util import (
-    ask_user,
-    download_with_resume,
-    instantiate_from_config,
-    url_attachment_name,
-    Chdir
-)
-
-
+from .util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name, Chdir
diff --git a/invokeai/backend/util/devices.py b/invokeai/backend/util/devices.py
index 3fbdaba41a..eeabcc35db 100644
--- a/invokeai/backend/util/devices.py
+++ b/invokeai/backend/util/devices.py
@@ -12,6 +12,7 @@ CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
 config = InvokeAIAppConfig.get_config()
 
+
 def choose_torch_device() -> torch.device:
     """Convenience routine for guessing which GPU device to run model on"""
     if config.always_use_cpu:
diff --git a/invokeai/backend/util/hotfixes.py b/invokeai/backend/util/hotfixes.py
index 67e46a426f..4710682ac1 100644
--- a/invokeai/backend/util/hotfixes.py
+++ b/invokeai/backend/util/hotfixes.py
@@ -20,6 +20,7 @@ from diffusers.models.controlnet import ControlNetConditioningEmbedding, Control
 
 # Modified ControlNetModel with encoder_attention_mask argument added
 
+
 class ControlNetModel(ModelMixin, ConfigMixin):
     """
     A ControlNet model.
@@ -618,9 +619,7 @@ class ControlNetModel(ModelMixin, ConfigMixin):
             mid_block_res_sample = mid_block_res_sample * conditioning_scale
 
         if self.config.global_pool_conditions:
-            down_block_res_samples = [
-                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
-            ]
+            down_block_res_samples = [torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples]
             mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
 
         if not return_dict:
@@ -630,5 +629,6 @@ class ControlNetModel(ModelMixin, ConfigMixin):
             down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
         )
 
+
 diffusers.ControlNetModel = ControlNetModel
-diffusers.models.controlnet.ControlNetModel = ControlNetModel
\ No newline at end of file
+diffusers.models.controlnet.ControlNetModel = ControlNetModel
diff --git a/invokeai/backend/util/logging.py b/invokeai/backend/util/logging.py
index 09ae600633..3a8d721aa5 100644
--- a/invokeai/backend/util/logging.py
+++ b/invokeai/backend/util/logging.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
 
 """
-invokeai.util.logging
+invokeai.backend.util.logging
 
 Logging class for InvokeAI that produces console messages
 
@@ -186,89 +186,109 @@ from invokeai.app.services.config import InvokeAIAppConfig, get_invokeai_config
 
 try:
     import syslog
+
     SYSLOG_AVAILABLE = True
 except:
     SYSLOG_AVAILABLE = False
 
+
 # module level functions
 def debug(msg, *args, **kwargs):
     InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
 
+
 def info(msg, *args, **kwargs):
     InvokeAILogger.getLogger().info(msg, *args, **kwargs)
 
+
 def warning(msg, *args, **kwargs):
     InvokeAILogger.getLogger().warning(msg, *args, **kwargs)
 
+
 def error(msg, *args, **kwargs):
     InvokeAILogger.getLogger().error(msg, *args, **kwargs)
 
+
 def critical(msg, *args, **kwargs):
     InvokeAILogger.getLogger().critical(msg, *args, **kwargs)
 
+
 def log(level, msg, *args, **kwargs):
     InvokeAILogger.getLogger().log(level, msg, *args, **kwargs)
 
+
 def disable(level=logging.CRITICAL):
     InvokeAILogger.getLogger().disable(level)
 
+
 def basicConfig(**kwargs):
     InvokeAILogger.getLogger().basicConfig(**kwargs)
 
+
 def getLogger(name: str = None) -> logging.Logger:
     return InvokeAILogger.getLogger(name)
 
 
-_FACILITY_MAP = dict(
-    LOG_KERN = syslog.LOG_KERN,
-    LOG_USER = syslog.LOG_USER,
-    LOG_MAIL = syslog.LOG_MAIL,
-    LOG_DAEMON = syslog.LOG_DAEMON,
-    LOG_AUTH = syslog.LOG_AUTH,
-    LOG_LPR = syslog.LOG_LPR,
-    LOG_NEWS = syslog.LOG_NEWS,
-    LOG_UUCP = syslog.LOG_UUCP,
-    LOG_CRON = syslog.LOG_CRON,
-    LOG_SYSLOG = syslog.LOG_SYSLOG,
-    LOG_LOCAL0 = syslog.LOG_LOCAL0,
-    LOG_LOCAL1 = syslog.LOG_LOCAL1,
-    LOG_LOCAL2 = syslog.LOG_LOCAL2,
-    LOG_LOCAL3 = syslog.LOG_LOCAL3,
-    LOG_LOCAL4 = syslog.LOG_LOCAL4,
-    LOG_LOCAL5 = syslog.LOG_LOCAL5,
-    LOG_LOCAL6 = syslog.LOG_LOCAL6,
-    LOG_LOCAL7 = syslog.LOG_LOCAL7,
-) if SYSLOG_AVAILABLE else dict()
-
-_SOCK_MAP = dict(
-    SOCK_STREAM = socket.SOCK_STREAM,
-    SOCK_DGRAM = socket.SOCK_DGRAM,
+_FACILITY_MAP = (
+    dict(
+        LOG_KERN=syslog.LOG_KERN,
+        LOG_USER=syslog.LOG_USER,
+        LOG_MAIL=syslog.LOG_MAIL,
+        LOG_DAEMON=syslog.LOG_DAEMON,
+        LOG_AUTH=syslog.LOG_AUTH,
+        LOG_LPR=syslog.LOG_LPR,
+        LOG_NEWS=syslog.LOG_NEWS,
+        LOG_UUCP=syslog.LOG_UUCP,
+        LOG_CRON=syslog.LOG_CRON,
+        LOG_SYSLOG=syslog.LOG_SYSLOG,
+        LOG_LOCAL0=syslog.LOG_LOCAL0,
+        LOG_LOCAL1=syslog.LOG_LOCAL1,
+        LOG_LOCAL2=syslog.LOG_LOCAL2,
+        LOG_LOCAL3=syslog.LOG_LOCAL3,
+        LOG_LOCAL4=syslog.LOG_LOCAL4,
+        LOG_LOCAL5=syslog.LOG_LOCAL5,
+        LOG_LOCAL6=syslog.LOG_LOCAL6,
+        LOG_LOCAL7=syslog.LOG_LOCAL7,
+    )
+    if SYSLOG_AVAILABLE
+    else dict()
 )
 
+_SOCK_MAP = dict(
+    SOCK_STREAM=socket.SOCK_STREAM,
+    SOCK_DGRAM=socket.SOCK_DGRAM,
+)
+
+
 class InvokeAIFormatter(logging.Formatter):
-    '''
+    """
     Base class for logging formatter
 
-    '''
+    """
+
     def format(self, record):
         formatter = logging.Formatter(self.log_fmt(record.levelno))
         return formatter.format(record)
 
     @abstractmethod
-    def log_fmt(self, levelno: int)->str:
+    def log_fmt(self, levelno: int) -> str:
         pass
-    
+
+
 class InvokeAISyslogFormatter(InvokeAIFormatter):
-    '''
+    """
     Formatting for syslog
-    '''
-    def log_fmt(self, levelno: int)->str:
-        return '%(name)s [%(process)d] <%(levelname)s> %(message)s'
+    """
+
+    def log_fmt(self, levelno: int) -> str:
+        return "%(name)s [%(process)d] <%(levelname)s> %(message)s"
+
 
 class InvokeAILegacyLogFormatter(InvokeAIFormatter):
-    '''
+    """
     Formatting for the InvokeAI Logger (legacy version)
-    '''
+    """
+
     FORMATS = {
         logging.DEBUG: "   | %(message)s",
         logging.INFO: ">> %(message)s",
@@ -276,20 +296,25 @@ class InvokeAILegacyLogFormatter(InvokeAIFormatter):
         logging.ERROR: "*** %(message)s",
         logging.CRITICAL: "### %(message)s",
     }
-    def log_fmt(self,levelno:int)->str:
+
+    def log_fmt(self, levelno: int) -> str:
         return self.FORMATS.get(levelno)
 
+
 class InvokeAIPlainLogFormatter(InvokeAIFormatter):
-    '''
+    """
     Custom Formatting for the InvokeAI Logger (plain version)
-    '''
-    def log_fmt(self, levelno: int)->str:
+    """
+
+    def log_fmt(self, levelno: int) -> str:
         return "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
 
+
 class InvokeAIColorLogFormatter(InvokeAIFormatter):
-    '''
+    """
     Custom Formatting for the InvokeAI Logger
-    '''
+    """
+
     # Color Codes
     grey = "\x1b[38;20m"
     yellow = "\x1b[33;20m"
@@ -308,32 +333,34 @@ class InvokeAIColorLogFormatter(InvokeAIFormatter):
         logging.INFO: grey + log_format + reset,
         logging.WARNING: yellow + log_format + reset,
         logging.ERROR: red + log_format + reset,
-        logging.CRITICAL: bold_red + log_format + reset
+        logging.CRITICAL: bold_red + log_format + reset,
     }
 
-    def log_fmt(self, levelno: int)->str:
+    def log_fmt(self, levelno: int) -> str:
         return self.FORMATS.get(levelno)
 
+
 LOG_FORMATTERS = {
-    'plain': InvokeAIPlainLogFormatter,
-    'color': InvokeAIColorLogFormatter,
-    'syslog': InvokeAISyslogFormatter,
-    'legacy': InvokeAILegacyLogFormatter,
+    "plain": InvokeAIPlainLogFormatter,
+    "color": InvokeAIColorLogFormatter,
+    "syslog": InvokeAISyslogFormatter,
+    "legacy": InvokeAILegacyLogFormatter,
 }
 
+
 class InvokeAILogger(object):
     loggers = dict()
 
     @classmethod
-    def getLogger(cls,
-                  name: str = 'InvokeAI',
-                  config: InvokeAIAppConfig=InvokeAIAppConfig.get_config())->logging.Logger:
+    def getLogger(
+        cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
+    ) -> logging.Logger:
         if name in cls.loggers:
             logger = cls.loggers[name]
             logger.handlers.clear()
         else:
             logger = logging.getLogger(name)
-        logger.setLevel(config.log_level.upper()) # yes, strings work here
+        logger.setLevel(config.log_level.upper())  # yes, strings work here
         for ch in cls.getLoggers(config):
             logger.addHandler(ch)
             cls.loggers[name] = logger
@@ -344,82 +371,80 @@ class InvokeAILogger(object):
         handler_strs = config.log_handlers
         handlers = list()
         for handler in handler_strs:
-            handler_name,*args = handler.split('=',2)
+            handler_name, *args = handler.split("=", 2)
             args = args[0] if len(args) > 0 else None
 
             # console and file get the fancy formatter.
             # syslog gets a simple one
             # http gets no custom formatter
             formatter = LOG_FORMATTERS[config.log_format]
-            if handler_name=='console':
+            if handler_name == "console":
                 ch = logging.StreamHandler()
                 ch.setFormatter(formatter())
                 handlers.append(ch)
-                
-            elif handler_name=='syslog':
+
+            elif handler_name == "syslog":
                 ch = cls._parse_syslog_args(args)
                 handlers.append(ch)
-                
-            elif handler_name=='file':
+
+            elif handler_name == "file":
                 ch = cls._parse_file_args(args)
                 ch.setFormatter(formatter())
                 handlers.append(ch)
-                
-            elif handler_name=='http':
+
+            elif handler_name == "http":
                 ch = cls._parse_http_args(args)
                 handlers.append(ch)
         return handlers
 
     @staticmethod
-    def _parse_syslog_args(
-            args: str=None
-    )-> logging.Handler:
+    def _parse_syslog_args(args: str = None) -> logging.Handler:
         if not SYSLOG_AVAILABLE:
             raise ValueError("syslog is not available on this system")
         if not args:
-            args='/dev/log' if Path('/dev/log').exists() else 'address:localhost:514'
+            args = "/dev/log" if Path("/dev/log").exists() else "address:localhost:514"
         syslog_args = dict()
         try:
-            for a in args.split(','):
-                arg_name,*arg_value = a.split(':',2)
-                if arg_name=='address':
-                    host,*port = arg_value
-                    port = 514 if len(port)==0 else int(port[0])
-                    syslog_args['address'] = (host,port)
-                elif arg_name=='facility':
-                    syslog_args['facility'] = _FACILITY_MAP[arg_value[0]]
-                elif arg_name=='socktype':
-                    syslog_args['socktype'] = _SOCK_MAP[arg_value[0]]
+            for a in args.split(","):
+                arg_name, *arg_value = a.split(":", 2)
+                if arg_name == "address":
+                    host, *port = arg_value
+                    port = 514 if len(port) == 0 else int(port[0])
+                    syslog_args["address"] = (host, port)
+                elif arg_name == "facility":
+                    syslog_args["facility"] = _FACILITY_MAP[arg_value[0]]
+                elif arg_name == "socktype":
+                    syslog_args["socktype"] = _SOCK_MAP[arg_value[0]]
                 else:
-                    syslog_args['address'] = arg_name
+                    syslog_args["address"] = arg_name
         except:
             raise ValueError(f"{args} is not a value argument list for syslog logging")
         return logging.handlers.SysLogHandler(**syslog_args)
-            
+
     @staticmethod
-    def _parse_file_args(args: str=None)-> logging.Handler:
+    def _parse_file_args(args: str = None) -> logging.Handler:
         if not args:
             raise ValueError("please provide filename for file logging using format 'file=/path/to/logfile.txt'")
         return logging.FileHandler(args)
 
     @staticmethod
-    def _parse_http_args(args: str=None)-> logging.Handler:
+    def _parse_http_args(args: str = None) -> logging.Handler:
         if not args:
             raise ValueError("please provide destination for http logging using format 'http=url'")
-        arg_list = args.split(',')
+        arg_list = args.split(",")
         url = urllib.parse.urlparse(arg_list.pop(0))
-        if url.scheme != 'http':
+        if url.scheme != "http":
             raise ValueError(f"the http logging module can only log to HTTP URLs, but {url.scheme} was specified")
         host = url.hostname
         path = url.path
         port = url.port or 80
-        
+
         syslog_args = dict()
         for a in arg_list:
-            arg_name, *arg_value = a.split(':',2)
-            if arg_name=='method':
-                arg_value = arg_value[0] if len(arg_value)>0 else 'GET'
+            arg_name, *arg_value = a.split(":", 2)
+            if arg_name == "method":
+                arg_value = arg_value[0] if len(arg_value) > 0 else "GET"
                 syslog_args[arg_name] = arg_value
             else:  # TODO: Provide support for SSL context and credentials
                 pass
-        return logging.handlers.HTTPHandler(f'{host}:{port}',path,**syslog_args)
+        return logging.handlers.HTTPHandler(f"{host}:{port}", path, **syslog_args)
diff --git a/invokeai/backend/util/mps_fixes.py b/invokeai/backend/util/mps_fixes.py
index e4d8da20a9..409eff1c8b 100644
--- a/invokeai/backend/util/mps_fixes.py
+++ b/invokeai/backend/util/mps_fixes.py
@@ -8,6 +8,8 @@ if torch.backends.mps.is_available():
 
 
 _torch_layer_norm = torch.nn.functional.layer_norm
+
+
 def new_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
     if input.device.type == "mps" and input.dtype == torch.float16:
         input = input.float()
@@ -19,20 +21,26 @@ def new_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
     else:
         return _torch_layer_norm(input, normalized_shape, weight, bias, eps)
 
+
 torch.nn.functional.layer_norm = new_layer_norm
 
 
 _torch_tensor_permute = torch.Tensor.permute
+
+
 def new_torch_tensor_permute(input, *dims):
     result = _torch_tensor_permute(input, *dims)
     if input.device == "mps" and input.dtype == torch.float16:
         result = result.contiguous()
     return result
 
+
 torch.Tensor.permute = new_torch_tensor_permute
 
 
 _torch_lerp = torch.lerp
+
+
 def new_torch_lerp(input, end, weight, *, out=None):
     if input.device.type == "mps" and input.dtype == torch.float16:
         input = input.float()
@@ -52,20 +60,36 @@ def new_torch_lerp(input, end, weight, *, out=None):
     else:
         return _torch_lerp(input, end, weight, out=out)
 
+
 torch.lerp = new_torch_lerp
 
 
 _torch_interpolate = torch.nn.functional.interpolate
-def new_torch_interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False):
+
+
+def new_torch_interpolate(
+    input,
+    size=None,
+    scale_factor=None,
+    mode="nearest",
+    align_corners=None,
+    recompute_scale_factor=None,
+    antialias=False,
+):
     if input.device.type == "mps" and input.dtype == torch.float16:
-        return _torch_interpolate(input.float(), size, scale_factor, mode, align_corners, recompute_scale_factor, antialias).half()
+        return _torch_interpolate(
+            input.float(), size, scale_factor, mode, align_corners, recompute_scale_factor, antialias
+        ).half()
     else:
         return _torch_interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias)
 
+
 torch.nn.functional.interpolate = new_torch_interpolate
 
 # TODO: refactor it
 _SlicedAttnProcessor = diffusers.models.attention_processor.SlicedAttnProcessor
+
+
 class ChunkedSlicedAttnProcessor:
     r"""
     Processor for implementing sliced attention.
@@ -78,7 +102,7 @@ class ChunkedSlicedAttnProcessor:
 
     def __init__(self, slice_size):
         assert isinstance(slice_size, int)
-        slice_size = 1 # TODO: maybe implement chunking in batches too when enough memory
+        slice_size = 1  # TODO: maybe implement chunking in batches too when enough memory
         self.slice_size = slice_size
         self._sliced_attn_processor = _SlicedAttnProcessor(slice_size)
 
@@ -121,7 +145,9 @@ class ChunkedSlicedAttnProcessor:
             (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
         )
 
-        chunk_tmp_tensor = torch.empty(self.slice_size, query.shape[1], key.shape[1], dtype=query.dtype, device=query.device)
+        chunk_tmp_tensor = torch.empty(
+            self.slice_size, query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
+        )
 
         for i in range(batch_size_attention // self.slice_size):
             start_idx = i * self.slice_size
@@ -131,7 +157,15 @@ class ChunkedSlicedAttnProcessor:
             key_slice = key[start_idx:end_idx]
             attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
 
-            self.get_attention_scores_chunked(attn, query_slice, key_slice, attn_mask_slice, hidden_states[start_idx:end_idx], value[start_idx:end_idx], chunk_tmp_tensor)
+            self.get_attention_scores_chunked(
+                attn,
+                query_slice,
+                key_slice,
+                attn_mask_slice,
+                hidden_states[start_idx:end_idx],
+                value[start_idx:end_idx],
+                chunk_tmp_tensor,
+            )
 
         hidden_states = attn.batch_to_head_dim(hidden_states)
 
@@ -150,7 +184,6 @@ class ChunkedSlicedAttnProcessor:
 
         return hidden_states
 
-
     def get_attention_scores_chunked(self, attn, query, key, attention_mask, hidden_states, value, chunk):
         # batch size = 1
         assert query.shape[0] == 1
@@ -163,14 +196,14 @@ class ChunkedSlicedAttnProcessor:
             query = query.float()
             key = key.float()
 
-        #out_item_size = query.dtype.itemsize
-        #if attn.upcast_attention:
+        # out_item_size = query.dtype.itemsize
+        # if attn.upcast_attention:
         #    out_item_size = torch.float32.itemsize
         out_item_size = query.element_size()
         if attn.upcast_attention:
             out_item_size = 4
 
-        chunk_size = 2 ** 29
+        chunk_size = 2**29
 
         out_size = query.shape[1] * key.shape[1] * out_item_size
         chunks_count = min(query.shape[1], math.ceil((out_size - 1) / chunk_size))
@@ -181,8 +214,8 @@ class ChunkedSlicedAttnProcessor:
         def _get_chunk_view(tensor, start, length):
             if start + length > tensor.shape[1]:
                 length = tensor.shape[1] - start
-            #print(f"view: [{tensor.shape[0]},{tensor.shape[1]},{tensor.shape[2]}] - start: {start}, length: {length}")
-            return tensor[:,start:start+length]
+            # print(f"view: [{tensor.shape[0]},{tensor.shape[1]},{tensor.shape[2]}] - start: {start}, length: {length}")
+            return tensor[:, start : start + length]
 
         for chunk_pos in range(0, query.shape[1], chunk_step):
             if attention_mask is not None:
@@ -196,7 +229,7 @@ class ChunkedSlicedAttnProcessor:
                 )
             else:
                 torch.baddbmm(
-                    torch.zeros((1,1,1), device=query.device, dtype=query.dtype),
+                    torch.zeros((1, 1, 1), device=query.device, dtype=query.dtype),
                     _get_chunk_view(query, chunk_pos, chunk_step),
                     key,
                     beta=0,
@@ -206,7 +239,7 @@ class ChunkedSlicedAttnProcessor:
             chunk = chunk.softmax(dim=-1)
             torch.bmm(chunk, value, out=_get_chunk_view(hidden_states, chunk_pos, chunk_step))
 
-        #del chunk
+        # del chunk
 
 
 diffusers.models.attention_processor.SlicedAttnProcessor = ChunkedSlicedAttnProcessor
diff --git a/invokeai/backend/util/util.py b/invokeai/backend/util/util.py
index 1cc632e483..f3c182c063 100644
--- a/invokeai/backend/util/util.py
+++ b/invokeai/backend/util/util.py
@@ -32,9 +32,7 @@ def log_txt_as_img(wh, xc, size=10):
         draw = ImageDraw.Draw(txt)
         font = ImageFont.load_default()
         nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(
-            xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
-        )
+        lines = "\n".join(xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc))
 
         try:
             draw.text((0, 0), lines, fill="black", font=font)
@@ -81,9 +79,7 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        logger.debug(
-            f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params."
-        )
+        logger.debug(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
     return total_params
 
 
@@ -154,21 +150,12 @@ def parallel_data_prefetch(
         proc = Thread
     # spawn processes
     if target_data_type == "ndarray":
-        arguments = [
-            [func, Q, part, i, use_worker_id]
-            for i, part in enumerate(np.array_split(data, n_proc))
-        ]
+        arguments = [[func, Q, part, i, use_worker_id] for i, part in enumerate(np.array_split(data, n_proc))]
     else:
-        step = (
-            int(len(data) / n_proc + 1)
-            if len(data) % n_proc != 0
-            else int(len(data) / n_proc)
-        )
+        step = int(len(data) / n_proc + 1) if len(data) % n_proc != 0 else int(len(data) / n_proc)
         arguments = [
             [func, Q, part, i, use_worker_id]
-            for i, part in enumerate(
-                [data[i : i + step] for i in range(0, len(data), step)]
-            )
+            for i, part in enumerate([data[i : i + step] for i in range(0, len(data), step)])
         ]
     processes = []
     for i in range(n_proc):
@@ -220,9 +207,7 @@ def parallel_data_prefetch(
         return gather_res
 
 
-def rand_perlin_2d(
-    shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3
-):
+def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
     delta = (res[0] / shape[0], res[1] / shape[1])
     d = (shape[0] // res[0], shape[1] // res[1])
 
@@ -265,9 +250,9 @@ def rand_perlin_2d(
     n01 = dot(tile_grads([0, -1], [1, None]), [0, -1]).to(device)
     n11 = dot(tile_grads([1, None], [1, None]), [-1, -1]).to(device)
     t = fade(grid[: shape[0], : shape[1]])
-    noise = math.sqrt(2) * torch.lerp(
-        torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]
-    ).to(device)
+    noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(
+        device
+    )
     return noise.to(dtype=torch_dtype(device))
 
 
@@ -276,9 +261,7 @@ def ask_user(question: str, answers: list):
 
     user_prompt = f"\n>> {question} {answers}: "
     invalid_answer_msg = "Invalid answer. Please try again."
-    pose_question = chain(
-        [user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt]))
-    )
+    pose_question = chain([user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt])))
     user_answers = map(input, pose_question)
     valid_response = next(filter(answers.__contains__, user_answers))
     return valid_response
@@ -303,9 +286,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
 
     if dest.is_dir():
         try:
-            file_name = re.search(
-                'filename="(.+)"', resp.headers.get("Content-Disposition")
-            ).group(1)
+            file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
         except:
             file_name = os.path.basename(url)
         dest = dest / file_name
@@ -322,7 +303,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
         logger.warning("corrupt existing file found. re-downloading")
         os.remove(dest)
         exist_size = 0
-        
+
     if resp.status_code == 416 or (content_length > 0 and exist_size == content_length):
         logger.warning(f"{dest}: complete file found. Skipping.")
         return dest
@@ -377,16 +358,16 @@ def image_to_dataURL(image: Image.Image, image_format: str = "PNG") -> str:
     buffered = io.BytesIO()
     image.save(buffered, format=image_format)
     mime_type = Image.MIME.get(image_format.upper(), "image/" + image_format.lower())
-    image_base64 = f"data:{mime_type};base64," + base64.b64encode(
-        buffered.getvalue()
-    ).decode("UTF-8")
+    image_base64 = f"data:{mime_type};base64," + base64.b64encode(buffered.getvalue()).decode("UTF-8")
     return image_base64
 
+
 class Chdir(object):
-    '''Context manager to chdir to desired directory and change back after context exits:
+    """Context manager to chdir to desired directory and change back after context exits:
     Args:
         path (Path): The path to the cwd
-    '''
+    """
+
     def __init__(self, path: Path):
         self.path = path
         self.original = Path().absolute()
@@ -394,5 +375,5 @@ class Chdir(object):
     def __enter__(self):
         os.chdir(self.path)
 
-    def __exit__(self,*args):
+    def __exit__(self, *args):
         os.chdir(self.original)
diff --git a/invokeai/backend/web/invoke_ai_web_server.py b/invokeai/backend/web/invoke_ai_web_server.py
index eec02cd9dc..88eb77551f 100644
--- a/invokeai/backend/web/invoke_ai_web_server.py
+++ b/invokeai/backend/web/invoke_ai_web_server.py
@@ -64,10 +64,7 @@ class InvokeAIWebServer:
         self.ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}
 
     def allowed_file(self, filename: str) -> bool:
-        return (
-            "." in filename
-            and filename.rsplit(".", 1)[1].lower() in self.ALLOWED_EXTENSIONS
-        )
+        return "." in filename and filename.rsplit(".", 1)[1].lower() in self.ALLOWED_EXTENSIONS
 
     def run(self):
         self.setup_app()
@@ -99,9 +96,7 @@ class InvokeAIWebServer:
                 _cors = _cors.split(",")
             socketio_args["cors_allowed_origins"] = _cors
 
-        self.app = Flask(
-            __name__, static_url_path="", static_folder=frontend.__path__[0]
-        )
+        self.app = Flask(__name__, static_url_path="", static_folder=frontend.__path__[0])
 
         self.socketio = SocketIO(self.app, **socketio_args)
 
@@ -192,9 +187,7 @@ class InvokeAIWebServer:
 
                 (width, height) = pil_image.size
 
-                thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(file_path), self.thumbnail_image_path
-                )
+                thumbnail_path = save_thumbnail(pil_image, os.path.basename(file_path), self.thumbnail_image_path)
 
                 response = {
                     "url": self.get_url_from_image_path(file_path),
@@ -237,12 +230,8 @@ class InvokeAIWebServer:
                     f"Point your browser at http{'s' if useSSL else ''}://localhost:{self.port} or use the host's DNS name or IP address."
                 )
             else:
-                logger.info(
-                    "Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
-                )
-                logger.info(
-                    f"Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
-                )
+                logger.info("Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address.")
+                logger.info(f"Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}")
             if not useSSL:
                 self.socketio.run(app=self.app, host=self.host, port=self.port)
             else:
@@ -392,22 +381,16 @@ class InvokeAIWebServer:
         @socketio.on("convertToDiffusers")
         def convert_to_diffusers(model_to_convert: dict):
             try:
-                if model_info := self.generate.model_manager.model_info(
-                    model_name=model_to_convert["model_name"]
-                ):
+                if model_info := self.generate.model_manager.model_info(model_name=model_to_convert["model_name"]):
                     if "weights" in model_info:
                         ckpt_path = Path(model_info["weights"])
                         original_config_file = Path(model_info["config"])
                         model_name = model_to_convert["model_name"]
                         model_description = model_info["description"]
                     else:
-                        self.socketio.emit(
-                            "error", {"message": "Model is not a valid checkpoint file"}
-                        )
+                        self.socketio.emit("error", {"message": "Model is not a valid checkpoint file"})
                 else:
-                    self.socketio.emit(
-                        "error", {"message": "Could not retrieve model info."}
-                    )
+                    self.socketio.emit("error", {"message": "Could not retrieve model info."})
 
                 if not ckpt_path.is_absolute():
                     ckpt_path = Path(Globals.root, ckpt_path)
@@ -415,22 +398,13 @@ class InvokeAIWebServer:
                 if original_config_file and not original_config_file.is_absolute():
                     original_config_file = Path(Globals.root, original_config_file)
 
-                diffusers_path = Path(
-                    ckpt_path.parent.absolute(), f"{model_name}_diffusers"
-                )
+                diffusers_path = Path(ckpt_path.parent.absolute(), f"{model_name}_diffusers")
 
                 if model_to_convert["save_location"] == "root":
-                    diffusers_path = Path(
-                        global_converted_ckpts_dir(), f"{model_name}_diffusers"
-                    )
+                    diffusers_path = Path(global_converted_ckpts_dir(), f"{model_name}_diffusers")
 
-                if (
-                    model_to_convert["save_location"] == "custom"
-                    and model_to_convert["custom_location"] is not None
-                ):
-                    diffusers_path = Path(
-                        model_to_convert["custom_location"], f"{model_name}_diffusers"
-                    )
+                if model_to_convert["save_location"] == "custom" and model_to_convert["custom_location"] is not None:
+                    diffusers_path = Path(model_to_convert["custom_location"], f"{model_name}_diffusers")
 
                 if diffusers_path.exists():
                     shutil.rmtree(diffusers_path)
@@ -462,10 +436,7 @@ class InvokeAIWebServer:
         def merge_diffusers_models(model_merge_info: dict):
             try:
                 models_to_merge = model_merge_info["models_to_merge"]
-                model_ids_or_paths = [
-                    self.generate.model_manager.model_name_or_path(x)
-                    for x in models_to_merge
-                ]
+                model_ids_or_paths = [self.generate.model_manager.model_name_or_path(x) for x in models_to_merge]
                 merged_pipe = merge_diffusion_models(
                     model_ids_or_paths,
                     model_merge_info["alpha"],
@@ -487,15 +458,11 @@ class InvokeAIWebServer:
                     commit_to_conf=opt.conf,
                 )
 
-                if vae := self.generate.model_manager.config[models_to_merge[0]].get(
-                    "vae", None
-                ):
+                if vae := self.generate.model_manager.config[models_to_merge[0]].get("vae", None):
                     logger.info(f"Using configured VAE assigned to {models_to_merge[0]}")
                     merged_model_config.update(vae=vae)
 
-                self.generate.model_manager.import_diffuser_model(
-                    dump_path, **merged_model_config
-                )
+                self.generate.model_manager.import_diffuser_model(dump_path, **merged_model_config)
                 new_model_list = self.generate.model_manager.list_models()
 
                 socketio.emit(
@@ -525,9 +492,7 @@ class InvokeAIWebServer:
                         )
                         os.remove(thumbnail_path)
                     except Exception as e:
-                        socketio.emit(
-                            "error", {"message": f"Unable to delete {f}: {str(e)}"}
-                        )
+                        socketio.emit("error", {"message": f"Unable to delete {f}: {str(e)}"})
                         pass
 
                 socketio.emit("tempFolderEmptied")
@@ -550,9 +515,7 @@ class InvokeAIWebServer:
 
                 (width, height) = pil_image.size
 
-                thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(new_path), self.thumbnail_image_path
-                )
+                thumbnail_path = save_thumbnail(pil_image, os.path.basename(new_path), self.thumbnail_image_path)
 
                 image_array = [
                     {
@@ -577,18 +540,14 @@ class InvokeAIWebServer:
         @socketio.on("requestLatestImages")
         def handle_request_latest_images(category, latest_mtime):
             try:
-                base_path = (
-                    self.result_path if category == "result" else self.init_image_path
-                )
+                base_path = self.result_path if category == "result" else self.init_image_path
 
                 paths = []
 
                 for ext in ("*.png", "*.jpg", "*.jpeg"):
                     paths.extend(glob.glob(os.path.join(base_path, ext)))
 
-                image_paths = sorted(
-                    paths, key=lambda x: os.path.getmtime(x), reverse=True
-                )
+                image_paths = sorted(paths, key=lambda x: os.path.getmtime(x), reverse=True)
 
                 image_paths = list(
                     filter(
@@ -609,16 +568,12 @@ class InvokeAIWebServer:
                         pil_image = Image.open(path)
                         (width, height) = pil_image.size
 
-                        thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(path), self.thumbnail_image_path
-                        )
+                        thumbnail_path = save_thumbnail(pil_image, os.path.basename(path), self.thumbnail_image_path)
 
                         image_array.append(
                             {
                                 "url": self.get_url_from_image_path(path),
-                                "thumbnail": self.get_url_from_image_path(
-                                    thumbnail_path
-                                ),
+                                "thumbnail": self.get_url_from_image_path(thumbnail_path),
                                 "mtime": os.path.getmtime(path),
                                 "metadata": metadata.get("sd-metadata"),
                                 "dreamPrompt": metadata.get("Dream"),
@@ -628,9 +583,7 @@ class InvokeAIWebServer:
                             }
                         )
                     except Exception as e:
-                        socketio.emit(
-                            "error", {"message": f"Unable to load {path}: {str(e)}"}
-                        )
+                        socketio.emit("error", {"message": f"Unable to load {path}: {str(e)}"})
                         pass
 
                 socketio.emit(
@@ -645,17 +598,13 @@ class InvokeAIWebServer:
             try:
                 page_size = 50
 
-                base_path = (
-                    self.result_path if category == "result" else self.init_image_path
-                )
+                base_path = self.result_path if category == "result" else self.init_image_path
 
                 paths = []
                 for ext in ("*.png", "*.jpg", "*.jpeg"):
                     paths.extend(glob.glob(os.path.join(base_path, ext)))
 
-                image_paths = sorted(
-                    paths, key=lambda x: os.path.getmtime(x), reverse=True
-                )
+                image_paths = sorted(paths, key=lambda x: os.path.getmtime(x), reverse=True)
 
                 if earliest_mtime:
                     image_paths = list(
@@ -679,16 +628,12 @@ class InvokeAIWebServer:
                         pil_image = Image.open(path)
                         (width, height) = pil_image.size
 
-                        thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(path), self.thumbnail_image_path
-                        )
+                        thumbnail_path = save_thumbnail(pil_image, os.path.basename(path), self.thumbnail_image_path)
 
                         image_array.append(
                             {
                                 "url": self.get_url_from_image_path(path),
-                                "thumbnail": self.get_url_from_image_path(
-                                    thumbnail_path
-                                ),
+                                "thumbnail": self.get_url_from_image_path(thumbnail_path),
                                 "mtime": os.path.getmtime(path),
                                 "metadata": metadata.get("sd-metadata"),
                                 "dreamPrompt": metadata.get("Dream"),
@@ -699,9 +644,7 @@ class InvokeAIWebServer:
                         )
                     except Exception as e:
                         logger.info(f"Unable to load {path}")
-                        socketio.emit(
-                            "error", {"message": f"Unable to load {path}: {str(e)}"}
-                        )
+                        socketio.emit("error", {"message": f"Unable to load {path}: {str(e)}"})
                         pass
 
                 socketio.emit(
@@ -716,9 +659,7 @@ class InvokeAIWebServer:
                 self.handle_exceptions(e)
 
         @socketio.on("generateImage")
-        def handle_generate_image_event(
-            generation_parameters, esrgan_parameters, facetool_parameters
-        ):
+        def handle_generate_image_event(generation_parameters, esrgan_parameters, facetool_parameters):
             try:
                 # truncate long init_mask/init_img base64 if needed
                 printable_parameters = {
@@ -726,14 +667,10 @@ class InvokeAIWebServer:
                 }
 
                 if "init_img" in generation_parameters:
-                    printable_parameters["init_img"] = (
-                        printable_parameters["init_img"][:64] + "..."
-                    )
+                    printable_parameters["init_img"] = printable_parameters["init_img"][:64] + "..."
 
                 if "init_mask" in generation_parameters:
-                    printable_parameters["init_mask"] = (
-                        printable_parameters["init_mask"][:64] + "..."
-                    )
+                    printable_parameters["init_mask"] = printable_parameters["init_mask"][:64] + "..."
 
                 logger.info(f"Image Generation Parameters:\n\n{printable_parameters}\n")
                 logger.info(f"ESRGAN Parameters: {esrgan_parameters}")
@@ -750,18 +687,14 @@ class InvokeAIWebServer:
         @socketio.on("runPostprocessing")
         def handle_run_postprocessing(original_image, postprocessing_parameters):
             try:
-                logger.info(
-                    f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
-                )
+                logger.info(f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}')
 
                 progress = Progress()
 
                 socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
-                original_image_path = self.get_image_path_from_url(
-                    original_image["url"]
-                )
+                original_image_path = self.get_image_path_from_url(original_image["url"])
 
                 image = Image.open(original_image_path)
 
@@ -801,14 +734,10 @@ class InvokeAIWebServer:
                         strength=postprocessing_parameters["facetool_strength"],
                         fidelity=postprocessing_parameters["codeformer_fidelity"],
                         seed=seed,
-                        device="cpu"
-                        if str(self.generate.device) == "mps"
-                        else self.generate.device,
+                        device="cpu" if str(self.generate.device) == "mps" else self.generate.device,
                     )
                 else:
-                    raise TypeError(
-                        f'{postprocessing_parameters["type"]} is not a valid postprocessing type'
-                    )
+                    raise TypeError(f'{postprocessing_parameters["type"]} is not a valid postprocessing type')
 
                 progress.set_current_status("common.statusSavingImage")
                 socketio.emit("progressUpdate", progress.to_formatted_dict())
@@ -832,9 +761,7 @@ class InvokeAIWebServer:
                     postprocessing=postprocessing_parameters["type"],
                 )
 
-                thumbnail_path = save_thumbnail(
-                    image, os.path.basename(path), self.thumbnail_image_path
-                )
+                thumbnail_path = save_thumbnail(image, os.path.basename(path), self.thumbnail_image_path)
 
                 self.write_log_message(
                     f'[Postprocessed] "{original_image_path}" > "{path}": {postprocessing_parameters}'
@@ -901,17 +828,13 @@ class InvokeAIWebServer:
             "app_version": APP_VERSION,
         }
 
-    def generate_images(
-        self, generation_parameters, esrgan_parameters, facetool_parameters
-    ):
+    def generate_images(self, generation_parameters, esrgan_parameters, facetool_parameters):
         try:
             self.canceled.clear()
 
             step_index = 1
             prior_variations = (
-                generation_parameters["with_variations"]
-                if "with_variations" in generation_parameters
-                else []
+                generation_parameters["with_variations"] if "with_variations" in generation_parameters else []
             )
 
             actual_generation_mode = generation_parameters["generation_mode"]
@@ -943,9 +866,7 @@ class InvokeAIWebServer:
 
                 original_bounding_box = generation_parameters["bounding_box"].copy()
 
-                initial_image = dataURL_to_image(
-                    generation_parameters["init_img"]
-                ).convert("RGBA")
+                initial_image = dataURL_to_image(generation_parameters["init_img"]).convert("RGBA")
 
                 """
                 The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass
@@ -962,13 +883,9 @@ class InvokeAIWebServer:
                 generation_parameters["bounding_box"]["y"] = 0
 
                 # Convert mask dataURL to an image and convert to greyscale
-                mask_image = dataURL_to_image(
-                    generation_parameters["init_mask"]
-                ).convert("L")
+                mask_image = dataURL_to_image(generation_parameters["init_mask"]).convert("L")
 
-                actual_generation_mode = get_canvas_generation_mode(
-                    initial_image, mask_image
-                )
+                actual_generation_mode = get_canvas_generation_mode(initial_image, mask_image)
 
                 """
                 Apply the mask to the init image, creating a "mask" image with
@@ -1018,9 +935,7 @@ class InvokeAIWebServer:
             elif generation_parameters["generation_mode"] == "img2img":
                 init_img_url = generation_parameters["init_img"]
                 init_img_path = self.get_image_path_from_url(init_img_url)
-                generation_parameters["init_img"] = Image.open(init_img_path).convert(
-                    "RGB"
-                )
+                generation_parameters["init_img"] = Image.open(init_img_path).convert("RGB")
 
             def image_progress(intermediate_state: PipelineIntermediateState):
                 if self.canceled.is_set():
@@ -1046,9 +961,7 @@ class InvokeAIWebServer:
                 }
 
                 progress.set_current_step(step + 1)
-                progress.set_current_status(
-                    f"{generation_messages[actual_generation_mode]}"
-                )
+                progress.set_current_status(f"{generation_messages[actual_generation_mode]}")
                 progress.set_current_status_has_steps(True)
 
                 if (
@@ -1057,9 +970,7 @@ class InvokeAIWebServer:
                     and step < generation_parameters["steps"] - 1
                 ):
                     image = self.generate.sample_to_image(sample)
-                    metadata = self.parameters_to_generated_image_metadata(
-                        generation_parameters
-                    )
+                    metadata = self.parameters_to_generated_image_metadata(generation_parameters)
                     command = parameters_to_command(generation_parameters)
 
                     (width, height) = image.size
@@ -1140,15 +1051,10 @@ class InvokeAIWebServer:
                 all_parameters = generation_parameters
                 postprocessing = False
 
-                if (
-                    "variation_amount" in all_parameters
-                    and all_parameters["variation_amount"] > 0
-                ):
+                if "variation_amount" in all_parameters and all_parameters["variation_amount"] > 0:
                     first_seed = first_seed or seed
                     this_variation = [[seed, all_parameters["variation_amount"]]]
-                    all_parameters["with_variations"] = (
-                        prior_variations + this_variation
-                    )
+                    all_parameters["with_variations"] = prior_variations + this_variation
                     all_parameters["seed"] = first_seed
                 elif "with_variations" in all_parameters:
                     all_parameters["seed"] = first_seed
@@ -1186,9 +1092,7 @@ class InvokeAIWebServer:
                     if facetool_parameters["type"] == "gfpgan":
                         progress.set_current_status("common.statusRestoringFacesGFPGAN")
                     elif facetool_parameters["type"] == "codeformer":
-                        progress.set_current_status(
-                            "common.statusRestoringFacesCodeFormer"
-                        )
+                        progress.set_current_status("common.statusRestoringFacesCodeFormer")
 
                     progress.set_current_status_has_steps(False)
                     self.socketio.emit("progressUpdate", progress.to_formatted_dict())
@@ -1206,18 +1110,12 @@ class InvokeAIWebServer:
                             strength=facetool_parameters["strength"],
                             fidelity=facetool_parameters["codeformer_fidelity"],
                             seed=seed,
-                            device="cpu"
-                            if str(self.generate.device) == "mps"
-                            else self.generate.device,
+                            device="cpu" if str(self.generate.device) == "mps" else self.generate.device,
                         )
-                        all_parameters["codeformer_fidelity"] = facetool_parameters[
-                            "codeformer_fidelity"
-                        ]
+                        all_parameters["codeformer_fidelity"] = facetool_parameters["codeformer_fidelity"]
 
                     postprocessing = True
-                    all_parameters["facetool_strength"] = facetool_parameters[
-                        "strength"
-                    ]
+                    all_parameters["facetool_strength"] = facetool_parameters["strength"]
                     all_parameters["facetool_type"] = facetool_parameters["type"]
 
                 progress.set_current_status("common.statusSavingImage")
@@ -1226,9 +1124,7 @@ class InvokeAIWebServer:
 
                 # restore the stashed URLS and discard the paths, we are about to send the result to client
                 all_parameters["init_img"] = (
-                    init_img_url
-                    if generation_parameters["generation_mode"] == "img2img"
-                    else ""
+                    init_img_url if generation_parameters["generation_mode"] == "img2img" else ""
                 )
 
                 if "init_mask" in all_parameters:
@@ -1246,8 +1142,7 @@ class InvokeAIWebServer:
 
                 generated_image_outdir = (
                     self.result_path
-                    if generation_parameters["generation_mode"]
-                    in ["txt2img", "img2img"]
+                    if generation_parameters["generation_mode"] in ["txt2img", "img2img"]
                     else self.temp_image_path
                 )
 
@@ -1259,9 +1154,7 @@ class InvokeAIWebServer:
                     postprocessing=postprocessing,
                 )
 
-                thumbnail_path = save_thumbnail(
-                    image, os.path.basename(path), self.thumbnail_image_path
-                )
+                thumbnail_path = save_thumbnail(image, os.path.basename(path), self.thumbnail_image_path)
 
                 logger.info(f'Image generated: "{path}"\n')
                 self.write_log_message(f'[Generated] "{path}": {command}')
@@ -1281,14 +1174,10 @@ class InvokeAIWebServer:
                     tokens = (
                         None
                         if type(parsed_prompt) is Blend
-                        else get_tokens_for_prompt_object(
-                                model.tokenizer, parsed_prompt
-                        )
+                        else get_tokens_for_prompt_object(model.tokenizer, parsed_prompt)
                     )
                 attention_maps_image_base64_url = (
-                    None
-                    if attention_maps_image is None
-                    else image_to_dataURL(attention_maps_image)
+                    None if attention_maps_image is None else image_to_dataURL(attention_maps_image)
                 )
 
                 self.socketio.emit(
@@ -1382,9 +1271,7 @@ class InvokeAIWebServer:
                 }
 
                 if parameters["facetool_type"] == "codeformer":
-                    facetool_parameters["fidelity"] = float(
-                        parameters["codeformer_fidelity"]
-                    )
+                    facetool_parameters["fidelity"] = float(parameters["codeformer_fidelity"])
 
                 postprocessing.append(facetool_parameters)
 
@@ -1398,9 +1285,7 @@ class InvokeAIWebServer:
                     }
                 )
 
-            rfc_dict["postprocessing"] = (
-                postprocessing if len(postprocessing) > 0 else None
-            )
+            rfc_dict["postprocessing"] = postprocessing if len(postprocessing) > 0 else None
 
             # semantic drift
             rfc_dict["sampler"] = parameters["sampler_name"]
@@ -1409,22 +1294,15 @@ class InvokeAIWebServer:
             variations = []
 
             if "with_variations" in parameters:
-                variations = [
-                    {"seed": x[0], "weight": x[1]}
-                    for x in parameters["with_variations"]
-                ]
+                variations = [{"seed": x[0], "weight": x[1]} for x in parameters["with_variations"]]
 
             rfc_dict["variations"] = variations
 
             if rfc_dict["type"] == "img2img":
                 rfc_dict["strength"] = parameters["strength"]
                 rfc_dict["fit"] = parameters["fit"]  # TODO: Noncompliant
-                rfc_dict["orig_hash"] = calculate_init_img_hash(
-                    self.get_image_path_from_url(parameters["init_img"])
-                )
-                rfc_dict["init_image_path"] = parameters[
-                    "init_img"
-                ]  # TODO: Noncompliant
+                rfc_dict["orig_hash"] = calculate_init_img_hash(self.get_image_path_from_url(parameters["init_img"]))
+                rfc_dict["init_image_path"] = parameters["init_img"]  # TODO: Noncompliant
 
             metadata["image"] = rfc_dict
 
@@ -1433,9 +1311,7 @@ class InvokeAIWebServer:
         except Exception as e:
             self.handle_exceptions(e)
 
-    def parameters_to_post_processed_image_metadata(
-        self, parameters, original_image_path
-    ):
+    def parameters_to_post_processed_image_metadata(self, parameters, original_image_path):
         try:
             current_metadata = retrieve_metadata(original_image_path)["sd-metadata"]
             postprocessing_metadata = {}
@@ -1447,9 +1323,7 @@ class InvokeAIWebServer:
             if "image" not in current_metadata:
                 current_metadata["image"] = {}
 
-                orig_hash = calculate_init_img_hash(
-                    self.get_image_path_from_url(original_image_path)
-                )
+                orig_hash = calculate_init_img_hash(self.get_image_path_from_url(original_image_path))
 
                 postprocessing_metadata["orig_path"] = (original_image_path,)
                 postprocessing_metadata["orig_hash"] = orig_hash
@@ -1473,9 +1347,7 @@ class InvokeAIWebServer:
             if "postprocessing" in current_metadata["image"] and isinstance(
                 current_metadata["image"]["postprocessing"], list
             ):
-                current_metadata["image"]["postprocessing"].append(
-                    postprocessing_metadata
-                )
+                current_metadata["image"]["postprocessing"].append(postprocessing_metadata)
             else:
                 current_metadata["image"]["postprocessing"] = [postprocessing_metadata]
 
@@ -1556,29 +1428,17 @@ class InvokeAIWebServer:
         """Given a url to an image used by the client, returns the absolute file path to that image"""
         try:
             if "init-images" in url:
-                return os.path.abspath(
-                    os.path.join(self.init_image_path, os.path.basename(url))
-                )
+                return os.path.abspath(os.path.join(self.init_image_path, os.path.basename(url)))
             elif "mask-images" in url:
-                return os.path.abspath(
-                    os.path.join(self.mask_image_path, os.path.basename(url))
-                )
+                return os.path.abspath(os.path.join(self.mask_image_path, os.path.basename(url)))
             elif "intermediates" in url:
-                return os.path.abspath(
-                    os.path.join(self.intermediate_path, os.path.basename(url))
-                )
+                return os.path.abspath(os.path.join(self.intermediate_path, os.path.basename(url)))
             elif "temp-images" in url:
-                return os.path.abspath(
-                    os.path.join(self.temp_image_path, os.path.basename(url))
-                )
+                return os.path.abspath(os.path.join(self.temp_image_path, os.path.basename(url)))
             elif "thumbnails" in url:
-                return os.path.abspath(
-                    os.path.join(self.thumbnail_image_path, os.path.basename(url))
-                )
+                return os.path.abspath(os.path.join(self.thumbnail_image_path, os.path.basename(url)))
             else:
-                return os.path.abspath(
-                    os.path.join(self.result_path, os.path.basename(url))
-                )
+                return os.path.abspath(os.path.join(self.result_path, os.path.basename(url)))
         except Exception as e:
             self.handle_exceptions(e)
 
@@ -1632,18 +1492,14 @@ class Progress:
         self.total_steps = (
             self._calculate_real_steps(
                 steps=generation_parameters["steps"],
-                strength=generation_parameters["strength"]
-                if "strength" in generation_parameters
-                else None,
+                strength=generation_parameters["strength"] if "strength" in generation_parameters else None,
                 has_init_image="init_img" in generation_parameters,
             )
             if generation_parameters
             else 1
         )
         self.current_iteration = 1
-        self.total_iterations = (
-            generation_parameters["iterations"] if generation_parameters else 1
-        )
+        self.total_iterations = generation_parameters["iterations"] if generation_parameters else 1
         self.current_status = "common.statusPreparing"
         self.is_processing = True
         self.current_status_has_steps = False
@@ -1703,9 +1559,7 @@ class CanceledException(Exception):
     pass
 
 
-def copy_image_from_bounding_box(
-    image: ImageType, x: int, y: int, width: int, height: int
-) -> ImageType:
+def copy_image_from_bounding_box(image: ImageType, x: int, y: int, width: int, height: int) -> ImageType:
     """
     Returns a copy an image, cropped to a bounding box.
     """
@@ -1740,9 +1594,7 @@ def image_to_dataURL(image: ImageType, image_format: str = "PNG") -> str:
     buffered = io.BytesIO()
     image.save(buffered, format=image_format)
     mime_type = Image.MIME.get(image_format.upper(), "image/" + image_format.lower())
-    image_base64 = f"data:{mime_type};base64," + base64.b64encode(
-        buffered.getvalue()
-    ).decode("UTF-8")
+    image_base64 = f"data:{mime_type};base64," + base64.b64encode(buffered.getvalue()).decode("UTF-8")
     return image_base64
 
 
diff --git a/invokeai/backend/web/modules/get_canvas_generation_mode.py b/invokeai/backend/web/modules/get_canvas_generation_mode.py
index 55955cc33c..6d680016e7 100644
--- a/invokeai/backend/web/modules/get_canvas_generation_mode.py
+++ b/invokeai/backend/web/modules/get_canvas_generation_mode.py
@@ -40,9 +40,7 @@ def get_canvas_generation_mode(
     init_img_has_transparency = check_for_any_transparency(init_img)
 
     if init_img_has_transparency:
-        init_img_is_fully_transparent = (
-            True if init_img_alpha_mask.getbbox() is None else False
-        )
+        init_img_is_fully_transparent = True if init_img_alpha_mask.getbbox() is None else False
 
     """
     Mask images are white in areas where no change should be made, black where changes
diff --git a/invokeai/backend/web/modules/parameters.py b/invokeai/backend/web/modules/parameters.py
index 440f21a947..8ab74adb92 100644
--- a/invokeai/backend/web/modules/parameters.py
+++ b/invokeai/backend/web/modules/parameters.py
@@ -10,7 +10,7 @@ SAMPLER_CHOICES = [
     "lms_k",
     "pndm",
     "heun",
-    'heun_k',
+    "heun_k",
     "euler",
     "euler_k",
     "euler_a",
@@ -76,9 +76,7 @@ def parameters_to_command(params):
     if "variation_amount" in params and params["variation_amount"] > 0:
         switches.append(f'-v {params["variation_amount"]}')
         if "with_variations" in params:
-            seed_weight_pairs = ",".join(
-                f"{seed}:{weight}" for seed, weight in params["with_variations"]
-            )
+            seed_weight_pairs = ",".join(f"{seed}:{weight}" for seed, weight in params["with_variations"])
             switches.append(f"-V {seed_weight_pairs}")
 
     return " ".join(switches)
diff --git a/invokeai/configs/INITIAL_MODELS.yaml b/invokeai/configs/INITIAL_MODELS.yaml
index e148ee9b55..86357e7b56 100644
--- a/invokeai/configs/INITIAL_MODELS.yaml
+++ b/invokeai/configs/INITIAL_MODELS.yaml
@@ -16,14 +16,18 @@ sd-2/main/stable-diffusion-2-inpainting:
    description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
    repo_id: stabilityai/stable-diffusion-2-inpainting
    recommended: False
-sdxl/main/stable-diffusion-xl-base-0-9:
-   description: Stable Diffusion XL base model (12 GB; access token required)
-   repo_id: stabilityai/stable-diffusion-xl-base-0.9
-   recommended: False
-sdxl-refiner/main/stable-diffusion-xl-refiner-0-9:
-   description: Stable Diffusion XL refiner model (12 GB; access token required)
-   repo_id: stabilityai/stable-diffusion-xl-refiner-0.9
+sdxl/main/stable-diffusion-xl-base-1-0:
+   description: Stable Diffusion XL base model (12 GB)
+   repo_id: stabilityai/stable-diffusion-xl-base-1.0
    recommended: False
+sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
+   description: Stable Diffusion XL refiner model (12 GB)
+   repo_id: stabilityai/stable-diffusion-xl-refiner-1.0
+   recommended: false
+sdxl/vae/sdxl-1-0-vae-fix:
+   description: Fine tuned version of the SDXL-1.0 VAE
+   repo_id: madebyollin/sdxl-vae-fp16-fix
+   recommended: true
 sd-1/main/Analog-Diffusion:
    description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
    repo_id: wavymulder/Analog-Diffusion
@@ -48,10 +52,6 @@ sd-1/main/openjourney:
    description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
    repo_id: prompthero/openjourney
    recommended: False
-sd-1/main/portraitplus:
-   description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
-   repo_id: wavymulder/portraitplus
-   recommended: False
 sd-1/main/seek.art_MEGA:
    repo_id: coreco/seek.art_MEGA
    description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
@@ -60,10 +60,6 @@ sd-1/main/trinart_stable_diffusion_v2:
    description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
    repo_id: naclbit/trinart_stable_diffusion_v2
    recommended: False
-sd-1/main/waifu-diffusion:
-   description: An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)
-   repo_id: hakurei/waifu-diffusion
-   recommended: False
 sd-1/controlnet/canny:
    repo_id: lllyasviel/control_v11p_sd15_canny
    recommended: True
diff --git a/invokeai/configs/stable-diffusion/sd_xl_base.yaml b/invokeai/configs/stable-diffusion/sd_xl_base.yaml
new file mode 100644
index 0000000000..2022dac950
--- /dev/null
+++ b/invokeai/configs/stable-diffusion/sd_xl_base.yaml
@@ -0,0 +1,98 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2816
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
+        context_dim: 2048
+        spatial_transformer_attn_type: softmax-xformers
+        legacy: False
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+            params:
+              layer: hidden
+              layer_idx: 11
+          # crossattn and vector cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+              legacy: False
+          # vector cond
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: target_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
\ No newline at end of file
diff --git a/invokeai/configs/stable-diffusion/sd_xl_refiner.yaml b/invokeai/configs/stable-diffusion/sd_xl_refiner.yaml
new file mode 100644
index 0000000000..cab5fe283d
--- /dev/null
+++ b/invokeai/configs/stable-diffusion/sd_xl_refiner.yaml
@@ -0,0 +1,91 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2560
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 384
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 4
+        context_dim: [1280, 1280, 1280, 1280]  # 1280
+        spatial_transformer_attn_type: softmax-xformers
+        legacy: False
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn and vector cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              legacy: False
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+          # vector cond
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: aesthetic_score
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by one
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
diff --git a/invokeai/frontend/__init__.py b/invokeai/frontend/__init__.py
index 98fdf870e9..19eafe46c4 100644
--- a/invokeai/frontend/__init__.py
+++ b/invokeai/frontend/__init__.py
@@ -1,3 +1,3 @@
-'''
+"""
 Initialization file for invokeai.frontend
-'''
+"""
diff --git a/invokeai/frontend/install/invokeai_update.py b/invokeai/frontend/install/invokeai_update.py
index f73f670496..56d1a313c7 100644
--- a/invokeai/frontend/install/invokeai_update.py
+++ b/invokeai/frontend/install/invokeai_update.py
@@ -1,7 +1,7 @@
-'''
+"""
 Minimalist updater script. Prompts user for the tag or branch to update to and runs
 pip install <path_to_git_source>.
-'''
+"""
 import os
 import platform
 import pkg_resources
@@ -15,10 +15,10 @@ from rich.style import Style
 
 from invokeai.version import __version__
 
-INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
-INVOKE_AI_TAG="https://github.com/invoke-ai/InvokeAI/archive/refs/tags"
-INVOKE_AI_BRANCH="https://github.com/invoke-ai/InvokeAI/archive/refs/heads"
-INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+INVOKE_AI_SRC = "https://github.com/invoke-ai/InvokeAI/archive"
+INVOKE_AI_TAG = "https://github.com/invoke-ai/InvokeAI/archive/refs/tags"
+INVOKE_AI_BRANCH = "https://github.com/invoke-ai/InvokeAI/archive/refs/heads"
+INVOKE_AI_REL = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
 
 OS = platform.uname().system
 ARCH = platform.uname().machine
@@ -29,34 +29,38 @@ if OS == "Windows":
 else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
-def get_versions()->dict:
+
+def get_versions() -> dict:
     return requests.get(url=INVOKE_AI_REL).json()
 
-def invokeai_is_running()->bool:
+
+def invokeai_is_running() -> bool:
     for p in psutil.process_iter():
         try:
             cmdline = p.cmdline()
-            matches = [x for x in cmdline if x.endswith(('invokeai','invokeai.exe'))]
+            matches = [x for x in cmdline if x.endswith(("invokeai", "invokeai.exe"))]
             if matches:
-                print(f':exclamation: [bold red]An InvokeAI instance appears to be running as process {p.pid}[/red bold]')
+                print(
+                    f":exclamation: [bold red]An InvokeAI instance appears to be running as process {p.pid}[/red bold]"
+                )
                 return True
-        except (psutil.AccessDenied,psutil.NoSuchProcess):
+        except (psutil.AccessDenied, psutil.NoSuchProcess):
             continue
     return False
 
+
 def welcome(versions: dict):
-    
     @group()
     def text():
-        yield f'InvokeAI Version: [bold yellow]{__version__}'
-        yield ''
-        yield 'This script will update InvokeAI to the latest release, or to a development version of your choice.'
-        yield ''
-        yield '[bold yellow]Options:'
-        yield f'''[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
+        yield f"InvokeAI Version: [bold yellow]{__version__}"
+        yield ""
+        yield "This script will update InvokeAI to the latest release, or to a development version of your choice."
+        yield ""
+        yield "[bold yellow]Options:"
+        yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
 [2] Update to the bleeding-edge development version ([italic]main[/italic])
 [3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
-[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to'''        
+[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
 
     console.rule()
     print(
@@ -72,20 +76,22 @@ def welcome(versions: dict):
     )
     console.line()
 
+
 def get_extras():
-    extras = ''
+    extras = ""
     try:
-        dist = pkg_resources.get_distribution('xformers')
-        extras = '[xformers]'
+        dist = pkg_resources.get_distribution("xformers")
+        extras = "[xformers]"
     except pkg_resources.DistributionNotFound:
         pass
     return extras
 
+
 def main():
     versions = get_versions()
     if invokeai_is_running():
-        print(f':exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]')
-        input('Press any key to continue...')
+        print(f":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
+        input("Press any key to continue...")
         return
 
     welcome(versions)
@@ -93,36 +99,36 @@ def main():
     tag = None
     branch = None
     release = None
-    choice = Prompt.ask('Choice:',choices=['1','2','3','4'],default='1')
-    
-    if choice=='1':
-        release = versions[0]['tag_name']
-    elif choice=='2':
-        release = 'main'
-    elif choice=='3':
-        tag = Prompt.ask('Enter an InvokeAI tag name')
-    elif choice=='4':
-        branch = Prompt.ask('Enter an InvokeAI branch name')
+    choice = Prompt.ask("Choice:", choices=["1", "2", "3", "4"], default="1")
+
+    if choice == "1":
+        release = versions[0]["tag_name"]
+    elif choice == "2":
+        release = "main"
+    elif choice == "3":
+        tag = Prompt.ask("Enter an InvokeAI tag name")
+    elif choice == "4":
+        branch = Prompt.ask("Enter an InvokeAI branch name")
 
     extras = get_extras()
 
-    print(f':crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]')
+    print(f":crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]")
     if release:
         cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_SRC}/{release}.zip" --use-pep517 --upgrade'
     elif tag:
         cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_TAG}/{tag}.zip" --use-pep517 --upgrade'
     else:
         cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_BRANCH}/{branch}.zip" --use-pep517 --upgrade'
-    print('')
-    print('')
-    if os.system(cmd)==0:
-        print(f':heavy_check_mark: Upgrade successful')
+    print("")
+    print("")
+    if os.system(cmd) == 0:
+        print(f":heavy_check_mark: Upgrade successful")
     else:
-        print(f':exclamation: [bold red]Upgrade failed[/red bold]')
-    
+        print(f":exclamation: [bold red]Upgrade failed[/red bold]")
+
+
 if __name__ == "__main__":
     try:
         main()
     except KeyboardInterrupt:
         pass
-
diff --git a/invokeai/frontend/install/model_install.py b/invokeai/frontend/install/model_install.py
index c4b19157a4..ea9efe1908 100644
--- a/invokeai/frontend/install/model_install.py
+++ b/invokeai/frontend/install/model_install.py
@@ -56,18 +56,18 @@ logger = InvokeAILogger.getLogger()
 # build a table mapping all non-printable characters to None
 # for stripping control characters
 # from https://stackoverflow.com/questions/92438/stripping-non-printable-characters-from-a-string-in-python
-NOPRINT_TRANS_TABLE = {
-    i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
-}
+NOPRINT_TRANS_TABLE = {i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()}
 
-def make_printable(s:str)->str:
-    '''Replace non-printable characters in a string'''
+
+def make_printable(s: str) -> str:
+    """Replace non-printable characters in a string"""
     return s.translate(NOPRINT_TRANS_TABLE)
 
+
 class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
     # for responsive resizing set to False, but this seems to cause a crash!
     FIX_MINIMUM_SIZE_WHEN_CREATED = True
-    
+
     # for persistence
     current_tab = 0
 
@@ -82,24 +82,18 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.subprocess_connection = None
 
         if not config.model_conf_path.exists():
-            with open(config.model_conf_path,'w') as file:
-                print('# InvokeAI model configuration file',file=file)
+            with open(config.model_conf_path, "w") as file:
+                print("# InvokeAI model configuration file", file=file)
         self.installer = ModelInstall(config)
         self.all_models = self.installer.all_models()
         self.starter_models = self.installer.starter_models()
-        self.model_labels = self._get_model_labels()        
+        self.model_labels = self._get_model_labels()
         window_width, window_height = get_terminal_size()
 
         self.nextrely -= 1
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields,",
-            editable=False,
-            color="CAUTION",
-        )
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Use cursor arrows to make a selection, and space to toggle checkboxes.",
+            value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
             editable=False,
             color="CAUTION",
         )
@@ -107,17 +101,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.tabs = self.add_widget_intelligent(
             SingleSelectColumns,
             values=[
-                'STARTER MODELS',
-                'MORE MODELS',
-                'CONTROLNETS',
-                'LORA/LYCORIS',
-                'TEXTUAL INVERSION',
+                "STARTER MODELS",
+                "MORE MODELS",
+                "CONTROLNETS",
+                "LORA/LYCORIS",
+                "TEXTUAL INVERSION",
             ],
             value=[self.current_tab],
-            columns = 5,
-            max_height = 2,
+            columns=5,
+            max_height=2,
             relx=8,
-            scroll_exit = True,
+            scroll_exit=True,
         )
         self.tabs.on_changed = self._toggle_tables
 
@@ -127,104 +121,105 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         self.nextrely = top_of_table
         self.pipeline_models = self.add_pipeline_widgets(
-            model_type=ModelType.Main,
-            window_width=window_width,
-            exclude = self.starter_models
+            model_type=ModelType.Main, window_width=window_width, exclude=self.starter_models
         )
         # self.pipeline_models['autoload_pending'] = True
-        bottom_of_table = max(bottom_of_table,self.nextrely)
+        bottom_of_table = max(bottom_of_table, self.nextrely)
 
         self.nextrely = top_of_table
         self.controlnet_models = self.add_model_widgets(
             model_type=ModelType.ControlNet,
             window_width=window_width,
         )
-        bottom_of_table = max(bottom_of_table,self.nextrely)
+        bottom_of_table = max(bottom_of_table, self.nextrely)
 
         self.nextrely = top_of_table
         self.lora_models = self.add_model_widgets(
             model_type=ModelType.Lora,
             window_width=window_width,
         )
-        bottom_of_table = max(bottom_of_table,self.nextrely)
+        bottom_of_table = max(bottom_of_table, self.nextrely)
 
         self.nextrely = top_of_table
         self.ti_models = self.add_model_widgets(
             model_type=ModelType.TextualInversion,
             window_width=window_width,
         )
-        bottom_of_table = max(bottom_of_table,self.nextrely)
-                
-        self.nextrely = bottom_of_table+1
+        bottom_of_table = max(bottom_of_table, self.nextrely)
+
+        self.nextrely = bottom_of_table + 1
 
         self.monitor = self.add_widget_intelligent(
             BufferBox,
-            name='Log Messages',
+            name="Log Messages",
             editable=False,
-            max_height = 10,
+            max_height=8,
         )
-        
+
         self.nextrely += 1
         done_label = "APPLY CHANGES"
         back_label = "BACK"
+        cancel_label = "CANCEL"
+        current_position = self.nextrely
         if self.multipage:
             self.back_button = self.add_widget_intelligent(
                 npyscreen.ButtonPress,
                 name=back_label,
-                rely=-3,
                 when_pressed_function=self.on_back,
             )
         else:
+            self.nextrely = current_position
+            self.cancel_button = self.add_widget_intelligent(
+                npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
+            )
+            self.nextrely = current_position
             self.ok_button = self.add_widget_intelligent(
                 npyscreen.ButtonPress,
                 name=done_label,
                 relx=(window_width - len(done_label)) // 2,
-                rely=-3,
-                when_pressed_function=self.on_execute
+                when_pressed_function=self.on_execute,
             )
 
         label = "APPLY CHANGES & EXIT"
+        self.nextrely = current_position
         self.done = self.add_widget_intelligent(
             npyscreen.ButtonPress,
             name=label,
-            rely=-3,
-            relx=window_width-len(label)-15,
+            relx=window_width - len(label) - 15,
             when_pressed_function=self.on_done,
         )
 
         # This restores the selected page on return from an installation
-        for i in range(1,self.current_tab+1):
+        for i in range(1, self.current_tab + 1):
             self.tabs.h_cursor_line_down(1)
         self._toggle_tables([self.current_tab])
 
-    ############# diffusers tab ##########        
-    def add_starter_pipelines(self)->dict[str, npyscreen.widget]:
-        '''Add widgets responsible for selecting diffusers models'''
+    ############# diffusers tab ##########
+    def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
+        """Add widgets responsible for selecting diffusers models"""
         widgets = dict()
         models = self.all_models
         starters = self.starter_models
         starter_model_labels = self.model_labels
-        
-        self.installed_models = sorted(
-            [x for x in starters if models[x].installed]
-        )
+
+        self.installed_models = sorted([x for x in starters if models[x].installed])
 
         widgets.update(
-            label1 = self.add_widget_intelligent(
+            label1=self.add_widget_intelligent(
                 CenteredTitleText,
                 name="Select from a starter set of Stable Diffusion models from HuggingFace.",
                 editable=False,
                 labelColor="CAUTION",
             )
         )
-        
+
         self.nextrely -= 1
         # if user has already installed some initial models, then don't patronize them
         # by showing more recommendations
-        show_recommended = len(self.installed_models)==0
+        show_recommended = len(self.installed_models) == 0
         keys = [x for x in models.keys() if x in starters]
         widgets.update(
-            models_selected = self.add_widget_intelligent(
+            models_selected=self.add_widget_intelligent(
                 MultiSelectColumns,
                 columns=1,
                 name="Install Starter Models",
@@ -232,40 +227,43 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 value=[
                     keys.index(x)
                     for x in keys
-                    if (show_recommended and models[x].recommended) \
-                    or (x in self.installed_models)
+                    if (show_recommended and models[x].recommended) or (x in self.installed_models)
                 ],
                 max_height=len(starters) + 1,
                 relx=4,
                 scroll_exit=True,
             ),
-            models = keys,
+            models=keys,
         )
 
         self.nextrely += 1
         return widgets
 
     ############# Add a set of model install widgets ########
-    def add_model_widgets(self,
-                          model_type: ModelType,
-                          window_width: int=120,
-                          install_prompt: str=None,
-                          exclude: set=set(),
-                          )->dict[str,npyscreen.widget]:
-        '''Generic code to create model selection widgets'''
+    def add_model_widgets(
+        self,
+        model_type: ModelType,
+        window_width: int = 120,
+        install_prompt: str = None,
+        exclude: set = set(),
+    ) -> dict[str, npyscreen.widget]:
+        """Generic code to create model selection widgets"""
         widgets = dict()
-        model_list = [x for x in self.all_models if self.all_models[x].model_type==model_type and not x in exclude]
+        model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and not x in exclude]
         model_labels = [self.model_labels[x] for x in model_list]
 
-        show_recommended = len(self.installed_models)==0
+        show_recommended = len(self.installed_models) == 0
         if len(model_list) > 0:
             max_width = max([len(x) for x in model_labels])
-            columns = window_width // (max_width+8)  # 8 characters for "[x] " and padding
-            columns = min(len(model_list),columns) or 1
-            prompt = install_prompt or f"Select the desired {model_type.value.title()} models to install. Unchecked models will be purged from disk."
+            columns = window_width // (max_width + 8)  # 8 characters for "[x] " and padding
+            columns = min(len(model_list), columns) or 1
+            prompt = (
+                install_prompt
+                or f"Select the desired {model_type.value.title()} models to install. Unchecked models will be purged from disk."
+            )
 
             widgets.update(
-                label1 = self.add_widget_intelligent(
+                label1=self.add_widget_intelligent(
                     CenteredTitleText,
                     name=prompt,
                     editable=False,
@@ -274,7 +272,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             )
 
             widgets.update(
-                models_selected = self.add_widget_intelligent(
+                models_selected=self.add_widget_intelligent(
                     MultiSelectColumns,
                     columns=columns,
                     name=f"Install {model_type} Models",
@@ -282,21 +280,20 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                     value=[
                         model_list.index(x)
                         for x in model_list
-                        if (show_recommended and self.all_models[x].recommended) \
-                            or self.all_models[x].installed
+                        if (show_recommended and self.all_models[x].recommended) or self.all_models[x].installed
                     ],
-                    max_height=len(model_list)//columns + 1,
+                    max_height=len(model_list) // columns + 1,
                     relx=4,
                     scroll_exit=True,
                 ),
-                models = model_list,
+                models=model_list,
             )
 
         self.nextrely += 1
         widgets.update(
-            download_ids = self.add_widget_intelligent(
+            download_ids=self.add_widget_intelligent(
                 TextBox,
-                name = "Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
+                name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
                 max_height=4,
                 scroll_exit=True,
                 editable=True,
@@ -305,16 +302,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         return widgets
 
     ### Tab for arbitrary diffusers widgets ###
-    def add_pipeline_widgets(self,
-                             model_type: ModelType=ModelType.Main,
-                             window_width: int=120,
-                             **kwargs,
-                             )->dict[str,npyscreen.widget]:
-        '''Similar to add_model_widgets() but adds some additional widgets at the bottom
-        to support the autoload directory'''
+    def add_pipeline_widgets(
+        self,
+        model_type: ModelType = ModelType.Main,
+        window_width: int = 120,
+        **kwargs,
+    ) -> dict[str, npyscreen.widget]:
+        """Similar to add_model_widgets() but adds some additional widgets at the bottom
+        to support the autoload directory"""
         widgets = self.add_model_widgets(
-            model_type = model_type,
-            window_width = window_width,
+            model_type=model_type,
+            window_width=window_width,
             install_prompt=f"Additional {model_type.value.title()} models already installed.",
             **kwargs,
         )
@@ -323,7 +321,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
     def resize(self):
         super().resize()
-        if (s := self.starter_pipelines.get("models_selected")):
+        if s := self.starter_pipelines.get("models_selected"):
             keys = [x for x in self.all_models.keys() if x in self.starter_models]
             s.values = [self.model_labels[x] for x in keys]
 
@@ -338,27 +336,27 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         ]
 
         for group in widgets:
-            for k,v in group.items():
+            for k, v in group.items():
                 try:
                     v.hidden = True
                     v.editable = False
                 except:
                     pass
-        for k,v in widgets[selected_tab].items():
+        for k, v in widgets[selected_tab].items():
             try:
                 v.hidden = False
-                if not isinstance(v,(npyscreen.FixedText, npyscreen.TitleFixedText, CenteredTitleText)):
+                if not isinstance(v, (npyscreen.FixedText, npyscreen.TitleFixedText, CenteredTitleText)):
                     v.editable = True
             except:
                 pass
         self.__class__.current_tab = selected_tab  # for persistence
         self.display()
 
-    def _get_model_labels(self) -> dict[str,str]:
+    def _get_model_labels(self) -> dict[str, str]:
         window_width, window_height = get_terminal_size()
         checkbox_width = 4
         spacing_width = 2
-        
+
         models = self.all_models
         label_width = max([len(models[x].name) for x in models])
         description_width = window_width - label_width - checkbox_width - spacing_width
@@ -366,30 +364,28 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         result = dict()
         for x in models.keys():
             description = models[x].description
-            description = description[0 : description_width - 3] + "..." \
-                if description and len(description) > description_width \
-                   else description if description else ''
-            result[x] =  f"%-{label_width}s %s" % (models[x].name, description)
+            description = (
+                description[0 : description_width - 3] + "..."
+                if description and len(description) > description_width
+                else description
+                if description
+                else ""
+            )
+            result[x] = f"%-{label_width}s %s" % (models[x].name, description)
         return result
-            
+
     def _get_columns(self) -> int:
         window_width, window_height = get_terminal_size()
-        cols = (
-            4
-            if window_width > 240
-            else 3
-            if window_width > 160
-            else 2
-            if window_width > 80
-            else 1
-        )
+        cols = 4 if window_width > 240 else 3 if window_width > 160 else 2 if window_width > 80 else 1
         return min(cols, len(self.installed_models))
 
-    def confirm_deletions(self, selections: InstallSelections)->bool:
+    def confirm_deletions(self, selections: InstallSelections) -> bool:
         remove_models = selections.remove_models
         if len(remove_models) > 0:
             mods = "\n".join([ModelManager.parse_key(x)[0] for x in remove_models])
-            return npyscreen.notify_ok_cancel(f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}")
+            return npyscreen.notify_ok_cancel(
+                f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
+            )
         else:
             return True
 
@@ -398,20 +394,20 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         app = self.parentApp
         if not self.confirm_deletions(app.install_selections):
             return
-            
-        self.monitor.entry_widget.buffer(['Processing...'],scroll_end=True)
+
+        self.monitor.entry_widget.buffer(["Processing..."], scroll_end=True)
         self.ok_button.hidden = True
         self.display()
-        
+
         # for communication with the subprocess
         parent_conn, child_conn = Pipe()
         p = Process(
-            target = process_and_execute,
+            target=process_and_execute,
             kwargs=dict(
-                opt = app.program_opts,
-                selections = app.install_selections,
-                conn_out = child_conn,
-            )
+                opt=app.program_opts,
+                selections=app.install_selections,
+                conn_out=child_conn,
+            ),
         )
         p.start()
         child_conn.close()
@@ -428,7 +424,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.parentApp.setNextForm(None)
         self.parentApp.user_cancelled = True
         self.editing = False
-        
+
     def on_done(self):
         self.marshall_arguments()
         if not self.confirm_deletions(self.parentApp.install_selections):
@@ -436,75 +432,79 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.parentApp.setNextForm(None)
         self.parentApp.user_cancelled = False
         self.editing = False
-        
+
     ########## This routine monitors the child process that is performing model installation and removal #####
     def while_waiting(self):
-        '''Called during idle periods. Main task is to update the Log Messages box with messages
-        from the child process that does the actual installation/removal'''
+        """Called during idle periods. Main task is to update the Log Messages box with messages
+        from the child process that does the actual installation/removal"""
         c = self.subprocess_connection
         if not c:
             return
-        
+
         monitor_widget = self.monitor.entry_widget
         while c.poll():
             try:
-                data = c.recv_bytes().decode('utf-8')
-                data.strip('\n')
+                data = c.recv_bytes().decode("utf-8")
+                data.strip("\n")
 
                 # processing child is requesting user input to select the
                 # right configuration file
-                if data.startswith('*need v2 config'):
-                    _,model_path,*_ = data.split(":",2)
+                if data.startswith("*need v2 config"):
+                    _, model_path, *_ = data.split(":", 2)
                     self._return_v2_config(model_path)
 
                 # processing child is done
-                elif data=='*done*':
+                elif data == "*done*":
                     self._close_subprocess_and_regenerate_form()
                     break
 
                 # update the log message box
                 else:
-                    data=make_printable(data)
-                    data=data.replace('[A','')
+                    data = make_printable(data)
+                    data = data.replace("[A", "")
                     monitor_widget.buffer(
-                        textwrap.wrap(data,
-                                      width=monitor_widget.width,
-                                      subsequent_indent='   ',
-                                      ),
-                        scroll_end=True
+                        textwrap.wrap(
+                            data,
+                            width=monitor_widget.width,
+                            subsequent_indent="   ",
+                        ),
+                        scroll_end=True,
                     )
                     self.display()
-            except (EOFError,OSError):
+            except (EOFError, OSError):
                 self.subprocess_connection = None
 
-    def _return_v2_config(self,model_path: str):
+    def _return_v2_config(self, model_path: str):
         c = self.subprocess_connection
         model_name = Path(model_path).name
         message = select_stable_diffusion_config_file(model_name=model_name)
-        c.send_bytes(message.encode('utf-8'))
+        c.send_bytes(message.encode("utf-8"))
 
     def _close_subprocess_and_regenerate_form(self):
         app = self.parentApp
         self.subprocess_connection.close()
         self.subprocess_connection = None
-        self.monitor.entry_widget.buffer(['** Action Complete **'])
+        self.monitor.entry_widget.buffer(["** Action Complete **"])
         self.display()
-        
+
         # rebuild the form, saving and restoring some of the fields that need to be preserved.
         saved_messages = self.monitor.entry_widget.values
         # autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
         # autoscan = self.pipeline_models['autoscan_on_startup'].value
-        
+
         app.main_form = app.addForm(
-            "MAIN", addModelsForm, name="Install Stable Diffusion Models", multipage=self.multipage,
+            "MAIN",
+            addModelsForm,
+            name="Install Stable Diffusion Models",
+            multipage=self.multipage,
         )
         app.switchForm("MAIN")
-        
+
         app.main_form.monitor.entry_widget.values = saved_messages
-        app.main_form.monitor.entry_widget.buffer([''],scroll_end=True)
+        app.main_form.monitor.entry_widget.buffer([""], scroll_end=True)
         # app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
         # app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
-        
+
     def marshall_arguments(self):
         """
         Assemble arguments and store as attributes of the application:
@@ -519,21 +519,29 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         all_models = self.all_models
 
         # Defined models (in INITIAL_CONFIG.yaml or models.yaml) to add/remove
-        ui_sections = [self.starter_pipelines, self.pipeline_models,
-                       self.controlnet_models, self.lora_models, self.ti_models]
+        ui_sections = [
+            self.starter_pipelines,
+            self.pipeline_models,
+            self.controlnet_models,
+            self.lora_models,
+            self.ti_models,
+        ]
         for section in ui_sections:
-            if not 'models_selected' in section:
+            if not "models_selected" in section:
                 continue
-            selected = set([section['models'][x] for x in section['models_selected'].value])
+            selected = set([section["models"][x] for x in section["models_selected"].value])
             models_to_install = [x for x in selected if not self.all_models[x].installed]
-            models_to_remove = [x for x in section['models'] if x not in selected and self.all_models[x].installed]
+            models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
             selections.remove_models.extend(models_to_remove)
-            selections.install_models.extend(all_models[x].path or all_models[x].repo_id \
-                                             for x in models_to_install if all_models[x].path or all_models[x].repo_id)
+            selections.install_models.extend(
+                all_models[x].path or all_models[x].repo_id
+                for x in models_to_install
+                if all_models[x].path or all_models[x].repo_id
+            )
 
         # models located in the 'download_ids" section
         for section in ui_sections:
-            if downloads := section.get('download_ids'):
+            if downloads := section.get("download_ids"):
                 selections.install_models.extend(downloads.value.split())
 
         # load directory and whether to scan on startup
@@ -542,8 +550,9 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         #     self.parentApp.autoload_pending = False
         # selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
 
+
 class AddModelApplication(npyscreen.NPSAppManaged):
-    def __init__(self,opt):
+    def __init__(self, opt):
         super().__init__()
         self.program_opts = opt
         self.user_cancelled = False
@@ -553,76 +562,83 @@ class AddModelApplication(npyscreen.NPSAppManaged):
     def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
         self.main_form = self.addForm(
-            "MAIN", addModelsForm, name="Install Stable Diffusion Models", cycle_widgets=True,
+            "MAIN",
+            addModelsForm,
+            name="Install Stable Diffusion Models",
+            cycle_widgets=False,
         )
 
-class StderrToMessage():
+
+class StderrToMessage:
     def __init__(self, connection: Connection):
         self.connection = connection
 
-    def write(self, data:str):
-        self.connection.send_bytes(data.encode('utf-8'))
+    def write(self, data: str):
+        self.connection.send_bytes(data.encode("utf-8"))
 
     def flush(self):
         pass
 
+
 # --------------------------------------------------------
-def ask_user_for_prediction_type(model_path: Path,
-                                 tui_conn: Connection=None
-                                 )->SchedulerPredictionType:
+def ask_user_for_prediction_type(model_path: Path, tui_conn: Connection = None) -> SchedulerPredictionType:
     if tui_conn:
-        logger.debug('Waiting for user response...')
-        return _ask_user_for_pt_tui(model_path, tui_conn)        
+        logger.debug("Waiting for user response...")
+        return _ask_user_for_pt_tui(model_path, tui_conn)
     else:
         return _ask_user_for_pt_cmdline(model_path)
 
-def _ask_user_for_pt_cmdline(model_path: Path)->SchedulerPredictionType:
+
+def _ask_user_for_pt_cmdline(model_path: Path) -> SchedulerPredictionType:
     choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
     print(
-f"""
+        f"""
 Please select the type of the V2 checkpoint named {model_path.name}:
 [1] A model based on Stable Diffusion v2 trained on 512 pixel images (SD-2-base)
 [2] A model based on Stable Diffusion v2 trained on 768 pixel images (SD-2-768)
 [3] Skip this model and come back later.
 """
-        )
+    )
     choice = None
     ok = False
     while not ok:
         try:
-            choice = input('select> ').strip()
-            choice = choices[int(choice)-1]
+            choice = input("select> ").strip()
+            choice = choices[int(choice) - 1]
             ok = True
         except (ValueError, IndexError):
-            print(f'{choice} is not a valid choice')
+            print(f"{choice} is not a valid choice")
         except EOFError:
             return
     return choice
-        
-def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection)->SchedulerPredictionType:
+
+
+def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPredictionType:
     try:
-        tui_conn.send_bytes(f'*need v2 config for:{model_path}'.encode('utf-8'))
+        tui_conn.send_bytes(f"*need v2 config for:{model_path}".encode("utf-8"))
         # note that we don't do any status checking here
-        response = tui_conn.recv_bytes().decode('utf-8')
+        response = tui_conn.recv_bytes().decode("utf-8")
         if response is None:
             return None
-        elif response == 'epsilon':
+        elif response == "epsilon":
             return SchedulerPredictionType.epsilon
-        elif response == 'v':
+        elif response == "v":
             return SchedulerPredictionType.VPrediction
-        elif response == 'abort':
-            logger.info('Conversion aborted')
+        elif response == "abort":
+            logger.info("Conversion aborted")
             return None
         else:
             return response
     except:
         return None
-        
+
+
 # --------------------------------------------------------
-def process_and_execute(opt: Namespace,
-                        selections: InstallSelections,
-                        conn_out: Connection=None,
-                        ):
+def process_and_execute(
+    opt: Namespace,
+    selections: InstallSelections,
+    conn_out: Connection = None,
+):
     # set up so that stderr is sent to conn_out
     if conn_out:
         translator = StderrToMessage(conn_out)
@@ -632,66 +648,57 @@ def process_and_execute(opt: Namespace,
         logger.handlers.clear()
         logger.addHandler(logging.StreamHandler(translator))
 
-    installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x,conn_out))
+    installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x, conn_out))
     installer.install(selections)
 
     if conn_out:
-        conn_out.send_bytes('*done*'.encode('utf-8'))
+        conn_out.send_bytes("*done*".encode("utf-8"))
         conn_out.close()
 
-def do_listings(opt)->bool:
+
+def do_listings(opt) -> bool:
     """List installed models of various sorts, and return
     True if any were requested."""
     model_manager = ModelManager(config.model_conf_path)
-    if opt.list_models == 'diffusers':
+    if opt.list_models == "diffusers":
         print("Diffuser models:")
         model_manager.print_models()
-    elif opt.list_models == 'controlnets':
+    elif opt.list_models == "controlnets":
         print("Installed Controlnet Models:")
         cnm = model_manager.list_controlnet_models()
-        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]),prefix='   '))
-    elif opt.list_models == 'loras':
+        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix="   "))
+    elif opt.list_models == "loras":
         print("Installed LoRA/LyCORIS Models:")
         cnm = model_manager.list_lora_models()
-        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]),prefix='   '))
-    elif opt.list_models == 'tis':
+        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix="   "))
+    elif opt.list_models == "tis":
         print("Installed Textual Inversion Embeddings:")
         cnm = model_manager.list_ti_models()
-        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]),prefix='   '))
+        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix="   "))
     else:
         return False
     return True
 
+
 # --------------------------------------------------------
 def select_and_download_models(opt: Namespace):
-    precision = (
-        "float32"
-        if opt.full_precision
-        else choose_precision(torch.device(choose_torch_device()))
-    )
+    precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
     config.precision = precision
     helper = lambda x: ask_user_for_prediction_type(x)
     # if do_listings(opt):
     # pass
-    
+
     installer = ModelInstall(config, prediction_type_helper=helper)
     if opt.list_models:
         installer.list_models(opt.list_models)
     elif opt.add or opt.delete:
-        selections = InstallSelections(
-            install_models = opt.add or [],
-            remove_models = opt.delete or []
-        )
+        selections = InstallSelections(install_models=opt.add or [], remove_models=opt.delete or [])
         installer.install(selections)
     elif opt.default_only:
-        selections = InstallSelections(
-            install_models = installer.default_model()
-        )
+        selections = InstallSelections(install_models=installer.default_model())
         installer.install(selections)
     elif opt.yes_to_all:
-        selections = InstallSelections(
-            install_models = installer.recommended_models()
-        )
+        selections = InstallSelections(install_models=installer.recommended_models())
         installer.install(selections)
 
     # this is where the TUI is called
@@ -706,15 +713,15 @@ def select_and_download_models(opt: Namespace):
         try:
             installApp.run()
         except KeyboardInterrupt as e:
-            if hasattr(installApp,'main_form'):
-                if installApp.main_form.subprocess \
-                   and installApp.main_form.subprocess.is_alive():
-                    logger.info('Terminating subprocesses')
+            if hasattr(installApp, "main_form"):
+                if installApp.main_form.subprocess and installApp.main_form.subprocess.is_alive():
+                    logger.info("Terminating subprocesses")
                     installApp.main_form.subprocess.terminate()
                     installApp.main_form.subprocess = None
             raise e
         process_and_execute(opt, installApp.install_selections)
 
+
 # -------------------------------------
 def main():
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
@@ -769,19 +776,17 @@ def main():
         help="path to root of install directory",
     )
     opt = parser.parse_args()
-    
+
     invoke_args = []
     if opt.root:
-        invoke_args.extend(['--root',opt.root])
+        invoke_args.extend(["--root", opt.root])
     if opt.full_precision:
-        invoke_args.extend(['--precision','float32'])
+        invoke_args.extend(["--precision", "float32"])
     config.parse_args(invoke_args)
     logger = InvokeAILogger().getLogger(config=config)
 
     if not config.model_conf_path.exists():
-        logger.info(
-            "Your InvokeAI root directory is not set up. Calling invokeai-configure."
-        )
+        logger.info("Your InvokeAI root directory is not set up. Calling invokeai-configure.")
         from invokeai.frontend.install import invokeai_configure
 
         invokeai_configure()
@@ -799,20 +804,18 @@ def main():
         logger.info("Goodbye! Come back soon.")
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
-                "Insufficient vertical space for the interface. Please make your window taller and try again"
-            )
-        input('Press any key to continue...')
+            logger.error("Insufficient vertical space for the interface. Please make your window taller and try again")
+        input("Press any key to continue...")
     except Exception as e:
         if str(e).startswith("addwstr"):
             logger.error(
                 "Insufficient horizontal space for the interface. Please make your window wider and try again."
             )
         else:
-            print(f'An exception has occurred: {str(e)} Details:')
+            print(f"An exception has occurred: {str(e)} Details:")
             print(traceback.format_exc(), file=sys.stderr)
-        input('Press any key to continue...')
-    
+        input("Press any key to continue...")
+
 
 # -------------------------------------
 if __name__ == "__main__":
diff --git a/invokeai/frontend/install/widgets.py b/invokeai/frontend/install/widgets.py
index 82d3753eb4..10da15bf13 100644
--- a/invokeai/frontend/install/widgets.py
+++ b/invokeai/frontend/install/widgets.py
@@ -14,40 +14,42 @@ import textwrap
 import npyscreen.wgmultiline as wgmultiline
 from npyscreen import fmPopup
 from shutil import get_terminal_size
-from curses import BUTTON2_CLICKED,BUTTON3_CLICKED
+from curses import BUTTON2_CLICKED, BUTTON3_CLICKED
 
 # minimum size for UIs
-MIN_COLS = 136
-MIN_LINES = 45
+MIN_COLS = 130
+MIN_LINES = 38
+
 
 # -------------------------------------
 def set_terminal_size(columns: int, lines: int):
     ts = get_terminal_size()
-    width = max(columns,ts.columns)
-    height = max(lines,ts.lines)
+    width = max(columns, ts.columns)
+    height = max(lines, ts.lines)
 
     OS = platform.uname().system
     if OS == "Windows":
         pass
         # not working reliably - ask user to adjust the window
-        #_set_terminal_size_powershell(width,height)
+        # _set_terminal_size_powershell(width,height)
     elif OS in ["Darwin", "Linux"]:
-        _set_terminal_size_unix(width,height)
+        _set_terminal_size_unix(width, height)
 
     # check whether it worked....
     ts = get_terminal_size()
     pause = False
     if ts.columns < columns:
-        print('\033[1mThis window is too narrow for the user interface. Please make it wider.\033[0m')
+        print("\033[1mThis window is too narrow for the user interface.\033[0m")
         pause = True
     if ts.lines < lines:
-        print('\033[1mThis window is too short for the user interface. Please make it taller.\033[0m')
+        print("\033[1mThis window is too short for the user interface.\033[0m")
         pause = True
     if pause:
-        input('Press any key to continue..')
+        input("Maximize the window then press any key to continue..")
+
 
 def _set_terminal_size_powershell(width: int, height: int):
-    script=f'''
+    script = f"""
 $pshost = get-host
 $pswindow = $pshost.ui.rawui
 $newsize = $pswindow.buffersize
@@ -58,8 +60,9 @@ $newsize = $pswindow.windowsize
 $newsize.height = {height}
 $newsize.width = {width}
 $pswindow.windowsize = $newsize
-'''
-    subprocess.run(["powershell","-Command","-"],input=script,text=True)
+"""
+    subprocess.run(["powershell", "-Command", "-"], input=script, text=True)
+
 
 def _set_terminal_size_unix(width: int, height: int):
     import fcntl
@@ -67,15 +70,16 @@ def _set_terminal_size_unix(width: int, height: int):
 
     # These terminals accept the size command and report that the
     # size changed, but they lie!!!
-    for bad_terminal in ['TERMINATOR_UUID', 'ALACRITTY_WINDOW_ID']:
+    for bad_terminal in ["TERMINATOR_UUID", "ALACRITTY_WINDOW_ID"]:
         if os.environ.get(bad_terminal):
             return
-    
+
     winsize = struct.pack("HHHH", height, width, 0, 0)
     fcntl.ioctl(sys.stdout.fileno(), termios.TIOCSWINSZ, winsize)
     sys.stdout.write("\x1b[8;{height};{width}t".format(height=height, width=width))
     sys.stdout.flush()
 
+
 def set_min_terminal_size(min_cols: int, min_lines: int):
     # make sure there's enough room for the ui
     term_cols, term_lines = get_terminal_size()
@@ -85,6 +89,7 @@ def set_min_terminal_size(min_cols: int, min_lines: int):
     lines = max(term_lines, min_lines)
     set_terminal_size(cols, lines)
 
+
 class IntSlider(npyscreen.Slider):
     def translate_value(self):
         stri = "%2d / %2d" % (self.value, self.out_of)
@@ -92,23 +97,25 @@ class IntSlider(npyscreen.Slider):
         stri = stri.rjust(l)
         return stri
 
+
 # -------------------------------------
 # fix npyscreen form so that cursor wraps both forward and backward
 class CyclingForm(object):
     def find_previous_editable(self, *args):
         done = False
-        n = self.editw-1
+        n = self.editw - 1
         while not done:
             if self._widgets__[n].editable and not self._widgets__[n].hidden:
                 self.editw = n
                 done = True
             n -= 1
-            if n<0:
+            if n < 0:
                 if self.cycle_widgets:
-                    n = len(self._widgets__)-1
+                    n = len(self._widgets__) - 1
                 else:
                     done = True
-                    
+
+
 # -------------------------------------
 class CenteredTitleText(npyscreen.TitleText):
     def __init__(self, *args, **keywords):
@@ -159,7 +166,8 @@ class FloatSlider(npyscreen.Slider):
 class FloatTitleSlider(npyscreen.TitleText):
     _entry_type = FloatSlider
 
-class SelectColumnBase():
+
+class SelectColumnBase:
     def make_contained_widgets(self):
         self._my_widgets = []
         column_width = self.width // self.columns
@@ -217,11 +225,12 @@ class SelectColumnBase():
         row_no = rel_y // self._contained_widget_height
         self.cursor_line = column_no * column_height + row_no
         if bstate & curses.BUTTON1_DOUBLE_CLICKED:
-            if hasattr(self,'on_mouse_double_click'):
+            if hasattr(self, "on_mouse_double_click"):
                 self.on_mouse_double_click(self.cursor_line)
         self.display()
 
-class MultiSelectColumns( SelectColumnBase, npyscreen.MultiSelect):
+
+class MultiSelectColumns(SelectColumnBase, npyscreen.MultiSelect):
     def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
         self.columns = columns
         self.value_cnt = len(values)
@@ -231,15 +240,17 @@ class MultiSelectColumns( SelectColumnBase, npyscreen.MultiSelect):
     def on_mouse_double_click(self, cursor_line):
         self.h_select_toggle(cursor_line)
 
-class SingleSelectWithChanged(npyscreen.SelectOne):
-    def __init__(self,*args,**kwargs):
-        super().__init__(*args,**kwargs)
 
-    def h_select(self,ch):
+class SingleSelectWithChanged(npyscreen.SelectOne):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def h_select(self, ch):
         super().h_select(ch)
         if self.on_changed:
             self.on_changed(self.value)
 
+
 class SingleSelectColumns(SelectColumnBase, SingleSelectWithChanged):
     def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
         self.columns = columns
@@ -254,23 +265,25 @@ class SingleSelectColumns(SelectColumnBase, SingleSelectWithChanged):
     def when_cursor_moved(self):
         self.h_select(self.cursor_line)
 
-    def h_cursor_line_right(self,ch):
-        self.h_exit_down('bye bye')
+    def h_cursor_line_right(self, ch):
+        self.h_exit_down("bye bye")
+
 
 class TextBoxInner(npyscreen.MultiLineEdit):
-
-    def __init__(self,*args,**kwargs):
-        super().__init__(*args,**kwargs)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
         self.yank = None
-        self.handlers.update({
-            "^A": self.h_cursor_to_start,
-            "^E": self.h_cursor_to_end,
-            "^K": self.h_kill,
-            "^F": self.h_cursor_right,
-            "^B": self.h_cursor_left,
-            "^Y": self.h_yank,
-            "^V": self.h_paste,
-        })
+        self.handlers.update(
+            {
+                "^A": self.h_cursor_to_start,
+                "^E": self.h_cursor_to_end,
+                "^K": self.h_kill,
+                "^F": self.h_cursor_right,
+                "^B": self.h_cursor_left,
+                "^Y": self.h_yank,
+                "^V": self.h_paste,
+            }
+        )
 
     def h_cursor_to_start(self, input):
         self.cursor_position = 0
@@ -279,27 +292,27 @@ class TextBoxInner(npyscreen.MultiLineEdit):
         self.cursor_position = len(self.value)
 
     def h_kill(self, input):
-        self.yank = self.value[self.cursor_position:]
-        self.value = self.value[:self.cursor_position]
+        self.yank = self.value[self.cursor_position :]
+        self.value = self.value[: self.cursor_position]
 
     def h_yank(self, input):
         if self.yank:
             self.paste(self.yank)
 
     def paste(self, text: str):
-        self.value = self.value[:self.cursor_position] + text + self.value[self.cursor_position:]
+        self.value = self.value[: self.cursor_position] + text + self.value[self.cursor_position :]
         self.cursor_position += len(text)
 
-    def h_paste(self, input: int=0):
+    def h_paste(self, input: int = 0):
         try:
             text = pyperclip.paste()
         except ModuleNotFoundError:
             text = "To paste with the mouse on Linux, please install the 'xclip' program."
         self.paste(text)
-        
+
     def handle_mouse_event(self, mouse_event):
         mouse_id, rel_x, rel_y, z, bstate = self.interpret_mouse_event(mouse_event)
-        if bstate & (BUTTON2_CLICKED|BUTTON3_CLICKED):
+        if bstate & (BUTTON2_CLICKED | BUTTON3_CLICKED):
             self.h_paste()
 
     # def update(self, clear=True):
@@ -320,77 +333,87 @@ class TextBoxInner(npyscreen.MultiLineEdit):
     #         self.rely, self.relx + WIDTH, curses.ACS_VLINE, HEIGHT
     #     )
 
-        # # draw corners
-        # self.parent.curses_pad.addch(
-        #     self.rely,
-        #     self.relx,
-        #     curses.ACS_ULCORNER,
-        # )
-        # self.parent.curses_pad.addch(
-        #     self.rely,
-        #     self.relx + WIDTH,
-        #     curses.ACS_URCORNER,
-        # )
-        # self.parent.curses_pad.addch(
-        #     self.rely + HEIGHT,
-        #     self.relx,
-        #     curses.ACS_LLCORNER,
-        # )
-        # self.parent.curses_pad.addch(
-        #     self.rely + HEIGHT,
-        #     self.relx + WIDTH,
-        #     curses.ACS_LRCORNER,
-        # )
+    # # draw corners
+    # self.parent.curses_pad.addch(
+    #     self.rely,
+    #     self.relx,
+    #     curses.ACS_ULCORNER,
+    # )
+    # self.parent.curses_pad.addch(
+    #     self.rely,
+    #     self.relx + WIDTH,
+    #     curses.ACS_URCORNER,
+    # )
+    # self.parent.curses_pad.addch(
+    #     self.rely + HEIGHT,
+    #     self.relx,
+    #     curses.ACS_LLCORNER,
+    # )
+    # self.parent.curses_pad.addch(
+    #     self.rely + HEIGHT,
+    #     self.relx + WIDTH,
+    #     curses.ACS_LRCORNER,
+    # )
+
+    # # fool our superclass into thinking drawing area is smaller - this is really hacky but it seems to work
+    # (relx, rely, height, width) = (self.relx, self.rely, self.height, self.width)
+    # self.relx += 1
+    # self.rely += 1
+    # self.height -= 1
+    # self.width -= 1
+    # super().update(clear=False)
+    # (self.relx, self.rely, self.height, self.width) = (relx, rely, height, width)
 
-        # # fool our superclass into thinking drawing area is smaller - this is really hacky but it seems to work
-        # (relx, rely, height, width) = (self.relx, self.rely, self.height, self.width)
-        # self.relx += 1
-        # self.rely += 1
-        # self.height -= 1
-        # self.width -= 1
-        # super().update(clear=False)
-        # (self.relx, self.rely, self.height, self.width) = (relx, rely, height, width)
 
 class TextBox(npyscreen.BoxTitle):
     _contained_widget = TextBoxInner
 
+
 class BufferBox(npyscreen.BoxTitle):
     _contained_widget = npyscreen.BufferPager
 
+
 class ConfirmCancelPopup(fmPopup.ActionPopup):
     DEFAULT_COLUMNS = 100
+
     def on_ok(self):
         self.value = True
+
     def on_cancel(self):
         self.value = False
-        
+
+
 class FileBox(npyscreen.BoxTitle):
     _contained_widget = npyscreen.Filename
-    
+
+
 class PrettyTextBox(npyscreen.BoxTitle):
     _contained_widget = TextBox
-    
+
+
 def _wrap_message_lines(message, line_length):
     lines = []
-    for line in message.split('\n'):
+    for line in message.split("\n"):
         lines.extend(textwrap.wrap(line.rstrip(), line_length))
     return lines
-    
+
+
 def _prepare_message(message):
     if isinstance(message, list) or isinstance(message, tuple):
-        return "\n".join([ s.rstrip() for s in message])
-        #return "\n".join(message)
+        return "\n".join([s.rstrip() for s in message])
+        # return "\n".join(message)
     else:
         return message
-    
+
+
 def select_stable_diffusion_config_file(
-        form_color: str='DANGER',
-        wrap:bool =True,
-        model_name:str='Unknown',
+    form_color: str = "DANGER",
+    wrap: bool = True,
+    model_name: str = "Unknown",
 ):
     message = f"Please select the correct base model for the V2 checkpoint named '{model_name}'. Press <CANCEL> to skip installation."
     title = "CONFIG FILE SELECTION"
-    options=[
+    options = [
         "An SD v2.x base model (512 pixels; no 'parameterization:' line in its yaml file)",
         "An SD v2.x v-predictive model (768 pixels; 'parameterization: \"v\"' line in its yaml file)",
         "Skip installation for now and come back later",
@@ -403,22 +426,22 @@ def select_stable_diffusion_config_file(
         lines=16,
     )
     F.preserve_selected_widget = True
-    
+
     mlw = F.add(
         wgmultiline.Pager,
         max_height=4,
         editable=False,
     )
-    mlw_width = mlw.width-1
+    mlw_width = mlw.width - 1
     if wrap:
         message = _wrap_message_lines(message, mlw_width)
     mlw.values = message
 
     choice = F.add(
         npyscreen.SelectOne,
-        values = options,
-        value = [0],
-        max_height = len(options)+1,
+        values=options,
+        value=[0],
+        max_height=len(options) + 1,
         scroll_exit=True,
     )
 
@@ -426,6 +449,6 @@ def select_stable_diffusion_config_file(
     F.edit()
     if not F.value:
         return None
-    assert choice.value[0] in range(0,3),'invalid choice'
-    choices = ['epsilon','v','abort']
+    assert choice.value[0] in range(0, 3), "invalid choice"
+    choices = ["epsilon", "v", "abort"]
     return choices[choice.value[0]]
diff --git a/invokeai/frontend/legacy_launch_invokeai.py b/invokeai/frontend/legacy_launch_invokeai.py
index 349fa5b945..e4509db6e5 100644
--- a/invokeai/frontend/legacy_launch_invokeai.py
+++ b/invokeai/frontend/legacy_launch_invokeai.py
@@ -2,18 +2,22 @@ import os
 import sys
 import argparse
 
+
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--web', action='store_true')
-    opts,_ = parser.parse_known_args()
+    parser.add_argument("--web", action="store_true")
+    opts, _ = parser.parse_known_args()
 
     if opts.web:
-        sys.argv.pop(sys.argv.index('--web'))
+        sys.argv.pop(sys.argv.index("--web"))
         from invokeai.app.api_app import invoke_api
+
         invoke_api()
     else:
         from invokeai.app.cli_app import invoke_cli
+
         invoke_cli()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()
diff --git a/invokeai/frontend/merge/__init__.py b/invokeai/frontend/merge/__init__.py
index f1fc66c39e..3a2e4474a5 100644
--- a/invokeai/frontend/merge/__init__.py
+++ b/invokeai/frontend/merge/__init__.py
@@ -2,4 +2,3 @@
 Initialization file for invokeai.frontend.merge
 """
 from .merge_diffusers import main as invokeai_merge_diffusers
-
diff --git a/invokeai/frontend/merge/merge_diffusers.py b/invokeai/frontend/merge/merge_diffusers.py
index c20d913883..81a7b3f6c8 100644
--- a/invokeai/frontend/merge/merge_diffusers.py
+++ b/invokeai/frontend/merge/merge_diffusers.py
@@ -20,13 +20,17 @@ from omegaconf import OmegaConf
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import (
-    ModelMerger, MergeInterpolationMethod,
-    ModelManager, ModelType, BaseModelType,
+    ModelMerger,
+    MergeInterpolationMethod,
+    ModelManager,
+    ModelType,
+    BaseModelType,
 )
 from invokeai.frontend.install.widgets import FloatTitleSlider, TextBox, SingleSelectColumns
 
 config = InvokeAIAppConfig.get_config()
 
+
 def _parse_args() -> Namespace:
     parser = argparse.ArgumentParser(description="InvokeAI model merging")
     parser.add_argument(
@@ -134,14 +138,14 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         self.base_select = self.add_widget_intelligent(
             SingleSelectColumns,
             values=[
-                'Models Built on SD-1.x',
-                'Models Built on SD-2.x',
+                "Models Built on SD-1.x",
+                "Models Built on SD-2.x",
             ],
             value=[self.current_base],
-            columns = 4,
-            max_height = 2,
+            columns=4,
+            max_height=2,
             relx=8,
-            scroll_exit = True,
+            scroll_exit=True,
         )
         self.base_select.on_changed = self._populate_models
         self.add_widget_intelligent(
@@ -300,15 +304,11 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
     def validate_field_values(self) -> bool:
         bad_fields = []
         model_names = self.model_names
-        selected_models = set(
-            (model_names[self.model1.value[0]], model_names[self.model2.value[0]])
-        )
+        selected_models = set((model_names[self.model1.value[0]], model_names[self.model2.value[0]]))
         if self.model3.value[0] > 0:
             selected_models.add(model_names[self.model3.value[0] - 1])
         if len(selected_models) < 2:
-            bad_fields.append(
-                f"Please select two or three DIFFERENT models to compare. You selected {selected_models}"
-            )
+            bad_fields.append(f"Please select two or three DIFFERENT models to compare. You selected {selected_models}")
         if len(bad_fields) > 0:
             message = "The following problems were detected and must be corrected:"
             for problem in bad_fields:
@@ -318,7 +318,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         else:
             return True
 
-    def get_model_names(self, base_model: BaseModelType=None) -> List[str]:
+    def get_model_names(self, base_model: BaseModelType = None) -> List[str]:
         model_names = [
             info["name"]
             for info in self.model_manager.list_models(model_type=ModelType.Main, base_model=base_model)
@@ -326,20 +326,21 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         ]
         return sorted(model_names)
 
-    def _populate_models(self,value=None):
+    def _populate_models(self, value=None):
         base_model = tuple(BaseModelType)[value[0]]
         self.model_names = self.get_model_names(base_model)
-        
+
         models_plus_none = self.model_names.copy()
         models_plus_none.insert(0, "None")
         self.model1.values = self.model_names
         self.model2.values = self.model_names
         self.model3.values = models_plus_none
-                  
+
         self.display()
 
+
 class Mergeapp(npyscreen.NPSAppManaged):
-    def __init__(self, model_manager:ModelManager):
+    def __init__(self, model_manager: ModelManager):
         super().__init__()
         self.model_manager = model_manager
 
@@ -367,9 +368,7 @@ def run_cli(args: Namespace):
 
     if not args.merged_model_name:
         args.merged_model_name = "+".join(args.model_names)
-        logger.info(
-            f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
-        )
+        logger.info(f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"')
 
     model_manager = ModelManager(config.model_conf_path)
     assert (
@@ -383,7 +382,7 @@ def run_cli(args: Namespace):
 
 def main():
     args = _parse_args()
-    config.parse_args(['--root',str(args.root_dir)])
+    config.parse_args(["--root", str(args.root_dir)])
 
     try:
         if args.front_end:
@@ -392,13 +391,9 @@ def main():
             run_cli(args)
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
-                "You need to have at least two diffusers models defined in models.yaml in order to merge"
-            )
+            logger.error("You need to have at least two diffusers models defined in models.yaml in order to merge")
         else:
-            logger.error(
-                "Not enough room for the user interface. Try making this window larger."
-            )
+            logger.error("Not enough room for the user interface. Try making this window larger.")
         sys.exit(-1)
     except Exception as e:
         logger.error(e)
diff --git a/invokeai/frontend/training/textual_inversion.py b/invokeai/frontend/training/textual_inversion.py
index e1c7b3749f..25debf4bdc 100755
--- a/invokeai/frontend/training/textual_inversion.py
+++ b/invokeai/frontend/training/textual_inversion.py
@@ -23,16 +23,14 @@ from omegaconf import OmegaConf
 import invokeai.backend.util.logging as logger
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from ...backend.training import (
-    do_textual_inversion_training,
-    parse_args
-)
+from ...backend.training import do_textual_inversion_training, parse_args
 
 TRAINING_DATA = "text-inversion-training-data"
 TRAINING_DIR = "text-inversion-output"
 CONF_FILE = "preferences.conf"
 config = None
 
+
 class textualInversionForm(npyscreen.FormMultiPageAction):
     resolutions = [512, 768, 1024]
     lr_schedulers = [
@@ -111,9 +109,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             npyscreen.TitleSelectOne,
             name="Learnable property:",
             values=self.learnable_properties,
-            value=self.learnable_properties.index(
-                saved_args.get("learnable_property", "object")
-            ),
+            value=self.learnable_properties.index(saved_args.get("learnable_property", "object")),
             max_height=4,
             scroll_exit=True,
         )
@@ -243,9 +239,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
     def initializer_changed(self):
         placeholder = self.placeholder_token.value
         self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
-        self.train_data_dir.value = str(
-            config.root_dir / TRAINING_DATA / placeholder
-        )
+        self.train_data_dir.value = str(config.root_dir / TRAINING_DATA / placeholder)
         self.output_dir.value = str(config.root_dir / TRAINING_DIR / placeholder)
         self.resume_from_checkpoint.value = Path(self.output_dir.value).exists()
 
@@ -254,9 +248,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             self.parentApp.setNextForm(None)
             self.editing = False
             self.parentApp.ti_arguments = self.marshall_arguments()
-            npyscreen.notify(
-                "Launching textual inversion training. This will take a while..."
-            )
+            npyscreen.notify("Launching textual inversion training. This will take a while...")
         else:
             self.editing = True
 
@@ -266,13 +258,9 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
     def validate_field_values(self) -> bool:
         bad_fields = []
         if self.model.value is None:
-            bad_fields.append(
-                "Model Name must correspond to a known model in models.yaml"
-            )
+            bad_fields.append("Model Name must correspond to a known model in models.yaml")
         if not re.match("^[a-zA-Z0-9.-]+$", self.placeholder_token.value):
-            bad_fields.append(
-                "Trigger term must only contain alphanumeric characters, the dot and hyphen"
-            )
+            bad_fields.append("Trigger term must only contain alphanumeric characters, the dot and hyphen")
         if self.train_data_dir.value is None:
             bad_fields.append("Data Training Directory cannot be empty")
         if self.output_dir.value is None:
@@ -288,16 +276,8 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
 
     def get_model_names(self) -> Tuple[List[str], int]:
         conf = OmegaConf.load(config.root_dir / "configs/models.yaml")
-        model_names = [
-            idx
-            for idx in sorted(list(conf.keys()))
-            if conf[idx].get("format", None) == "diffusers"
-        ]
-        defaults = [
-            idx
-            for idx in range(len(model_names))
-            if "default" in conf[model_names[idx]]
-        ]
+        model_names = [idx for idx in sorted(list(conf.keys())) if conf[idx].get("format", None) == "diffusers"]
+        defaults = [idx for idx in range(len(model_names)) if "default" in conf[model_names[idx]]]
         default = defaults[0] if len(defaults) > 0 else 0
         return (model_names, default)
 
@@ -310,9 +290,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             resolution=self.resolutions[self.resolution.value[0]],
             lr_scheduler=self.lr_schedulers[self.lr_scheduler.value[0]],
             mixed_precision=self.precisions[self.mixed_precision.value[0]],
-            learnable_property=self.learnable_properties[
-                self.learnable_property.value[0]
-            ],
+            learnable_property=self.learnable_properties[self.learnable_property.value[0]],
         )
 
         # all the strings and booleans
@@ -374,9 +352,7 @@ def copy_to_embeddings_folder(args: dict):
     os.makedirs(destination, exist_ok=True)
     logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}")
     shutil.copy(source, destination)
-    if (
-        input("Delete training logs and intermediate checkpoints? [y] ") or "y"
-    ).startswith(("y", "Y")):
+    if (input("Delete training logs and intermediate checkpoints? [y] ") or "y").startswith(("y", "Y")):
         shutil.rmtree(Path(args["output_dir"]))
     else:
         logger.info(f'Keeping {args["output_dir"]}')
@@ -423,7 +399,7 @@ def do_front_end(args: Namespace):
         save_args(args)
 
         try:
-            do_textual_inversion_training(InvokeAIAppConfig.get_config(),**args)
+            do_textual_inversion_training(InvokeAIAppConfig.get_config(), **args)
             copy_to_embeddings_folder(args)
         except Exception as e:
             logger.error("An exception occurred during training. The exception was:")
@@ -434,19 +410,19 @@ def do_front_end(args: Namespace):
 
 def main():
     global config
-    
+
     args = parse_args()
     config = InvokeAIAppConfig.get_config()
 
     # change root if needed
     if args.root_dir:
         config.root = args.root_dir
-    
+
     try:
         if args.front_end:
             do_front_end(args)
         else:
-            do_textual_inversion_training(config,**vars(args))
+            do_textual_inversion_training(config, **vars(args))
     except AssertionError as e:
         logger.error(e)
         sys.exit(-1)
@@ -454,13 +430,9 @@ def main():
         pass
     except (widget.NotEnoughSpaceForWidget, Exception) as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
-                "You need to have at least one diffusers models defined in models.yaml in order to train"
-            )
+            logger.error("You need to have at least one diffusers models defined in models.yaml in order to train")
         elif str(e).startswith("addwstr"):
-            logger.error(
-                "Not enough window space for the interface. Please make your window larger and try again."
-            )
+            logger.error("Not enough window space for the interface. Please make your window larger and try again.")
         else:
             logger.error(e)
         sys.exit(-1)
diff --git a/invokeai/frontend/web/__init__.py b/invokeai/frontend/web/__init__.py
index 010129ece2..e9758b27b6 100644
--- a/invokeai/frontend/web/__init__.py
+++ b/invokeai/frontend/web/__init__.py
@@ -1,3 +1,3 @@
-'''
+"""
 Initialization file for invokeai.frontend.web
-'''
+"""
diff --git a/invokeai/frontend/web/dist/assets/App-06ea4e5e.js b/invokeai/frontend/web/dist/assets/App-06ea4e5e.js
deleted file mode 100644
index a7597f8d24..0000000000
--- a/invokeai/frontend/web/dist/assets/App-06ea4e5e.js
+++ /dev/null
@@ -1,169 +0,0 @@
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Math.abs(i-e)<Math.abs(t[s]-e)?u:s},0),o=Math.abs(t[r]-e);return n===0||o<n?t[r]:e},hg=function(e){return e=e.toString(),e==="auto"||e.endsWith("px")||e.endsWith("%")||e.endsWith("vh")||e.endsWith("vw")||e.endsWith("vmax")||e.endsWith("vmin")?e:e+"px"},Bd=function(e,t,n,r){if(e&&typeof e=="string"){if(e.endsWith("px"))return Number(e.replace("px",""));if(e.endsWith("%")){var o=Number(e.replace("%",""))/100;return t*o}if(e.endsWith("vw")){var o=Number(e.replace("vw",""))/100;return n*o}if(e.endsWith("vh")){var o=Number(e.replace("vh",""))/100;return r*o}}return e},a$=function(e,t,n,r,o,s,i){return r=Bd(r,e.width,t,n),o=Bd(o,e.height,t,n),s=Bd(s,e.width,t,n),i=Bd(i,e.height,t,n),{maxWidth:typeof r>"u"?void 0:Number(r),maxHeight:typeof o>"u"?void 0:Number(o),minWidth:typeof s>"u"?void 0:Number(s),minHeight:typeof i>"u"?void 0:Number(i)}},i$=["as","style","className","grid","snap","bounds","boundsByDirection","size","defaultSize","minWidth","minHeight","maxWidth","maxHeight","lockAspectRatio","lockAspectRatioExtraWidth","lockAspectRatioExtraHeight","enable","handleStyles","handleClasses","handleWrapperStyle","handleWrapperClass","children","onResizeStart","onResize","onResizeStop","handleComponent","scale","resizeRatio","snapGap"],C2="__resizable_base__",l$=function(e){r$(t,e);function t(n){var r=e.call(this,n)||this;return r.ratio=1,r.resizable=null,r.parentLeft=0,r.parentTop=0,r.resizableLeft=0,r.resizableRight=0,r.resizableTop=0,r.resizableBottom=0,r.targetLeft=0,r.targetTop=0,r.appendBase=function(){if(!r.resizable||!r.window)return null;var o=r.parentNode;if(!o)return null;var s=r.window.document.createElement("div");return s.style.width="100%",s.style.height="100%",s.style.position="absolute",s.style.transform="scale(0, 0)",s.style.left="0",s.style.flex="0 0 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this.resizable?this.resizable.parentNode:null},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"window",{get:function(){return!this.resizable||!this.resizable.ownerDocument?null:this.resizable.ownerDocument.defaultView},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"propsSize",{get:function(){return this.props.size||this.props.defaultSize||o$},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"size",{get:function(){var n=0,r=0;if(this.resizable&&this.window){var o=this.resizable.offsetWidth,s=this.resizable.offsetHeight,i=this.resizable.style.position;i!=="relative"&&(this.resizable.style.position="relative"),n=this.resizable.style.width!=="auto"?this.resizable.offsetWidth:o,r=this.resizable.style.height!=="auto"?this.resizable.offsetHeight:s,this.resizable.style.position=i}return{width:n,height:r}},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"sizeStyle",{get:function(){var n=this,r=this.props.size,o=function(u){if(typeof n.state[u]>"u"||n.state[u]==="auto")return"auto";if(n.propsSize&&n.propsSize[u]&&n.propsSize[u].toString().endsWith("%")){if(n.state[u].toString().endsWith("%"))return n.state[u].toString();var d=n.getParentSize(),p=Number(n.state[u].toString().replace("px","")),h=p/d[u]*100;return h+"%"}return hg(n.state[u])},s=r&&typeof r.width<"u"&&!this.state.isResizing?hg(r.width):o("width"),i=r&&typeof r.height<"u"&&!this.state.isResizing?hg(r.height):o("height");return{width:s,height:i}},enumerable:!1,configurable:!0}),t.prototype.getParentSize=function(){if(!this.parentNode)return this.window?{width:this.window.innerWidth,height:this.window.innerHeight}:{width:0,height:0};var n=this.appendBase();if(!n)return{width:0,height:0};var r=!1,o=this.parentNode.style.flexWrap;o!=="wrap"&&(r=!0,this.parentNode.style.flexWrap="wrap"),n.style.position="relative",n.style.minWidth="100%",n.style.minHeight="100%";var s={width:n.offsetWidth,height:n.offsetHeight};return r&&(this.parentNode.style.flexWrap=o),this.removeBase(n),s},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(!(!this.resizable||!this.window)){var n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var o=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof o>"u"||o==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var o=this.props.boundsByDirection,s=this.state.direction,i=o&&pl("left",s),u=o&&pl("top",s),d,p;if(this.props.bounds==="parent"){var h=this.parentNode;h&&(d=i?this.resizableRight-this.parentLeft:h.offsetWidth+(this.parentLeft-this.resizableLeft),p=u?this.resizableBottom-this.parentTop:h.offsetHeight+(this.parentTop-this.resizableTop))}else 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pl("right",u)&&(b=d.width+(n-d.x)*s/o,h&&(S=(b-C)/this.ratio+x)),pl("left",u)&&(b=d.width-(n-d.x)*s/o,h&&(S=(b-C)/this.ratio+x)),pl("bottom",u)&&(S=d.height+(r-d.y)*s/o,h&&(b=(S-x)*this.ratio+C)),pl("top",u)&&(S=d.height-(r-d.y)*s/o,h&&(b=(S-x)*this.ratio+C)),{newWidth:b,newHeight:S}},t.prototype.calculateNewSizeFromAspectRatio=function(n,r,o,s){var i=this.props,u=i.lockAspectRatio,d=i.lockAspectRatioExtraHeight,p=i.lockAspectRatioExtraWidth,h=typeof s.width>"u"?10:s.width,g=typeof o.width>"u"||o.width<0?n:o.width,v=typeof s.height>"u"?10:s.height,b=typeof o.height>"u"||o.height<0?r:o.height,S=d||0,x=p||0;if(u){var C=(v-S)*this.ratio+x,_=(b-S)*this.ratio+x,k=(h-x)/this.ratio+S,P=(g-x)/this.ratio+S,I=Math.max(h,C),E=Math.min(g,_),O=Math.max(v,k),R=Math.min(b,P);n=zd(n,I,E),r=zd(r,O,R)}else n=zd(n,h,g),r=zd(r,v,b);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var o=this.props.bounds.getBoundingClientRect();this.targetLeft=o.left,this.targetTop=o.top}if(this.resizable){var s=this.resizable.getBoundingClientRect(),i=s.left,u=s.top,d=s.right,p=s.bottom;this.resizableLeft=i,this.resizableRight=d,this.resizableTop=u,this.resizableBottom=p}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var o=0,s=0;if(n.nativeEvent&&s$(n.nativeEvent)?(o=n.nativeEvent.clientX,s=n.nativeEvent.clientY):n.nativeEvent&&Ld(n.nativeEvent)&&(o=n.nativeEvent.touches[0].clientX,s=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var i=this.props.onResizeStart(n,r,this.resizable);if(i===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var u,d=this.window.getComputedStyle(this.resizable);if(d.flexBasis!=="auto"){var p=this.parentNode;if(p){var h=this.window.getComputedStyle(p).flexDirection;this.flexDir=h.startsWith("row")?"row":"column",u=d.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var g={original:{x:o,y:s,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:Ts(Ts({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:u};this.setState(g)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&Ld(n))try{n.preventDefault(),n.stopPropagation()}catch{}var o=this.props,s=o.maxWidth,i=o.maxHeight,u=o.minWidth,d=o.minHeight,p=Ld(n)?n.touches[0].clientX:n.clientX,h=Ld(n)?n.touches[0].clientY:n.clientY,g=this.state,v=g.direction,b=g.original,S=g.width,x=g.height,C=this.getParentSize(),_=a$(C,this.window.innerWidth,this.window.innerHeight,s,i,u,d);s=_.maxWidth,i=_.maxHeight,u=_.minWidth,d=_.minHeight;var k=this.calculateNewSizeFromDirection(p,h),P=k.newHeight,I=k.newWidth,E=this.calculateNewMaxFromBoundary(s,i);this.props.snap&&this.props.snap.x&&(I=S2(I,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(P=S2(P,this.props.snap.y,this.props.snapGap));var O=this.calculateNewSizeFromAspectRatio(I,P,{width:E.maxWidth,height:E.maxHeight},{width:u,height:d});if(I=O.newWidth,P=O.newHeight,this.props.grid){var R=w2(I,this.props.grid[0]),D=w2(P,this.props.grid[1]),T=this.props.snapGap||0;I=T===0||Math.abs(R-I)<=T?R:I,P=T===0||Math.abs(D-P)<=T?D:P}var B={width:I-b.width,height:P-b.height};if(S&&typeof S=="string"){if(S.endsWith("%")){var W=I/C.width*100;I=W+"%"}else if(S.endsWith("vw")){var J=I/this.window.innerWidth*100;I=J+"vw"}else if(S.endsWith("vh")){var F=I/this.window.innerHeight*100;I=F+"vh"}}if(x&&typeof x=="string"){if(x.endsWith("%")){var W=P/C.height*100;P=W+"%"}else if(x.endsWith("vw")){var J=P/this.window.innerWidth*100;P=J+"vw"}else if(x.endsWith("vh")){var F=P/this.window.innerHeight*100;P=F+"vh"}}var N={width:this.createSizeForCssProperty(I,"width"),height:this.createSizeForCssProperty(P,"height")};this.flexDir==="row"?N.flexBasis=N.width:this.flexDir==="column"&&(N.flexBasis=N.height),nc.flushSync(function(){r.setState(N)}),this.props.onResize&&this.props.onResize(n,v,this.resizable,B)}},t.prototype.onMouseUp=function(n){var r=this.state,o=r.isResizing,s=r.direction,i=r.original;if(!(!o||!this.resizable)){var u={width:this.size.width-i.width,height:this.size.height-i.height};this.props.onResizeStop&&this.props.onResizeStop(n,s,this.resizable,u),this.props.size&&this.setState(this.props.size),this.unbindEvents(),this.setState({isResizing:!1,backgroundStyle:Ts(Ts({},this.state.backgroundStyle),{cursor:"auto"})})}},t.prototype.updateSize=function(n){this.setState({width:n.width,height:n.height})},t.prototype.renderResizer=function(){var n=this,r=this.props,o=r.enable,s=r.handleStyles,i=r.handleClasses,u=r.handleWrapperStyle,d=r.handleWrapperClass,p=r.handleComponent;if(!o)return null;var h=Object.keys(o).map(function(g){return o[g]!==!1?f.createElement(n$,{key:g,direction:g,onResizeStart:n.onResizeStart,replaceStyles:s&&s[g],className:i&&i[g]},p&&p[g]?p[g]:null):null});return f.createElement("div",{className:d,style:u},h)},t.prototype.render=function(){var n=this,r=Object.keys(this.props).reduce(function(i,u){return i$.indexOf(u)!==-1||(i[u]=n.props[u]),i},{}),o=Ts(Ts(Ts({position:"relative",userSelect:this.state.isResizing?"none":"auto"},this.props.style),this.sizeStyle),{maxWidth:this.props.maxWidth,maxHeight:this.props.maxHeight,minWidth:this.props.minWidth,minHeight:this.props.minHeight,boxSizing:"border-box",flexShrink:0});this.state.flexBasis&&(o.flexBasis=this.state.flexBasis);var s=this.props.as||"div";return f.createElement(s,Ts({ref:this.ref,style:o,className:this.props.className},r),this.state.isResizing&&f.createElement("div",{style:this.state.backgroundStyle}),this.props.children,this.renderResizer())},t.defaultProps={as:"div",onResizeStart:function(){},onResize:function(){},onResizeStop:function(){},enable:{top:!0,right:!0,bottom:!0,left:!0,topRight:!0,bottomRight:!0,bottomLeft:!0,topLeft:!0},style:{},grid:[1,1],lockAspectRatio:!1,lockAspectRatioExtraWidth:0,lockAspectRatioExtraHeight:0,scale:1,resizeRatio:1,snapGap:0},t}(f.PureComponent);const c$=({direction:e,langDirection:t})=>({top:e==="bottom",right:t!=="rtl"&&e==="left"||t==="rtl"&&e==="right",bottom:e==="top",left:t!=="rtl"&&e==="right"||t==="rtl"&&e==="left"}),u$=({direction:e,minWidth:t,maxWidth:n,minHeight:r,maxHeight:o})=>{const s=t??(["left","right"].includes(e)?10:void 0),i=n??(["left","right"].includes(e)?"95vw":void 0),u=r??(["top","bottom"].includes(e)?10:void 0),d=o??(["top","bottom"].includes(e)?"95vh":void 0);return{...s?{minWidth:s}:{},...i?{maxWidth:i}:{},...u?{minHeight:u}:{},...d?{maxHeight:d}:{}}},oa="0.75rem",Fd="1rem",Mc="5px",d$=({isResizable:e,direction:t})=>{const n=`calc((2 * ${oa} + ${Mc}) / -2)`;return 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Et={},Ft=Symbol.for("RTKQ/skipToken"),Wt=Ft,_n={status:t.uninitialized},In=(0,pe.createNextState)(_n,function(){}),ln=(0,pe.createNextState)(_n,function(){}),Cr=I(Eo),vn=WeakMap?new WeakMap:void 0,sr=function(q){var Y=q.endpointName,le=q.queryArgs,we="",ce=vn==null?void 0:vn.get(le);if(typeof ce=="string")we=ce;else{var nt=JSON.stringify(le,function(Je,Dt){return(0,Cr.isPlainObject)(Dt)?Object.keys(Dt).sort().reduce(function(st,dt){return st[dt]=Dt[dt],st},{}):Dt});(0,Cr.isPlainObject)(le)&&(vn==null||vn.set(le,nt)),we=nt}return Y+"("+we+")"},Ie=I(Eo),it=I(Az);function Xe(){for(var q=[],Y=0;Y<arguments.length;Y++)q[Y]=arguments[Y];return function(le){var we=(0,it.defaultMemoize)(function(st){var dt,De;return(De=le.extractRehydrationInfo)==null?void 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Fe=ye[De],ht=Fe[0],xt=Fe[1];if(st.overrideExisting||!(ht in nt.endpointDefinitions)){nt.endpointDefinitions[ht]=xt;for(var Ot=0,Lt=Dt;Ot<Lt.length;Ot++)Lt[Ot].injectEndpoint(ht,xt)}}return Je},enhanceEndpoints:function(st){var dt=st.addTagTypes,De=st.endpoints;if(dt)for(var ye=0,Fe=dt;ye<Fe.length;ye++){var ht=Fe[ye];ce.tagTypes.includes(ht)||ce.tagTypes.push(ht)}if(De)for(var xt=0,Ot=Object.entries(De);xt<Ot.length;xt++){var Lt=Ot[xt],Kt=Lt[0],qt=Lt[1];typeof qt=="function"?qt(nt.endpointDefinitions[Kt]):Object.assign(nt.endpointDefinitions[Kt]||{},qt)}return Je}},Dt=q.map(function(st){return st.init(Je,ce,nt)});return Je.injectEndpoints({endpoints:le.endpoints})}}function ot(){return function(){throw new Error("When using `fakeBaseQuery`, all queries & mutations must use the `queryFn` definition syntax.")}}var Nt,ur=I(Eo),yn=function(q){var Y=q.reducerPath,le=q.api,we=q.context,ce=q.internalState,nt=le.internalActions,Je=nt.removeQueryResult,Dt=nt.unsubscribeQueryResult;function st(ye){var Fe=ce.currentSubscriptions[ye];return!!Fe&&!function(ht){for(var xt in ht)return!1;return!0}(Fe)}var dt={};function De(ye,Fe,ht,xt){var Ot,Lt=we.endpointDefinitions[Fe],Kt=(Ot=Lt==null?void 0:Lt.keepUnusedDataFor)!=null?Ot:xt.keepUnusedDataFor;if(Kt!==1/0){var qt=Math.max(0,Math.min(Kt,2147482647e-3));if(!st(ye)){var $t=dt[ye];$t&&clearTimeout($t),dt[ye]=setTimeout(function(){st(ye)||ht.dispatch(Je({queryCacheKey:ye})),delete dt[ye]},1e3*qt)}}}return function(ye,Fe,ht){var xt;if(Dt.match(ye)){var Ot=Fe.getState()[Y];De(xn=ye.payload.queryCacheKey,(xt=Ot.queries[xn])==null?void 0:xt.endpointName,Fe,Ot.config)}if(le.util.resetApiState.match(ye))for(var Lt=0,Kt=Object.entries(dt);Lt<Kt.length;Lt++){var qt=Kt[Lt],$t=qt[0],Oe=qt[1];Oe&&clearTimeout(Oe),delete dt[$t]}if(we.hasRehydrationInfo(ye)){Ot=Fe.getState()[Y];for(var ut=we.extractRehydrationInfo(ye).queries,_t=0,At=Object.entries(ut);_t<At.length;_t++){var xn,zt=At[_t],tn=zt[1];De(xn=zt[0],tn==null?void 0:tn.endpointName,Fe,Ot.config)}}}},bn=I(Eo),$r=function(q){var Y=q.reducerPath,le=q.context,we=q.context.endpointDefinitions,ce=q.mutationThunk,nt=q.api,Je=q.assertTagType,Dt=q.refetchQuery,st=nt.internalActions.removeQueryResult,dt=(0,bn.isAnyOf)((0,bn.isFulfilled)(ce),(0,bn.isRejectedWithValue)(ce));function De(ye,Fe){var ht=Fe.getState(),xt=ht[Y],Ot=nt.util.selectInvalidatedBy(ht,ye);le.batch(function(){for(var Lt,Kt=0,qt=Array.from(Ot.values());Kt<qt.length;Kt++){var $t=qt[Kt].queryCacheKey,Oe=xt.queries[$t],ut=(Lt=xt.subscriptions[$t])!=null?Lt:{};Oe&&(Object.keys(ut).length===0?Fe.dispatch(st({queryCacheKey:$t})):Oe.status!==t.uninitialized&&Fe.dispatch(Dt(Oe,$t)))}})}return function(ye,Fe){dt(ye)&&De(vt(ye,"invalidatesTags",we,Je),Fe),nt.util.invalidateTags.match(ye)&&De(ve(ye.payload,void 0,void 0,void 0,void 0,Je),Fe)}},dr=function(q){var Y=q.reducerPath,le=q.queryThunk,we=q.api,ce=q.refetchQuery,nt=q.internalState,Je={};function Dt(ye,Fe){var ht=ye.queryCacheKey,xt=Fe.getState()[Y].queries[ht];if(xt&&xt.status!==t.uninitialized){var Ot=De(nt.currentSubscriptions[ht]);if(Number.isFinite(Ot)){var Lt=Je[ht];Lt!=null&&Lt.timeout&&(clearTimeout(Lt.timeout),Lt.timeout=void 0);var Kt=Date.now()+Ot,qt=Je[ht]={nextPollTimestamp:Kt,pollingInterval:Ot,timeout:setTimeout(function(){qt.timeout=void 0,Fe.dispatch(ce(xt,ht))},Ot)}}}}function st(ye,Fe){var ht=ye.queryCacheKey,xt=Fe.getState()[Y].queries[ht];if(xt&&xt.status!==t.uninitialized){var Ot=De(nt.currentSubscriptions[ht]);if(Number.isFinite(Ot)){var Lt=Je[ht],Kt=Date.now()+Ot;(!Lt||Kt<Lt.nextPollTimestamp)&&Dt({queryCacheKey:ht},Fe)}else dt(ht)}}function dt(ye){var Fe=Je[ye];Fe!=null&&Fe.timeout&&clearTimeout(Fe.timeout),delete Je[ye]}function De(ye){ye===void 0&&(ye={});var Fe=Number.POSITIVE_INFINITY;for(var ht in ye)ye[ht].pollingInterval&&(Fe=Math.min(ye[ht].pollingInterval,Fe));return Fe}return function(ye,Fe){(we.internalActions.updateSubscriptionOptions.match(ye)||we.internalActions.unsubscribeQueryResult.match(ye))&&st(ye.payload,Fe),(le.pending.match(ye)||le.rejected.match(ye)&&ye.meta.condition)&&st(ye.meta.arg,Fe),(le.fulfilled.match(ye)||le.rejected.match(ye)&&!ye.meta.condition)&&Dt(ye.meta.arg,Fe),we.util.resetApiState.match(ye)&&function(){for(var ht=0,xt=Object.keys(Je);ht<xt.length;ht++)dt(xt[ht])}()}},An=I(Eo),Tn=new Error("Promise never resolved before cacheEntryRemoved."),Nn=function(q){var Y=q.api,le=q.reducerPath,we=q.context,ce=q.queryThunk,nt=q.mutationThunk,Je=(0,An.isAsyncThunkAction)(ce),Dt=(0,An.isAsyncThunkAction)(nt),st=(0,An.isFulfilled)(ce,nt),dt={};function De(ye,Fe,ht,xt,Ot){var Lt=we.endpointDefinitions[ye],Kt=Lt==null?void 0:Lt.onCacheEntryAdded;if(Kt){var qt={},$t=new Promise(function(zt){qt.cacheEntryRemoved=zt}),Oe=Promise.race([new Promise(function(zt){qt.valueResolved=zt}),$t.then(function(){throw Tn})]);Oe.catch(function(){}),dt[ht]=qt;var ut=Y.endpoints[ye].select(Lt.type===H.query?Fe:ht),_t=xt.dispatch(function(zt,tn,Jt){return Jt}),At=_(C({},xt),{getCacheEntry:function(){return ut(xt.getState())},requestId:Ot,extra:_t,updateCachedData:Lt.type===H.query?function(zt){return xt.dispatch(Y.util.updateQueryData(ye,Fe,zt))}:void 0,cacheDataLoaded:Oe,cacheEntryRemoved:$t}),xn=Kt(Fe,At);Promise.resolve(xn).catch(function(zt){if(zt!==Tn)throw zt})}}return function(ye,Fe,ht){var xt=function(ut){return Je(ut)?ut.meta.arg.queryCacheKey:Dt(ut)?ut.meta.requestId:Y.internalActions.removeQueryResult.match(ut)?ut.payload.queryCacheKey:Y.internalActions.removeMutationResult.match(ut)?be(ut.payload):""}(ye);if(ce.pending.match(ye)){var Ot=ht[le].queries[xt],Lt=Fe.getState()[le].queries[xt];!Ot&&Lt&&De(ye.meta.arg.endpointName,ye.meta.arg.originalArgs,xt,Fe,ye.meta.requestId)}else if(nt.pending.match(ye))(Lt=Fe.getState()[le].mutations[xt])&&De(ye.meta.arg.endpointName,ye.meta.arg.originalArgs,xt,Fe,ye.meta.requestId);else 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At=Y.endpoints[Kt].select($t.type===H.query?qt:Ot),xn=De.dispatch(function(Un,yt,wn){return wn}),zt=_(C({},De),{getCacheEntry:function(){return At(De.getState())},requestId:Ot,extra:xn,updateCachedData:$t.type===H.query?function(Un){return De.dispatch(Y.util.updateQueryData(Kt,qt,Un))}:void 0,queryFulfilled:_t});Oe(qt,zt)}}else if(Dt(dt)){var tn=dt.meta,Jt=tn.baseQueryMeta;(ye=st[Ot=tn.requestId])==null||ye.resolve({data:dt.payload,meta:Jt}),delete st[Ot]}else if(Je(dt)){var _r=dt.meta;Jt=_r.baseQueryMeta,(ht=st[Ot=_r.requestId])==null||ht.reject({error:(Fe=dt.payload)!=null?Fe:dt.error,isUnhandledError:!_r.rejectedWithValue,meta:Jt}),delete st[Ot]}}},zr=function(q){var Y=q.api,le=q.context.apiUid;return function(we,ce){Y.util.resetApiState.match(we)&&ce.dispatch(Y.internalActions.middlewareRegistered(le))}},Lr=I(Rc),es=typeof queueMicrotask=="function"?queueMicrotask.bind(typeof window<"u"?window:typeof al<"u"?al:globalThis):function(q){return(Nt||(Nt=Promise.resolve())).then(q).catch(function(Y){return setTimeout(function(){throw Y},0)})};function Dr(q){for(var Y=[],le=1;le<arguments.length;le++)Y[le-1]=arguments[le];Object.assign.apply(Object,o([q],Y))}var ks=I(Rc),ts=Symbol(),Xs=function(){return{name:ts,init:function(q,Y,le){var we=Y.baseQuery,ce=Y.reducerPath,nt=Y.serializeQueryArgs,Je=Y.keepUnusedDataFor,Dt=Y.refetchOnMountOrArgChange,st=Y.refetchOnFocus,dt=Y.refetchOnReconnect;(0,ks.enablePatches)();var De=function(yt){return yt};Object.assign(q,{reducerPath:ce,endpoints:{},internalActions:{onOnline:xe,onOffline:me,onFocus:ee,onFocusLost:ie},util:{}});var ye=function(yt){var wn=this,kn=yt.reducerPath,Gn=yt.baseQuery,ar=yt.context.endpointDefinitions,En=yt.serializeQueryArgs,On=yt.api,nn=function(Be,wt){return E(wn,[Be,wt],function(pt,Ze){var ft,Rt,lt,Qe,ke,Se,Pe,Te,Ge,kt,St,Ne,Qt,dn=Ze.signal,on=Ze.abort,ir=Ze.rejectWithValue,Fn=Ze.fulfillWithValue,an=Ze.dispatch,$n=Ze.getState,to=Ze.extra;return r(this,function(rn){switch(rn.label){case 0:ft=ar[pt.endpointName],rn.label=1;case 1:return rn.trys.push([1,8,,13]),Rt=jt,lt=void 0,Qe={signal:dn,abort:on,dispatch:an,getState:$n,extra:to,endpoint:pt.endpointName,type:pt.type,forced:pt.type==="query"?pn(pt,$n()):void 0},(ke=pt.type==="query"?pt[ge]:void 0)?(lt=ke(),[3,6]):[3,2];case 2:return ft.query?[4,Gn(ft.query(pt.originalArgs),Qe,ft.extraOptions)]:[3,4];case 3:return lt=rn.sent(),ft.transformResponse&&(Rt=ft.transformResponse),[3,6];case 4:return[4,ft.queryFn(pt.originalArgs,Qe,ft.extraOptions,function(Mn){return Gn(Mn,Qe,ft.extraOptions)})];case 5:lt=rn.sent(),rn.label=6;case 6:if(lt.error)throw new G(lt.error,lt.meta);return Se=Fn,[4,Rt(lt.data,lt.meta,pt.originalArgs)];case 7:return[2,Se.apply(void 0,[rn.sent(),(Ne={fulfilledTimeStamp:Date.now(),baseQueryMeta:lt.meta},Ne[Ue.SHOULD_AUTOBATCH]=!0,Ne)])];case 8:if(Pe=rn.sent(),!((Te=Pe)instanceof G))return[3,12];Ge=jt,ft.query&&ft.transformErrorResponse&&(Ge=ft.transformErrorResponse),rn.label=9;case 9:return rn.trys.push([9,11,,12]),kt=ir,[4,Ge(Te.value,Te.meta,pt.originalArgs)];case 10:return[2,kt.apply(void 0,[rn.sent(),(Qt={baseQueryMeta:Te.meta},Qt[Ue.SHOULD_AUTOBATCH]=!0,Qt)])];case 11:return St=rn.sent(),Te=St,[3,12];case 12:throw console.error(Te),Te;case 13:return[2]}})})};function pn(Be,wt){var pt,Ze,ft,Rt,lt=(Ze=(pt=wt[kn])==null?void 0:pt.queries)==null?void 0:Ze[Be.queryCacheKey],Qe=(ft=wt[kn])==null?void 0:ft.config.refetchOnMountOrArgChange,ke=lt==null?void 0:lt.fulfilledTimeStamp,Se=(Rt=Be.forceRefetch)!=null?Rt:Be.subscribe&&Qe;return!!Se&&(Se===!0||(Number(new Date)-Number(ke))/1e3>=Se)}var Sn=(0,Ue.createAsyncThunk)(kn+"/executeQuery",nn,{getPendingMeta:function(){var Be;return(Be={startedTimeStamp:Date.now()})[Ue.SHOULD_AUTOBATCH]=!0,Be},condition:function(Be,wt){var pt,Ze,ft,Rt=(0,wt.getState)(),lt=(Ze=(pt=Rt[kn])==null?void 0:pt.queries)==null?void 0:Ze[Be.queryCacheKey],Qe=lt==null?void 0:lt.fulfilledTimeStamp,ke=Be.originalArgs,Se=lt==null?void 0:lt.originalArgs,Pe=ar[Be.endpointName];return!(!_e(Be)&&((lt==null?void 0:lt.status)==="pending"||!pn(Be,Rt)&&(!ne(Pe)||!((ft=Pe==null?void 0:Pe.forceRefetch)!=null&&ft.call(Pe,{currentArg:ke,previousArg:Se,endpointState:lt,state:Rt})))&&Qe))},dispatchConditionRejection:!0}),cn=(0,Ue.createAsyncThunk)(kn+"/executeMutation",nn,{getPendingMeta:function(){var Be;return(Be={startedTimeStamp:Date.now()})[Ue.SHOULD_AUTOBATCH]=!0,Be}});function un(Be){return function(wt){var pt,Ze;return((Ze=(pt=wt==null?void 0:wt.meta)==null?void 0:pt.arg)==null?void 0:Ze.endpointName)===Be}}return{queryThunk:Sn,mutationThunk:cn,prefetch:function(Be,wt,pt){return function(Ze,ft){var Rt=function(Pe){return"force"in Pe}(pt)&&pt.force,lt=function(Pe){return"ifOlderThan"in Pe}(pt)&&pt.ifOlderThan,Qe=function(Pe){return Pe===void 0&&(Pe=!0),On.endpoints[Be].initiate(wt,{forceRefetch:Pe})},ke=On.endpoints[Be].select(wt)(ft());if(Rt)Ze(Qe());else if(lt){var Se=ke==null?void 0:ke.fulfilledTimeStamp;if(!Se)return void Ze(Qe());(Number(new Date)-Number(new Date(Se)))/1e3>=lt&&Ze(Qe())}else Ze(Qe(!1))}},updateQueryData:function(Be,wt,pt){return function(Ze,ft){var Rt,lt,Qe=On.endpoints[Be].select(wt)(ft()),ke={patches:[],inversePatches:[],undo:function(){return Ze(On.util.patchQueryData(Be,wt,ke.inversePatches))}};if(Qe.status===t.uninitialized)return ke;if("data"in Qe)if((0,Le.isDraftable)(Qe.data)){var Se=(0,Le.produceWithPatches)(Qe.data,pt),Pe=Se[2];(Rt=ke.patches).push.apply(Rt,Se[1]),(lt=ke.inversePatches).push.apply(lt,Pe)}else{var Te=pt(Qe.data);ke.patches.push({op:"replace",path:[],value:Te}),ke.inversePatches.push({op:"replace",path:[],value:Qe.data})}return Ze(On.util.patchQueryData(Be,wt,ke.patches)),ke}},upsertQueryData:function(Be,wt,pt){return function(Ze){var ft;return Ze(On.endpoints[Be].initiate(wt,((ft={subscribe:!1,forceRefetch:!0})[ge]=function(){return{data:pt}},ft)))}},patchQueryData:function(Be,wt,pt){return function(Ze){Ze(On.internalActions.queryResultPatched({queryCacheKey:En({queryArgs:wt,endpointDefinition:ar[Be],endpointName:Be}),patches:pt}))}},buildMatchThunkActions:function(Be,wt){return{matchPending:(0,We.isAllOf)((0,We.isPending)(Be),un(wt)),matchFulfilled:(0,We.isAllOf)((0,We.isFulfilled)(Be),un(wt)),matchRejected:(0,We.isAllOf)((0,We.isRejected)(Be),un(wt))}}}}({baseQuery:we,reducerPath:ce,context:le,api:q,serializeQueryArgs:nt}),Fe=ye.queryThunk,ht=ye.mutationThunk,xt=ye.patchQueryData,Ot=ye.updateQueryData,Lt=ye.upsertQueryData,Kt=ye.prefetch,qt=ye.buildMatchThunkActions,$t=function(yt){var wn=yt.reducerPath,kn=yt.queryThunk,Gn=yt.mutationThunk,ar=yt.context,En=ar.endpointDefinitions,On=ar.apiUid,nn=ar.extractRehydrationInfo,pn=ar.hasRehydrationInfo,Sn=yt.assertTagType,cn=yt.config,un=(0,Ce.createAction)(wn+"/resetApiState"),Be=(0,Ce.createSlice)({name:wn+"/queries",initialState:Et,reducers:{removeQueryResult:{reducer:function(Qe,ke){delete Qe[ke.payload.queryCacheKey]},prepare:(0,Ce.prepareAutoBatched)()},queryResultPatched:function(Qe,ke){var Se=ke.payload,Pe=Se.patches;bt(Qe,Se.queryCacheKey,function(Te){Te.data=(0,Pt.applyPatches)(Te.data,Pe.concat())})}},extraReducers:function(Qe){Qe.addCase(kn.pending,function(ke,Se){var Pe,Te=Se.meta,Ge=Se.meta.arg,kt=_e(Ge);(Ge.subscribe||kt)&&(ke[Pe=Ge.queryCacheKey]!=null||(ke[Pe]={status:t.uninitialized,endpointName:Ge.endpointName})),bt(ke,Ge.queryCacheKey,function(St){St.status=t.pending,St.requestId=kt&&St.requestId?St.requestId:Te.requestId,Ge.originalArgs!==void 0&&(St.originalArgs=Ge.originalArgs),St.startedTimeStamp=Te.startedTimeStamp})}).addCase(kn.fulfilled,function(ke,Se){var Pe=Se.meta,Te=Se.payload;bt(ke,Pe.arg.queryCacheKey,function(Ge){var kt;if(Ge.requestId===Pe.requestId||_e(Pe.arg)){var St=En[Pe.arg.endpointName].merge;if(Ge.status=t.fulfilled,St)if(Ge.data!==void 0){var Ne=Pe.fulfilledTimeStamp,Qt=Pe.arg,dn=Pe.baseQueryMeta,on=Pe.requestId,ir=(0,Ce.createNextState)(Ge.data,function(Fn){return St(Fn,Te,{arg:Qt.originalArgs,baseQueryMeta:dn,fulfilledTimeStamp:Ne,requestId:on})});Ge.data=ir}else Ge.data=Te;else Ge.data=(kt=En[Pe.arg.endpointName].structuralSharing)==null||kt?D((0,jn.isDraft)(Ge.data)?(0,Pt.original)(Ge.data):Ge.data,Te):Te;delete Ge.error,Ge.fulfilledTimeStamp=Pe.fulfilledTimeStamp}})}).addCase(kn.rejected,function(ke,Se){var Pe=Se.meta,Te=Pe.condition,Ge=Pe.requestId,kt=Se.error,St=Se.payload;bt(ke,Pe.arg.queryCacheKey,function(Ne){if(!Te){if(Ne.requestId!==Ge)return;Ne.status=t.rejected,Ne.error=St??kt}})}).addMatcher(pn,function(ke,Se){for(var Pe=nn(Se).queries,Te=0,Ge=Object.entries(Pe);Te<Ge.length;Te++){var kt=Ge[Te],St=kt[1];(St==null?void 0:St.status)!==t.fulfilled&&(St==null?void 0:St.status)!==t.rejected||(ke[kt[0]]=St)}})}}),wt=(0,Ce.createSlice)({name:wn+"/mutations",initialState:Et,reducers:{removeMutationResult:{reducer:function(Qe,ke){var Se=be(ke.payload);Se in Qe&&delete Qe[Se]},prepare:(0,Ce.prepareAutoBatched)()}},extraReducers:function(Qe){Qe.addCase(Gn.pending,function(ke,Se){var Pe=Se.meta,Te=Pe.requestId,Ge=Pe.arg,kt=Pe.startedTimeStamp;Ge.track&&(ke[be(Se.meta)]={requestId:Te,status:t.pending,endpointName:Ge.endpointName,startedTimeStamp:kt})}).addCase(Gn.fulfilled,function(ke,Se){var Pe=Se.payload,Te=Se.meta;Te.arg.track&&Ae(ke,Te,function(Ge){Ge.requestId===Te.requestId&&(Ge.status=t.fulfilled,Ge.data=Pe,Ge.fulfilledTimeStamp=Te.fulfilledTimeStamp)})}).addCase(Gn.rejected,function(ke,Se){var Pe=Se.payload,Te=Se.error,Ge=Se.meta;Ge.arg.track&&Ae(ke,Ge,function(kt){kt.requestId===Ge.requestId&&(kt.status=t.rejected,kt.error=Pe??Te)})}).addMatcher(pn,function(ke,Se){for(var Pe=nn(Se).mutations,Te=0,Ge=Object.entries(Pe);Te<Ge.length;Te++){var kt=Ge[Te],St=kt[0],Ne=kt[1];(Ne==null?void 0:Ne.status)!==t.fulfilled&&(Ne==null?void 0:Ne.status)!==t.rejected||St===(Ne==null?void 0:Ne.requestId)||(ke[St]=Ne)}})}}),pt=(0,Ce.createSlice)({name:wn+"/invalidation",initialState:Et,reducers:{},extraReducers:function(Qe){Qe.addCase(Be.actions.removeQueryResult,function(ke,Se){for(var Pe=Se.payload.queryCacheKey,Te=0,Ge=Object.values(ke);Te<Ge.length;Te++)for(var kt=0,St=Object.values(Ge[Te]);kt<St.length;kt++){var Ne=St[kt],Qt=Ne.indexOf(Pe);Qt!==-1&&Ne.splice(Qt,1)}}).addMatcher(pn,function(ke,Se){for(var Pe,Te,Ge,kt,St=nn(Se).provided,Ne=0,Qt=Object.entries(St);Ne<Qt.length;Ne++)for(var dn=Qt[Ne],on=dn[0],ir=0,Fn=Object.entries(dn[1]);ir<Fn.length;ir++)for(var an=Fn[ir],$n=an[0],to=an[1],rn=(kt=(Te=(Pe=ke[on])!=null?Pe:ke[on]={})[Ge=$n||"__internal_without_id"])!=null?kt:Te[Ge]=[],Mn=0,Br=to;Mn<Br.length;Mn++){var kr=Br[Mn];rn.includes(kr)||rn.push(kr)}}).addMatcher((0,Ce.isAnyOf)((0,Ce.isFulfilled)(kn),(0,Ce.isRejectedWithValue)(kn)),function(ke,Se){for(var Pe,Te,Ge,kt,St=vt(Se,"providesTags",En,Sn),Ne=Se.meta.arg.queryCacheKey,Qt=0,dn=Object.values(ke);Qt<dn.length;Qt++)for(var on=0,ir=Object.values(dn[Qt]);on<ir.length;on++){var Fn=ir[on],an=Fn.indexOf(Ne);an!==-1&&Fn.splice(an,1)}for(var $n=0,to=St;$n<to.length;$n++){var rn=to[$n],Mn=rn.type,Br=rn.id,kr=(kt=(Te=(Pe=ke[Mn])!=null?Pe:ke[Mn]={})[Ge=Br||"__internal_without_id"])!=null?kt:Te[Ge]=[];kr.includes(Ne)||kr.push(Ne)}})}}),Ze=(0,Ce.createSlice)({name:wn+"/subscriptions",initialState:Et,reducers:{updateSubscriptionOptions:function(Qe,ke){},unsubscribeQueryResult:function(Qe,ke){},internal_probeSubscription:function(Qe,ke){}}}),ft=(0,Ce.createSlice)({name:wn+"/internalSubscriptions",initialState:Et,reducers:{subscriptionsUpdated:{reducer:function(Qe,ke){return(0,Pt.applyPatches)(Qe,ke.payload)},prepare:(0,Ce.prepareAutoBatched)()}}}),Rt=(0,Ce.createSlice)({name:wn+"/config",initialState:C({online:typeof navigator>"u"||navigator.onLine===void 0||navigator.onLine,focused:typeof document>"u"||document.visibilityState!=="hidden",middlewareRegistered:!1},cn),reducers:{middlewareRegistered:function(Qe,ke){Qe.middlewareRegistered=Qe.middlewareRegistered!=="conflict"&&On===ke.payload||"conflict"}},extraReducers:function(Qe){Qe.addCase(xe,function(ke){ke.online=!0}).addCase(me,function(ke){ke.online=!1}).addCase(ee,function(ke){ke.focused=!0}).addCase(ie,function(ke){ke.focused=!1}).addMatcher(pn,function(ke){return C({},ke)})}}),lt=(0,Ce.combineReducers)({queries:Be.reducer,mutations:wt.reducer,provided:pt.reducer,subscriptions:ft.reducer,config:Rt.reducer});return{reducer:function(Qe,ke){return lt(un.match(ke)?void 0:Qe,ke)},actions:_(C(C(C(C(C({},Rt.actions),Be.actions),Ze.actions),ft.actions),wt.actions),{unsubscribeMutationResult:wt.actions.removeMutationResult,resetApiState:un})}}({context:le,queryThunk:Fe,mutationThunk:ht,reducerPath:ce,assertTagType:De,config:{refetchOnFocus:st,refetchOnReconnect:dt,refetchOnMountOrArgChange:Dt,keepUnusedDataFor:Je,reducerPath:ce}}),Oe=$t.reducer,ut=$t.actions;Dr(q.util,{patchQueryData:xt,updateQueryData:Ot,upsertQueryData:Lt,prefetch:Kt,resetApiState:ut.resetApiState}),Dr(q.internalActions,ut);var _t=function(yt){var wn=yt.reducerPath,kn=yt.queryThunk,Gn=yt.api,ar=yt.context,En=ar.apiUid,On={invalidateTags:(0,ur.createAction)(wn+"/invalidateTags")},nn=[zr,yn,$r,dr,Nn,_o];return{middleware:function(Sn){var cn=!1,un=_(C({},yt),{internalState:{currentSubscriptions:{}},refetchQuery:pn}),Be=nn.map(function(Ze){return Ze(un)}),wt=function(Ze){var ft=Ze.api,Rt=Ze.queryThunk,lt=Ze.internalState,Qe=ft.reducerPath+"/subscriptions",ke=null,Se=!1,Pe=ft.internalActions,Te=Pe.updateSubscriptionOptions,Ge=Pe.unsubscribeQueryResult;return function(kt,St){var Ne,Qt;if(ke||(ke=JSON.parse(JSON.stringify(lt.currentSubscriptions))),ft.util.resetApiState.match(kt))return ke=lt.currentSubscriptions={},[!0,!1];if(ft.internalActions.internal_probeSubscription.match(kt)){var dn=kt.payload;return[!1,!!((Ne=lt.currentSubscriptions[dn.queryCacheKey])!=null&&Ne[dn.requestId])]}var on=function(an,$n){var to,rn,Mn,Br,kr,Pa,Uu,ko,Js;if(Te.match($n)){var Ps=$n.payload,Zs=Ps.queryCacheKey,no=Ps.requestId;return(to=an==null?void 0:an[Zs])!=null&&to[no]&&(an[Zs][no]=Ps.options),!0}if(Ge.match($n)){var ro=$n.payload;return no=ro.requestId,an[Zs=ro.queryCacheKey]&&delete an[Zs][no],!0}if(ft.internalActions.removeQueryResult.match($n))return delete an[$n.payload.queryCacheKey],!0;if(Rt.pending.match($n)){var oo=$n.meta;if(no=oo.requestId,(Vr=oo.arg).subscribe)return(Do=(Mn=an[rn=Vr.queryCacheKey])!=null?Mn:an[rn]={})[no]=(kr=(Br=Vr.subscriptionOptions)!=null?Br:Do[no])!=null?kr:{},!0}if(Rt.rejected.match($n)){var Do,jo=$n.meta,Vr=jo.arg;if(no=jo.requestId,jo.condition&&Vr.subscribe)return(Do=(Uu=an[Pa=Vr.queryCacheKey])!=null?Uu:an[Pa]={})[no]=(Js=(ko=Vr.subscriptionOptions)!=null?ko:Do[no])!=null?Js:{},!0}return!1}(lt.currentSubscriptions,kt);if(on){Se||(es(function(){var an=JSON.parse(JSON.stringify(lt.currentSubscriptions)),$n=(0,Lr.produceWithPatches)(ke,function(){return an});St.next(ft.internalActions.subscriptionsUpdated($n[1])),ke=an,Se=!1}),Se=!0);var ir=!!((Qt=kt.type)!=null&&Qt.startsWith(Qe)),Fn=Rt.rejected.match(kt)&&kt.meta.condition&&!!kt.meta.arg.subscribe;return[!ir&&!Fn,!1]}return[!0,!1]}}(un),pt=function(Ze){var ft=Ze.reducerPath,Rt=Ze.context,lt=Ze.refetchQuery,Qe=Ze.internalState,ke=Ze.api.internalActions.removeQueryResult;function Se(Pe,Te){var Ge=Pe.getState()[ft],kt=Ge.queries,St=Qe.currentSubscriptions;Rt.batch(function(){for(var Ne=0,Qt=Object.keys(St);Ne<Qt.length;Ne++){var dn=Qt[Ne],on=kt[dn],ir=St[dn];ir&&on&&(Object.values(ir).some(function(Fn){return Fn[Te]===!0})||Object.values(ir).every(function(Fn){return Fn[Te]===void 0})&&Ge.config[Te])&&(Object.keys(ir).length===0?Pe.dispatch(ke({queryCacheKey:dn})):on.status!==t.uninitialized&&Pe.dispatch(lt(on,dn)))}})}return function(Pe,Te){ee.match(Pe)&&Se(Te,"refetchOnFocus"),xe.match(Pe)&&Se(Te,"refetchOnReconnect")}}(un);return function(Ze){return function(ft){cn||(cn=!0,Sn.dispatch(Gn.internalActions.middlewareRegistered(En)));var Rt,lt=_(C({},Sn),{next:Ze}),Qe=Sn.getState(),ke=wt(ft,lt,Qe),Se=ke[1];if(Rt=ke[0]?Ze(ft):Se,Sn.getState()[wn]&&(pt(ft,lt,Qe),function(Ge){return!!Ge&&typeof Ge.type=="string"&&Ge.type.startsWith(wn+"/")}(ft)||ar.hasRehydrationInfo(ft)))for(var Pe=0,Te=Be;Pe<Te.length;Pe++)(0,Te[Pe])(ft,lt,Qe);return Rt}}},actions:On};function pn(Sn,cn,un){return un===void 0&&(un={}),kn(C({type:"query",endpointName:Sn.endpointName,originalArgs:Sn.originalArgs,subscribe:!1,forceRefetch:!0,queryCacheKey:cn},un))}}({reducerPath:ce,context:le,queryThunk:Fe,mutationThunk:ht,api:q,assertTagType:De}),At=_t.middleware;Dr(q.util,_t.actions),Dr(q,{reducer:Oe,middleware:At});var xn=function(yt){var wn=yt.serializeQueryArgs,kn=yt.reducerPath,Gn=function(nn){return In},ar=function(nn){return ln};return{buildQuerySelector:function(nn,pn){return function(Sn){var cn=wn({queryArgs:Sn,endpointDefinition:pn,endpointName:nn});return(0,pe.createSelector)(Sn===Ft?Gn:function(un){var Be,wt,pt;return(pt=(wt=(Be=On(un))==null?void 0:Be.queries)==null?void 0:wt[cn])!=null?pt:In},En)}},buildMutationSelector:function(){return function(nn){var pn,Sn;return Sn=typeof nn=="object"?(pn=be(nn))!=null?pn:Ft:nn,(0,pe.createSelector)(Sn===Ft?ar:function(cn){var un,Be,wt;return(wt=(Be=(un=On(cn))==null?void 0:un.mutations)==null?void 0:Be[Sn])!=null?wt:ln},En)}},selectInvalidatedBy:function(nn,pn){for(var Sn,cn=nn[kn],un=new Set,Be=0,wt=pn.map(de);Be<wt.length;Be++){var pt=wt[Be],Ze=cn.provided[pt.type];if(Ze)for(var ft=0,Rt=(Sn=pt.id!==void 0?Ze[pt.id]:O(Object.values(Ze)))!=null?Sn:[];ft<Rt.length;ft++)un.add(Rt[ft])}return O(Array.from(un.values()).map(function(lt){var Qe=cn.queries[lt];return Qe?[{queryCacheKey:lt,endpointName:Qe.endpointName,originalArgs:Qe.originalArgs}]:[]}))}};function En(nn){return C(C({},nn),{status:pn=nn.status,isUninitialized:pn===t.uninitialized,isLoading:pn===t.pending,isSuccess:pn===t.fulfilled,isError:pn===t.rejected});var pn}function On(nn){return nn[kn]}}({serializeQueryArgs:nt,reducerPath:ce}),zt=xn.buildQuerySelector,tn=xn.buildMutationSelector;Dr(q.util,{selectInvalidatedBy:xn.selectInvalidatedBy});var Jt=function(yt){var wn=yt.serializeQueryArgs,kn=yt.queryThunk,Gn=yt.mutationThunk,ar=yt.api,En=yt.context,On=new Map,nn=new Map,pn=ar.internalActions,Sn=pn.unsubscribeQueryResult,cn=pn.removeMutationResult,un=pn.updateSubscriptionOptions;return{buildInitiateQuery:function(Be,wt){var pt=function(Ze,ft){var Rt=ft===void 0?{}:ft,lt=Rt.subscribe,Qe=lt===void 0||lt,ke=Rt.forceRefetch,Se=Rt.subscriptionOptions,Pe=Rt[ge];return function(Te,Ge){var kt,St,Ne=wn({queryArgs:Ze,endpointDefinition:wt,endpointName:Be}),Qt=kn(((kt={type:"query",subscribe:Qe,forceRefetch:ke,subscriptionOptions:Se,endpointName:Be,originalArgs:Ze,queryCacheKey:Ne})[ge]=Pe,kt)),dn=ar.endpoints[Be].select(Ze),on=Te(Qt),ir=dn(Ge()),Fn=on.requestId,an=on.abort,$n=ir.requestId!==Fn,to=(St=On.get(Te))==null?void 0:St[Ne],rn=function(){return dn(Ge())},Mn=Object.assign(Pe?on.then(rn):$n&&!to?Promise.resolve(ir):Promise.all([to,on]).then(rn),{arg:Ze,requestId:Fn,subscriptionOptions:Se,queryCacheKey:Ne,abort:an,unwrap:function(){return E(this,null,function(){var kr;return r(this,function(Pa){switch(Pa.label){case 0:return[4,Mn];case 1:if((kr=Pa.sent()).isError)throw kr.error;return[2,kr.data]}})})},refetch:function(){return Te(pt(Ze,{subscribe:!1,forceRefetch:!0}))},unsubscribe:function(){Qe&&Te(Sn({queryCacheKey:Ne,requestId:Fn}))},updateSubscriptionOptions:function(kr){Mn.subscriptionOptions=kr,Te(un({endpointName:Be,requestId:Fn,queryCacheKey:Ne,options:kr}))}});if(!to&&!$n&&!Pe){var Br=On.get(Te)||{};Br[Ne]=Mn,On.set(Te,Br),Mn.then(function(){delete Br[Ne],Object.keys(Br).length||On.delete(Te)})}return Mn}};return pt},buildInitiateMutation:function(Be){return function(wt,pt){var Ze=pt===void 0?{}:pt,ft=Ze.track,Rt=ft===void 0||ft,lt=Ze.fixedCacheKey;return function(Qe,ke){var Se=Gn({type:"mutation",endpointName:Be,originalArgs:wt,track:Rt,fixedCacheKey:lt}),Pe=Qe(Se),Te=Pe.requestId,Ge=Pe.abort,kt=Pe.unwrap,St=Pe.unwrap().then(function(on){return{data:on}}).catch(function(on){return{error:on}}),Ne=function(){Qe(cn({requestId:Te,fixedCacheKey:lt}))},Qt=Object.assign(St,{arg:Pe.arg,requestId:Te,abort:Ge,unwrap:kt,unsubscribe:Ne,reset:Ne}),dn=nn.get(Qe)||{};return nn.set(Qe,dn),dn[Te]=Qt,Qt.then(function(){delete dn[Te],Object.keys(dn).length||nn.delete(Qe)}),lt&&(dn[lt]=Qt,Qt.then(function(){dn[lt]===Qt&&(delete dn[lt],Object.keys(dn).length||nn.delete(Qe))})),Qt}}},getRunningQueryThunk:function(Be,wt){return function(pt){var Ze,ft=wn({queryArgs:wt,endpointDefinition:En.endpointDefinitions[Be],endpointName:Be});return(Ze=On.get(pt))==null?void 0:Ze[ft]}},getRunningMutationThunk:function(Be,wt){return function(pt){var Ze;return(Ze=nn.get(pt))==null?void 0:Ze[wt]}},getRunningQueriesThunk:function(){return function(Be){return Object.values(On.get(Be)||{}).filter(Re)}},getRunningMutationsThunk:function(){return function(Be){return Object.values(nn.get(Be)||{}).filter(Re)}},getRunningOperationPromises:function(){var Be=function(wt){return Array.from(wt.values()).flatMap(function(pt){return pt?Object.values(pt):[]})};return o(o([],Be(On)),Be(nn)).filter(Re)},removalWarning:function(){throw new Error(`This method had to be removed due to a conceptual bug in RTK.
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-       See https://redux-toolkit.js.org/rtk-query/usage/server-side-rendering for new guidance on SSR.`)}}}({queryThunk:Fe,mutationThunk:ht,api:q,serializeQueryArgs:nt,context:le}),_r=Jt.buildInitiateQuery,Un=Jt.buildInitiateMutation;return Dr(q.util,{getRunningOperationPromises:Jt.getRunningOperationPromises,getRunningOperationPromise:Jt.removalWarning,getRunningMutationThunk:Jt.getRunningMutationThunk,getRunningMutationsThunk:Jt.getRunningMutationsThunk,getRunningQueryThunk:Jt.getRunningQueryThunk,getRunningQueriesThunk:Jt.getRunningQueriesThunk}),{name:ts,injectEndpoint:function(yt,wn){var kn,Gn=q;(kn=Gn.endpoints)[yt]!=null||(kn[yt]={}),ne(wn)?Dr(Gn.endpoints[yt],{name:yt,select:zt(yt,wn),initiate:_r(yt,wn)},qt(Fe,yt)):wn.type===H.mutation&&Dr(Gn.endpoints[yt],{name:yt,select:tn(),initiate:Un(yt)},qt(ht,yt))}}}}},js=Xe(Xs())})(c_);l_.exports=c_;var Xr=l_.exports;const 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diff --git a/invokeai/frontend/web/dist/assets/App-d6f88f50.js b/invokeai/frontend/web/dist/assets/App-d6f88f50.js
new file mode 100644
index 0000000000..9e418a7691
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0,bottom:"-5px"}),left:_r(_r({},wS),{left:"-5px"}),topRight:_r(_r({},zf),{right:"-10px",top:"-10px",cursor:"ne-resize"}),bottomRight:_r(_r({},zf),{right:"-10px",bottom:"-10px",cursor:"se-resize"}),bottomLeft:_r(_r({},zf),{left:"-10px",bottom:"-10px",cursor:"sw-resize"}),topLeft:_r(_r({},zf),{left:"-10px",top:"-10px",cursor:"nw-resize"})},jH=function(e){_H(t,e);function t(){var n=e!==null&&e.apply(this,arguments)||this;return n.onMouseDown=function(r){n.props.onResizeStart(r,n.props.direction)},n.onTouchStart=function(r){n.props.onResizeStart(r,n.props.direction)},n}return t.prototype.render=function(){return f.createElement("div",{className:this.props.className||"",style:_r(_r({position:"absolute",userSelect:"none"},PH[this.props.direction]),this.props.replaceStyles||{}),onMouseDown:this.onMouseDown,onTouchStart:this.onTouchStart},this.props.children)},t}(f.PureComponent),IH=globalThis&&globalThis.__extends||function(){var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(r,o){r.__proto__=o}||function(r,o){for(var s in o)Object.prototype.hasOwnProperty.call(o,s)&&(r[s]=o[s])},e(t,n)};return function(t,n){e(t,n);function r(){this.constructor=t}t.prototype=n===null?Object.create(n):(r.prototype=n.prototype,new r)}}(),qs=globalThis&&globalThis.__assign||function(){return qs=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++){t=arguments[n];for(var o in t)Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o])}return e},qs.apply(this,arguments)},EH={width:"auto",height:"auto"},Lf=function(e,t,n){return Math.max(Math.min(e,n),t)},SS=function(e,t){return Math.round(e/t)*t},Bl=function(e,t){return new RegExp(e,"i").test(t)},Bf=function(e){return!!(e.touches&&e.touches.length)},OH=function(e){return!!((e.clientX||e.clientX===0)&&(e.clientY||e.clientY===0))},CS=function(e,t,n){n===void 0&&(n=0);var r=t.reduce(function(s,a,c){return Math.abs(a-e)<Math.abs(t[s]-e)?c:s},0),o=Math.abs(t[r]-e);return n===0||o<n?t[r]:e},O0=function(e){return e=e.toString(),e==="auto"||e.endsWith("px")||e.endsWith("%")||e.endsWith("vh")||e.endsWith("vw")||e.endsWith("vmax")||e.endsWith("vmin")?e:e+"px"},Ff=function(e,t,n,r){if(e&&typeof e=="string"){if(e.endsWith("px"))return Number(e.replace("px",""));if(e.endsWith("%")){var o=Number(e.replace("%",""))/100;return t*o}if(e.endsWith("vw")){var o=Number(e.replace("vw",""))/100;return n*o}if(e.endsWith("vh")){var o=Number(e.replace("vh",""))/100;return r*o}}return e},RH=function(e,t,n,r,o,s,a){return r=Ff(r,e.width,t,n),o=Ff(o,e.height,t,n),s=Ff(s,e.width,t,n),a=Ff(a,e.height,t,n),{maxWidth:typeof r>"u"?void 0:Number(r),maxHeight:typeof o>"u"?void 0:Number(o),minWidth:typeof s>"u"?void 0:Number(s),minHeight:typeof a>"u"?void 0:Number(a)}},MH=["as","style","className","grid","snap","bounds","boundsByDirection","size","defaultSize","minWidth","minHeight","maxWidth","maxHeight","lockAspectRatio","lockAspectRatioExtraWidth","lockAspectRatioExtraHeight","enable","handleStyles","handleClasses","handleWrapperStyle","handleWrapperClass","children","onResizeStart","onResize","onResizeStop","handleComponent","scale","resizeRatio","snapGap"],kS="__resizable_base__",DH=function(e){IH(t,e);function t(n){var r=e.call(this,n)||this;return r.ratio=1,r.resizable=null,r.parentLeft=0,r.parentTop=0,r.resizableLeft=0,r.resizableRight=0,r.resizableTop=0,r.resizableBottom=0,r.targetLeft=0,r.targetTop=0,r.appendBase=function(){if(!r.resizable||!r.window)return null;var o=r.parentNode;if(!o)return null;var s=r.window.document.createElement("div");return s.style.width="100%",s.style.height="100%",s.style.position="absolute",s.style.transform="scale(0, 0)",s.style.left="0",s.style.flex="0 0 100%",s.classList?s.classList.add(kS):s.className+=kS,o.appendChild(s),s},r.removeBase=function(o){var s=r.parentNode;s&&s.removeChild(o)},r.ref=function(o){o&&(r.resizable=o)},r.state={isResizing:!1,width:typeof(r.propsSize&&r.propsSize.width)>"u"?"auto":r.propsSize&&r.propsSize.width,height:typeof(r.propsSize&&r.propsSize.height)>"u"?"auto":r.propsSize&&r.propsSize.height,direction:"right",original:{x:0,y:0,width:0,height:0},backgroundStyle:{height:"100%",width:"100%",backgroundColor:"rgba(0,0,0,0)",cursor:"auto",opacity:0,position:"fixed",zIndex:9999,top:"0",left:"0",bottom:"0",right:"0"},flexBasis:void 0},r.onResizeStart=r.onResizeStart.bind(r),r.onMouseMove=r.onMouseMove.bind(r),r.onMouseUp=r.onMouseUp.bind(r),r}return Object.defineProperty(t.prototype,"parentNode",{get:function(){return this.resizable?this.resizable.parentNode:null},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"window",{get:function(){return!this.resizable||!this.resizable.ownerDocument?null:this.resizable.ownerDocument.defaultView},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"propsSize",{get:function(){return this.props.size||this.props.defaultSize||EH},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"size",{get:function(){var n=0,r=0;if(this.resizable&&this.window){var o=this.resizable.offsetWidth,s=this.resizable.offsetHeight,a=this.resizable.style.position;a!=="relative"&&(this.resizable.style.position="relative"),n=this.resizable.style.width!=="auto"?this.resizable.offsetWidth:o,r=this.resizable.style.height!=="auto"?this.resizable.offsetHeight:s,this.resizable.style.position=a}return{width:n,height:r}},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"sizeStyle",{get:function(){var n=this,r=this.props.size,o=function(c){if(typeof n.state[c]>"u"||n.state[c]==="auto")return"auto";if(n.propsSize&&n.propsSize[c]&&n.propsSize[c].toString().endsWith("%")){if(n.state[c].toString().endsWith("%"))return n.state[c].toString();var d=n.getParentSize(),p=Number(n.state[c].toString().replace("px","")),h=p/d[c]*100;return h+"%"}return O0(n.state[c])},s=r&&typeof r.width<"u"&&!this.state.isResizing?O0(r.width):o("width"),a=r&&typeof r.height<"u"&&!this.state.isResizing?O0(r.height):o("height");return{width:s,height:a}},enumerable:!1,configurable:!0}),t.prototype.getParentSize=function(){if(!this.parentNode)return this.window?{width:this.window.innerWidth,height:this.window.innerHeight}:{width:0,height:0};var n=this.appendBase();if(!n)return{width:0,height:0};var r=!1,o=this.parentNode.style.flexWrap;o!=="wrap"&&(r=!0,this.parentNode.style.flexWrap="wrap"),n.style.position="relative",n.style.minWidth="100%",n.style.minHeight="100%";var s={width:n.offsetWidth,height:n.offsetHeight};return r&&(this.parentNode.style.flexWrap=o),this.removeBase(n),s},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(!(!this.resizable||!this.window)){var n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var o=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof o>"u"||o==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var o=this.props.boundsByDirection,s=this.state.direction,a=o&&Bl("left",s),c=o&&Bl("top",s),d,p;if(this.props.bounds==="parent"){var h=this.parentNode;h&&(d=a?this.resizableRight-this.parentLeft:h.offsetWidth+(this.parentLeft-this.resizableLeft),p=c?this.resizableBottom-this.parentTop:h.offsetHeight+(this.parentTop-this.resizableTop))}else this.props.bounds==="window"?this.window&&(d=a?this.resizableRight:this.window.innerWidth-this.resizableLeft,p=c?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(d=a?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),p=c?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return d&&Number.isFinite(d)&&(n=n&&n<d?n:d),p&&Number.isFinite(p)&&(r=r&&r<p?r:p),{maxWidth:n,maxHeight:r}},t.prototype.calculateNewSizeFromDirection=function(n,r){var o=this.props.scale||1,s=this.props.resizeRatio||1,a=this.state,c=a.direction,d=a.original,p=this.props,h=p.lockAspectRatio,m=p.lockAspectRatioExtraHeight,v=p.lockAspectRatioExtraWidth,b=d.width,w=d.height,y=m||0,S=v||0;return Bl("right",c)&&(b=d.width+(n-d.x)*s/o,h&&(w=(b-S)/this.ratio+y)),Bl("left",c)&&(b=d.width-(n-d.x)*s/o,h&&(w=(b-S)/this.ratio+y)),Bl("bottom",c)&&(w=d.height+(r-d.y)*s/o,h&&(b=(w-y)*this.ratio+S)),Bl("top",c)&&(w=d.height-(r-d.y)*s/o,h&&(b=(w-y)*this.ratio+S)),{newWidth:b,newHeight:w}},t.prototype.calculateNewSizeFromAspectRatio=function(n,r,o,s){var a=this.props,c=a.lockAspectRatio,d=a.lockAspectRatioExtraHeight,p=a.lockAspectRatioExtraWidth,h=typeof s.width>"u"?10:s.width,m=typeof o.width>"u"||o.width<0?n:o.width,v=typeof s.height>"u"?10:s.height,b=typeof o.height>"u"||o.height<0?r:o.height,w=d||0,y=p||0;if(c){var S=(v-w)*this.ratio+y,k=(b-w)*this.ratio+y,_=(h-y)/this.ratio+w,I=(m-y)/this.ratio+w,P=Math.max(h,S),E=Math.min(m,k),O=Math.max(v,_),R=Math.min(b,I);n=Lf(n,P,E),r=Lf(r,O,R)}else n=Lf(n,h,m),r=Lf(r,v,b);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var o=this.props.bounds.getBoundingClientRect();this.targetLeft=o.left,this.targetTop=o.top}if(this.resizable){var s=this.resizable.getBoundingClientRect(),a=s.left,c=s.top,d=s.right,p=s.bottom;this.resizableLeft=a,this.resizableRight=d,this.resizableTop=c,this.resizableBottom=p}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var o=0,s=0;if(n.nativeEvent&&OH(n.nativeEvent)?(o=n.nativeEvent.clientX,s=n.nativeEvent.clientY):n.nativeEvent&&Bf(n.nativeEvent)&&(o=n.nativeEvent.touches[0].clientX,s=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var a=this.props.onResizeStart(n,r,this.resizable);if(a===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var c,d=this.window.getComputedStyle(this.resizable);if(d.flexBasis!=="auto"){var p=this.parentNode;if(p){var h=this.window.getComputedStyle(p).flexDirection;this.flexDir=h.startsWith("row")?"row":"column",c=d.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var m={original:{x:o,y:s,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:qs(qs({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:c};this.setState(m)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&Bf(n))try{n.preventDefault(),n.stopPropagation()}catch{}var o=this.props,s=o.maxWidth,a=o.maxHeight,c=o.minWidth,d=o.minHeight,p=Bf(n)?n.touches[0].clientX:n.clientX,h=Bf(n)?n.touches[0].clientY:n.clientY,m=this.state,v=m.direction,b=m.original,w=m.width,y=m.height,S=this.getParentSize(),k=RH(S,this.window.innerWidth,this.window.innerHeight,s,a,c,d);s=k.maxWidth,a=k.maxHeight,c=k.minWidth,d=k.minHeight;var _=this.calculateNewSizeFromDirection(p,h),I=_.newHeight,P=_.newWidth,E=this.calculateNewMaxFromBoundary(s,a);this.props.snap&&this.props.snap.x&&(P=CS(P,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(I=CS(I,this.props.snap.y,this.props.snapGap));var O=this.calculateNewSizeFromAspectRatio(P,I,{width:E.maxWidth,height:E.maxHeight},{width:c,height:d});if(P=O.newWidth,I=O.newHeight,this.props.grid){var R=SS(P,this.props.grid[0]),M=SS(I,this.props.grid[1]),A=this.props.snapGap||0;P=A===0||Math.abs(R-P)<=A?R:P,I=A===0||Math.abs(M-I)<=A?M:I}var T={width:P-b.width,height:I-b.height};if(w&&typeof w=="string"){if(w.endsWith("%")){var $=P/S.width*100;P=$+"%"}else if(w.endsWith("vw")){var Q=P/this.window.innerWidth*100;P=Q+"vw"}else if(w.endsWith("vh")){var B=P/this.window.innerHeight*100;P=B+"vh"}}if(y&&typeof y=="string"){if(y.endsWith("%")){var $=I/S.height*100;I=$+"%"}else if(y.endsWith("vw")){var Q=I/this.window.innerWidth*100;I=Q+"vw"}else if(y.endsWith("vh")){var B=I/this.window.innerHeight*100;I=B+"vh"}}var V={width:this.createSizeForCssProperty(P,"width"),height:this.createSizeForCssProperty(I,"height")};this.flexDir==="row"?V.flexBasis=V.width:this.flexDir==="column"&&(V.flexBasis=V.height),_i.flushSync(function(){r.setState(V)}),this.props.onResize&&this.props.onResize(n,v,this.resizable,T)}},t.prototype.onMouseUp=function(n){var r=this.state,o=r.isResizing,s=r.direction,a=r.original;if(!(!o||!this.resizable)){var c={width:this.size.width-a.width,height:this.size.height-a.height};this.props.onResizeStop&&this.props.onResizeStop(n,s,this.resizable,c),this.props.size&&this.setState(this.props.size),this.unbindEvents(),this.setState({isResizing:!1,backgroundStyle:qs(qs({},this.state.backgroundStyle),{cursor:"auto"})})}},t.prototype.updateSize=function(n){this.setState({width:n.width,height:n.height})},t.prototype.renderResizer=function(){var n=this,r=this.props,o=r.enable,s=r.handleStyles,a=r.handleClasses,c=r.handleWrapperStyle,d=r.handleWrapperClass,p=r.handleComponent;if(!o)return null;var h=Object.keys(o).map(function(m){return o[m]!==!1?f.createElement(jH,{key:m,direction:m,onResizeStart:n.onResizeStart,replaceStyles:s&&s[m],className:a&&a[m]},p&&p[m]?p[m]:null):null});return f.createElement("div",{className:d,style:c},h)},t.prototype.render=function(){var n=this,r=Object.keys(this.props).reduce(function(a,c){return MH.indexOf(c)!==-1||(a[c]=n.props[c]),a},{}),o=qs(qs(qs({position:"relative",userSelect:this.state.isResizing?"none":"auto"},this.props.style),this.sizeStyle),{maxWidth:this.props.maxWidth,maxHeight:this.props.maxHeight,minWidth:this.props.minWidth,minHeight:this.props.minHeight,boxSizing:"border-box",flexShrink:0});this.state.flexBasis&&(o.flexBasis=this.state.flexBasis);var s=this.props.as||"div";return f.createElement(s,qs({ref:this.ref,style:o,className:this.props.className},r),this.state.isResizing&&f.createElement("div",{style:this.state.backgroundStyle}),this.props.children,this.renderResizer())},t.defaultProps={as:"div",onResizeStart:function(){},onResize:function(){},onResizeStop:function(){},enable:{top:!0,right:!0,bottom:!0,left:!0,topRight:!0,bottomRight:!0,bottomLeft:!0,topLeft:!0},style:{},grid:[1,1],lockAspectRatio:!1,lockAspectRatioExtraWidth:0,lockAspectRatioExtraHeight:0,scale:1,resizeRatio:1,snapGap:0},t}(f.PureComponent);const AH=({direction:e,langDirection:t})=>({top:e==="bottom",right:t!=="rtl"&&e==="left"||t==="rtl"&&e==="right",bottom:e==="top",left:t!=="rtl"&&e==="right"||t==="rtl"&&e==="left"}),TH=({direction:e,minWidth:t,maxWidth:n,minHeight:r,maxHeight:o})=>{const s=t??(["left","right"].includes(e)?10:void 0),a=n??(["left","right"].includes(e)?"95vw":void 0),c=r??(["top","bottom"].includes(e)?10:void 0),d=o??(["top","bottom"].includes(e)?"95vh":void 0);return{...s?{minWidth:s}:{},...a?{maxWidth:a}:{},...c?{minHeight:c}:{},...d?{maxHeight:d}:{}}},ba="0.75rem",Hf="1rem",pu="5px",NH=({isResizable:e,direction:t})=>{const n=`calc((2 * ${ba} + ${pu}) / -2)`;return 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wt,Bt,mt,ot,Re,Ie,De,We,tt,Dt,Rt,Ve,nn,yn=at.signal,hn=at.abort,gr=at.rejectWithValue,Xn=at.fulfillWithValue,gn=at.dispatch,Vn=at.getState,ao=at.extra;return r(this,function(fn){switch(fn.label){case 0:wt=mr[St.endpointName],fn.label=1;case 1:return fn.trys.push([1,8,,13]),Bt=kt,mt=void 0,ot={signal:yn,abort:hn,dispatch:gn,getState:Vn,extra:ao,endpoint:St.endpointName,type:St.type,forced:St.type==="query"?Sn(St,Vn()):void 0},(Re=St.type==="query"?St[xe]:void 0)?(mt=Re(),[3,6]):[3,2];case 2:return wt.query?[4,Jn(wt.query(St.originalArgs),ot,wt.extraOptions)]:[3,4];case 3:return mt=fn.sent(),wt.transformResponse&&(Bt=wt.transformResponse),[3,6];case 4:return[4,wt.queryFn(St.originalArgs,ot,wt.extraOptions,function($n){return Jn($n,ot,wt.extraOptions)})];case 5:mt=fn.sent(),fn.label=6;case 6:if(mt.error)throw new q(mt.error,mt.meta);return Ie=Xn,[4,Bt(mt.data,mt.meta,St.originalArgs)];case 7:return[2,Ie.apply(void 0,[fn.sent(),(Ve={fulfilledTimeStamp:Date.now(),baseQueryMeta:mt.meta},Ve[$e.SHOULD_AUTOBATCH]=!0,Ve)])];case 8:if(De=fn.sent(),!((We=De)instanceof q))return[3,12];tt=kt,wt.query&&wt.transformErrorResponse&&(tt=wt.transformErrorResponse),fn.label=9;case 9:return fn.trys.push([9,11,,12]),Dt=gr,[4,tt(We.value,We.meta,St.originalArgs)];case 10:return[2,Dt.apply(void 0,[fn.sent(),(nn={baseQueryMeta:We.meta},nn[$e.SHOULD_AUTOBATCH]=!0,nn)])];case 11:return Rt=fn.sent(),We=Rt,[3,12];case 12:throw console.error(We),We;case 13:return[2]}})})};function Sn(Ke,Ot){var St,at,wt,Bt,mt=(at=(St=Ot[Rn])==null?void 0:St.queries)==null?void 0:at[Ke.queryCacheKey],ot=(wt=Ot[Rn])==null?void 0:wt.config.refetchOnMountOrArgChange,Re=mt==null?void 0:mt.fulfilledTimeStamp,Ie=(Bt=Ke.forceRefetch)!=null?Bt:Ke.subscribe&&ot;return!!Ie&&(Ie===!0||(Number(new Date)-Number(Re))/1e3>=Ie)}var En=(0,$e.createAsyncThunk)(Rn+"/executeQuery",dn,{getPendingMeta:function(){var Ke;return(Ke={startedTimeStamp:Date.now()})[$e.SHOULD_AUTOBATCH]=!0,Ke},condition:function(Ke,Ot){var St,at,wt,Bt=(0,Ot.getState)(),mt=(at=(St=Bt[Rn])==null?void 0:St.queries)==null?void 0:at[Ke.queryCacheKey],ot=mt==null?void 0:mt.fulfilledTimeStamp,Re=Ke.originalArgs,Ie=mt==null?void 0:mt.originalArgs,De=mr[Ke.endpointName];return!(!de(Ke)&&((mt==null?void 0:mt.status)==="pending"||!Sn(Ke,Bt)&&(!oe(De)||!((wt=De==null?void 0:De.forceRefetch)!=null&&wt.call(De,{currentArg:Re,previousArg:Ie,endpointState:mt,state:Bt})))&&ot))},dispatchConditionRejection:!0}),vn=(0,$e.createAsyncThunk)(Rn+"/executeMutation",dn,{getPendingMeta:function(){var Ke;return(Ke={startedTimeStamp:Date.now()})[$e.SHOULD_AUTOBATCH]=!0,Ke}});function bn(Ke){return function(Ot){var St,at;return((at=(St=Ot==null?void 0:Ot.meta)==null?void 0:St.arg)==null?void 0:at.endpointName)===Ke}}return{queryThunk:En,mutationThunk:vn,prefetch:function(Ke,Ot,St){return function(at,wt){var Bt=function(De){return"force"in De}(St)&&St.force,mt=function(De){return"ifOlderThan"in De}(St)&&St.ifOlderThan,ot=function(De){return De===void 0&&(De=!0),Nn.endpoints[Ke].initiate(Ot,{forceRefetch:De})},Re=Nn.endpoints[Ke].select(Ot)(wt());if(Bt)at(ot());else if(mt){var Ie=Re==null?void 0:Re.fulfilledTimeStamp;if(!Ie)return void at(ot());(Number(new Date)-Number(new Date(Ie)))/1e3>=mt&&at(ot())}else at(ot(!1))}},updateQueryData:function(Ke,Ot,St){return function(at,wt){var Bt,mt,ot=Nn.endpoints[Ke].select(Ot)(wt()),Re={patches:[],inversePatches:[],undo:function(){return at(Nn.util.patchQueryData(Ke,Ot,Re.inversePatches))}};if(ot.status===t.uninitialized)return Re;if("data"in ot)if((0,Oe.isDraftable)(ot.data)){var Ie=(0,Oe.produceWithPatches)(ot.data,St),De=Ie[2];(Bt=Re.patches).push.apply(Bt,Ie[1]),(mt=Re.inversePatches).push.apply(mt,De)}else{var We=St(ot.data);Re.patches.push({op:"replace",path:[],value:We}),Re.inversePatches.push({op:"replace",path:[],value:ot.data})}return at(Nn.util.patchQueryData(Ke,Ot,Re.patches)),Re}},upsertQueryData:function(Ke,Ot,St){return function(at){var wt;return at(Nn.endpoints[Ke].initiate(Ot,((wt={subscribe:!1,forceRefetch:!0})[xe]=function(){return{data:St}},wt)))}},patchQueryData:function(Ke,Ot,St){return function(at){at(Nn.internalActions.queryResultPatched({queryCacheKey:Tn({queryArgs:Ot,endpointDefinition:mr[Ke],endpointName:Ke}),patches:St}))}},buildMatchThunkActions:function(Ke,Ot){return{matchPending:(0,Te.isAllOf)((0,Te.isPending)(Ke),bn(Ot)),matchFulfilled:(0,Te.isAllOf)((0,Te.isFulfilled)(Ke),bn(Ot)),matchRejected:(0,Te.isAllOf)((0,Te.isRejected)(Ke),bn(Ot))}}}}({baseQuery:_e,reducerPath:me,context:he,api:J,serializeQueryArgs:ut}),Je=Ce.queryThunk,jt=Ce.mutationThunk,Et=Ce.patchQueryData,Nt=Ce.updateQueryData,qt=Ce.upsertQueryData,tn=Ce.prefetch,Zt=Ce.buildMatchThunkActions,Ut=function(It){var In=It.reducerPath,Rn=It.queryThunk,Jn=It.mutationThunk,mr=It.context,Tn=mr.endpointDefinitions,Nn=mr.apiUid,dn=mr.extractRehydrationInfo,Sn=mr.hasRehydrationInfo,En=It.assertTagType,vn=It.config,bn=(0,ge.createAction)(In+"/resetApiState"),Ke=(0,ge.createSlice)({name:In+"/queries",initialState:Pt,reducers:{removeQueryResult:{reducer:function(ot,Re){delete ot[Re.payload.queryCacheKey]},prepare:(0,ge.prepareAutoBatched)()},queryResultPatched:function(ot,Re){var Ie=Re.payload,De=Ie.patches;bt(ot,Ie.queryCacheKey,function(We){We.data=(0,vt.applyPatches)(We.data,De.concat())})}},extraReducers:function(ot){ot.addCase(Rn.pending,function(Re,Ie){var De,We=Ie.meta,tt=Ie.meta.arg,Dt=de(tt);(tt.subscribe||Dt)&&(Re[De=tt.queryCacheKey]!=null||(Re[De]={status:t.uninitialized,endpointName:tt.endpointName})),bt(Re,tt.queryCacheKey,function(Rt){Rt.status=t.pending,Rt.requestId=Dt&&Rt.requestId?Rt.requestId:We.requestId,tt.originalArgs!==void 0&&(Rt.originalArgs=tt.originalArgs),Rt.startedTimeStamp=We.startedTimeStamp})}).addCase(Rn.fulfilled,function(Re,Ie){var De=Ie.meta,We=Ie.payload;bt(Re,De.arg.queryCacheKey,function(tt){var Dt;if(tt.requestId===De.requestId||de(De.arg)){var Rt=Tn[De.arg.endpointName].merge;if(tt.status=t.fulfilled,Rt)if(tt.data!==void 0){var Ve=De.fulfilledTimeStamp,nn=De.arg,yn=De.baseQueryMeta,hn=De.requestId,gr=(0,ge.createNextState)(tt.data,function(Xn){return Rt(Xn,We,{arg:nn.originalArgs,baseQueryMeta:yn,fulfilledTimeStamp:Ve,requestId:hn})});tt.data=gr}else tt.data=We;else tt.data=(Dt=Tn[De.arg.endpointName].structuralSharing)==null||Dt?M((0,on.isDraft)(tt.data)?(0,vt.original)(tt.data):tt.data,We):We;delete tt.error,tt.fulfilledTimeStamp=De.fulfilledTimeStamp}})}).addCase(Rn.rejected,function(Re,Ie){var De=Ie.meta,We=De.condition,tt=De.requestId,Dt=Ie.error,Rt=Ie.payload;bt(Re,De.arg.queryCacheKey,function(Ve){if(!We){if(Ve.requestId!==tt)return;Ve.status=t.rejected,Ve.error=Rt??Dt}})}).addMatcher(Sn,function(Re,Ie){for(var De=dn(Ie).queries,We=0,tt=Object.entries(De);We<tt.length;We++){var Dt=tt[We],Rt=Dt[1];(Rt==null?void 0:Rt.status)!==t.fulfilled&&(Rt==null?void 0:Rt.status)!==t.rejected||(Re[Dt[0]]=Rt)}})}}),Ot=(0,ge.createSlice)({name:In+"/mutations",initialState:Pt,reducers:{removeMutationResult:{reducer:function(ot,Re){var Ie=Se(Re.payload);Ie in ot&&delete ot[Ie]},prepare:(0,ge.prepareAutoBatched)()}},extraReducers:function(ot){ot.addCase(Jn.pending,function(Re,Ie){var De=Ie.meta,We=De.requestId,tt=De.arg,Dt=De.startedTimeStamp;tt.track&&(Re[Se(Ie.meta)]={requestId:We,status:t.pending,endpointName:tt.endpointName,startedTimeStamp:Dt})}).addCase(Jn.fulfilled,function(Re,Ie){var De=Ie.payload,We=Ie.meta;We.arg.track&&Me(Re,We,function(tt){tt.requestId===We.requestId&&(tt.status=t.fulfilled,tt.data=De,tt.fulfilledTimeStamp=We.fulfilledTimeStamp)})}).addCase(Jn.rejected,function(Re,Ie){var De=Ie.payload,We=Ie.error,tt=Ie.meta;tt.arg.track&&Me(Re,tt,function(Dt){Dt.requestId===tt.requestId&&(Dt.status=t.rejected,Dt.error=De??We)})}).addMatcher(Sn,function(Re,Ie){for(var De=dn(Ie).mutations,We=0,tt=Object.entries(De);We<tt.length;We++){var Dt=tt[We],Rt=Dt[0],Ve=Dt[1];(Ve==null?void 0:Ve.status)!==t.fulfilled&&(Ve==null?void 0:Ve.status)!==t.rejected||Rt===(Ve==null?void 0:Ve.requestId)||(Re[Rt]=Ve)}})}}),St=(0,ge.createSlice)({name:In+"/invalidation",initialState:Pt,reducers:{},extraReducers:function(ot){ot.addCase(Ke.actions.removeQueryResult,function(Re,Ie){for(var De=Ie.payload.queryCacheKey,We=0,tt=Object.values(Re);We<tt.length;We++)for(var Dt=0,Rt=Object.values(tt[We]);Dt<Rt.length;Dt++){var Ve=Rt[Dt],nn=Ve.indexOf(De);nn!==-1&&Ve.splice(nn,1)}}).addMatcher(Sn,function(Re,Ie){for(var De,We,tt,Dt,Rt=dn(Ie).provided,Ve=0,nn=Object.entries(Rt);Ve<nn.length;Ve++)for(var yn=nn[Ve],hn=yn[0],gr=0,Xn=Object.entries(yn[1]);gr<Xn.length;gr++)for(var gn=Xn[gr],Vn=gn[0],ao=gn[1],fn=(Dt=(We=(De=Re[hn])!=null?De:Re[hn]={})[tt=Vn||"__internal_without_id"])!=null?Dt:We[tt]=[],$n=0,Ur=ao;$n<Ur.length;$n++){var Rr=Ur[$n];fn.includes(Rr)||fn.push(Rr)}}).addMatcher((0,ge.isAnyOf)((0,ge.isFulfilled)(Rn),(0,ge.isRejectedWithValue)(Rn)),function(Re,Ie){for(var De,We,tt,Dt,Rt=ct(Ie,"providesTags",Tn,En),Ve=Ie.meta.arg.queryCacheKey,nn=0,yn=Object.values(Re);nn<yn.length;nn++)for(var hn=0,gr=Object.values(yn[nn]);hn<gr.length;hn++){var Xn=gr[hn],gn=Xn.indexOf(Ve);gn!==-1&&Xn.splice(gn,1)}for(var Vn=0,ao=Rt;Vn<ao.length;Vn++){var fn=ao[Vn],$n=fn.type,Ur=fn.id,Rr=(Dt=(We=(De=Re[$n])!=null?De:Re[$n]={})[tt=Ur||"__internal_without_id"])!=null?Dt:We[tt]=[];Rr.includes(Ve)||Rr.push(Ve)}})}}),at=(0,ge.createSlice)({name:In+"/subscriptions",initialState:Pt,reducers:{updateSubscriptionOptions:function(ot,Re){},unsubscribeQueryResult:function(ot,Re){},internal_probeSubscription:function(ot,Re){}}}),wt=(0,ge.createSlice)({name:In+"/internalSubscriptions",initialState:Pt,reducers:{subscriptionsUpdated:{reducer:function(ot,Re){return(0,vt.applyPatches)(ot,Re.payload)},prepare:(0,ge.prepareAutoBatched)()}}}),Bt=(0,ge.createSlice)({name:In+"/config",initialState:S({online:typeof navigator>"u"||navigator.onLine===void 0||navigator.onLine,focused:typeof document>"u"||document.visibilityState!=="hidden",middlewareRegistered:!1},vn),reducers:{middlewareRegistered:function(ot,Re){ot.middlewareRegistered=ot.middlewareRegistered!=="conflict"&&Nn===Re.payload||"conflict"}},extraReducers:function(ot){ot.addCase(ye,function(Re){Re.online=!0}).addCase(ce,function(Re){Re.online=!1}).addCase(Y,function(Re){Re.focused=!0}).addCase(ae,function(Re){Re.focused=!1}).addMatcher(Sn,function(Re){return S({},Re)})}}),mt=(0,ge.combineReducers)({queries:Ke.reducer,mutations:Ot.reducer,provided:St.reducer,subscriptions:wt.reducer,config:Bt.reducer});return{reducer:function(ot,Re){return mt(bn.match(Re)?void 0:ot,Re)},actions:k(S(S(S(S(S({},Bt.actions),Ke.actions),at.actions),wt.actions),Ot.actions),{unsubscribeMutationResult:Ot.actions.removeMutationResult,resetApiState:bn})}}({context:he,queryThunk:Je,mutationThunk:jt,reducerPath:me,assertTagType:He,config:{refetchOnFocus:ft,refetchOnReconnect:xt,refetchOnMountOrArgChange:Ht,keepUnusedDataFor:st,reducerPath:me}}),Be=Ut.reducer,yt=Ut.actions;$r(J.util,{patchQueryData:Et,updateQueryData:Nt,upsertQueryData:qt,prefetch:tn,resetApiState:yt.resetApiState}),$r(J.internalActions,yt);var Mt=function(It){var In=It.reducerPath,Rn=It.queryThunk,Jn=It.api,mr=It.context,Tn=mr.apiUid,Nn={invalidateTags:(0,lr.createAction)(In+"/invalidateTags")},dn=[Wr,pn,Hr,yr,Wn,Mo];return{middleware:function(En){var vn=!1,bn=k(S({},It),{internalState:{currentSubscriptions:{}},refetchQuery:Sn}),Ke=dn.map(function(at){return at(bn)}),Ot=function(at){var wt=at.api,Bt=at.queryThunk,mt=at.internalState,ot=wt.reducerPath+"/subscriptions",Re=null,Ie=!1,De=wt.internalActions,We=De.updateSubscriptionOptions,tt=De.unsubscribeQueryResult;return function(Dt,Rt){var Ve,nn;if(Re||(Re=JSON.parse(JSON.stringify(mt.currentSubscriptions))),wt.util.resetApiState.match(Dt))return Re=mt.currentSubscriptions={},[!0,!1];if(wt.internalActions.internal_probeSubscription.match(Dt)){var yn=Dt.payload;return[!1,!!((Ve=mt.currentSubscriptions[yn.queryCacheKey])!=null&&Ve[yn.requestId])]}var hn=function(gn,Vn){var ao,fn,$n,Ur,Rr,Va,Wd,Do,fa;if(We.match(Vn)){var Ls=Vn.payload,pa=Ls.queryCacheKey,io=Ls.requestId;return(ao=gn==null?void 0:gn[pa])!=null&&ao[io]&&(gn[pa][io]=Ls.options),!0}if(tt.match(Vn)){var lo=Vn.payload;return io=lo.requestId,gn[pa=lo.queryCacheKey]&&delete gn[pa][io],!0}if(wt.internalActions.removeQueryResult.match(Vn))return delete gn[Vn.payload.queryCacheKey],!0;if(Bt.pending.match(Vn)){var co=Vn.meta;if(io=co.requestId,(Yr=co.arg).subscribe)return(Ho=($n=gn[fn=Yr.queryCacheKey])!=null?$n:gn[fn]={})[io]=(Rr=(Ur=Yr.subscriptionOptions)!=null?Ur:Ho[io])!=null?Rr:{},!0}if(Bt.rejected.match(Vn)){var Ho,Ao=Vn.meta,Yr=Ao.arg;if(io=Ao.requestId,Ao.condition&&Yr.subscribe)return(Ho=(Wd=gn[Va=Yr.queryCacheKey])!=null?Wd:gn[Va]={})[io]=(fa=(Do=Yr.subscriptionOptions)!=null?Do:Ho[io])!=null?fa:{},!0}return!1}(mt.currentSubscriptions,Dt);if(hn){Ie||(fs(function(){var gn=JSON.parse(JSON.stringify(mt.currentSubscriptions)),Vn=(0,Vr.produceWithPatches)(Re,function(){return gn});Rt.next(wt.internalActions.subscriptionsUpdated(Vn[1])),Re=gn,Ie=!1}),Ie=!0);var gr=!!((nn=Dt.type)!=null&&nn.startsWith(ot)),Xn=Bt.rejected.match(Dt)&&Dt.meta.condition&&!!Dt.meta.arg.subscribe;return[!gr&&!Xn,!1]}return[!0,!1]}}(bn),St=function(at){var wt=at.reducerPath,Bt=at.context,mt=at.refetchQuery,ot=at.internalState,Re=at.api.internalActions.removeQueryResult;function Ie(De,We){var tt=De.getState()[wt],Dt=tt.queries,Rt=ot.currentSubscriptions;Bt.batch(function(){for(var Ve=0,nn=Object.keys(Rt);Ve<nn.length;Ve++){var yn=nn[Ve],hn=Dt[yn],gr=Rt[yn];gr&&hn&&(Object.values(gr).some(function(Xn){return Xn[We]===!0})||Object.values(gr).every(function(Xn){return Xn[We]===void 0})&&tt.config[We])&&(Object.keys(gr).length===0?De.dispatch(Re({queryCacheKey:yn})):hn.status!==t.uninitialized&&De.dispatch(mt(hn,yn)))}})}return function(De,We){Y.match(De)&&Ie(We,"refetchOnFocus"),ye.match(De)&&Ie(We,"refetchOnReconnect")}}(bn);return function(at){return function(wt){vn||(vn=!0,En.dispatch(Jn.internalActions.middlewareRegistered(Tn)));var Bt,mt=k(S({},En),{next:at}),ot=En.getState(),Re=Ot(wt,mt,ot),Ie=Re[1];if(Bt=Re[0]?at(wt):Ie,En.getState()[In]&&(St(wt,mt,ot),function(tt){return!!tt&&typeof tt.type=="string"&&tt.type.startsWith(In+"/")}(wt)||mr.hasRehydrationInfo(wt)))for(var De=0,We=Ke;De<We.length;De++)(0,We[De])(wt,mt,ot);return Bt}}},actions:Nn};function Sn(En,vn,bn){return bn===void 0&&(bn={}),Rn(S({type:"query",endpointName:En.endpointName,originalArgs:En.originalArgs,subscribe:!1,forceRefetch:!0,queryCacheKey:vn},bn))}}({reducerPath:me,context:he,queryThunk:Je,mutationThunk:jt,api:J,assertTagType:He}),Wt=Mt.middleware;$r(J.util,Mt.actions),$r(J,{reducer:Be,middleware:Wt});var jn=function(It){var In=It.serializeQueryArgs,Rn=It.reducerPath,Jn=function(dn){return $t},mr=function(dn){return zt};return{buildQuerySelector:function(dn,Sn){return function(En){var vn=In({queryArgs:En,endpointDefinition:Sn,endpointName:dn});return(0,re.createSelector)(En===Tt?Jn:function(bn){var Ke,Ot,St;return(St=(Ot=(Ke=Nn(bn))==null?void 0:Ke.queries)==null?void 0:Ot[vn])!=null?St:$t},Tn)}},buildMutationSelector:function(){return function(dn){var Sn,En;return En=typeof dn=="object"?(Sn=Se(dn))!=null?Sn:Tt:dn,(0,re.createSelector)(En===Tt?mr:function(vn){var bn,Ke,Ot;return(Ot=(Ke=(bn=Nn(vn))==null?void 0:bn.mutations)==null?void 0:Ke[En])!=null?Ot:zt},Tn)}},selectInvalidatedBy:function(dn,Sn){for(var En,vn=dn[Rn],bn=new Set,Ke=0,Ot=Sn.map(ie);Ke<Ot.length;Ke++){var St=Ot[Ke],at=vn.provided[St.type];if(at)for(var wt=0,Bt=(En=St.id!==void 0?at[St.id]:O(Object.values(at)))!=null?En:[];wt<Bt.length;wt++)bn.add(Bt[wt])}return O(Array.from(bn.values()).map(function(mt){var ot=vn.queries[mt];return ot?[{queryCacheKey:mt,endpointName:ot.endpointName,originalArgs:ot.originalArgs}]:[]}))}};function Tn(dn){return S(S({},dn),{status:Sn=dn.status,isUninitialized:Sn===t.uninitialized,isLoading:Sn===t.pending,isSuccess:Sn===t.fulfilled,isError:Sn===t.rejected});var Sn}function Nn(dn){return dn[Rn]}}({serializeQueryArgs:ut,reducerPath:me}),Gt=jn.buildQuerySelector,un=jn.buildMutationSelector;$r(J.util,{selectInvalidatedBy:jn.selectInvalidatedBy});var sn=function(It){var In=It.serializeQueryArgs,Rn=It.queryThunk,Jn=It.mutationThunk,mr=It.api,Tn=It.context,Nn=new Map,dn=new Map,Sn=mr.internalActions,En=Sn.unsubscribeQueryResult,vn=Sn.removeMutationResult,bn=Sn.updateSubscriptionOptions;return{buildInitiateQuery:function(Ke,Ot){var St=function(at,wt){var Bt=wt===void 0?{}:wt,mt=Bt.subscribe,ot=mt===void 0||mt,Re=Bt.forceRefetch,Ie=Bt.subscriptionOptions,De=Bt[xe];return function(We,tt){var Dt,Rt,Ve=In({queryArgs:at,endpointDefinition:Ot,endpointName:Ke}),nn=Rn(((Dt={type:"query",subscribe:ot,forceRefetch:Re,subscriptionOptions:Ie,endpointName:Ke,originalArgs:at,queryCacheKey:Ve})[xe]=De,Dt)),yn=mr.endpoints[Ke].select(at),hn=We(nn),gr=yn(tt()),Xn=hn.requestId,gn=hn.abort,Vn=gr.requestId!==Xn,ao=(Rt=Nn.get(We))==null?void 0:Rt[Ve],fn=function(){return yn(tt())},$n=Object.assign(De?hn.then(fn):Vn&&!ao?Promise.resolve(gr):Promise.all([ao,hn]).then(fn),{arg:at,requestId:Xn,subscriptionOptions:Ie,queryCacheKey:Ve,abort:gn,unwrap:function(){return E(this,null,function(){var Rr;return r(this,function(Va){switch(Va.label){case 0:return[4,$n];case 1:if((Rr=Va.sent()).isError)throw Rr.error;return[2,Rr.data]}})})},refetch:function(){return We(St(at,{subscribe:!1,forceRefetch:!0}))},unsubscribe:function(){ot&&We(En({queryCacheKey:Ve,requestId:Xn}))},updateSubscriptionOptions:function(Rr){$n.subscriptionOptions=Rr,We(bn({endpointName:Ke,requestId:Xn,queryCacheKey:Ve,options:Rr}))}});if(!ao&&!Vn&&!De){var Ur=Nn.get(We)||{};Ur[Ve]=$n,Nn.set(We,Ur),$n.then(function(){delete Ur[Ve],Object.keys(Ur).length||Nn.delete(We)})}return $n}};return St},buildInitiateMutation:function(Ke){return function(Ot,St){var at=St===void 0?{}:St,wt=at.track,Bt=wt===void 0||wt,mt=at.fixedCacheKey;return function(ot,Re){var Ie=Jn({type:"mutation",endpointName:Ke,originalArgs:Ot,track:Bt,fixedCacheKey:mt}),De=ot(Ie),We=De.requestId,tt=De.abort,Dt=De.unwrap,Rt=De.unwrap().then(function(hn){return{data:hn}}).catch(function(hn){return{error:hn}}),Ve=function(){ot(vn({requestId:We,fixedCacheKey:mt}))},nn=Object.assign(Rt,{arg:De.arg,requestId:We,abort:tt,unwrap:Dt,unsubscribe:Ve,reset:Ve}),yn=dn.get(ot)||{};return dn.set(ot,yn),yn[We]=nn,nn.then(function(){delete yn[We],Object.keys(yn).length||dn.delete(ot)}),mt&&(yn[mt]=nn,nn.then(function(){yn[mt]===nn&&(delete yn[mt],Object.keys(yn).length||dn.delete(ot))})),nn}}},getRunningQueryThunk:function(Ke,Ot){return function(St){var at,wt=In({queryArgs:Ot,endpointDefinition:Tn.endpointDefinitions[Ke],endpointName:Ke});return(at=Nn.get(St))==null?void 0:at[wt]}},getRunningMutationThunk:function(Ke,Ot){return function(St){var at;return(at=dn.get(St))==null?void 0:at[Ot]}},getRunningQueriesThunk:function(){return function(Ke){return Object.values(Nn.get(Ke)||{}).filter(ke)}},getRunningMutationsThunk:function(){return function(Ke){return Object.values(dn.get(Ke)||{}).filter(ke)}},getRunningOperationPromises:function(){var Ke=function(Ot){return Array.from(Ot.values()).flatMap(function(St){return St?Object.values(St):[]})};return o(o([],Ke(Nn)),Ke(dn)).filter(ke)},removalWarning:function(){throw new Error(`This method had to be removed due to a conceptual bug in RTK.
+       Please see https://github.com/reduxjs/redux-toolkit/pull/2481 for details.
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+ * @license React
+ * react-reconciler.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
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$r(C)?(u.flags|=128,u.child=l.child,u=qO.bind(null,l),da(C,u),null):(l=j.treeContext,ke&&(Wo=he(C),To=u,Zn=!0,hs=null,Xc=!1,l!==null&&(lo[co++]=Ao,lo[co++]=Yr,lo[co++]=Ho,Ao=l.id,Yr=l.overflow,Ho=u)),u=Tg(u,x.children),u.flags|=4096,u)}function Zx(l,u,g){l.lanes|=u;var x=l.alternate;x!==null&&(x.lanes|=u),fg(l.return,u,g)}function Ng(l,u,g,x,C){var j=l.memoizedState;j===null?l.memoizedState={isBackwards:u,rendering:null,renderingStartTime:0,last:x,tail:g,tailMode:C}:(j.isBackwards=u,j.rendering=null,j.renderingStartTime=0,j.last=x,j.tail=g,j.tailMode=C)}function e2(l,u,g){var x=u.pendingProps,C=x.revealOrder,j=x.tail;if(uo(l,u,x.children,g),x=cr.current,x&2)x=x&1|2,u.flags|=128;else{if(l!==null&&l.flags&128)e:for(l=u.child;l!==null;){if(l.tag===13)l.memoizedState!==null&&Zx(l,g,u);else if(l.tag===19)Zx(l,g,u);else if(l.child!==null){l.child.return=l,l=l.child;continue}if(l===u)break e;for(;l.sibling===null;){if(l.return===null||l.return===u)break 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+ * @license React
+ * react-reconciler-constants.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */wl.ConcurrentRoot=1;wl.ContinuousEventPriority=4;wl.DefaultEventPriority=16;wl.DiscreteEventPriority=1;wl.IdleEventPriority=536870912;wl.LegacyRoot=0;aO.exports=wl;var iO=aO.exports;const o5={children:!0,ref:!0,key:!0,style:!0,forwardedRef:!0,unstable_applyCache:!0,unstable_applyDrawHitFromCache:!0};let s5=!1,a5=!1;const Qy=".react-konva-event",Bie=`ReactKonva: You have a Konva node with draggable = true and position defined but no onDragMove or onDragEnd events are handled.
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+For more info see: https://github.com/konvajs/react-konva/issues/256
+`,Fie=`ReactKonva: You are using "zIndex" attribute for a Konva node.
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lue=fe([mn,ir],(e,t)=>{const{layer:n,isMaskEnabled:r}=e;return{layer:n,isMaskEnabled:r,isStaging:t}},{memoizeOptions:{resultEqualityCheck:Jt}});function cue(){const e=te(),{t}=be(),{layer:n,isMaskEnabled:r,isStaging:o}=z(lue),s=()=>{e(Tp(n==="mask"?"base":"mask"))};rt(["q"],()=>{s()},{enabled:()=>!o,preventDefault:!0},[n]);const a=c=>{const d=c;e(Tp(d)),d==="mask"&&!r&&e(hd(!0))};return i.jsx(Xr,{tooltip:`${t("unifiedCanvas.layer")} (Q)`,"aria-label":`${t("unifiedCanvas.layer")} (Q)`,value:n,data:k_,onChange:a,disabled:o,w:"full"})}function uue(){const e=te(),{t}=be(),n=Ea(),r=z(ir),o=z(a=>a.system.isProcessing);rt(["shift+m"],()=>{s()},{enabled:()=>!r,preventDefault:!0},[n,o]);const s=()=>{e(x_())};return i.jsx(Le,{"aria-label":`${t("unifiedCanvas.mergeVisible")} (Shift+M)`,tooltip:`${t("unifiedCanvas.mergeVisible")} (Shift+M)`,icon:i.jsx(bP,{}),onClick:s,isDisabled:r})}function due(){const 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i.jsx(Le,{"aria-label":t("unifiedCanvas.clearCanvas"),tooltip:t("unifiedCanvas.clearCanvas"),icon:i.jsx(us,{}),onClick:r,isDisabled:n,colorScheme:"error"})}function hue(){const e=Ea(),t=te(),{t:n}=be();rt(["r"],()=>{o()},{enabled:()=>!0,preventDefault:!0},[e]);const r=gO(()=>o(!1),()=>o(!0)),o=(s=!1)=>{const a=Ea();if(!a)return;const c=a.getClientRect({skipTransform:!0});t(y_({contentRect:c,shouldScaleTo1:s}))};return i.jsx(Le,{"aria-label":`${n("unifiedCanvas.resetView")} (R)`,tooltip:`${n("unifiedCanvas.resetView")} (R)`,icon:i.jsx(pP,{}),onClick:r})}function mue(){const e=z(ir),t=Ea(),n=z(a=>a.system.isProcessing),r=te(),{t:o}=be();rt(["shift+s"],()=>{s()},{enabled:()=>!e,preventDefault:!0},[t,n]);const s=()=>{r(w_())};return i.jsx(Le,{"aria-label":`${o("unifiedCanvas.saveToGallery")} (Shift+S)`,tooltip:`${o("unifiedCanvas.saveToGallery")} (Shift+S)`,icon:i.jsx(_m,{}),onClick:s,isDisabled:e})}const gue=fe([mn,ir,go],(e,t,n)=>{const{isProcessing:r}=n,{tool:o}=e;return{tool:o,isStaging:t,isProcessing:r}},{memoizeOptions:{resultEqualityCheck:Jt}}),vue=()=>{const e=te(),{t}=be(),{tool:n,isStaging:r}=z(gue);rt(["b"],()=>{o()},{enabled:()=>!r,preventDefault:!0},[]),rt(["e"],()=>{s()},{enabled:()=>!r,preventDefault:!0},[n]),rt(["c"],()=>{a()},{enabled:()=>!r,preventDefault:!0},[n]),rt(["shift+f"],()=>{c()},{enabled:()=>!r,preventDefault:!0}),rt(["delete","backspace"],()=>{d()},{enabled:()=>!r,preventDefault:!0});const o=()=>e(ea("brush")),s=()=>e(ea("eraser")),a=()=>e(ea("colorPicker")),c=()=>e(v_()),d=()=>e(b_());return i.jsxs(F,{flexDirection:"column",gap:2,children:[i.jsxs(nr,{children:[i.jsx(Le,{"aria-label":`${t("unifiedCanvas.brush")} (B)`,tooltip:`${t("unifiedCanvas.brush")} (B)`,icon:i.jsx(xP,{}),isChecked:n==="brush"&&!r,onClick:o,isDisabled:r}),i.jsx(Le,{"aria-label":`${t("unifiedCanvas.eraser")} (E)`,tooltip:`${t("unifiedCanvas.eraser")} 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i.jsxs(F,{sx:{gap:2,p:4,borderRadius:4,flexDirection:"column",w:"full",bg:"base.150",_dark:{bg:"base.750"}},children:[i.jsxs(F,{alignItems:"center",gap:2,children:[i.jsx(Ye,{sx:{fontSize:"sm",width:"full",color:"base.700",_dark:{color:"base.300"}},children:t("parameters.aspectRatio")}),i.jsx(pl,{}),i.jsx(O8,{}),i.jsx(Le,{tooltip:t("ui.swapSizes"),"aria-label":t("ui.swapSizes"),size:"sm",icon:i.jsx(l8,{}),fontSize:20,onClick:()=>e(DD())})]}),i.jsx(Yue,{}),i.jsx(que,{})]})}const Que=fe(Xe,({ui:e,generation:t})=>{const{shouldUseSliders:n}=e,{shouldRandomizeSeed:r}=t;return{shouldUseSliders:n,activeLabel:r?void 0:"Manual Seed"}},Ge),Jue=()=>{const{shouldUseSliders:e,activeLabel:t}=z(Que);return 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rt("*",()=>{rP("shift")?!t&&e(Po(!0)):t&&e(Po(!1))},{keyup:!0,keydown:!0},[t]),rt("o",()=>{e($D()),o==="unifiedCanvas"&&n&&e(Co())}),rt(["shift+o"],()=>{e(zD()),o==="unifiedCanvas"&&e(Co())}),rt("g",()=>{e(LD()),o==="unifiedCanvas"&&r&&e(Co())}),rt(["shift+g"],()=>{e(T5()),o==="unifiedCanvas"&&e(Co())}),rt("1",()=>{e(Kl("txt2img"))}),rt("2",()=>{e(Kl("img2img"))}),rt("3",()=>{e(Kl("unifiedCanvas"))}),rt("4",()=>{e(Kl("nodes"))}),null},xde=f.memo(yde),wde={},Sde=({config:e=wde,headerComponent:t})=>{const n=z(U6),r=dF(),o=te();return f.useEffect(()=>{Bn.changeLanguage(n)},[n]),f.useEffect(()=>{q5(e)&&(r.info({namespace:"App",config:e},"Received config"),o(BD(e)))},[o,e,r]),f.useEffect(()=>{o(FD())},[o]),i.jsxs(i.Fragment,{children:[i.jsxs(ol,{w:"100vw",h:"100vh",position:"relative",overflow:"hidden",children:[i.jsx(SH,{children:i.jsxs(ol,{sx:{gap:4,p:4,gridAutoRows:"min-content 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diff --git a/invokeai/frontend/web/dist/assets/MantineProvider-b5842fc1.js b/invokeai/frontend/web/dist/assets/MantineProvider-b5842fc1.js
deleted file mode 100644
index d479487e00..0000000000
--- a/invokeai/frontend/web/dist/assets/MantineProvider-b5842fc1.js
+++ /dev/null
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diff --git a/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-94e0a1e5.js b/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-94e0a1e5.js
new file mode 100644
index 0000000000..c11ffa2797
--- /dev/null
+++ b/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-94e0a1e5.js
@@ -0,0 +1,310 @@
+import{A as m,fW as Ze,z as y,a4 as Xa,fX as Ya,af as va,aj as d,fY as b,fZ as t,f_ as Ka,f$ as h,g0 as ua,g1 as Za,g2 as Ja,aI as Qa,g3 as et,ad as rt,g4 as at}from"./index-bad7ff83.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./MantineProvider-c592f969.js";var za=String.raw,Ca=za`
+  :root,
+  :host {
+    --chakra-vh: 100vh;
+  }
+
+  @supports (height: -webkit-fill-available) {
+    :root,
+    :host {
+      --chakra-vh: -webkit-fill-available;
+    }
+  }
+
+  @supports (height: -moz-fill-available) {
+    :root,
+    :host {
+      --chakra-vh: -moz-fill-available;
+    }
+  }
+
+  @supports (height: 100dvh) {
+    :root,
+    :host {
+      --chakra-vh: 100dvh;
+    }
+  }
+`,$t=()=>m.jsx(Ze,{styles:Ca}),kt=({scope:e=""})=>m.jsx(Ze,{styles:za`
+      html {
+        line-height: 1.5;
+        -webkit-text-size-adjust: 100%;
+        font-family: system-ui, sans-serif;
+        -webkit-font-smoothing: antialiased;
+        text-rendering: optimizeLegibility;
+        -moz-osx-font-smoothing: grayscale;
+        touch-action: manipulation;
+      }
+
+      body {
+        position: relative;
+        min-height: 100%;
+        margin: 0;
+        font-feature-settings: "kern";
+      }
+
+      ${e} :where(*, *::before, *::after) {
+        border-width: 0;
+        border-style: solid;
+        box-sizing: border-box;
+        word-wrap: break-word;
+      }
+
+      main {
+        display: block;
+      }
+
+      ${e} hr {
+        border-top-width: 1px;
+        box-sizing: content-box;
+        height: 0;
+        overflow: visible;
+      }
+
+      ${e} :where(pre, code, kbd,samp) {
+        font-family: SFMono-Regular, Menlo, Monaco, Consolas, monospace;
+        font-size: 1em;
+      }
+
+      ${e} a {
+        background-color: transparent;
+        color: inherit;
+        text-decoration: inherit;
+      }
+
+      ${e} abbr[title] {
+        border-bottom: none;
+        text-decoration: underline;
+        -webkit-text-decoration: underline dotted;
+        text-decoration: underline dotted;
+      }
+
+      ${e} :where(b, strong) {
+        font-weight: bold;
+      }
+
+      ${e} small {
+        font-size: 80%;
+      }
+
+      ${e} :where(sub,sup) {
+        font-size: 75%;
+        line-height: 0;
+        position: relative;
+        vertical-align: baseline;
+      }
+
+      ${e} sub {
+        bottom: -0.25em;
+      }
+
+      ${e} sup {
+        top: -0.5em;
+      }
+
+      ${e} img {
+        border-style: none;
+      }
+
+      ${e} :where(button, input, optgroup, select, textarea) {
+        font-family: inherit;
+        font-size: 100%;
+        line-height: 1.15;
+        margin: 0;
+      }
+
+      ${e} :where(button, input) {
+        overflow: visible;
+      }
+
+      ${e} :where(button, select) {
+        text-transform: none;
+      }
+
+      ${e} :where(
+          button::-moz-focus-inner,
+          [type="button"]::-moz-focus-inner,
+          [type="reset"]::-moz-focus-inner,
+          [type="submit"]::-moz-focus-inner
+        ) {
+        border-style: none;
+        padding: 0;
+      }
+
+      ${e} fieldset {
+        padding: 0.35em 0.75em 0.625em;
+      }
+
+      ${e} legend {
+        box-sizing: border-box;
+        color: inherit;
+        display: table;
+        max-width: 100%;
+        padding: 0;
+        white-space: normal;
+      }
+
+      ${e} progress {
+        vertical-align: baseline;
+      }
+
+      ${e} textarea {
+        overflow: auto;
+      }
+
+      ${e} :where([type="checkbox"], [type="radio"]) {
+        box-sizing: border-box;
+        padding: 0;
+      }
+
+      ${e} input[type="number"]::-webkit-inner-spin-button,
+      ${e} input[type="number"]::-webkit-outer-spin-button {
+        -webkit-appearance: none !important;
+      }
+
+      ${e} input[type="number"] {
+        -moz-appearance: textfield;
+      }
+
+      ${e} input[type="search"] {
+        -webkit-appearance: textfield;
+        outline-offset: -2px;
+      }
+
+      ${e} input[type="search"]::-webkit-search-decoration {
+        -webkit-appearance: none !important;
+      }
+
+      ${e} ::-webkit-file-upload-button {
+        -webkit-appearance: button;
+        font: inherit;
+      }
+
+      ${e} details {
+        display: block;
+      }
+
+      ${e} summary {
+        display: list-item;
+      }
+
+      template {
+        display: none;
+      }
+
+      [hidden] {
+        display: none !important;
+      }
+
+      ${e} :where(
+          blockquote,
+          dl,
+          dd,
+          h1,
+          h2,
+          h3,
+          h4,
+          h5,
+          h6,
+          hr,
+          figure,
+          p,
+          pre
+        ) {
+        margin: 0;
+      }
+
+      ${e} button {
+        background: transparent;
+        padding: 0;
+      }
+
+      ${e} fieldset {
+        margin: 0;
+        padding: 0;
+      }
+
+      ${e} :where(ol, ul) {
+        margin: 0;
+        padding: 0;
+      }
+
+      ${e} textarea {
+        resize: vertical;
+      }
+
+      ${e} :where(button, [role="button"]) {
+        cursor: pointer;
+      }
+
+      ${e} button::-moz-focus-inner {
+        border: 0 !important;
+      }
+
+      ${e} table {
+        border-collapse: collapse;
+      }
+
+      ${e} :where(h1, h2, h3, h4, h5, h6) {
+        font-size: inherit;
+        font-weight: inherit;
+      }
+
+      ${e} :where(button, input, optgroup, select, textarea) {
+        padding: 0;
+        line-height: inherit;
+        color: inherit;
+      }
+
+      ${e} :where(img, svg, video, canvas, audio, iframe, embed, object) {
+        display: block;
+      }
+
+      ${e} :where(img, video) {
+        max-width: 100%;
+        height: auto;
+      }
+
+      [data-js-focus-visible]
+        :focus:not([data-focus-visible-added]):not(
+          [data-focus-visible-disabled]
+        ) {
+        outline: none;
+        box-shadow: none;
+      }
+
+      ${e} select::-ms-expand {
+        display: none;
+      }
+
+      ${Ca}
+    `}),Ce={light:"chakra-ui-light",dark:"chakra-ui-dark"};function _t(e={}){const{preventTransition:r=!0}=e,a={setDataset:i=>{const n=r?a.preventTransition():void 0;document.documentElement.dataset.theme=i,document.documentElement.style.colorScheme=i,n==null||n()},setClassName(i){document.body.classList.add(i?Ce.dark:Ce.light),document.body.classList.remove(i?Ce.light:Ce.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(i){var n;return((n=a.query().matches)!=null?n:i==="dark")?"dark":"light"},addListener(i){const n=a.query(),l=s=>{i(s.matches?"dark":"light")};return typeof n.addListener=="function"?n.addListener(l):n.addEventListener("change",l),()=>{typeof n.removeListener=="function"?n.removeListener(l):n.removeEventListener("change",l)}},preventTransition(){const i=document.createElement("style");return i.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(i),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(i)})})}}};return a}var wt="chakra-ui-color-mode";function Aa(e){return{ssr:!1,type:"localStorage",get(r){if(!(globalThis!=null&&globalThis.document))return r;let a;try{a=localStorage.getItem(e)||r}catch{}return a||r},set(r){try{localStorage.setItem(e,r)}catch{}}}}var zt=Aa(wt),Pr=()=>{};function Ir(e,r){return e.type==="cookie"&&e.ssr?e.get(r):r}function Pa(e){const{value:r,children:a,options:{useSystemColorMode:i,initialColorMode:n,disableTransitionOnChange:l}={},colorModeManager:s=zt}=e,c=n==="dark"?"dark":"light",[v,x]=y.useState(()=>Ir(s,c)),[I,O]=y.useState(()=>Ir(s)),{getSystemTheme:oe,setClassName:_e,setDataset:zr,addListener:Cr}=y.useMemo(()=>_t({preventTransition:l}),[l]),we=n==="system"&&!v?I:v,C=y.useCallback(ie=>{const ze=ie==="system"?oe():ie;x(ze),_e(ze==="dark"),zr(ze),s.set(ze)},[s,oe,_e,zr]);Xa(()=>{n==="system"&&O(oe())},[]),y.useEffect(()=>{const ie=s.get();if(ie){C(ie);return}if(n==="system"){C("system");return}C(c)},[s,c,n,C]);const Ar=y.useCallback(()=>{C(we==="dark"?"light":"dark")},[we,C]);y.useEffect(()=>{if(i)return Cr(C)},[i,Cr,C]);const Ua=y.useMemo(()=>({colorMode:r??we,toggleColorMode:r?Pr:Ar,setColorMode:r?Pr:C,forced:r!==void 0}),[we,Ar,C,r]);return m.jsx(Ya.Provider,{value:Ua,children:a})}Pa.displayName="ColorModeProvider";var 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+          var(--invokeai-colors-base-800) 100%)`}}}})),zc=h({variants:{invokeAI:wc},defaultProps:{size:"md",variant:"invokeAI"}}),Cc=f("popper-arrow-bg"),Ac=t(e=>({borderRadius:"base",shadow:"dark-lg",bg:o("base.700","base.200")(e),[Cc.variable]:o("colors.base.700","colors.base.200")(e),pb:1.5})),Pc=h({baseStyle:Ac}),Ic={config:{cssVarPrefix:"invokeai",initialColorMode:"dark",useSystemColorMode:!1},layerStyles:{body:{bg:"base.50",color:"base.900",".chakra-ui-dark &":{bg:"base.900",color:"base.50"}},first:{bg:"base.100",color:"base.900",".chakra-ui-dark &":{bg:"base.850",color:"base.100"}},second:{bg:"base.200",color:"base.900",".chakra-ui-dark &":{bg:"base.800",color:"base.100"}}},styles:{global:()=>({layerStyle:"body","*":{...Gd}})},direction:"ltr",fonts:{body:"'Inter Variable', sans-serif"},shadows:{light:{accent:"0 0 10px 0 var(--invokeai-colors-accent-300)",accentHover:"0 0 10px 0 var(--invokeai-colors-accent-400)",ok:"0 0 7px var(--invokeai-colors-ok-600)",working:"0 0 7px var(--invokeai-colors-working-600)",error:"0 0 7px var(--invokeai-colors-error-600)"},dark:{accent:"0 0 10px 0 var(--invokeai-colors-accent-600)",accentHover:"0 0 10px 0 var(--invokeai-colors-accent-500)",ok:"0 0 7px var(--invokeai-colors-ok-400)",working:"0 0 7px var(--invokeai-colors-working-400)",error:"0 0 7px var(--invokeai-colors-error-400)"},selected:{light:"0px 0px 0px 1px var(--invokeai-colors-base-150), 0px 0px 0px 4px var(--invokeai-colors-accent-400)",dark:"0px 0px 0px 1px var(--invokeai-colors-base-900), 0px 0px 0px 4px var(--invokeai-colors-accent-500)"},hoverSelected:{light:"0px 0px 0px 1px var(--invokeai-colors-base-150), 0px 0px 0px 4px var(--invokeai-colors-accent-500)",dark:"0px 0px 0px 1px var(--invokeai-colors-base-900), 0px 0px 0px 4px var(--invokeai-colors-accent-400)"},hoverUnselected:{light:"0px 0px 0px 1px var(--invokeai-colors-base-150), 0px 0px 0px 3px var(--invokeai-colors-accent-500)",dark:"0px 0px 0px 1px var(--invokeai-colors-base-900), 0px 0px 0px 3px var(--invokeai-colors-accent-400)"},nodeSelectedOutline:"0 0 0 2px var(--invokeai-colors-accent-450)"},colors:R0,components:{Button:X0,Input:vd,Editable:nd,Textarea:zc,Tabs:$c,Progress:Nd,Accordion:N0,FormLabel:sd,Switch:fc,NumberInput:Fd,Select:Jd,Skeleton:ec,Slider:sc,Popover:Hd,Modal:kd,Checkbox:Q0,Menu:St,Text:_c,Tooltip:Pc}};const Fc=()=>y.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),Tc=Aa("@@invokeai-color-mode");function Ec({children:e}){const{i18n:r}=pt(),a=r.dir(),i=y.useMemo(()=>A0({...Ic,direction:a}),[a]);y.useEffect(()=>{document.body.dir=a},[a]);const n=Fc();return m.jsx(xt,{theme:n,children:m.jsx(D0,{theme:i,colorModeManager:Tc,children:e})})}export{Ec as default};
diff --git a/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-a6269375.js b/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-a6269375.js
deleted file mode 100644
index b1b3abea4e..0000000000
--- a/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-a6269375.js
+++ /dev/null
@@ -1,302 +0,0 @@
-import{v as g,gm as Ke,s as x,L as Ba,gn as Ea,ae as ca,ah as c,go as b,gp as Da,gq as t,gr as Ra,gs as h,gt as ba,gu as ja,gv as Ha,b2 as Wa,gw as Va,Z as La,gx as qa,gy as Oa,gz as Na,gA as Ga,aY as Ua}from"./index-e2437518.js";import{e as n,o as Sr,h as Ya,i as Ka,j as Xa,k as Za,l as Ja,n as Qa,p as et,q as rt,s as at,t as tt,v as ot,w as nt,x as it,M as lt}from"./MantineProvider-b5842fc1.js";var va=String.raw,ua=va`
-  :root,
-  :host {
-    --chakra-vh: 100vh;
-  }
-
-  @supports (height: -webkit-fill-available) {
-    :root,
-    :host {
-      --chakra-vh: -webkit-fill-available;
-    }
-  }
-
-  @supports (height: -moz-fill-available) {
-    :root,
-    :host {
-      --chakra-vh: -moz-fill-available;
-    }
-  }
-
-  @supports (height: 100dvh) {
-    :root,
-    :host {
-      --chakra-vh: 100dvh;
-    }
-  }
-`,st=()=>g.jsx(Ke,{styles:ua}),dt=({scope:e=""})=>g.jsx(Ke,{styles:va`
-      html {
-        line-height: 1.5;
-        -webkit-text-size-adjust: 100%;
-        font-family: system-ui, sans-serif;
-        -webkit-font-smoothing: antialiased;
-        text-rendering: optimizeLegibility;
-        -moz-osx-font-smoothing: grayscale;
-        touch-action: manipulation;
-      }
-
-      body {
-        position: relative;
-        min-height: 100%;
-        margin: 0;
-        font-feature-settings: "kern";
-      }
-
-      ${e} :where(*, *::before, *::after) {
-        border-width: 0;
-        border-style: solid;
-        box-sizing: border-box;
-        word-wrap: break-word;
-      }
-
-      main {
-        display: block;
-      }
-
-      ${e} hr {
-        border-top-width: 1px;
-        box-sizing: content-box;
-        height: 0;
-        overflow: visible;
-      }
-
-      ${e} :where(pre, code, kbd,samp) {
-        font-family: SFMono-Regular, Menlo, Monaco, Consolas, monospace;
-        font-size: 1em;
-      }
-
-      ${e} a {
-        background-color: transparent;
-        color: inherit;
-        text-decoration: inherit;
-      }
-
-      ${e} abbr[title] {
-        border-bottom: none;
-        text-decoration: underline;
-        -webkit-text-decoration: underline dotted;
-        text-decoration: underline dotted;
-      }
-
-      ${e} :where(b, strong) {
-        font-weight: bold;
-      }
-
-      ${e} small {
-        font-size: 80%;
-      }
-
-      ${e} :where(sub,sup) {
-        font-size: 75%;
-        line-height: 0;
-        position: relative;
-        vertical-align: baseline;
-      }
-
-      ${e} sub {
-        bottom: -0.25em;
-      }
-
-      ${e} sup {
-        top: -0.5em;
-      }
-
-      ${e} img {
-        border-style: none;
-      }
-
-      ${e} :where(button, input, optgroup, select, textarea) {
-        font-family: inherit;
-        font-size: 100%;
-        line-height: 1.15;
-        margin: 0;
-      }
-
-      ${e} :where(button, input) {
-        overflow: visible;
-      }
-
-      ${e} :where(button, select) {
-        text-transform: none;
-      }
-
-      ${e} :where(
-          button::-moz-focus-inner,
-          [type="button"]::-moz-focus-inner,
-          [type="reset"]::-moz-focus-inner,
-          [type="submit"]::-moz-focus-inner
-        ) {
-        border-style: none;
-        padding: 0;
-      }
-
-      ${e} fieldset {
-        padding: 0.35em 0.75em 0.625em;
-      }
-
-      ${e} legend {
-        box-sizing: border-box;
-        color: inherit;
-        display: table;
-        max-width: 100%;
-        padding: 0;
-        white-space: normal;
-      }
-
-      ${e} progress {
-        vertical-align: baseline;
-      }
-
-      ${e} textarea {
-        overflow: auto;
-      }
-
-      ${e} :where([type="checkbox"], [type="radio"]) {
-        box-sizing: border-box;
-        padding: 0;
-      }
-
-      ${e} input[type="number"]::-webkit-inner-spin-button,
-      ${e} input[type="number"]::-webkit-outer-spin-button {
-        -webkit-appearance: none !important;
-      }
-
-      ${e} input[type="number"] {
-        -moz-appearance: textfield;
-      }
-
-      ${e} input[type="search"] {
-        -webkit-appearance: textfield;
-        outline-offset: -2px;
-      }
-
-      ${e} input[type="search"]::-webkit-search-decoration {
-        -webkit-appearance: none !important;
-      }
-
-      ${e} ::-webkit-file-upload-button {
-        -webkit-appearance: button;
-        font: inherit;
-      }
-
-      ${e} details {
-        display: block;
-      }
-
-      ${e} summary {
-        display: list-item;
-      }
-
-      template {
-        display: none;
-      }
-
-      [hidden] {
-        display: none !important;
-      }
-
-      ${e} :where(
-          blockquote,
-          dl,
-          dd,
-          h1,
-          h2,
-          h3,
-          h4,
-          h5,
-          h6,
-          hr,
-          figure,
-          p,
-          pre
-        ) {
-        margin: 0;
-      }
-
-      ${e} button {
-        background: transparent;
-        padding: 0;
-      }
-
-      ${e} fieldset {
-        margin: 0;
-        padding: 0;
-      }
-
-      ${e} :where(ol, ul) {
-        margin: 0;
-        padding: 0;
-      }
-
-      ${e} textarea {
-        resize: vertical;
-      }
-
-      ${e} :where(button, [role="button"]) {
-        cursor: pointer;
-      }
-
-      ${e} button::-moz-focus-inner {
-        border: 0 !important;
-      }
-
-      ${e} table {
-        border-collapse: collapse;
-      }
-
-      ${e} :where(h1, h2, h3, h4, h5, h6) {
-        font-size: inherit;
-        font-weight: inherit;
-      }
-
-      ${e} :where(button, input, optgroup, select, textarea) {
-        padding: 0;
-        line-height: inherit;
-        color: inherit;
-      }
-
-      ${e} :where(img, svg, video, canvas, audio, iframe, embed, object) {
-        display: block;
-      }
-
-      ${e} :where(img, video) {
-        max-width: 100%;
-        height: auto;
-      }
-
-      [data-js-focus-visible]
-        :focus:not([data-focus-visible-added]):not(
-          [data-focus-visible-disabled]
-        ) {
-        outline: none;
-        box-shadow: none;
-      }
-
-      ${e} select::-ms-expand {
-        display: none;
-      }
-
-      ${ua}
-    `}),_e={light:"chakra-ui-light",dark:"chakra-ui-dark"};function ct(e={}){const{preventTransition:r=!0}=e,a={setDataset:o=>{const i=r?a.preventTransition():void 0;document.documentElement.dataset.theme=o,document.documentElement.style.colorScheme=o,i==null||i()},setClassName(o){document.body.classList.add(o?_e.dark:_e.light),document.body.classList.remove(o?_e.light:_e.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(o){var i;return((i=a.query().matches)!=null?i:o==="dark")?"dark":"light"},addListener(o){const i=a.query(),l=s=>{o(s.matches?"dark":"light")};return typeof i.addListener=="function"?i.addListener(l):i.addEventListener("change",l),()=>{typeof i.removeListener=="function"?i.removeListener(l):i.removeEventListener("change",l)}},preventTransition(){const o=document.createElement("style");return o.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(o),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(o)})})}}};return a}var bt="chakra-ui-color-mode";function fa(e){return{ssr:!1,type:"localStorage",get(r){if(!(globalThis!=null&&globalThis.document))return r;let a;try{a=localStorage.getItem(e)||r}catch{}return a||r},set(r){try{localStorage.setItem(e,r)}catch{}}}}var vt=fa(bt),Cr=()=>{};function Ar(e,r){return e.type==="cookie"&&e.ssr?e.get(r):r}function ma(e){const{value:r,children:a,options:{useSystemColorMode:o,initialColorMode:i,disableTransitionOnChange:l}={},colorModeManager:s=vt}=e,d=i==="dark"?"dark":"light",[v,$]=x.useState(()=>Ar(s,d)),[p,S]=x.useState(()=>Ar(s)),{getSystemTheme:T,setClassName:L,setDataset:Qe,addListener:wr}=x.useMemo(()=>ct({preventTransition:l}),[l]),$e=i==="system"&&!v?p:v,F=x.useCallback(te=>{const ke=te==="system"?T():te;$(ke),L(ke==="dark"),Qe(ke),s.set(ke)},[s,T,L,Qe]);Ba(()=>{i==="system"&&S(T())},[]),x.useEffect(()=>{const te=s.get();if(te){F(te);return}if(i==="system"){F("system");return}F(d)},[s,d,i,F]);const zr=x.useCallback(()=>{F($e==="dark"?"light":"dark")},[$e,F]);x.useEffect(()=>{if(o)return wr(F)},[o,wr,F]);const Ma=x.useMemo(()=>({colorMode:r??$e,toggleColorMode:r?Cr:zr,setColorMode:r?Cr:F,forced:r!==void 0}),[$e,zr,F,r]);return g.jsx(Ea.Provider,{value:Ma,children:a})}ma.displayName="ColorModeProvider";var 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1)"},gt={"ultra-fast":"50ms",faster:"100ms",fast:"150ms",normal:"200ms",slow:"300ms",slower:"400ms","ultra-slow":"500ms"},yt={property:mt,easing:ht,duration:gt},pt=yt,St={hide:-1,auto:"auto",base:0,docked:10,dropdown:1e3,sticky:1100,banner:1200,overlay:1300,modal:1400,popover:1500,skipLink:1600,toast:1700,tooltip:1800},xt=St,$t={none:0,"1px":"1px solid","2px":"2px solid","4px":"4px solid","8px":"8px solid"},kt=$t,_t={base:"0em",sm:"30em",md:"48em",lg:"62em",xl:"80em","2xl":"96em"},wt=_t,zt={transparent:"transparent",current:"currentColor",black:"#000000",white:"#FFFFFF",whiteAlpha:{50:"rgba(255, 255, 255, 0.04)",100:"rgba(255, 255, 255, 0.06)",200:"rgba(255, 255, 255, 0.08)",300:"rgba(255, 255, 255, 0.16)",400:"rgba(255, 255, 255, 0.24)",500:"rgba(255, 255, 255, 0.36)",600:"rgba(255, 255, 255, 0.48)",700:"rgba(255, 255, 255, 0.64)",800:"rgba(255, 255, 255, 0.80)",900:"rgba(255, 255, 255, 0.92)"},blackAlpha:{50:"rgba(0, 0, 0, 0.04)",100:"rgba(0, 0, 0, 0.06)",200:"rgba(0, 0, 0, 0.08)",300:"rgba(0, 0, 0, 0.16)",400:"rgba(0, 0, 0, 0.24)",500:"rgba(0, 0, 0, 0.36)",600:"rgba(0, 0, 0, 0.48)",700:"rgba(0, 0, 0, 0.64)",800:"rgba(0, 0, 0, 0.80)",900:"rgba(0, 0, 0, 0.92)"},gray:{50:"#F7FAFC",100:"#EDF2F7",200:"#E2E8F0",300:"#CBD5E0",400:"#A0AEC0",500:"#718096",600:"#4A5568",700:"#2D3748",800:"#1A202C",900:"#171923"},red:{50:"#FFF5F5",100:"#FED7D7",200:"#FEB2B2",300:"#FC8181",400:"#F56565",500:"#E53E3E",600:"#C53030",700:"#9B2C2C",800:"#822727",900:"#63171B"},orange:{50:"#FFFAF0",100:"#FEEBC8",200:"#FBD38D",300:"#F6AD55",400:"#ED8936",500:"#DD6B20",600:"#C05621",700:"#9C4221",800:"#7B341E",900:"#652B19"},yellow:{50:"#FFFFF0",100:"#FEFCBF",200:"#FAF089",300:"#F6E05E",400:"#ECC94B",500:"#D69E2E",600:"#B7791F",700:"#975A16",800:"#744210",900:"#5F370E"},green:{50:"#F0FFF4",100:"#C6F6D5",200:"#9AE6B4",300:"#68D391",400:"#48BB78",500:"#38A169",600:"#2F855A",700:"#276749",800:"#22543D",900:"#1C4532"},teal:{50:"#E6FFFA",100:"#B2F5EA",200:"#81E6D9",300:"#4FD1C5",400:"#38B2AC",500:"#319795",600:"#2C7A7B",700:"#285E61",800:"#234E52",900:"#1D4044"},blue:{50:"#ebf8ff",100:"#bee3f8",200:"#90cdf4",300:"#63b3ed",400:"#4299e1",500:"#3182ce",600:"#2b6cb0",700:"#2c5282",800:"#2a4365",900:"#1A365D"},cyan:{50:"#EDFDFD",100:"#C4F1F9",200:"#9DECF9",300:"#76E4F7",400:"#0BC5EA",500:"#00B5D8",600:"#00A3C4",700:"#0987A0",800:"#086F83",900:"#065666"},purple:{50:"#FAF5FF",100:"#E9D8FD",200:"#D6BCFA",300:"#B794F4",400:"#9F7AEA",500:"#805AD5",600:"#6B46C1",700:"#553C9A",800:"#44337A",900:"#322659"},pink:{50:"#FFF5F7",100:"#FED7E2",200:"#FBB6CE",300:"#F687B3",400:"#ED64A6",500:"#D53F8C",600:"#B83280",700:"#97266D",800:"#702459",900:"#521B41"},linkedin:{50:"#E8F4F9",100:"#CFEDFB",200:"#9BDAF3",300:"#68C7EC",400:"#34B3E4",500:"#00A0DC",600:"#008CC9",700:"#0077B5",800:"#005E93",900:"#004471"},facebook:{50:"#E8F4F9",100:"#D9DEE9",200:"#B7C2DA",300:"#6482C0",400:"#4267B2",500:"#385898",600:"#314E89",700:"#29487D",800:"#223B67",900:"#1E355B"},messenger:{50:"#D0E6FF",100:"#B9DAFF",200:"#A2CDFF",300:"#7AB8FF",400:"#2E90FF",500:"#0078FF",600:"#0063D1",700:"#0052AC",800:"#003C7E",900:"#002C5C"},whatsapp:{50:"#dffeec",100:"#b9f5d0",200:"#90edb3",300:"#65e495",400:"#3cdd78",500:"#22c35e",600:"#179848",700:"#0c6c33",800:"#01421c",900:"#001803"},twitter:{50:"#E5F4FD",100:"#C8E9FB",200:"#A8DCFA",300:"#83CDF7",400:"#57BBF5",500:"#1DA1F2",600:"#1A94DA",700:"#1681BF",800:"#136B9E",900:"#0D4D71"},telegram:{50:"#E3F2F9",100:"#C5E4F3",200:"#A2D4EC",300:"#7AC1E4",400:"#47A9DA",500:"#0088CC",600:"#007AB8",700:"#006BA1",800:"#005885",900:"#003F5E"}},Ct=zt,At={none:"0",sm:"0.125rem",base:"0.25rem",md:"0.375rem",lg:"0.5rem",xl:"0.75rem","2xl":"1rem","3xl":"1.5rem",full:"9999px"},Pt=At,Tt={xs:"0 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-      var(--invokeai-colors-base-200) 70%,
-      var(--invokeai-colors-base-200) 100%)`},_dark:{"::-webkit-resizer":{backgroundImage:`linear-gradient(135deg,
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-        var(--invokeai-colors-base-900) 70%,
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-        var(--invokeai-colors-base-800) 100%)`}}})),Dc=h({variants:{invokeAI:Ec},defaultProps:{size:"md",variant:"invokeAI"}}),Rc=m("popper-arrow-bg"),jc=t(e=>({borderRadius:"base",shadow:"dark-lg",bg:n("base.700","base.200")(e),[Rc.variable]:n("colors.base.700","colors.base.200")(e),pb:1.5})),Hc=h({baseStyle:jc}),Wc={config:{cssVarPrefix:"invokeai",initialColorMode:"dark",useSystemColorMode:!1},layerStyles:{body:{bg:"base.50",color:"base.900",".chakra-ui-dark &":{bg:"base.900",color:"base.50"}},first:{bg:"base.100",color:"base.900",".chakra-ui-dark &":{bg:"base.850",color:"base.100"}},second:{bg:"base.200",color:"base.900",".chakra-ui-dark &":{bg:"base.800",color:"base.100"}}},styles:{global:()=>({layerStyle:"body","*":{...ac}})},direction:"ltr",fonts:{body:"'Inter Variable', sans-serif"},shadows:{light:{accent:"0 0 10px 0 var(--invokeai-colors-accent-300)",accentHover:"0 0 10px 0 var(--invokeai-colors-accent-400)",ok:"0 0 7px var(--invokeai-colors-ok-600)",working:"0 0 7px var(--invokeai-colors-working-600)",error:"0 0 7px var(--invokeai-colors-error-600)"},dark:{accent:"0 0 10px 0 var(--invokeai-colors-accent-600)",accentHover:"0 0 10px 0 var(--invokeai-colors-accent-500)",ok:"0 0 7px var(--invokeai-colors-ok-400)",working:"0 0 7px var(--invokeai-colors-working-400)",error:"0 0 7px var(--invokeai-colors-error-400)"},selected:{light:"0px 0px 0px 1px var(--invokeai-colors-base-150), 0px 0px 0px 4px var(--invokeai-colors-accent-400)",dark:"0px 0px 0px 1px var(--invokeai-colors-base-900), 0px 0px 0px 4px var(--invokeai-colors-accent-500)"},hoverSelected:{light:"0px 0px 0px 1px var(--invokeai-colors-base-150), 0px 0px 0px 4px var(--invokeai-colors-accent-500)",dark:"0px 0px 0px 1px var(--invokeai-colors-base-900), 0px 0px 0px 4px var(--invokeai-colors-accent-400)"},hoverUnselected:{light:"0px 0px 0px 1px var(--invokeai-colors-base-150), 0px 0px 0px 3px var(--invokeai-colors-accent-500)",dark:"0px 0px 0px 1px var(--invokeai-colors-base-900), 0px 0px 0px 3px var(--invokeai-colors-accent-400)"},nodeSelectedOutline:"0 0 0 2px var(--invokeai-colors-accent-450)"},colors:U0,components:{Button:od,Input:$d,Editable:hd,Textarea:Dc,Tabs:Ic,Progress:rc,Accordion:rd,FormLabel:yd,Switch:_c,NumberInput:Vd,Select:sc,Skeleton:cc,Slider:yc,Popover:Kd,Modal:Md,Checkbox:dd,Menu:it,Text:Bc,Tooltip:Hc}};const Vc=()=>x.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),Lc=fa("@@invokeai-color-mode");function Nc({children:e}){const{i18n:r}=Ua(),a=r.dir(),o=x.useMemo(()=>j0({...Wc,direction:a}),[a]);x.useEffect(()=>{document.body.dir=a},[a]);const i=Vc();return g.jsx(lt,{theme:i,children:g.jsx(G0,{theme:o,colorModeManager:Lc,children:e})})}export{Nc as default};
diff --git a/invokeai/frontend/web/dist/assets/index-bad7ff83.js b/invokeai/frontend/web/dist/assets/index-bad7ff83.js
new file mode 100644
index 0000000000..604784e6c9
--- /dev/null
+++ b/invokeai/frontend/web/dist/assets/index-bad7ff83.js
@@ -0,0 +1,125 @@
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+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
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Ce{constructor(t){this.spa=this.safeParseAsync,this._def=t,this.parse=this.parse.bind(this),this.safeParse=this.safeParse.bind(this),this.parseAsync=this.parseAsync.bind(this),this.safeParseAsync=this.safeParseAsync.bind(this),this.spa=this.spa.bind(this),this.refine=this.refine.bind(this),this.refinement=this.refinement.bind(this),this.superRefine=this.superRefine.bind(this),this.optional=this.optional.bind(this),this.nullable=this.nullable.bind(this),this.nullish=this.nullish.bind(this),this.array=this.array.bind(this),this.promise=this.promise.bind(this),this.or=this.or.bind(this),this.and=this.and.bind(this),this.transform=this.transform.bind(this),this.brand=this.brand.bind(this),this.default=this.default.bind(this),this.catch=this.catch.bind(this),this.describe=this.describe.bind(this),this.pipe=this.pipe.bind(this),this.isNullable=this.isNullable.bind(this),this.isOptional=this.isOptional.bind(this)}get description(){return this._def.description}_getType(t){return ms(t.data)}_getOrReturnCtx(t,n){return n||{common:t.parent.common,data:t.data,parsedType:ms(t.data),schemaErrorMap:this._def.errorMap,path:t.path,parent:t.parent}}_processInputParams(t){return{status:new jn,ctx:{common:t.parent.common,data:t.data,parsedType:ms(t.data),schemaErrorMap:this._def.errorMap,path:t.path,parent:t.parent}}}_parseSync(t){const n=this._parse(t);if(ym(n))throw new Error("Synchronous parse encountered promise.");return n}_parseAsync(t){const n=this._parse(t);return Promise.resolve(n)}parse(t,n){const r=this.safeParse(t,n);if(r.success)return r.data;throw r.error}safeParse(t,n){var r;const i={common:{issues:[],async:(r=n==null?void 0:n.async)!==null&&r!==void 0?r:!1,contextualErrorMap:n==null?void 0:n.errorMap},path:(n==null?void 0:n.path)||[],schemaErrorMap:this._def.errorMap,parent:null,data:t,parsedType:ms(t)},o=this._parseSync({data:t,path:i.path,parent:i});return q4(i,o)}async parseAsync(t,n){const r=await this.safeParseAsync(t,n);if(r.success)return r.data;throw r.error}async safeParseAsync(t,n){const r={common:{issues:[],contextualErrorMap:n==null?void 0:n.errorMap,async:!0},path:(n==null?void 0:n.path)||[],schemaErrorMap:this._def.errorMap,parent:null,data:t,parsedType:ms(t)},i=this._parse({data:t,path:r.path,parent:r}),o=await(ym(i)?i:Promise.resolve(i));return q4(r,o)}refine(t,n){const r=i=>typeof n=="string"||typeof n>"u"?{message:n}:typeof n=="function"?n(i):n;return this._refinement((i,o)=>{const s=t(i),a=()=>o.addIssue({code:ee.custom,...r(i)});return typeof Promise<"u"&&s instanceof Promise?s.then(l=>l?!0:(a(),!1)):s?!0:(a(),!1)})}refinement(t,n){return this._refinement((r,i)=>t(r)?!0:(i.addIssue(typeof n=="function"?n(r,i):n),!1))}_refinement(t){return new Ti({schema:this,typeName:ye.ZodEffects,effect:{type:"refinement",refinement:t}})}superRefine(t){return this._refinement(t)}optional(){return Ro.create(this,this._def)}nullable(){return el.create(this,this._def)}nullish(){return this.nullable().optional()}array(){return Si.create(this,this._def)}promise(){return Ju.create(this,this._def)}or(t){return mf.create([this,t],this._def)}and(t){return yf.create(this,t,this._def)}transform(t){return new Ti({...we(this._def),schema:this,typeName:ye.ZodEffects,effect:{type:"transform",transform:t}})}default(t){const n=typeof t=="function"?t:()=>t;return new wf({...we(this._def),innerType:this,defaultValue:n,typeName:ye.ZodDefault})}brand(){return new SO({typeName:ye.ZodBranded,type:this,...we(this._def)})}catch(t){const n=typeof t=="function"?t:()=>t;return new _m({...we(this._def),innerType:this,catchValue:n,typeName:ye.ZodCatch})}describe(t){const n=this.constructor;return new n({...this._def,description:t})}pipe(t){return mh.create(this,t)}isOptional(){return this.safeParse(void 0).success}isNullable(){return this.safeParse(null).success}}const dY=/^c[^\s-]{8,}$/i,fY=/^[a-z][a-z0-9]*$/,hY=/[0-9A-HJKMNP-TV-Z]{26}/,pY=/^([a-f0-9]{8}-[a-f0-9]{4}-[1-5][a-f0-9]{3}-[a-f0-9]{4}-[a-f0-9]{12}|00000000-0000-0000-0000-000000000000)$/i,gY=/^(([^<>()[\]\\.,;:\s@\"]+(\.[^<>()[\]\\.,;:\s@\"]+)*)|(\".+\"))@((\[(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\])|(\[IPv6:(([a-f0-9]{1,4}:){7}|::([a-f0-9]{1,4}:){0,6}|([a-f0-9]{1,4}:){1}:([a-f0-9]{1,4}:){0,5}|([a-f0-9]{1,4}:){2}:([a-f0-9]{1,4}:){0,4}|([a-f0-9]{1,4}:){3}:([a-f0-9]{1,4}:){0,3}|([a-f0-9]{1,4}:){4}:([a-f0-9]{1,4}:){0,2}|([a-f0-9]{1,4}:){5}:([a-f0-9]{1,4}:){0,1})([a-f0-9]{1,4}|(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2})))\])|([A-Za-z0-9]([A-Za-z0-9-]*[A-Za-z0-9])*(\.[A-Za-z]{2,})+))$/,mY=/^(\p{Extended_Pictographic}|\p{Emoji_Component})+$/u,yY=/^(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))$/,vY=/^(([a-f0-9]{1,4}:){7}|::([a-f0-9]{1,4}:){0,6}|([a-f0-9]{1,4}:){1}:([a-f0-9]{1,4}:){0,5}|([a-f0-9]{1,4}:){2}:([a-f0-9]{1,4}:){0,4}|([a-f0-9]{1,4}:){3}:([a-f0-9]{1,4}:){0,3}|([a-f0-9]{1,4}:){4}:([a-f0-9]{1,4}:){0,2}|([a-f0-9]{1,4}:){5}:([a-f0-9]{1,4}:){0,1})([a-f0-9]{1,4}|(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2})))$/,bY=e=>e.precision?e.offset?new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}(([+-]\\d{2}(:?\\d{2})?)|Z)$`):new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}Z$`):e.precision===0?e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"):e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?Z$");function SY(e,t){return!!((t==="v4"||!t)&&yY.test(e)||(t==="v6"||!t)&&vY.test(e))}class pi extends Ce{constructor(){super(...arguments),this._regex=(t,n,r)=>this.refinement(i=>t.test(i),{validation:n,code:ee.invalid_string,...ge.errToObj(r)}),this.nonempty=t=>this.min(1,ge.errToObj(t)),this.trim=()=>new pi({...this._def,checks:[...this._def.checks,{kind:"trim"}]}),this.toLowerCase=()=>new pi({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]}),this.toUpperCase=()=>new pi({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}_parse(t){if(this._def.coerce&&(t.data=String(t.data)),this._getType(t)!==re.string){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.string,received:o.parsedType}),be}const r=new jn;let i;for(const o of this._def.checks)if(o.kind==="min")t.data.length<o.value&&(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.too_small,minimum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="max")t.data.length>o.value&&(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="length"){const s=t.data.length>o.value,a=t.data.length<o.value;(s||a)&&(i=this._getOrReturnCtx(t,i),s?oe(i,{code:ee.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!0,message:o.message}):a&&oe(i,{code:ee.too_small,minimum:o.value,type:"string",inclusive:!0,exact:!0,message:o.message}),r.dirty())}else if(o.kind==="email")gY.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"email",code:ee.invalid_string,message:o.message}),r.dirty());else if(o.kind==="emoji")mY.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"emoji",code:ee.invalid_string,message:o.message}),r.dirty());else if(o.kind==="uuid")pY.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"uuid",code:ee.invalid_string,message:o.message}),r.dirty());else if(o.kind==="cuid")dY.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"cuid",code:ee.invalid_string,message:o.message}),r.dirty());else if(o.kind==="cuid2")fY.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"cuid2",code:ee.invalid_string,message:o.message}),r.dirty());else if(o.kind==="ulid")hY.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"ulid",code:ee.invalid_string,message:o.message}),r.dirty());else if(o.kind==="url")try{new URL(t.data)}catch{i=this._getOrReturnCtx(t,i),oe(i,{validation:"url",code:ee.invalid_string,message:o.message}),r.dirty()}else o.kind==="regex"?(o.regex.lastIndex=0,o.regex.test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"regex",code:ee.invalid_string,message:o.message}),r.dirty())):o.kind==="trim"?t.data=t.data.trim():o.kind==="includes"?t.data.includes(o.value,o.position)||(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.invalid_string,validation:{includes:o.value,position:o.position},message:o.message}),r.dirty()):o.kind==="toLowerCase"?t.data=t.data.toLowerCase():o.kind==="toUpperCase"?t.data=t.data.toUpperCase():o.kind==="startsWith"?t.data.startsWith(o.value)||(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.invalid_string,validation:{startsWith:o.value},message:o.message}),r.dirty()):o.kind==="endsWith"?t.data.endsWith(o.value)||(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.invalid_string,validation:{endsWith:o.value},message:o.message}),r.dirty()):o.kind==="datetime"?bY(o).test(t.data)||(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.invalid_string,validation:"datetime",message:o.message}),r.dirty()):o.kind==="ip"?SY(t.data,o.version)||(i=this._getOrReturnCtx(t,i),oe(i,{validation:"ip",code:ee.invalid_string,message:o.message}),r.dirty()):Ve.assertNever(o);return{status:r.value,value:t.data}}_addCheck(t){return new pi({...this._def,checks:[...this._def.checks,t]})}email(t){return this._addCheck({kind:"email",...ge.errToObj(t)})}url(t){return this._addCheck({kind:"url",...ge.errToObj(t)})}emoji(t){return this._addCheck({kind:"emoji",...ge.errToObj(t)})}uuid(t){return this._addCheck({kind:"uuid",...ge.errToObj(t)})}cuid(t){return this._addCheck({kind:"cuid",...ge.errToObj(t)})}cuid2(t){return this._addCheck({kind:"cuid2",...ge.errToObj(t)})}ulid(t){return this._addCheck({kind:"ulid",...ge.errToObj(t)})}ip(t){return this._addCheck({kind:"ip",...ge.errToObj(t)})}datetime(t){var n;return typeof t=="string"?this._addCheck({kind:"datetime",precision:null,offset:!1,message:t}):this._addCheck({kind:"datetime",precision:typeof(t==null?void 0:t.precision)>"u"?null:t==null?void 0:t.precision,offset:(n=t==null?void 0:t.offset)!==null&&n!==void 0?n:!1,...ge.errToObj(t==null?void 0:t.message)})}regex(t,n){return this._addCheck({kind:"regex",regex:t,...ge.errToObj(n)})}includes(t,n){return this._addCheck({kind:"includes",value:t,position:n==null?void 0:n.position,...ge.errToObj(n==null?void 0:n.message)})}startsWith(t,n){return this._addCheck({kind:"startsWith",value:t,...ge.errToObj(n)})}endsWith(t,n){return this._addCheck({kind:"endsWith",value:t,...ge.errToObj(n)})}min(t,n){return this._addCheck({kind:"min",value:t,...ge.errToObj(n)})}max(t,n){return this._addCheck({kind:"max",value:t,...ge.errToObj(n)})}length(t,n){return this._addCheck({kind:"length",value:t,...ge.errToObj(n)})}get isDatetime(){return!!this._def.checks.find(t=>t.kind==="datetime")}get isEmail(){return!!this._def.checks.find(t=>t.kind==="email")}get isURL(){return!!this._def.checks.find(t=>t.kind==="url")}get isEmoji(){return!!this._def.checks.find(t=>t.kind==="emoji")}get isUUID(){return!!this._def.checks.find(t=>t.kind==="uuid")}get isCUID(){return!!this._def.checks.find(t=>t.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(t=>t.kind==="cuid2")}get isULID(){return!!this._def.checks.find(t=>t.kind==="ulid")}get isIP(){return!!this._def.checks.find(t=>t.kind==="ip")}get minLength(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxLength(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}}pi.create=e=>{var t;return new pi({checks:[],typeName:ye.ZodString,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...we(e)})};function _Y(e,t){const n=(e.toString().split(".")[1]||"").length,r=(t.toString().split(".")[1]||"").length,i=n>r?n:r,o=parseInt(e.toFixed(i).replace(".","")),s=parseInt(t.toFixed(i).replace(".",""));return o%s/Math.pow(10,i)}class zs extends Ce{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(t){if(this._def.coerce&&(t.data=Number(t.data)),this._getType(t)!==re.number){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.number,received:o.parsedType}),be}let r;const i=new jn;for(const o of this._def.checks)o.kind==="int"?Ve.isInteger(t.data)||(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.invalid_type,expected:"integer",received:"float",message:o.message}),i.dirty()):o.kind==="min"?(o.inclusive?t.data<o.value:t.data<=o.value)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.too_small,minimum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="max"?(o.inclusive?t.data>o.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.too_big,maximum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="multipleOf"?_Y(t.data,o.value)!==0&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):o.kind==="finite"?Number.isFinite(t.data)||(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.not_finite,message:o.message}),i.dirty()):Ve.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,ge.toString(n))}gt(t,n){return this.setLimit("min",t,!1,ge.toString(n))}lte(t,n){return this.setLimit("max",t,!0,ge.toString(n))}lt(t,n){return this.setLimit("max",t,!1,ge.toString(n))}setLimit(t,n,r,i){return new zs({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:ge.toString(i)}]})}_addCheck(t){return new zs({...this._def,checks:[...this._def.checks,t]})}int(t){return this._addCheck({kind:"int",message:ge.toString(t)})}positive(t){return this._addCheck({kind:"min",value:0,inclusive:!1,message:ge.toString(t)})}negative(t){return this._addCheck({kind:"max",value:0,inclusive:!1,message:ge.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:0,inclusive:!0,message:ge.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:0,inclusive:!0,message:ge.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:ge.toString(n)})}finite(t){return this._addCheck({kind:"finite",message:ge.toString(t)})}safe(t){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:ge.toString(t)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:ge.toString(t)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}get isInt(){return!!this._def.checks.find(t=>t.kind==="int"||t.kind==="multipleOf"&&Ve.isInteger(t.value))}get isFinite(){let t=null,n=null;for(const r of this._def.checks){if(r.kind==="finite"||r.kind==="int"||r.kind==="multipleOf")return!0;r.kind==="min"?(n===null||r.value>n)&&(n=r.value):r.kind==="max"&&(t===null||r.value<t)&&(t=r.value)}return Number.isFinite(n)&&Number.isFinite(t)}}zs.create=e=>new zs({checks:[],typeName:ye.ZodNumber,coerce:(e==null?void 0:e.coerce)||!1,...we(e)});class Us extends Ce{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(t){if(this._def.coerce&&(t.data=BigInt(t.data)),this._getType(t)!==re.bigint){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.bigint,received:o.parsedType}),be}let r;const i=new jn;for(const o of this._def.checks)o.kind==="min"?(o.inclusive?t.data<o.value:t.data<=o.value)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.too_small,type:"bigint",minimum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="max"?(o.inclusive?t.data>o.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.too_big,type:"bigint",maximum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="multipleOf"?t.data%o.value!==BigInt(0)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):Ve.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,ge.toString(n))}gt(t,n){return this.setLimit("min",t,!1,ge.toString(n))}lte(t,n){return this.setLimit("max",t,!0,ge.toString(n))}lt(t,n){return this.setLimit("max",t,!1,ge.toString(n))}setLimit(t,n,r,i){return new Us({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:ge.toString(i)}]})}_addCheck(t){return new Us({...this._def,checks:[...this._def.checks,t]})}positive(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:ge.toString(t)})}negative(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:ge.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:ge.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:ge.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:ge.toString(n)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}}Us.create=e=>{var t;return new Us({checks:[],typeName:ye.ZodBigInt,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...we(e)})};class hf extends Ce{_parse(t){if(this._def.coerce&&(t.data=!!t.data),this._getType(t)!==re.boolean){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.boolean,received:r.parsedType}),be}return tr(t.data)}}hf.create=e=>new hf({typeName:ye.ZodBoolean,coerce:(e==null?void 0:e.coerce)||!1,...we(e)});class Za extends Ce{_parse(t){if(this._def.coerce&&(t.data=new Date(t.data)),this._getType(t)!==re.date){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.date,received:o.parsedType}),be}if(isNaN(t.data.getTime())){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_date}),be}const r=new jn;let i;for(const o of this._def.checks)o.kind==="min"?t.data.getTime()<o.value&&(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.too_small,message:o.message,inclusive:!0,exact:!1,minimum:o.value,type:"date"}),r.dirty()):o.kind==="max"?t.data.getTime()>o.value&&(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.too_big,message:o.message,inclusive:!0,exact:!1,maximum:o.value,type:"date"}),r.dirty()):Ve.assertNever(o);return{status:r.value,value:new Date(t.data.getTime())}}_addCheck(t){return new Za({...this._def,checks:[...this._def.checks,t]})}min(t,n){return this._addCheck({kind:"min",value:t.getTime(),message:ge.toString(n)})}max(t,n){return this._addCheck({kind:"max",value:t.getTime(),message:ge.toString(n)})}get minDate(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t!=null?new Date(t):null}get maxDate(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t!=null?new Date(t):null}}Za.create=e=>new Za({checks:[],coerce:(e==null?void 0:e.coerce)||!1,typeName:ye.ZodDate,...we(e)});class vm extends Ce{_parse(t){if(this._getType(t)!==re.symbol){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.symbol,received:r.parsedType}),be}return tr(t.data)}}vm.create=e=>new vm({typeName:ye.ZodSymbol,...we(e)});class pf extends Ce{_parse(t){if(this._getType(t)!==re.undefined){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.undefined,received:r.parsedType}),be}return tr(t.data)}}pf.create=e=>new pf({typeName:ye.ZodUndefined,...we(e)});class gf extends Ce{_parse(t){if(this._getType(t)!==re.null){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.null,received:r.parsedType}),be}return tr(t.data)}}gf.create=e=>new gf({typeName:ye.ZodNull,...we(e)});class Zu extends Ce{constructor(){super(...arguments),this._any=!0}_parse(t){return tr(t.data)}}Zu.create=e=>new Zu({typeName:ye.ZodAny,...we(e)});class Ba extends Ce{constructor(){super(...arguments),this._unknown=!0}_parse(t){return tr(t.data)}}Ba.create=e=>new Ba({typeName:ye.ZodUnknown,...we(e)});class Vo extends Ce{_parse(t){const n=this._getOrReturnCtx(t);return oe(n,{code:ee.invalid_type,expected:re.never,received:n.parsedType}),be}}Vo.create=e=>new Vo({typeName:ye.ZodNever,...we(e)});class bm extends Ce{_parse(t){if(this._getType(t)!==re.undefined){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.void,received:r.parsedType}),be}return tr(t.data)}}bm.create=e=>new bm({typeName:ye.ZodVoid,...we(e)});class Si extends Ce{_parse(t){const{ctx:n,status:r}=this._processInputParams(t),i=this._def;if(n.parsedType!==re.array)return oe(n,{code:ee.invalid_type,expected:re.array,received:n.parsedType}),be;if(i.exactLength!==null){const s=n.data.length>i.exactLength.value,a=n.data.length<i.exactLength.value;(s||a)&&(oe(n,{code:s?ee.too_big:ee.too_small,minimum:a?i.exactLength.value:void 0,maximum:s?i.exactLength.value:void 0,type:"array",inclusive:!0,exact:!0,message:i.exactLength.message}),r.dirty())}if(i.minLength!==null&&n.data.length<i.minLength.value&&(oe(n,{code:ee.too_small,minimum:i.minLength.value,type:"array",inclusive:!0,exact:!1,message:i.minLength.message}),r.dirty()),i.maxLength!==null&&n.data.length>i.maxLength.value&&(oe(n,{code:ee.too_big,maximum:i.maxLength.value,type:"array",inclusive:!0,exact:!1,message:i.maxLength.message}),r.dirty()),n.common.async)return Promise.all([...n.data].map((s,a)=>i.type._parseAsync(new eo(n,s,n.path,a)))).then(s=>jn.mergeArray(r,s));const o=[...n.data].map((s,a)=>i.type._parseSync(new eo(n,s,n.path,a)));return jn.mergeArray(r,o)}get element(){return this._def.type}min(t,n){return new Si({...this._def,minLength:{value:t,message:ge.toString(n)}})}max(t,n){return new Si({...this._def,maxLength:{value:t,message:ge.toString(n)}})}length(t,n){return new Si({...this._def,exactLength:{value:t,message:ge.toString(n)}})}nonempty(t){return this.min(1,t)}}Si.create=(e,t)=>new Si({type:e,minLength:null,maxLength:null,exactLength:null,typeName:ye.ZodArray,...we(t)});function Wl(e){if(e instanceof Tt){const t={};for(const n in e.shape){const r=e.shape[n];t[n]=Ro.create(Wl(r))}return new Tt({...e._def,shape:()=>t})}else return e instanceof Si?new Si({...e._def,type:Wl(e.element)}):e instanceof Ro?Ro.create(Wl(e.unwrap())):e instanceof el?el.create(Wl(e.unwrap())):e instanceof to?to.create(e.items.map(t=>Wl(t))):e}class Tt extends Ce{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const t=this._def.shape(),n=Ve.objectKeys(t);return this._cached={shape:t,keys:n}}_parse(t){if(this._getType(t)!==re.object){const u=this._getOrReturnCtx(t);return oe(u,{code:ee.invalid_type,expected:re.object,received:u.parsedType}),be}const{status:r,ctx:i}=this._processInputParams(t),{shape:o,keys:s}=this._getCached(),a=[];if(!(this._def.catchall instanceof Vo&&this._def.unknownKeys==="strip"))for(const u in i.data)s.includes(u)||a.push(u);const l=[];for(const u of s){const c=o[u],d=i.data[u];l.push({key:{status:"valid",value:u},value:c._parse(new eo(i,d,i.path,u)),alwaysSet:u in i.data})}if(this._def.catchall instanceof Vo){const u=this._def.unknownKeys;if(u==="passthrough")for(const c of a)l.push({key:{status:"valid",value:c},value:{status:"valid",value:i.data[c]}});else if(u==="strict")a.length>0&&(oe(i,{code:ee.unrecognized_keys,keys:a}),r.dirty());else if(u!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const u=this._def.catchall;for(const c of a){const d=i.data[c];l.push({key:{status:"valid",value:c},value:u._parse(new eo(i,d,i.path,c)),alwaysSet:c in i.data})}}return i.common.async?Promise.resolve().then(async()=>{const u=[];for(const c of l){const d=await c.key;u.push({key:d,value:await c.value,alwaysSet:c.alwaysSet})}return u}).then(u=>jn.mergeObjectSync(r,u)):jn.mergeObjectSync(r,l)}get shape(){return this._def.shape()}strict(t){return ge.errToObj,new Tt({...this._def,unknownKeys:"strict",...t!==void 0?{errorMap:(n,r)=>{var i,o,s,a;const l=(s=(o=(i=this._def).errorMap)===null||o===void 0?void 0:o.call(i,n,r).message)!==null&&s!==void 0?s:r.defaultError;return n.code==="unrecognized_keys"?{message:(a=ge.errToObj(t).message)!==null&&a!==void 0?a:l}:{message:l}}}:{}})}strip(){return new Tt({...this._def,unknownKeys:"strip"})}passthrough(){return new Tt({...this._def,unknownKeys:"passthrough"})}extend(t){return new Tt({...this._def,shape:()=>({...this._def.shape(),...t})})}merge(t){return new Tt({unknownKeys:t._def.unknownKeys,catchall:t._def.catchall,shape:()=>({...this._def.shape(),...t._def.shape()}),typeName:ye.ZodObject})}setKey(t,n){return this.augment({[t]:n})}catchall(t){return new Tt({...this._def,catchall:t})}pick(t){const n={};return Ve.objectKeys(t).forEach(r=>{t[r]&&this.shape[r]&&(n[r]=this.shape[r])}),new Tt({...this._def,shape:()=>n})}omit(t){const n={};return Ve.objectKeys(this.shape).forEach(r=>{t[r]||(n[r]=this.shape[r])}),new Tt({...this._def,shape:()=>n})}deepPartial(){return Wl(this)}partial(t){const n={};return Ve.objectKeys(this.shape).forEach(r=>{const i=this.shape[r];t&&!t[r]?n[r]=i:n[r]=i.optional()}),new Tt({...this._def,shape:()=>n})}required(t){const n={};return Ve.objectKeys(this.shape).forEach(r=>{if(t&&!t[r])n[r]=this.shape[r];else{let o=this.shape[r];for(;o instanceof Ro;)o=o._def.innerType;n[r]=o}}),new Tt({...this._def,shape:()=>n})}keyof(){return bO(Ve.objectKeys(this.shape))}}Tt.create=(e,t)=>new Tt({shape:()=>e,unknownKeys:"strip",catchall:Vo.create(),typeName:ye.ZodObject,...we(t)});Tt.strictCreate=(e,t)=>new Tt({shape:()=>e,unknownKeys:"strict",catchall:Vo.create(),typeName:ye.ZodObject,...we(t)});Tt.lazycreate=(e,t)=>new Tt({shape:e,unknownKeys:"strip",catchall:Vo.create(),typeName:ye.ZodObject,...we(t)});class mf extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t),r=this._def.options;function i(o){for(const a of o)if(a.result.status==="valid")return a.result;for(const a of o)if(a.result.status==="dirty")return n.common.issues.push(...a.ctx.common.issues),a.result;const s=o.map(a=>new bi(a.ctx.common.issues));return oe(n,{code:ee.invalid_union,unionErrors:s}),be}if(n.common.async)return Promise.all(r.map(async o=>{const s={...n,common:{...n.common,issues:[]},parent:null};return{result:await o._parseAsync({data:n.data,path:n.path,parent:s}),ctx:s}})).then(i);{let o;const s=[];for(const l of r){const u={...n,common:{...n.common,issues:[]},parent:null},c=l._parseSync({data:n.data,path:n.path,parent:u});if(c.status==="valid")return c;c.status==="dirty"&&!o&&(o={result:c,ctx:u}),u.common.issues.length&&s.push(u.common.issues)}if(o)return n.common.issues.push(...o.ctx.common.issues),o.result;const a=s.map(l=>new bi(l));return oe(n,{code:ee.invalid_union,unionErrors:a}),be}}get options(){return this._def.options}}mf.create=(e,t)=>new mf({options:e,typeName:ye.ZodUnion,...we(t)});const dg=e=>e instanceof bf?dg(e.schema):e instanceof Ti?dg(e.innerType()):e instanceof Sf?[e.value]:e instanceof Gs?e.options:e instanceof _f?Object.keys(e.enum):e instanceof wf?dg(e._def.innerType):e instanceof pf?[void 0]:e instanceof gf?[null]:null;class w0 extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==re.object)return oe(n,{code:ee.invalid_type,expected:re.object,received:n.parsedType}),be;const r=this.discriminator,i=n.data[r],o=this.optionsMap.get(i);return o?n.common.async?o._parseAsync({data:n.data,path:n.path,parent:n}):o._parseSync({data:n.data,path:n.path,parent:n}):(oe(n,{code:ee.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[r]}),be)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(t,n,r){const i=new Map;for(const o of n){const s=dg(o.shape[t]);if(!s)throw new Error(`A discriminator value for key \`${t}\` could not be extracted from all schema options`);for(const a of s){if(i.has(a))throw new Error(`Discriminator property ${String(t)} has duplicate value ${String(a)}`);i.set(a,o)}}return new w0({typeName:ye.ZodDiscriminatedUnion,discriminator:t,options:n,optionsMap:i,...we(r)})}}function D_(e,t){const n=ms(e),r=ms(t);if(e===t)return{valid:!0,data:e};if(n===re.object&&r===re.object){const i=Ve.objectKeys(t),o=Ve.objectKeys(e).filter(a=>i.indexOf(a)!==-1),s={...e,...t};for(const a of o){const l=D_(e[a],t[a]);if(!l.valid)return{valid:!1};s[a]=l.data}return{valid:!0,data:s}}else if(n===re.array&&r===re.array){if(e.length!==t.length)return{valid:!1};const i=[];for(let o=0;o<e.length;o++){const s=e[o],a=t[o],l=D_(s,a);if(!l.valid)return{valid:!1};i.push(l.data)}return{valid:!0,data:i}}else return n===re.date&&r===re.date&&+e==+t?{valid:!0,data:e}:{valid:!1}}class yf extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=(o,s)=>{if(I_(o)||I_(s))return be;const a=D_(o.value,s.value);return a.valid?((N_(o)||N_(s))&&n.dirty(),{status:n.value,value:a.data}):(oe(r,{code:ee.invalid_intersection_types}),be)};return r.common.async?Promise.all([this._def.left._parseAsync({data:r.data,path:r.path,parent:r}),this._def.right._parseAsync({data:r.data,path:r.path,parent:r})]).then(([o,s])=>i(o,s)):i(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}yf.create=(e,t,n)=>new yf({left:e,right:t,typeName:ye.ZodIntersection,...we(n)});class to extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.array)return oe(r,{code:ee.invalid_type,expected:re.array,received:r.parsedType}),be;if(r.data.length<this._def.items.length)return oe(r,{code:ee.too_small,minimum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),be;!this._def.rest&&r.data.length>this._def.items.length&&(oe(r,{code:ee.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),n.dirty());const o=[...r.data].map((s,a)=>{const l=this._def.items[a]||this._def.rest;return l?l._parse(new eo(r,s,r.path,a)):null}).filter(s=>!!s);return r.common.async?Promise.all(o).then(s=>jn.mergeArray(n,s)):jn.mergeArray(n,o)}get items(){return this._def.items}rest(t){return new to({...this._def,rest:t})}}to.create=(e,t)=>{if(!Array.isArray(e))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new to({items:e,typeName:ye.ZodTuple,rest:null,...we(t)})};class vf extends Ce{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.object)return oe(r,{code:ee.invalid_type,expected:re.object,received:r.parsedType}),be;const i=[],o=this._def.keyType,s=this._def.valueType;for(const a in r.data)i.push({key:o._parse(new eo(r,a,r.path,a)),value:s._parse(new eo(r,r.data[a],r.path,a))});return r.common.async?jn.mergeObjectAsync(n,i):jn.mergeObjectSync(n,i)}get element(){return this._def.valueType}static create(t,n,r){return n instanceof Ce?new vf({keyType:t,valueType:n,typeName:ye.ZodRecord,...we(r)}):new vf({keyType:pi.create(),valueType:t,typeName:ye.ZodRecord,...we(n)})}}class Sm extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.map)return oe(r,{code:ee.invalid_type,expected:re.map,received:r.parsedType}),be;const i=this._def.keyType,o=this._def.valueType,s=[...r.data.entries()].map(([a,l],u)=>({key:i._parse(new eo(r,a,r.path,[u,"key"])),value:o._parse(new eo(r,l,r.path,[u,"value"]))}));if(r.common.async){const a=new Map;return Promise.resolve().then(async()=>{for(const l of s){const u=await l.key,c=await l.value;if(u.status==="aborted"||c.status==="aborted")return be;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}})}else{const a=new Map;for(const l of s){const u=l.key,c=l.value;if(u.status==="aborted"||c.status==="aborted")return be;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}}}}Sm.create=(e,t,n)=>new Sm({valueType:t,keyType:e,typeName:ye.ZodMap,...we(n)});class Ja extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.set)return oe(r,{code:ee.invalid_type,expected:re.set,received:r.parsedType}),be;const i=this._def;i.minSize!==null&&r.data.size<i.minSize.value&&(oe(r,{code:ee.too_small,minimum:i.minSize.value,type:"set",inclusive:!0,exact:!1,message:i.minSize.message}),n.dirty()),i.maxSize!==null&&r.data.size>i.maxSize.value&&(oe(r,{code:ee.too_big,maximum:i.maxSize.value,type:"set",inclusive:!0,exact:!1,message:i.maxSize.message}),n.dirty());const o=this._def.valueType;function s(l){const u=new Set;for(const c of l){if(c.status==="aborted")return be;c.status==="dirty"&&n.dirty(),u.add(c.value)}return{status:n.value,value:u}}const a=[...r.data.values()].map((l,u)=>o._parse(new eo(r,l,r.path,u)));return r.common.async?Promise.all(a).then(l=>s(l)):s(a)}min(t,n){return new Ja({...this._def,minSize:{value:t,message:ge.toString(n)}})}max(t,n){return new Ja({...this._def,maxSize:{value:t,message:ge.toString(n)}})}size(t,n){return this.min(t,n).max(t,n)}nonempty(t){return this.min(1,t)}}Ja.create=(e,t)=>new Ja({valueType:e,minSize:null,maxSize:null,typeName:ye.ZodSet,...we(t)});class ku extends Ce{constructor(){super(...arguments),this.validate=this.implement}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==re.function)return oe(n,{code:ee.invalid_type,expected:re.function,received:n.parsedType}),be;function r(a,l){return gm({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,pm(),ff].filter(u=>!!u),issueData:{code:ee.invalid_arguments,argumentsError:l}})}function i(a,l){return gm({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,pm(),ff].filter(u=>!!u),issueData:{code:ee.invalid_return_type,returnTypeError:l}})}const o={errorMap:n.common.contextualErrorMap},s=n.data;return this._def.returns instanceof Ju?tr(async(...a)=>{const l=new bi([]),u=await this._def.args.parseAsync(a,o).catch(f=>{throw l.addIssue(r(a,f)),l}),c=await s(...u);return await this._def.returns._def.type.parseAsync(c,o).catch(f=>{throw l.addIssue(i(c,f)),l})}):tr((...a)=>{const l=this._def.args.safeParse(a,o);if(!l.success)throw new bi([r(a,l.error)]);const u=s(...l.data),c=this._def.returns.safeParse(u,o);if(!c.success)throw new bi([i(u,c.error)]);return c.data})}parameters(){return this._def.args}returnType(){return this._def.returns}args(...t){return new ku({...this._def,args:to.create(t).rest(Ba.create())})}returns(t){return new ku({...this._def,returns:t})}implement(t){return this.parse(t)}strictImplement(t){return this.parse(t)}static create(t,n,r){return new ku({args:t||to.create([]).rest(Ba.create()),returns:n||Ba.create(),typeName:ye.ZodFunction,...we(r)})}}class bf extends Ce{get schema(){return this._def.getter()}_parse(t){const{ctx:n}=this._processInputParams(t);return this._def.getter()._parse({data:n.data,path:n.path,parent:n})}}bf.create=(e,t)=>new bf({getter:e,typeName:ye.ZodLazy,...we(t)});class Sf extends Ce{_parse(t){if(t.data!==this._def.value){const n=this._getOrReturnCtx(t);return oe(n,{received:n.data,code:ee.invalid_literal,expected:this._def.value}),be}return{status:"valid",value:t.data}}get value(){return this._def.value}}Sf.create=(e,t)=>new Sf({value:e,typeName:ye.ZodLiteral,...we(t)});function bO(e,t){return new Gs({values:e,typeName:ye.ZodEnum,...we(t)})}class Gs extends Ce{_parse(t){if(typeof t.data!="string"){const n=this._getOrReturnCtx(t),r=this._def.values;return oe(n,{expected:Ve.joinValues(r),received:n.parsedType,code:ee.invalid_type}),be}if(this._def.values.indexOf(t.data)===-1){const n=this._getOrReturnCtx(t),r=this._def.values;return oe(n,{received:n.data,code:ee.invalid_enum_value,options:r}),be}return tr(t.data)}get options(){return this._def.values}get enum(){const t={};for(const n of this._def.values)t[n]=n;return t}get Values(){const t={};for(const n of this._def.values)t[n]=n;return t}get Enum(){const t={};for(const n of this._def.values)t[n]=n;return t}extract(t){return Gs.create(t)}exclude(t){return Gs.create(this.options.filter(n=>!t.includes(n)))}}Gs.create=bO;class _f extends Ce{_parse(t){const n=Ve.getValidEnumValues(this._def.values),r=this._getOrReturnCtx(t);if(r.parsedType!==re.string&&r.parsedType!==re.number){const i=Ve.objectValues(n);return oe(r,{expected:Ve.joinValues(i),received:r.parsedType,code:ee.invalid_type}),be}if(n.indexOf(t.data)===-1){const i=Ve.objectValues(n);return oe(r,{received:r.data,code:ee.invalid_enum_value,options:i}),be}return tr(t.data)}get enum(){return this._def.values}}_f.create=(e,t)=>new _f({values:e,typeName:ye.ZodNativeEnum,...we(t)});class Ju extends Ce{unwrap(){return this._def.type}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==re.promise&&n.common.async===!1)return oe(n,{code:ee.invalid_type,expected:re.promise,received:n.parsedType}),be;const r=n.parsedType===re.promise?n.data:Promise.resolve(n.data);return tr(r.then(i=>this._def.type.parseAsync(i,{path:n.path,errorMap:n.common.contextualErrorMap})))}}Ju.create=(e,t)=>new Ju({type:e,typeName:ye.ZodPromise,...we(t)});class Ti extends Ce{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===ye.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=this._def.effect||null;if(i.type==="preprocess"){const s=i.transform(r.data);return r.common.async?Promise.resolve(s).then(a=>this._def.schema._parseAsync({data:a,path:r.path,parent:r})):this._def.schema._parseSync({data:s,path:r.path,parent:r})}const o={addIssue:s=>{oe(r,s),s.fatal?n.abort():n.dirty()},get path(){return r.path}};if(o.addIssue=o.addIssue.bind(o),i.type==="refinement"){const s=a=>{const l=i.refinement(a,o);if(r.common.async)return Promise.resolve(l);if(l instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return a};if(r.common.async===!1){const a=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});return a.status==="aborted"?be:(a.status==="dirty"&&n.dirty(),s(a.value),{status:n.value,value:a.value})}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(a=>a.status==="aborted"?be:(a.status==="dirty"&&n.dirty(),s(a.value).then(()=>({status:n.value,value:a.value}))))}if(i.type==="transform")if(r.common.async===!1){const s=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});if(!mm(s))return s;const a=i.transform(s.value,o);if(a instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:n.value,value:a}}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(s=>mm(s)?Promise.resolve(i.transform(s.value,o)).then(a=>({status:n.value,value:a})):s);Ve.assertNever(i)}}Ti.create=(e,t,n)=>new Ti({schema:e,typeName:ye.ZodEffects,effect:t,...we(n)});Ti.createWithPreprocess=(e,t,n)=>new Ti({schema:t,effect:{type:"preprocess",transform:e},typeName:ye.ZodEffects,...we(n)});class Ro extends Ce{_parse(t){return this._getType(t)===re.undefined?tr(void 0):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}Ro.create=(e,t)=>new Ro({innerType:e,typeName:ye.ZodOptional,...we(t)});class el extends Ce{_parse(t){return this._getType(t)===re.null?tr(null):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}el.create=(e,t)=>new el({innerType:e,typeName:ye.ZodNullable,...we(t)});class wf extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t);let r=n.data;return n.parsedType===re.undefined&&(r=this._def.defaultValue()),this._def.innerType._parse({data:r,path:n.path,parent:n})}removeDefault(){return this._def.innerType}}wf.create=(e,t)=>new wf({innerType:e,typeName:ye.ZodDefault,defaultValue:typeof t.default=="function"?t.default:()=>t.default,...we(t)});class _m extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t),r={...n,common:{...n.common,issues:[]}},i=this._def.innerType._parse({data:r.data,path:r.path,parent:{...r}});return ym(i)?i.then(o=>({status:"valid",value:o.status==="valid"?o.value:this._def.catchValue({get error(){return new bi(r.common.issues)},input:r.data})})):{status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new bi(r.common.issues)},input:r.data})}}removeCatch(){return this._def.innerType}}_m.create=(e,t)=>new _m({innerType:e,typeName:ye.ZodCatch,catchValue:typeof t.catch=="function"?t.catch:()=>t.catch,...we(t)});class wm extends Ce{_parse(t){if(this._getType(t)!==re.nan){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.nan,received:r.parsedType}),be}return{status:"valid",value:t.data}}}wm.create=e=>new wm({typeName:ye.ZodNaN,...we(e)});const wY=Symbol("zod_brand");class SO extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t),r=n.data;return this._def.type._parse({data:r,path:n.path,parent:n})}unwrap(){return this._def.type}}class mh extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.common.async)return(async()=>{const o=await this._def.in._parseAsync({data:r.data,path:r.path,parent:r});return o.status==="aborted"?be:o.status==="dirty"?(n.dirty(),vO(o.value)):this._def.out._parseAsync({data:o.value,path:r.path,parent:r})})();{const i=this._def.in._parseSync({data:r.data,path:r.path,parent:r});return i.status==="aborted"?be:i.status==="dirty"?(n.dirty(),{status:"dirty",value:i.value}):this._def.out._parseSync({data:i.value,path:r.path,parent:r})}}static create(t,n){return new mh({in:t,out:n,typeName:ye.ZodPipeline})}}const _O=(e,t={},n)=>e?Zu.create().superRefine((r,i)=>{var o,s;if(!e(r)){const a=typeof t=="function"?t(r):typeof t=="string"?{message:t}:t,l=(s=(o=a.fatal)!==null&&o!==void 0?o:n)!==null&&s!==void 0?s:!0,u=typeof a=="string"?{message:a}:a;i.addIssue({code:"custom",...u,fatal:l})}}):Zu.create(),xY={object:Tt.lazycreate};var ye;(function(e){e.ZodString="ZodString",e.ZodNumber="ZodNumber",e.ZodNaN="ZodNaN",e.ZodBigInt="ZodBigInt",e.ZodBoolean="ZodBoolean",e.ZodDate="ZodDate",e.ZodSymbol="ZodSymbol",e.ZodUndefined="ZodUndefined",e.ZodNull="ZodNull",e.ZodAny="ZodAny",e.ZodUnknown="ZodUnknown",e.ZodNever="ZodNever",e.ZodVoid="ZodVoid",e.ZodArray="ZodArray",e.ZodObject="ZodObject",e.ZodUnion="ZodUnion",e.ZodDiscriminatedUnion="ZodDiscriminatedUnion",e.ZodIntersection="ZodIntersection",e.ZodTuple="ZodTuple",e.ZodRecord="ZodRecord",e.ZodMap="ZodMap",e.ZodSet="ZodSet",e.ZodFunction="ZodFunction",e.ZodLazy="ZodLazy",e.ZodLiteral="ZodLiteral",e.ZodEnum="ZodEnum",e.ZodEffects="ZodEffects",e.ZodNativeEnum="ZodNativeEnum",e.ZodOptional="ZodOptional",e.ZodNullable="ZodNullable",e.ZodDefault="ZodDefault",e.ZodCatch="ZodCatch",e.ZodPromise="ZodPromise",e.ZodBranded="ZodBranded",e.ZodPipeline="ZodPipeline"})(ye||(ye={}));const CY=(e,t={message:`Input not instance of ${e.name}`})=>_O(n=>n instanceof e,t),wO=pi.create,xO=zs.create,TY=wm.create,EY=Us.create,CO=hf.create,PY=Za.create,AY=vm.create,kY=pf.create,RY=gf.create,OY=Zu.create,MY=Ba.create,IY=Vo.create,NY=bm.create,DY=Si.create,LY=Tt.create,$Y=Tt.strictCreate,FY=mf.create,BY=w0.create,jY=yf.create,VY=to.create,zY=vf.create,UY=Sm.create,GY=Ja.create,HY=ku.create,qY=bf.create,WY=Sf.create,KY=Gs.create,XY=_f.create,YY=Ju.create,W4=Ti.create,QY=Ro.create,ZY=el.create,JY=Ti.createWithPreprocess,eQ=mh.create,tQ=()=>wO().optional(),nQ=()=>xO().optional(),rQ=()=>CO().optional(),iQ={string:e=>pi.create({...e,coerce:!0}),number:e=>zs.create({...e,coerce:!0}),boolean:e=>hf.create({...e,coerce:!0}),bigint:e=>Us.create({...e,coerce:!0}),date:e=>Za.create({...e,coerce:!0})},oQ=be;var jt=Object.freeze({__proto__:null,defaultErrorMap:ff,setErrorMap:uY,getErrorMap:pm,makeIssue:gm,EMPTY_PATH:cY,addIssueToContext:oe,ParseStatus:jn,INVALID:be,DIRTY:vO,OK:tr,isAborted:I_,isDirty:N_,isValid:mm,isAsync:ym,get util(){return Ve},get objectUtil(){return M_},ZodParsedType:re,getParsedType:ms,ZodType:Ce,ZodString:pi,ZodNumber:zs,ZodBigInt:Us,ZodBoolean:hf,ZodDate:Za,ZodSymbol:vm,ZodUndefined:pf,ZodNull:gf,ZodAny:Zu,ZodUnknown:Ba,ZodNever:Vo,ZodVoid:bm,ZodArray:Si,ZodObject:Tt,ZodUnion:mf,ZodDiscriminatedUnion:w0,ZodIntersection:yf,ZodTuple:to,ZodRecord:vf,ZodMap:Sm,ZodSet:Ja,ZodFunction:ku,ZodLazy:bf,ZodLiteral:Sf,ZodEnum:Gs,ZodNativeEnum:_f,ZodPromise:Ju,ZodEffects:Ti,ZodTransformer:Ti,ZodOptional:Ro,ZodNullable:el,ZodDefault:wf,ZodCatch:_m,ZodNaN:wm,BRAND:wY,ZodBranded:SO,ZodPipeline:mh,custom:_O,Schema:Ce,ZodSchema:Ce,late:xY,get ZodFirstPartyTypeKind(){return ye},coerce:iQ,any:OY,array:DY,bigint:EY,boolean:CO,date:PY,discriminatedUnion:BY,effect:W4,enum:KY,function:HY,instanceof:CY,intersection:jY,lazy:qY,literal:WY,map:UY,nan:TY,nativeEnum:XY,never:IY,null:RY,nullable:ZY,number:xO,object:LY,oboolean:rQ,onumber:nQ,optional:QY,ostring:tQ,pipeline:eQ,preprocess:JY,promise:YY,record:zY,set:GY,strictObject:$Y,string:wO,symbol:AY,transformer:W4,tuple:VY,undefined:kY,union:FY,unknown:MY,void:NY,NEVER:oQ,ZodIssueCode:ee,quotelessJson:lY,ZodError:bi});const sQ=jt.string(),vwe=e=>sQ.safeParse(e).success,aQ=jt.string(),bwe=e=>aQ.safeParse(e).success;jt.string();jt.string();const lQ=jt.number().int().min(1),Swe=e=>lQ.safeParse(e).success,uQ=jt.number().min(1),_we=e=>uQ.safeParse(e).success,cQ=jt.enum(["euler","deis","ddim","ddpm","dpmpp_2s","dpmpp_2m","dpmpp_2m_sde","dpmpp_sde","heun","kdpm_2","lms","pndm","unipc","euler_k","dpmpp_2s_k","dpmpp_2m_k","dpmpp_2m_sde_k","dpmpp_sde_k","heun_k","lms_k","euler_a","kdpm_2_a"]),wwe=e=>cQ.safeParse(e).success,xwe={euler:"Euler",deis:"DEIS",ddim:"DDIM",ddpm:"DDPM",dpmpp_sde:"DPM++ SDE",dpmpp_2s:"DPM++ 2S",dpmpp_2m:"DPM++ 2M",dpmpp_2m_sde:"DPM++ 2M SDE",heun:"Heun",kdpm_2:"KDPM 2",lms:"LMS",pndm:"PNDM",unipc:"UniPC",euler_k:"Euler Karras",dpmpp_sde_k:"DPM++ SDE Karras",dpmpp_2s_k:"DPM++ 2S Karras",dpmpp_2m_k:"DPM++ 2M Karras",dpmpp_2m_sde_k:"DPM++ 2M SDE Karras",heun_k:"Heun Karras",lms_k:"LMS Karras",euler_a:"Euler Ancestral",kdpm_2_a:"KDPM 2 Ancestral"},dQ=jt.number().int().min(0).max(aY),Cwe=e=>dQ.safeParse(e).success,fQ=jt.number().multipleOf(8).min(64),Twe=e=>fQ.safeParse(e).success,hQ=jt.number().multipleOf(8).min(64),Ewe=e=>hQ.safeParse(e).success,x0=jt.enum(["sd-1","sd-2","sdxl","sdxl-refiner"]),xf=jt.object({model_name:jt.string().min(1),base_model:x0}),Pwe=e=>xf.safeParse(e).success,pQ=jt.object({model_name:jt.string().min(1),base_model:x0}),Awe=jt.object({model_name:jt.string().min(1),base_model:x0}),kwe=jt.object({model_name:jt.string().min(1),base_model:x0}),gQ=jt.number().min(0).max(1),Rwe=e=>gQ.safeParse(e).success;jt.enum(["fp16","fp32"]);const Wo={cfgScale:7.5,height:512,img2imgStrength:.75,infillMethod:"patchmatch",iterations:1,perlin:0,positivePrompt:"",negativePrompt:"",scheduler:"euler",seamBlur:16,seamSize:96,seamSteps:30,seamStrength:.7,seed:0,seedWeights:"",shouldFitToWidthHeight:!0,shouldGenerateVariations:!1,shouldRandomizeSeed:!0,shouldUseNoiseSettings:!1,steps:50,threshold:0,tileSize:32,variationAmount:.1,width:512,shouldUseSymmetry:!1,horizontalSymmetrySteps:0,verticalSymmetrySteps:0,model:null,vae:null,vaePrecision:"fp32",seamlessXAxis:!1,seamlessYAxis:!1,clipSkip:0,shouldUseCpuNoise:!0,shouldShowAdvancedOptions:!1,aspectRatio:null},mQ=Wo,TO=Pt({name:"generation",initialState:mQ,reducers:{setPositivePrompt:(e,t)=>{e.positivePrompt=t.payload},setNegativePrompt:(e,t)=>{e.negativePrompt=t.payload},setIterations:(e,t)=>{e.iterations=t.payload},setSteps:(e,t)=>{e.steps=t.payload},clampSymmetrySteps:e=>{e.horizontalSymmetrySteps=Ss(e.horizontalSymmetrySteps,0,e.steps),e.verticalSymmetrySteps=Ss(e.verticalSymmetrySteps,0,e.steps)},setCfgScale:(e,t)=>{e.cfgScale=t.payload},setThreshold:(e,t)=>{e.threshold=t.payload},setPerlin:(e,t)=>{e.perlin=t.payload},setHeight:(e,t)=>{e.height=t.payload},setWidth:(e,t)=>{e.width=t.payload},toggleSize:e=>{const[t,n]=[e.width,e.height];e.width=n,e.height=t},setScheduler:(e,t)=>{e.scheduler=t.payload},setSeed:(e,t)=>{e.seed=t.payload,e.shouldRandomizeSeed=!1},setImg2imgStrength:(e,t)=>{e.img2imgStrength=t.payload},setSeamlessXAxis:(e,t)=>{e.seamlessXAxis=t.payload},setSeamlessYAxis:(e,t)=>{e.seamlessYAxis=t.payload},setShouldFitToWidthHeight:(e,t)=>{e.shouldFitToWidthHeight=t.payload},resetSeed:e=>{e.seed=-1},setShouldGenerateVariations:(e,t)=>{e.shouldGenerateVariations=t.payload},setVariationAmount:(e,t)=>{e.variationAmount=t.payload},setSeedWeights:(e,t)=>{e.seedWeights=t.payload,e.shouldGenerateVariations=!0,e.variationAmount=0},resetParametersState:e=>({...e,...Wo}),setShouldRandomizeSeed:(e,t)=>{e.shouldRandomizeSeed=t.payload},clearInitialImage:e=>{e.initialImage=void 0},setSeamSize:(e,t)=>{e.seamSize=t.payload},setSeamBlur:(e,t)=>{e.seamBlur=t.payload},setSeamStrength:(e,t)=>{e.seamStrength=t.payload},setSeamSteps:(e,t)=>{e.seamSteps=t.payload},setTileSize:(e,t)=>{e.tileSize=t.payload},setInfillMethod:(e,t)=>{e.infillMethod=t.payload},setShouldUseSymmetry:(e,t)=>{e.shouldUseSymmetry=t.payload},setHorizontalSymmetrySteps:(e,t)=>{e.horizontalSymmetrySteps=t.payload},setVerticalSymmetrySteps:(e,t)=>{e.verticalSymmetrySteps=t.payload},setShouldUseNoiseSettings:(e,t)=>{e.shouldUseNoiseSettings=t.payload},initialImageChanged:(e,t)=>{const{image_name:n,width:r,height:i}=t.payload;e.initialImage={imageName:n,width:r,height:i}},modelChanged:(e,t)=>{if(e.model=t.payload,e.model===null)return;const{maxClip:n}=sY[e.model.base_model];e.clipSkip=Ss(e.clipSkip,0,n)},vaeSelected:(e,t)=>{e.vae=t.payload},vaePrecisionChanged:(e,t)=>{e.vaePrecision=t.payload},setClipSkip:(e,t)=>{e.clipSkip=t.payload},shouldUseCpuNoiseChanged:(e,t)=>{e.shouldUseCpuNoise=t.payload},setShouldShowAdvancedOptions:(e,t)=>{e.shouldShowAdvancedOptions=t.payload,t.payload||(e.clipSkip=0)},setAspectRatio:(e,t)=>{const n=t.payload;e.aspectRatio=n,n&&(e.height=Ui(e.width/n,8))}},extraReducers:e=>{e.addCase(iY,(t,n)=>{var i;const r=(i=n.payload.sd)==null?void 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Either set keySeparator: false on init or make sure your translations are published in nested format.")}let C=[];const R=this.languageUtils.getFallbackCodes(this.options.fallbackLng,n.lng||this.language);if(this.options.saveMissingTo==="fallback"&&R&&R[0])for(let N=0;N<R.length;N++)C.push(R[N]);else this.options.saveMissingTo==="all"?C=this.languageUtils.toResolveHierarchy(n.lng||this.language):C.push(n.lng||this.language);const M=(N,O,D)=>{const L=x&&D!==f?D:$;this.options.missingKeyHandler?this.options.missingKeyHandler(N,l,O,L,I,n):this.backendConnector&&this.backendConnector.saveMissing&&this.backendConnector.saveMissing(N,l,O,L,I,n),this.emit("missingKey",N,l,O,f)};this.options.saveMissing&&(this.options.saveMissingPlurals&&w?C.forEach(N=>{this.pluralResolver.getSuffixes(N,n).forEach(O=>{M([N],s+O,n[`defaultValue${O}`]||E)})}):M(C,s,E))}f=this.extendTranslation(f,t,n,d,r),_&&f===s&&this.options.appendNamespaceToMissingKey&&(f=`${l}:${s}`),(_||b)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?f=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${l}:${s}`:s,b?f:void 0):f=this.options.parseMissingKeyHandler(f))}return i?(d.res=f,d):f}extendTranslation(t,n,r,i,o){var s=this;if(this.i18nFormat&&this.i18nFormat.parse)t=this.i18nFormat.parse(t,{...this.options.interpolation.defaultVariables,...r},i.usedLng,i.usedNS,i.usedKey,{resolved:i});else if(!r.skipInterpolation){r.interpolation&&this.interpolator.init({...r,interpolation:{...this.options.interpolation,...r.interpolation}});const u=typeof t=="string"&&(r&&r.interpolation&&r.interpolation.skipOnVariables!==void 0?r.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);let c;if(u){const f=t.match(this.interpolator.nestingRegexp);c=f&&f.length}let d=r.replace&&typeof r.replace!="string"?r.replace:r;if(this.options.interpolation.defaultVariables&&(d={...this.options.interpolation.defaultVariables,...d}),t=this.interpolator.interpolate(t,d,r.lng||this.language,r),u){const f=t.match(this.interpolator.nestingRegexp),h=f&&f.length;c<h&&(r.nest=!1)}!r.lng&&this.options.compatibilityAPI!=="v1"&&i&&i.res&&(r.lng=i.usedLng),r.nest!==!1&&(t=this.interpolator.nest(t,function(){for(var f=arguments.length,h=new Array(f),p=0;p<f;p++)h[p]=arguments[p];return o&&o[0]===h[0]&&!r.context?(s.logger.warn(`It seems you are nesting recursively key: ${h[0]} in key: ${n[0]}`),null):s.translate(...h,n)},r)),r.interpolation&&this.interpolator.reset()}const a=r.postProcess||this.options.postProcess,l=typeof a=="string"?[a]:a;return t!=null&&l&&l.length&&r.applyPostProcessor!==!1&&(t=LI.handle(l,t,n,this.options&&this.options.postProcessPassResolved?{i18nResolved:i,...r}:r,this)),t}resolve(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},r,i,o,s,a;return typeof t=="string"&&(t=[t]),t.forEach(l=>{if(this.isValidLookup(r))return;const u=this.extractFromKey(l,n),c=u.key;i=c;let d=u.namespaces;this.options.fallbackNS&&(d=d.concat(this.options.fallbackNS));const f=n.count!==void 0&&typeof n.count!="string",h=f&&!n.ordinal&&n.count===0&&this.pluralResolver.shouldUseIntlApi(),p=n.context!==void 0&&(typeof n.context=="string"||typeof n.context=="number")&&n.context!=="",m=n.lngs?n.lngs:this.languageUtils.toResolveHierarchy(n.lng||this.language,n.fallbackLng);d.forEach(S=>{this.isValidLookup(r)||(a=S,!EE[`${m[0]}-${S}`]&&this.utils&&this.utils.hasLoadedNamespace&&!this.utils.hasLoadedNamespace(a)&&(EE[`${m[0]}-${S}`]=!0,this.logger.warn(`key "${i}" for languages "${m.join(", ")}" won't get resolved as namespace "${a}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(v=>{if(this.isValidLookup(r))return;s=v;const y=[c];if(this.i18nFormat&&this.i18nFormat.addLookupKeys)this.i18nFormat.addLookupKeys(y,c,v,S,n);else{let b;f&&(b=this.pluralResolver.getSuffix(v,n.count,n));const _=`${this.options.pluralSeparator}zero`,w=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(f&&(y.push(c+b),n.ordinal&&b.indexOf(w)===0&&y.push(c+b.replace(w,this.options.pluralSeparator)),h&&y.push(c+_)),p){const x=`${c}${this.options.contextSeparator}${n.context}`;y.push(x),f&&(y.push(x+b),n.ordinal&&b.indexOf(w)===0&&y.push(x+b.replace(w,this.options.pluralSeparator)),h&&y.push(x+_))}}let g;for(;g=y.pop();)this.isValidLookup(r)||(o=g,r=this.getResource(v,S,g,n))}))})}),{res:r,usedKey:i,exactUsedKey:o,usedLng:s,usedNS:a}}isValidLookup(t){return t!==void 0&&!(!this.options.returnNull&&t===null)&&!(!this.options.returnEmptyString&&t==="")}getResource(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(t,n,r,i):this.resourceStore.getResource(t,n,r,i)}static hasDefaultValue(t){const n="defaultValue";for(const r in t)if(Object.prototype.hasOwnProperty.call(t,r)&&n===r.substring(0,n.length)&&t[r]!==void 0)return!0;return!1}}function $b(e){return e.charAt(0).toUpperCase()+e.slice(1)}class PE{constructor(t){this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=qi.create("languageUtils")}getScriptPartFromCode(t){if(t=Hm(t),!t||t.indexOf("-")<0)return null;const n=t.split("-");return n.length===2||(n.pop(),n[n.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(n.join("-"))}getLanguagePartFromCode(t){if(t=Hm(t),!t||t.indexOf("-")<0)return t;const n=t.split("-");return this.formatLanguageCode(n[0])}formatLanguageCode(t){if(typeof t=="string"&&t.indexOf("-")>-1){const n=["hans","hant","latn","cyrl","cans","mong","arab"];let r=t.split("-");return this.options.lowerCaseLng?r=r.map(i=>i.toLowerCase()):r.length===2?(r[0]=r[0].toLowerCase(),r[1]=r[1].toUpperCase(),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=$b(r[1].toLowerCase()))):r.length===3&&(r[0]=r[0].toLowerCase(),r[1].length===2&&(r[1]=r[1].toUpperCase()),r[0]!=="sgn"&&r[2].length===2&&(r[2]=r[2].toUpperCase()),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=$b(r[1].toLowerCase())),n.indexOf(r[2].toLowerCase())>-1&&(r[2]=$b(r[2].toLowerCase()))),r.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?t.toLowerCase():t}isSupportedCode(t){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(t=this.getLanguagePartFromCode(t)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(t)>-1}getBestMatchFromCodes(t){if(!t)return null;let n;return t.forEach(r=>{if(n)return;const i=this.formatLanguageCode(r);(!this.options.supportedLngs||this.isSupportedCode(i))&&(n=i)}),!n&&this.options.supportedLngs&&t.forEach(r=>{if(n)return;const i=this.getLanguagePartFromCode(r);if(this.isSupportedCode(i))return n=i;n=this.options.supportedLngs.find(o=>{if(o===i)return o;if(!(o.indexOf("-")<0&&i.indexOf("-")<0)&&o.indexOf(i)===0)return o})}),n||(n=this.getFallbackCodes(this.options.fallbackLng)[0]),n}getFallbackCodes(t,n){if(!t)return[];if(typeof t=="function"&&(t=t(n)),typeof t=="string"&&(t=[t]),Object.prototype.toString.apply(t)==="[object Array]")return t;if(!n)return t.default||[];let r=t[n];return r||(r=t[this.getScriptPartFromCode(n)]),r||(r=t[this.formatLanguageCode(n)]),r||(r=t[this.getLanguagePartFromCode(n)]),r||(r=t.default),r||[]}toResolveHierarchy(t,n){const r=this.getFallbackCodes(n||this.options.fallbackLng||[],t),i=[],o=s=>{s&&(this.isSupportedCode(s)?i.push(s):this.logger.warn(`rejecting language code not found in supportedLngs: ${s}`))};return typeof t=="string"&&(t.indexOf("-")>-1||t.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&o(this.formatLanguageCode(t)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&o(this.getScriptPartFromCode(t)),this.options.load!=="currentOnly"&&o(this.getLanguagePartFromCode(t))):typeof t=="string"&&o(this.formatLanguageCode(t)),r.forEach(s=>{i.indexOf(s)<0&&o(this.formatLanguageCode(s))}),i}}let oae=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],sae={1:function(e){return+(e>1)},2:function(e){return+(e!=1)},3:function(e){return 0},4:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},5:function(e){return e==0?0:e==1?1:e==2?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5},6:function(e){return e==1?0:e>=2&&e<=4?1:2},7:function(e){return e==1?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},8:function(e){return e==1?0:e==2?1:e!=8&&e!=11?2:3},9:function(e){return+(e>=2)},10:function(e){return e==1?0:e==2?1:e<7?2:e<11?3:4},11:function(e){return e==1||e==11?0:e==2||e==12?1:e>2&&e<20?2:3},12:function(e){return+(e%10!=1||e%100==11)},13:function(e){return+(e!==0)},14:function(e){return e==1?0:e==2?1:e==3?2:3},15:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2},16:function(e){return e%10==1&&e%100!=11?0:e!==0?1:2},17:function(e){return e==1||e%10==1&&e%100!=11?0:1},18:function(e){return e==0?0:e==1?1:2},19:function(e){return e==1?0:e==0||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3},20:function(e){return e==1?0:e==0||e%100>0&&e%100<20?1:2},21:function(e){return e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0},22:function(e){return e==1?0:e==2?1:(e<0||e>10)&&e%10==0?2:3}};const aae=["v1","v2","v3"],lae=["v4"],AE={zero:0,one:1,two:2,few:3,many:4,other:5};function uae(){const e={};return oae.forEach(t=>{t.lngs.forEach(n=>{e[n]={numbers:t.nr,plurals:sae[t.fc]}})}),e}class cae{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.languageUtils=t,this.options=n,this.logger=qi.create("pluralResolver"),(!this.options.compatibilityJSON||lae.includes(this.options.compatibilityJSON))&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=uae()}addRule(t,n){this.rules[t]=n}getRule(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(Hm(t),{type:n.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[t]||this.rules[this.languageUtils.getLanguagePartFromCode(t)]}needsPlural(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return this.shouldUseIntlApi()?r&&r.resolvedOptions().pluralCategories.length>1:r&&r.numbers.length>1}getPluralFormsOfKey(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(t,r).map(i=>`${n}${i}`)}getSuffixes(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return r?this.shouldUseIntlApi()?r.resolvedOptions().pluralCategories.sort((i,o)=>AE[i]-AE[o]).map(i=>`${this.options.prepend}${n.ordinal?`ordinal${this.options.prepend}`:""}${i}`):r.numbers.map(i=>this.getSuffix(t,i,n)):[]}getSuffix(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};const i=this.getRule(t,r);return i?this.shouldUseIntlApi()?`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${i.select(n)}`:this.getSuffixRetroCompatible(i,n):(this.logger.warn(`no plural rule found for: ${t}`),"")}getSuffixRetroCompatible(t,n){const r=t.noAbs?t.plurals(n):t.plurals(Math.abs(n));let i=t.numbers[r];this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1&&(i===2?i="plural":i===1&&(i=""));const o=()=>this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString();return this.options.compatibilityJSON==="v1"?i===1?"":typeof i=="number"?`_plural_${i.toString()}`:o():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1?o():this.options.prepend&&r.toString()?this.options.prepend+r.toString():r.toString()}shouldUseIntlApi(){return!aae.includes(this.options.compatibilityJSON)}}function kE(e,t,n){let r=arguments.length>3&&arguments[3]!==void 0?arguments[3]:".",i=arguments.length>4&&arguments[4]!==void 0?arguments[4]:!0,o=eae(e,t,n);return!o&&i&&typeof n=="string"&&(o=Gm(e,n,r),o===void 0&&(o=Gm(t,n,r))),o}class dae{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=qi.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||(n=>n),this.init(t)}init(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};t.interpolation||(t.interpolation={escapeValue:!0});const n=t.interpolation;this.escape=n.escape!==void 0?n.escape:nae,this.escapeValue=n.escapeValue!==void 0?n.escapeValue:!0,this.useRawValueToEscape=n.useRawValueToEscape!==void 0?n.useRawValueToEscape:!1,this.prefix=n.prefix?jl(n.prefix):n.prefixEscaped||"{{",this.suffix=n.suffix?jl(n.suffix):n.suffixEscaped||"}}",this.formatSeparator=n.formatSeparator?n.formatSeparator:n.formatSeparator||",",this.unescapePrefix=n.unescapeSuffix?"":n.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":n.unescapeSuffix||"",this.nestingPrefix=n.nestingPrefix?jl(n.nestingPrefix):n.nestingPrefixEscaped||jl("$t("),this.nestingSuffix=n.nestingSuffix?jl(n.nestingSuffix):n.nestingSuffixEscaped||jl(")"),this.nestingOptionsSeparator=n.nestingOptionsSeparator?n.nestingOptionsSeparator:n.nestingOptionsSeparator||",",this.maxReplaces=n.maxReplaces?n.maxReplaces:1e3,this.alwaysFormat=n.alwaysFormat!==void 0?n.alwaysFormat:!1,this.resetRegExp()}reset(){this.options&&this.init(this.options)}resetRegExp(){const t=`${this.prefix}(.+?)${this.suffix}`;this.regexp=new RegExp(t,"g");const n=`${this.prefix}${this.unescapePrefix}(.+?)${this.unescapeSuffix}${this.suffix}`;this.regexpUnescape=new RegExp(n,"g");const r=`${this.nestingPrefix}(.+?)${this.nestingSuffix}`;this.nestingRegexp=new RegExp(r,"g")}interpolate(t,n,r,i){let o,s,a;const l=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function u(p){return p.replace(/\$/g,"$$$$")}const c=p=>{if(p.indexOf(this.formatSeparator)<0){const y=kE(n,l,p,this.options.keySeparator,this.options.ignoreJSONStructure);return this.alwaysFormat?this.format(y,void 0,r,{...i,...n,interpolationkey:p}):y}const m=p.split(this.formatSeparator),S=m.shift().trim(),v=m.join(this.formatSeparator).trim();return this.format(kE(n,l,S,this.options.keySeparator,this.options.ignoreJSONStructure),v,r,{...i,...n,interpolationkey:S})};this.resetRegExp();const d=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,f=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 0?i.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables;return[{regex:this.regexpUnescape,safeValue:p=>u(p)},{regex:this.regexp,safeValue:p=>this.escapeValue?u(this.escape(p)):u(p)}].forEach(p=>{for(a=0;o=p.regex.exec(t);){const m=o[1].trim();if(s=c(m),s===void 0)if(typeof d=="function"){const v=d(t,o,i);s=typeof v=="string"?v:""}else if(i&&Object.prototype.hasOwnProperty.call(i,m))s="";else if(f){s=o[0];continue}else this.logger.warn(`missed to pass in variable ${m} for interpolating ${t}`),s="";else typeof s!="string"&&!this.useRawValueToEscape&&(s=xE(s));const S=p.safeValue(s);if(t=t.replace(o[0],S),f?(p.regex.lastIndex+=s.length,p.regex.lastIndex-=o[0].length):p.regex.lastIndex=0,a++,a>=this.maxReplaces)break}}),t}nest(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i,o,s;function a(l,u){const c=this.nestingOptionsSeparator;if(l.indexOf(c)<0)return l;const d=l.split(new RegExp(`${c}[ ]*{`));let f=`{${d[1]}`;l=d[0],f=this.interpolate(f,s);const h=f.match(/'/g),p=f.match(/"/g);(h&&h.length%2===0&&!p||p.length%2!==0)&&(f=f.replace(/'/g,'"'));try{s=JSON.parse(f),u&&(s={...u,...s})}catch(m){return this.logger.warn(`failed parsing options string in nesting for key ${l}`,m),`${l}${c}${f}`}return delete s.defaultValue,l}for(;i=this.nestingRegexp.exec(t);){let l=[];s={...r},s=s.replace&&typeof s.replace!="string"?s.replace:s,s.applyPostProcessor=!1,delete s.defaultValue;let u=!1;if(i[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(i[1])){const c=i[1].split(this.formatSeparator).map(d=>d.trim());i[1]=c.shift(),l=c,u=!0}if(o=n(a.call(this,i[1].trim(),s),s),o&&i[0]===t&&typeof o!="string")return o;typeof o!="string"&&(o=xE(o)),o||(this.logger.warn(`missed to resolve ${i[1]} for nesting ${t}`),o=""),u&&(o=l.reduce((c,d)=>this.format(c,d,r.lng,{...r,interpolationkey:i[1].trim()}),o.trim())),t=t.replace(i[0],o),this.regexp.lastIndex=0}return t}}function fae(e){let t=e.toLowerCase().trim();const n={};if(e.indexOf("(")>-1){const r=e.split("(");t=r[0].toLowerCase().trim();const i=r[1].substring(0,r[1].length-1);t==="currency"&&i.indexOf(":")<0?n.currency||(n.currency=i.trim()):t==="relativetime"&&i.indexOf(":")<0?n.range||(n.range=i.trim()):i.split(";").forEach(s=>{if(!s)return;const[a,...l]=s.split(":"),u=l.join(":").trim().replace(/^'+|'+$/g,"");n[a.trim()]||(n[a.trim()]=u),u==="false"&&(n[a.trim()]=!1),u==="true"&&(n[a.trim()]=!0),isNaN(u)||(n[a.trim()]=parseInt(u,10))})}return{formatName:t,formatOptions:n}}function Vl(e){const t={};return function(r,i,o){const s=i+JSON.stringify(o);let a=t[s];return a||(a=e(Hm(i),o),t[s]=a),a(r)}}class hae{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=qi.create("formatter"),this.options=t,this.formats={number:Vl((n,r)=>{const i=new Intl.NumberFormat(n,{...r});return o=>i.format(o)}),currency:Vl((n,r)=>{const i=new Intl.NumberFormat(n,{...r,style:"currency"});return o=>i.format(o)}),datetime:Vl((n,r)=>{const i=new Intl.DateTimeFormat(n,{...r});return o=>i.format(o)}),relativetime:Vl((n,r)=>{const i=new Intl.RelativeTimeFormat(n,{...r});return o=>i.format(o,r.range||"day")}),list:Vl((n,r)=>{const i=new Intl.ListFormat(n,{...r});return o=>i.format(o)})},this.init(t)}init(t){const r=(arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}}).interpolation;this.formatSeparator=r.formatSeparator?r.formatSeparator:r.formatSeparator||","}add(t,n){this.formats[t.toLowerCase().trim()]=n}addCached(t,n){this.formats[t.toLowerCase().trim()]=Vl(n)}format(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return n.split(this.formatSeparator).reduce((a,l)=>{const{formatName:u,formatOptions:c}=fae(l);if(this.formats[u]){let d=a;try{const f=i&&i.formatParams&&i.formatParams[i.interpolationkey]||{},h=f.locale||f.lng||i.locale||i.lng||r;d=this.formats[u](a,h,{...c,...i,...f})}catch(f){this.logger.warn(f)}return d}else this.logger.warn(`there was no format function for ${u}`);return a},t)}}function pae(e,t){e.pending[t]!==void 0&&(delete e.pending[t],e.pendingCount--)}class gae extends J0{constructor(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};super(),this.backend=t,this.store=n,this.services=r,this.languageUtils=r.languageUtils,this.options=i,this.logger=qi.create("backendConnector"),this.waitingReads=[],this.maxParallelReads=i.maxParallelReads||10,this.readingCalls=0,this.maxRetries=i.maxRetries>=0?i.maxRetries:5,this.retryTimeout=i.retryTimeout>=1?i.retryTimeout:350,this.state={},this.queue=[],this.backend&&this.backend.init&&this.backend.init(r,i.backend,i)}queueLoad(t,n,r,i){const o={},s={},a={},l={};return t.forEach(u=>{let c=!0;n.forEach(d=>{const f=`${u}|${d}`;!r.reload&&this.store.hasResourceBundle(u,d)?this.state[f]=2:this.state[f]<0||(this.state[f]===1?s[f]===void 0&&(s[f]=!0):(this.state[f]=1,c=!1,s[f]===void 0&&(s[f]=!0),o[f]===void 0&&(o[f]=!0),l[d]===void 0&&(l[d]=!0)))}),c||(a[u]=!0)}),(Object.keys(o).length||Object.keys(s).length)&&this.queue.push({pending:s,pendingCount:Object.keys(s).length,loaded:{},errors:[],callback:i}),{toLoad:Object.keys(o),pending:Object.keys(s),toLoadLanguages:Object.keys(a),toLoadNamespaces:Object.keys(l)}}loaded(t,n,r){const i=t.split("|"),o=i[0],s=i[1];n&&this.emit("failedLoading",o,s,n),r&&this.store.addResourceBundle(o,s,r),this.state[t]=n?-1:2;const a={};this.queue.forEach(l=>{Jse(l.loaded,[o],s),pae(l,t),n&&l.errors.push(n),l.pendingCount===0&&!l.done&&(Object.keys(l.loaded).forEach(u=>{a[u]||(a[u]={});const c=l.loaded[u];c.length&&c.forEach(d=>{a[u][d]===void 0&&(a[u][d]=!0)})}),l.done=!0,l.errors.length?l.callback(l.errors):l.callback())}),this.emit("loaded",a),this.queue=this.queue.filter(l=>!l.done)}read(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,o=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,s=arguments.length>5?arguments[5]:void 0;if(!t.length)return s(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:t,ns:n,fcName:r,tried:i,wait:o,callback:s});return}this.readingCalls++;const a=(u,c)=>{if(this.readingCalls--,this.waitingReads.length>0){const d=this.waitingReads.shift();this.read(d.lng,d.ns,d.fcName,d.tried,d.wait,d.callback)}if(u&&c&&i<this.maxRetries){setTimeout(()=>{this.read.call(this,t,n,r,i+1,o*2,s)},o);return}s(u,c)},l=this.backend[r].bind(this.backend);if(l.length===2){try{const u=l(t,n);u&&typeof u.then=="function"?u.then(c=>a(null,c)).catch(a):a(null,u)}catch(u){a(u)}return}return l(t,n,a)}prepareLoading(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),i&&i();typeof t=="string"&&(t=this.languageUtils.toResolveHierarchy(t)),typeof n=="string"&&(n=[n]);const o=this.queueLoad(t,n,r,i);if(!o.toLoad.length)return o.pending.length||i(),null;o.toLoad.forEach(s=>{this.loadOne(s)})}load(t,n,r){this.prepareLoading(t,n,{},r)}reload(t,n,r){this.prepareLoading(t,n,{reload:!0},r)}loadOne(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:"";const r=t.split("|"),i=r[0],o=r[1];this.read(i,o,"read",void 0,void 0,(s,a)=>{s&&this.logger.warn(`${n}loading namespace ${o} for language ${i} failed`,s),!s&&a&&this.logger.log(`${n}loaded namespace ${o} for language ${i}`,a),this.loaded(t,s,a)})}saveMissing(t,n,r,i,o){let s=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{},a=arguments.length>6&&arguments[6]!==void 0?arguments[6]:()=>{};if(this.services.utils&&this.services.utils.hasLoadedNamespace&&!this.services.utils.hasLoadedNamespace(n)){this.logger.warn(`did not save key "${r}" as the namespace "${n}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(r==null||r==="")){if(this.backend&&this.backend.create){const l={...s,isUpdate:o},u=this.backend.create.bind(this.backend);if(u.length<6)try{let c;u.length===5?c=u(t,n,r,i,l):c=u(t,n,r,i),c&&typeof c.then=="function"?c.then(d=>a(null,d)).catch(a):a(null,c)}catch(c){a(c)}else u(t,n,r,i,a,l)}!t||!t[0]||this.store.addResource(t[0],n,r,i)}}}function RE(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!1,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){let n={};if(typeof t[1]=="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),typeof t[2]=="object"||typeof t[3]=="object"){const r=t[3]||t[2];Object.keys(r).forEach(i=>{n[i]=r[i]})}return n},interpolation:{escapeValue:!0,format:(e,t,n,r)=>e,prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function OE(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function Rp(){}function mae(e){Object.getOwnPropertyNames(Object.getPrototypeOf(e)).forEach(n=>{typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}class Lf extends J0{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;if(super(),this.options=OE(t),this.services={},this.logger=qi,this.modules={external:[]},mae(this),n&&!this.isInitialized&&!t.isClone){if(!this.options.initImmediate)return this.init(t,n),this;setTimeout(()=>{this.init(t,n)},0)}}init(){var t=this;let n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},r=arguments.length>1?arguments[1]:void 0;typeof n=="function"&&(r=n,n={}),!n.defaultNS&&n.defaultNS!==!1&&n.ns&&(typeof n.ns=="string"?n.defaultNS=n.ns:n.ns.indexOf("translation")<0&&(n.defaultNS=n.ns[0]));const i=RE();this.options={...i,...this.options,...OE(n)},this.options.compatibilityAPI!=="v1"&&(this.options.interpolation={...i.interpolation,...this.options.interpolation}),n.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=n.keySeparator),n.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=n.nsSeparator);function o(c){return c?typeof c=="function"?new c:c:null}if(!this.options.isClone){this.modules.logger?qi.init(o(this.modules.logger),this.options):qi.init(null,this.options);let c;this.modules.formatter?c=this.modules.formatter:typeof Intl<"u"&&(c=hae);const d=new PE(this.options);this.store=new TE(this.options.resources,this.options);const f=this.services;f.logger=qi,f.resourceStore=this.store,f.languageUtils=d,f.pluralResolver=new cae(d,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),c&&(!this.options.interpolation.format||this.options.interpolation.format===i.interpolation.format)&&(f.formatter=o(c),f.formatter.init(f,this.options),this.options.interpolation.format=f.formatter.format.bind(f.formatter)),f.interpolator=new dae(this.options),f.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},f.backendConnector=new gae(o(this.modules.backend),f.resourceStore,f,this.options),f.backendConnector.on("*",function(h){for(var p=arguments.length,m=new Array(p>1?p-1:0),S=1;S<p;S++)m[S-1]=arguments[S];t.emit(h,...m)}),this.modules.languageDetector&&(f.languageDetector=o(this.modules.languageDetector),f.languageDetector.init&&f.languageDetector.init(f,this.options.detection,this.options)),this.modules.i18nFormat&&(f.i18nFormat=o(this.modules.i18nFormat),f.i18nFormat.init&&f.i18nFormat.init(this)),this.translator=new qm(this.services,this.options),this.translator.on("*",function(h){for(var p=arguments.length,m=new Array(p>1?p-1:0),S=1;S<p;S++)m[S-1]=arguments[S];t.emit(h,...m)}),this.modules.external.forEach(h=>{h.init&&h.init(this)})}if(this.format=this.options.interpolation.format,r||(r=Rp),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){const c=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);c.length>0&&c[0]!=="dev"&&(this.options.lng=c[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined"),["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"].forEach(c=>{this[c]=function(){return t.store[c](...arguments)}}),["addResource","addResources","addResourceBundle","removeResourceBundle"].forEach(c=>{this[c]=function(){return t.store[c](...arguments),t}});const l=Xc(),u=()=>{const c=(d,f)=>{this.isInitialized&&!this.initializedStoreOnce&&this.logger.warn("init: i18next is already initialized. 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Please check the object you are passing to i18next.use()");if(!t.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return t.type==="backend"&&(this.modules.backend=t),(t.type==="logger"||t.log&&t.warn&&t.error)&&(this.modules.logger=t),t.type==="languageDetector"&&(this.modules.languageDetector=t),t.type==="i18nFormat"&&(this.modules.i18nFormat=t),t.type==="postProcessor"&&LI.addPostProcessor(t),t.type==="formatter"&&(this.modules.formatter=t),t.type==="3rdParty"&&this.modules.external.push(t),this}setResolvedLanguage(t){if(!(!t||!this.languages)&&!(["cimode","dev"].indexOf(t)>-1))for(let n=0;n<this.languages.length;n++){const r=this.languages[n];if(!(["cimode","dev"].indexOf(r)>-1)&&this.store.hasLanguageSomeTranslations(r)){this.resolvedLanguage=r;break}}}changeLanguage(t,n){var r=this;this.isLanguageChangingTo=t;const i=Xc();this.emit("languageChanging",t);const o=l=>{this.language=l,this.languages=this.services.languageUtils.toResolveHierarchy(l),this.resolvedLanguage=void 0,this.setResolvedLanguage(l)},s=(l,u)=>{u?(o(u),this.translator.changeLanguage(u),this.isLanguageChangingTo=void 0,this.emit("languageChanged",u),this.logger.log("languageChanged",u)):this.isLanguageChangingTo=void 0,i.resolve(function(){return r.t(...arguments)}),n&&n(l,function(){return r.t(...arguments)})},a=l=>{!t&&!l&&this.services.languageDetector&&(l=[]);const u=typeof l=="string"?l:this.services.languageUtils.getBestMatchFromCodes(l);u&&(this.language||o(u),this.translator.language||this.translator.changeLanguage(u),this.services.languageDetector&&this.services.languageDetector.cacheUserLanguage&&this.services.languageDetector.cacheUserLanguage(u)),this.loadResources(u,c=>{s(c,u)})};return!t&&this.services.languageDetector&&!this.services.languageDetector.async?a(this.services.languageDetector.detect()):!t&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(a):this.services.languageDetector.detect(a):a(t),i}getFixedT(t,n,r){var i=this;const o=function(s,a){let l;if(typeof a!="object"){for(var u=arguments.length,c=new Array(u>2?u-2:0),d=2;d<u;d++)c[d-2]=arguments[d];l=i.options.overloadTranslationOptionHandler([s,a].concat(c))}else l={...a};l.lng=l.lng||o.lng,l.lngs=l.lngs||o.lngs,l.ns=l.ns||o.ns,l.keyPrefix=l.keyPrefix||r||o.keyPrefix;const f=i.options.keySeparator||".";let h;return l.keyPrefix&&Array.isArray(s)?h=s.map(p=>`${l.keyPrefix}${f}${p}`):h=l.keyPrefix?`${l.keyPrefix}${f}${s}`:s,i.t(h,l)};return typeof t=="string"?o.lng=t:o.lngs=t,o.ns=n,o.keyPrefix=r,o}t(){return this.translator&&this.translator.translate(...arguments)}exists(){return this.translator&&this.translator.exists(...arguments)}setDefaultNamespace(t){this.options.defaultNS=t}hasLoadedNamespace(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;const r=n.lng||this.resolvedLanguage||this.languages[0],i=this.options?this.options.fallbackLng:!1,o=this.languages[this.languages.length-1];if(r.toLowerCase()==="cimode")return!0;const s=(a,l)=>{const u=this.services.backendConnector.state[`${a}|${l}`];return u===-1||u===2};if(n.precheck){const a=n.precheck(this,s);if(a!==void 0)return a}return!!(this.hasResourceBundle(r,t)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||s(r,t)&&(!i||s(o,t)))}loadNamespaces(t,n){const r=Xc();return this.options.ns?(typeof t=="string"&&(t=[t]),t.forEach(i=>{this.options.ns.indexOf(i)<0&&this.options.ns.push(i)}),this.loadResources(i=>{r.resolve(),n&&n(i)}),r):(n&&n(),Promise.resolve())}loadLanguages(t,n){const r=Xc();typeof t=="string"&&(t=[t]);const i=this.options.preload||[],o=t.filter(s=>i.indexOf(s)<0);return o.length?(this.options.preload=i.concat(o),this.loadResources(s=>{r.resolve(),n&&n(s)}),r):(n&&n(),Promise.resolve())}dir(t){if(t||(t=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!t)return"rtl";const n=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],r=this.services&&this.services.languageUtils||new PE(RE());return n.indexOf(r.getLanguagePartFromCode(t))>-1||t.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}static createInstance(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;return new Lf(t,n)}cloneInstance(){let t=arguments.length>0&&arguments[0]!==void 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Please use public Util.radToDeg instead."),e.Util.radToDeg(g)},_getRotation(g){return t.Konva.angleDeg?e.Util.radToDeg(g):g},_capitalize(g){return g.charAt(0).toUpperCase()+g.slice(1)},throw(g){throw new Error(h+g)},error(g){console.error(h+g)},warn(g){t.Konva.showWarnings&&console.warn(f+g)},each(g,b){for(var _ in g)b(_,g[_])},_inRange(g,b,_){return b<=g&&g<_},_getProjectionToSegment(g,b,_,w,x,T){var P,E,A,$=(g-_)*(g-_)+(b-w)*(b-w);if($==0)P=g,E=b,A=(x-_)*(x-_)+(T-w)*(T-w);else{var I=((x-g)*(_-g)+(T-b)*(w-b))/$;I<0?(P=g,E=b,A=(g-x)*(g-x)+(b-T)*(b-T)):I>1?(P=_,E=w,A=(_-x)*(_-x)+(w-T)*(w-T)):(P=g+I*(_-g),E=b+I*(w-b),A=(P-x)*(P-x)+(E-T)*(E-T))}return[P,E,A]},_getProjectionToLine(g,b,_){var w=e.Util.cloneObject(g),x=Number.MAX_VALUE;return b.forEach(function(T,P){if(!(!_&&P===b.length-1)){var E=b[(P+1)%b.length],A=e.Util._getProjectionToSegment(T.x,T.y,E.x,E.y,g.x,g.y),$=A[0],I=A[1],C=A[2];C<x&&(w.x=$,w.y=I,x=C)}}),w},_prepareArrayForTween(g,b,_){var w,x=[],T=[];if(g.length>b.length){var P=b;b=g,g=P}for(w=0;w<g.length;w+=2)x.push({x:g[w],y:g[w+1]});for(w=0;w<b.length;w+=2)T.push({x:b[w],y:b[w+1]});var E=[];return T.forEach(function(A){var $=e.Util._getProjectionToLine(A,x,_);E.push($.x),E.push($.y)}),E},_prepareToStringify(g){var b;g.visitedByCircularReferenceRemoval=!0;for(var _ in g)if(g.hasOwnProperty(_)&&g[_]&&typeof g[_]=="object"){if(b=Object.getOwnPropertyDescriptor(g,_),g[_].visitedByCircularReferenceRemoval||e.Util._isElement(g[_]))if(b.configurable)delete g[_];else return null;else if(e.Util._prepareToStringify(g[_])===null)if(b.configurable)delete g[_];else return null}return delete g.visitedByCircularReferenceRemoval,g},_assign(g,b){for(var _ in b)g[_]=b[_];return g},_getFirstPointerId(g){return g.touches?g.changedTouches[0].identifier:g.pointerId||999},releaseCanvas(...g){t.Konva.releaseCanvasOnDestroy&&g.forEach(b=>{b.width=0,b.height=0})},drawRoundedRectPath(g,b,_,w){let x=0,T=0,P=0,E=0;typeof w=="number"?x=T=P=E=Math.min(w,b/2,_/2):(x=Math.min(w[0]||0,b/2,_/2),T=Math.min(w[1]||0,b/2,_/2),E=Math.min(w[2]||0,b/2,_/2),P=Math.min(w[3]||0,b/2,_/2)),g.moveTo(x,0),g.lineTo(b-T,0),g.arc(b-T,T,T,Math.PI*3/2,0,!1),g.lineTo(b,_-E),g.arc(b-E,_-E,E,0,Math.PI/2,!1),g.lineTo(P,_),g.arc(P,_-P,P,Math.PI/2,Math.PI,!1),g.lineTo(0,x),g.arc(x,x,x,Math.PI,Math.PI*3/2,!1)}}})(Ot);var wt={},Te={},de={};Object.defineProperty(de,"__esModule",{value:!0});de.getComponentValidator=de.getBooleanValidator=de.getNumberArrayValidator=de.getFunctionValidator=de.getStringOrGradientValidator=de.getStringValidator=de.getNumberOrAutoValidator=de.getNumberOrArrayOfNumbersValidator=de.getNumberValidator=de.alphaComponent=de.RGBComponent=void 0;const Ko=Pe,Nt=Ot;function Xo(e){return Nt.Util._isString(e)?'"'+e+'"':Object.prototype.toString.call(e)==="[object Number]"||Nt.Util._isBoolean(e)?e:Object.prototype.toString.call(e)}function mce(e){return e>255?255:e<0?0:Math.round(e)}de.RGBComponent=mce;function yce(e){return e>1?1:e<1e-4?1e-4:e}de.alphaComponent=yce;function vce(){if(Ko.Konva.isUnminified)return function(e,t){return Nt.Util._isNumber(e)||Nt.Util.warn(Xo(e)+' is a not valid value for "'+t+'" attribute. 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Use ""'+l+'" instead.')}o.prototype[a]=f,o.prototype[c]=f,o.prototype[d]=f})},afterSetFilter(){this._filterUpToDate=!1}}})(Te);var _i={},Oo={};Object.defineProperty(Oo,"__esModule",{value:!0});Oo.HitContext=Oo.SceneContext=Oo.Context=void 0;const sN=Ot,Pce=Pe;function Ace(e){var t=[],n=e.length,r=sN.Util,i,o;for(i=0;i<n;i++)o=e[i],r._isNumber(o)?o=Math.round(o*1e3)/1e3:r._isString(o)||(o=o+""),t.push(o);return t}var YE=",",kce="(",Rce=")",Oce="([",Mce="])",Ice=";",Nce="()",Dce="=",QE=["arc","arcTo","beginPath","bezierCurveTo","clearRect","clip","closePath","createLinearGradient","createPattern","createRadialGradient","drawImage","ellipse","fill","fillText","getImageData","createImageData","lineTo","moveTo","putImageData","quadraticCurveTo","rect","restore","rotate","save","scale","setLineDash","setTransform","stroke","strokeText","transform","translate"],Lce=["fillStyle","strokeStyle","shadowColor","shadowBlur","shadowOffsetX","shadowOffsetY","lineCap","lineDashOffset","lineJoin","lineWidth","miterLimit","font","textAlign","textBaseline","globalAlpha","globalCompositeOperation","imageSmoothingEnabled"];const $ce=100;class rv{constructor(t){this.canvas=t,Pce.Konva.enableTrace&&(this.traceArr=[],this._enableTrace())}fillShape(t){t.fillEnabled()&&this._fill(t)}_fill(t){}strokeShape(t){t.hasStroke()&&this._stroke(t)}_stroke(t){}fillStrokeShape(t){t.attrs.fillAfterStrokeEnabled?(this.strokeShape(t),this.fillShape(t)):(this.fillShape(t),this.strokeShape(t))}getTrace(t,n){var r=this.traceArr,i=r.length,o="",s,a,l,u;for(s=0;s<i;s++)a=r[s],l=a.method,l?(u=a.args,o+=l,t?o+=Nce:sN.Util._isArray(u[0])?o+=Oce+u.join(YE)+Mce:(n&&(u=u.map(c=>typeof c=="number"?Math.floor(c):c)),o+=kce+u.join(YE)+Rce)):(o+=a.property,t||(o+=Dce+a.val)),o+=Ice;return o}clearTrace(){this.traceArr=[]}_trace(t){var n=this.traceArr,r;n.push(t),r=n.length,r>=$ce&&n.shift()}reset(){var t=this.getCanvas().getPixelRatio();this.setTransform(1*t,0,0,1*t,0,0)}getCanvas(){return this.canvas}clear(t){var n=this.getCanvas();t?this.clearRect(t.x||0,t.y||0,t.width||0,t.height||0):this.clearRect(0,0,n.getWidth()/n.pixelRatio,n.getHeight()/n.pixelRatio)}_applyLineCap(t){const n=t.attrs.lineCap;n&&this.setAttr("lineCap",n)}_applyOpacity(t){var n=t.getAbsoluteOpacity();n!==1&&this.setAttr("globalAlpha",n)}_applyLineJoin(t){const n=t.attrs.lineJoin;n&&this.setAttr("lineJoin",n)}setAttr(t,n){this._context[t]=n}arc(t,n,r,i,o,s){this._context.arc(t,n,r,i,o,s)}arcTo(t,n,r,i,o){this._context.arcTo(t,n,r,i,o)}beginPath(){this._context.beginPath()}bezierCurveTo(t,n,r,i,o,s){this._context.bezierCurveTo(t,n,r,i,o,s)}clearRect(t,n,r,i){this._context.clearRect(t,n,r,i)}clip(...t){this._context.clip.apply(this._context,t)}closePath(){this._context.closePath()}createImageData(t,n){var r=arguments;if(r.length===2)return this._context.createImageData(t,n);if(r.length===1)return this._context.createImageData(t)}createLinearGradient(t,n,r,i){return this._context.createLinearGradient(t,n,r,i)}createPattern(t,n){return this._context.createPattern(t,n)}createRadialGradient(t,n,r,i,o,s){return this._context.createRadialGradient(t,n,r,i,o,s)}drawImage(t,n,r,i,o,s,a,l,u){var c=arguments,d=this._context;c.length===3?d.drawImage(t,n,r):c.length===5?d.drawImage(t,n,r,i,o):c.length===9&&d.drawImage(t,n,r,i,o,s,a,l,u)}ellipse(t,n,r,i,o,s,a,l){this._context.ellipse(t,n,r,i,o,s,a,l)}isPointInPath(t,n,r,i){return r?this._context.isPointInPath(r,t,n,i):this._context.isPointInPath(t,n,i)}fill(...t){this._context.fill.apply(this._context,t)}fillRect(t,n,r,i){this._context.fillRect(t,n,r,i)}strokeRect(t,n,r,i){this._context.strokeRect(t,n,r,i)}fillText(t,n,r,i){i?this._context.fillText(t,n,r,i):this._context.fillText(t,n,r)}measureText(t){return this._context.measureText(t)}getImageData(t,n,r,i){return this._context.getImageData(t,n,r,i)}lineTo(t,n){this._context.lineTo(t,n)}moveTo(t,n){this._context.moveTo(t,n)}rect(t,n,r,i){this._context.rect(t,n,r,i)}putImageData(t,n,r){this._context.putImageData(t,n,r)}quadraticCurveTo(t,n,r,i){this._context.quadraticCurveTo(t,n,r,i)}restore(){this._context.restore()}rotate(t){this._context.rotate(t)}save(){this._context.save()}scale(t,n){this._context.scale(t,n)}setLineDash(t){this._context.setLineDash?this._context.setLineDash(t):"mozDash"in this._context?this._context.mozDash=t:"webkitLineDash"in this._context&&(this._context.webkitLineDash=t)}getLineDash(){return this._context.getLineDash()}setTransform(t,n,r,i,o,s){this._context.setTransform(t,n,r,i,o,s)}stroke(t){t?this._context.stroke(t):this._context.stroke()}strokeText(t,n,r,i){this._context.strokeText(t,n,r,i)}transform(t,n,r,i,o,s){this._context.transform(t,n,r,i,o,s)}translate(t,n){this._context.translate(t,n)}_enableTrace(){var t=this,n=QE.length,r=this.setAttr,i,o,s=function(a){var 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n=t._getLinearGradient();n&&(this.setAttr("fillStyle",n),t._fillFunc(this))}_fillRadialGradient(t){const n=t._getRadialGradient();n&&(this.setAttr("fillStyle",n),t._fillFunc(this))}_fill(t){const n=t.fill(),r=t.getFillPriority();if(n&&r==="color"){this._fillColor(t);return}const i=t.getFillPatternImage();if(i&&r==="pattern"){this._fillPattern(t);return}const o=t.getFillLinearGradientColorStops();if(o&&r==="linear-gradient"){this._fillLinearGradient(t);return}const s=t.getFillRadialGradientColorStops();if(s&&r==="radial-gradient"){this._fillRadialGradient(t);return}n?this._fillColor(t):i?this._fillPattern(t):o?this._fillLinearGradient(t):s&&this._fillRadialGradient(t)}_strokeLinearGradient(t){const n=t.getStrokeLinearGradientStartPoint(),r=t.getStrokeLinearGradientEndPoint(),i=t.getStrokeLinearGradientColorStops(),o=this.createLinearGradient(n.x,n.y,r.x,r.y);if(i){for(var s=0;s<i.length;s+=2)o.addColorStop(i[s],i[s+1]);this.setAttr("strokeStyle",o)}}_stroke(t){var n=t.dash(),r=t.getStrokeScaleEnabled();if(t.hasStroke()){if(!r){this.save();var i=this.getCanvas().getPixelRatio();this.setTransform(i,0,0,i,0,0)}this._applyLineCap(t),n&&t.dashEnabled()&&(this.setLineDash(n),this.setAttr("lineDashOffset",t.dashOffset())),this.setAttr("lineWidth",t.strokeWidth()),t.getShadowForStrokeEnabled()||this.setAttr("shadowColor","rgba(0,0,0,0)");var o=t.getStrokeLinearGradientColorStops();o?this._strokeLinearGradient(t):this.setAttr("strokeStyle",t.stroke()),t._strokeFunc(this),r||this.restore()}}_applyShadow(t){var n,r,i,o=(n=t.getShadowRGBA())!==null&&n!==void 0?n:"black",s=(r=t.getShadowBlur())!==null&&r!==void 0?r:5,a=(i=t.getShadowOffset())!==null&&i!==void 0?i:{x:0,y:0},l=t.getAbsoluteScale(),u=this.canvas.getPixelRatio(),c=l.x*u,d=l.y*u;this.setAttr("shadowColor",o),this.setAttr("shadowBlur",s*Math.min(Math.abs(c),Math.abs(d))),this.setAttr("shadowOffsetX",a.x*c),this.setAttr("shadowOffsetY",a.y*d)}}Oo.SceneContext=Fce;class Bce extends rv{constructor(t){super(t),this._context=t._canvas.getContext("2d",{willReadFrequently:!0})}_fill(t){this.save(),this.setAttr("fillStyle",t.colorKey),t._fillFuncHit(this),this.restore()}strokeShape(t){t.hasHitStroke()&&this._stroke(t)}_stroke(t){if(t.hasHitStroke()){const o=t.getStrokeScaleEnabled();if(!o){this.save();var n=this.getCanvas().getPixelRatio();this.setTransform(n,0,0,n,0,0)}this._applyLineCap(t);var r=t.hitStrokeWidth(),i=r==="auto"?t.strokeWidth():r;this.setAttr("lineWidth",i),this.setAttr("strokeStyle",t.colorKey),t._strokeFuncHit(this),o||this.restore()}}}Oo.HitContext=Bce;Object.defineProperty(_i,"__esModule",{value:!0});_i.HitCanvas=_i.SceneCanvas=_i.Canvas=void 0;const Ym=Ot,aN=Oo,lN=Pe,jce=Te,Vce=de;var Np;function zce(){if(Np)return Np;var e=Ym.Util.createCanvasElement(),t=e.getContext("2d");return Np=function(){var n=lN.Konva._global.devicePixelRatio||1,r=t.webkitBackingStorePixelRatio||t.mozBackingStorePixelRatio||t.msBackingStorePixelRatio||t.oBackingStorePixelRatio||t.backingStorePixelRatio||1;return n/r}(),Ym.Util.releaseCanvas(e),Np}class iv{constructor(t){this.pixelRatio=1,this.width=0,this.height=0,this.isCache=!1;var n=t||{},r=n.pixelRatio||lN.Konva.pixelRatio||zce();this.pixelRatio=r,this._canvas=Ym.Util.createCanvasElement(),this._canvas.style.padding="0",this._canvas.style.margin="0",this._canvas.style.border="0",this._canvas.style.background="transparent",this._canvas.style.position="absolute",this._canvas.style.top="0",this._canvas.style.left="0"}getContext(){return this.context}getPixelRatio(){return this.pixelRatio}setPixelRatio(t){var n=this.pixelRatio;this.pixelRatio=t,this.setSize(this.getWidth()/n,this.getHeight()/n)}setWidth(t){this.width=this._canvas.width=t*this.pixelRatio,this._canvas.style.width=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}setHeight(t){this.height=this._canvas.height=t*this.pixelRatio,this._canvas.style.height=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}getWidth(){return this.width}getHeight(){return this.height}setSize(t,n){this.setWidth(t||0),this.setHeight(n||0)}toDataURL(t,n){try{return this._canvas.toDataURL(t,n)}catch{try{return this._canvas.toDataURL()}catch(i){return Ym.Util.error("Unable to get data URL. "+i.message+" For more info read https://konvajs.org/docs/posts/Tainted_Canvas.html."),""}}}}_i.Canvas=iv;jce.Factory.addGetterSetter(iv,"pixelRatio",void 0,(0,Vce.getNumberValidator)());class Uce extends iv{constructor(t={width:0,height:0,willReadFrequently:!1}){super(t),this.context=new aN.SceneContext(this,{willReadFrequently:t.willReadFrequently}),this.setSize(t.width,t.height)}}_i.SceneCanvas=Uce;class Gce extends iv{constructor(t={width:0,height:0}){super(t),this.hitCanvas=!0,this.context=new aN.HitContext(this),this.setSize(t.width,t.height)}}_i.HitCanvas=Gce;var ov={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.DD=void 0;const t=Pe,n=Ot;e.DD={get isDragging(){var r=!1;return e.DD._dragElements.forEach(i=>{i.dragStatus==="dragging"&&(r=!0)}),r},justDragged:!1,get node(){var r;return e.DD._dragElements.forEach(i=>{r=i.node}),r},_dragElements:new Map,_drag(r){const i=[];e.DD._dragElements.forEach((o,s)=>{const{node:a}=o,l=a.getStage();l.setPointersPositions(r),o.pointerId===void 0&&(o.pointerId=n.Util._getFirstPointerId(r));const u=l._changedPointerPositions.find(f=>f.id===o.pointerId);if(u){if(o.dragStatus!=="dragging"){var c=a.dragDistance(),d=Math.max(Math.abs(u.x-o.startPointerPos.x),Math.abs(u.y-o.startPointerPos.y));if(d<c||(a.startDrag({evt:r}),!a.isDragging()))return}a._setDragPosition(r,o),i.push(a)}}),i.forEach(o=>{o.fire("dragmove",{type:"dragmove",target:o,evt:r},!0)})},_endDragBefore(r){const i=[];e.DD._dragElements.forEach(o=>{const{node:s}=o,a=s.getStage();if(r&&a.setPointersPositions(r),!a._changedPointerPositions.find(c=>c.id===o.pointerId))return;(o.dragStatus==="dragging"||o.dragStatus==="stopped")&&(e.DD.justDragged=!0,t.Konva._mouseListenClick=!1,t.Konva._touchListenClick=!1,t.Konva._pointerListenClick=!1,o.dragStatus="stopped");const u=o.node.getLayer()||o.node instanceof t.Konva.Stage&&o.node;u&&i.indexOf(u)===-1&&i.push(u)}),i.forEach(o=>{o.draw()})},_endDragAfter(r){e.DD._dragElements.forEach((i,o)=>{i.dragStatus==="stopped"&&i.node.fire("dragend",{type:"dragend",target:i.node,evt:r},!0),i.dragStatus!=="dragging"&&e.DD._dragElements.delete(o)})}},t.Konva.isBrowser&&(window.addEventListener("mouseup",e.DD._endDragBefore,!0),window.addEventListener("touchend",e.DD._endDragBefore,!0),window.addEventListener("mousemove",e.DD._drag),window.addEventListener("touchmove",e.DD._drag),window.addEventListener("mouseup",e.DD._endDragAfter,!1),window.addEventListener("touchend",e.DD._endDragAfter,!1))})(ov);Object.defineProperty(wt,"__esModule",{value:!0});wt.Node=void 0;const ke=Ot,Ah=Te,Dp=_i,ya=Pe,Br=ov,zt=de;var bg="absoluteOpacity",Lp="allEventListeners",bo="absoluteTransform",ZE="absoluteScale",va="canvas",Hce="Change",qce="children",Wce="konva",g2="listening",JE="mouseenter",e6="mouseleave",t6="set",n6="Shape",Sg=" ",r6="stage",ss="transform",Kce="Stage",m2="visible",Xce=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(Sg);let Yce=1,Se=class y2{constructor(t){this._id=Yce++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(t),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(t){(t===ss||t===bo)&&this._cache.get(t)?this._cache.get(t).dirty=!0:t?this._cache.delete(t):this._cache.clear()}_getCache(t,n){var r=this._cache.get(t),i=t===ss||t===bo,o=r===void 0||i&&r.dirty===!0;return o&&(r=n.call(this),this._cache.set(t,r)),r}_calculate(t,n,r){if(!this._attachedDepsListeners.get(t)){const i=n.map(o=>o+"Change.konva").join(Sg);this.on(i,()=>{this._clearCache(t)}),this._attachedDepsListeners.set(t,!0)}return this._getCache(t,r)}_getCanvasCache(){return this._cache.get(va)}_clearSelfAndDescendantCache(t){this._clearCache(t),t===bo&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(va)){const{scene:t,filter:n,hit:r}=this._cache.get(va);ke.Util.releaseCanvas(t,n,r),this._cache.delete(va)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(t){var n=t||{},r={};(n.x===void 0||n.y===void 0||n.width===void 0||n.height===void 0)&&(r=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var i=Math.ceil(n.width||r.width),o=Math.ceil(n.height||r.height),s=n.pixelRatio,a=n.x===void 0?Math.floor(r.x):n.x,l=n.y===void 0?Math.floor(r.y):n.y,u=n.offset||0,c=n.drawBorder||!1,d=n.hitCanvasPixelRatio||1;if(!i||!o){ke.Util.error("Can not cache the node. Width or height of the node equals 0. Caching is skipped.");return}i+=u*2+1,o+=u*2+1,a-=u,l-=u;var f=new Dp.SceneCanvas({pixelRatio:s,width:i,height:o}),h=new Dp.SceneCanvas({pixelRatio:s,width:0,height:0,willReadFrequently:!0}),p=new Dp.HitCanvas({pixelRatio:d,width:i,height:o}),m=f.getContext(),S=p.getContext();return p.isCache=!0,f.isCache=!0,this._cache.delete(va),this._filterUpToDate=!1,n.imageSmoothingEnabled===!1&&(f.getContext()._context.imageSmoothingEnabled=!1,h.getContext()._context.imageSmoothingEnabled=!1),m.save(),S.save(),m.translate(-a,-l),S.translate(-a,-l),this._isUnderCache=!0,this._clearSelfAndDescendantCache(bg),this._clearSelfAndDescendantCache(ZE),this.drawScene(f,this),this.drawHit(p,this),this._isUnderCache=!1,m.restore(),S.restore(),c&&(m.save(),m.beginPath(),m.rect(0,0,i,o),m.closePath(),m.setAttr("strokeStyle","red"),m.setAttr("lineWidth",5),m.stroke(),m.restore()),this._cache.set(va,{scene:f,filter:h,hit:p,x:a,y:l}),this._requestDraw(),this}isCached(){return this._cache.has(va)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,s,a,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var c=l.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),{x:i,y:o,width:s-i,height:a-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),s,a,l,u;if(t){if(!this._filterUpToDate){var c=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(s=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/c,r.getHeight()/c),a=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l<s;l++){if(u=t[l],typeof u!="function"){ke.Util.error("Filter should be type of function, but got "+typeof u+" instead. Please check correct filters");continue}u.call(this,a),o.putImageData(a,0,0)}}catch(d){ke.Util.error("Unable to apply filter. "+d.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return i}return r}on(t,n){if(this._cache&&this._cache.delete(Lp),arguments.length===3)return this._delegate.apply(this,arguments);var r=t.split(Sg),i=r.length,o,s,a,l,u;for(o=0;o<i;o++)s=r[o],a=s.split("."),l=a[0],u=a[1]||"",this.eventListeners[l]||(this.eventListeners[l]=[]),this.eventListeners[l].push({name:u,handler:n});return this}off(t,n){var r=(t||"").split(Sg),i=r.length,o,s,a,l,u,c;if(this._cache&&this._cache.delete(Lp),!t)for(s in this.eventListeners)this._off(s);for(o=0;o<i;o++)if(a=r[o],l=a.split("."),u=l[0],c=l[1],u)this.eventListeners[u]&&this._off(u,c,n);else for(s in this.eventListeners)this._off(s,c,n);return this}dispatchEvent(t){var n={target:this,type:t.type,evt:t};return this.fire(t.type,n),this}addEventListener(t,n){return this.on(t,function(r){n.call(this,r.evt)}),this}removeEventListener(t){return this.off(t),this}_delegate(t,n,r){var i=this;this.on(t,function(o){for(var s=o.target.findAncestors(n,!0,i),a=0;a<s.length;a++)o=ke.Util.cloneObject(o),o.currentTarget=s[a],r.call(s[a],o)})}remove(){return this.isDragging()&&this.stopDrag(),Br.DD._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(bo),this._clearSelfAndDescendantCache(bg),this._clearSelfAndDescendantCache(ZE),this._clearSelfAndDescendantCache(r6),this._clearSelfAndDescendantCache(m2),this._clearSelfAndDescendantCache(g2)}_remove(){this._clearCaches();var t=this.getParent();t&&t.children&&(t.children.splice(this.index,1),t._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this.clearCache(),this}getAttr(t){var n="get"+ke.Util._capitalize(t);return ke.Util._isFunction(this[n])?this[n]():this.attrs[t]}getAncestors(){for(var t=this.getParent(),n=[];t;)n.push(t),t=t.getParent();return n}getAttrs(){return this.attrs||{}}setAttrs(t){return this._batchTransformChanges(()=>{var n,r;if(!t)return this;for(n in t)n!==qce&&(r=t6+ke.Util._capitalize(n),ke.Util._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(g2,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(m2,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;Br.DD._dragElements.forEach(s=>{s.dragStatus==="dragging"&&(s.node.nodeType==="Stage"||s.node.getLayer()===r)&&(i=!0)});var o=!n&&!ya.Konva.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,s,a;function l(u){for(i=[],o=u.length,s=0;s<o;s++)a=u[s],r++,a.nodeType!==n6&&(i=i.concat(a.getChildren().slice())),a._id===n._id&&(s=o);i.length>0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==Kce&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(ss),this._clearSelfAndDescendantCache(bo)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new ke.Transform,s=this.offset();return o.m=i.slice(),o.translate(s.x,s.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(ss);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(ss),this._clearSelfAndDescendantCache(bo),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,s;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,s=0;s<o;s++)t(r[s])}}rotate(t){return this.rotation(this.rotation()+t),this}moveToTop(){if(!this.parent)return ke.Util.warn("Node has no parent. moveToTop function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0):!1}moveUp(){if(!this.parent)return ke.Util.warn("Node has no parent. moveUp function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.splice(t+1,0,this),this.parent._setChildrenIndices(),!0):!1}moveDown(){if(!this.parent)return ke.Util.warn("Node has no parent. moveDown function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return ke.Util.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return ke.Util.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&ke.Util.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(bg,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,s,a;t.attrs={};for(r in n)i=n[r],a=ke.Util.isObject(i)&&!ke.Util._isPlainObject(i)&&!ke.Util._isArray(i),!a&&(o=typeof this[r]=="function"&&this[r],delete n[r],s=o?o.call(this):null,n[r]=i,s!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),ke.Util._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i<r;i++)if(o=n[i],ke.Util.isValidSelector(o)||(ke.Util.warn('Selector "'+o+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),ke.Util.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),ke.Util.warn("Konva is awesome, right?")),o.charAt(0)==="#"){if(this.id()===o.slice(1))return!0}else if(o.charAt(0)==="."){if(this.hasName(o.slice(1)))return!0}else if(this.className===o||this.nodeType===o)return!0;return!1}getLayer(){var t=this.getParent();return t?t.getLayer():null}getStage(){return this._getCache(r6,this._getStage)}_getStage(){var t=this.getParent();if(t)return t.getStage()}fire(t,n={},r){return n.target=n.target||this,r?this._fireAndBubble(t,n):this._fire(t,n),this}getAbsoluteTransform(t){return t?this._getAbsoluteTransform(t):this._getCache(bo,this._getAbsoluteTransform)}_getAbsoluteTransform(t){var n;if(t)return n=new ke.Transform,this._eachAncestorReverse(function(i){var o=i.transformsEnabled();o==="all"?n.multiply(i.getTransform()):o==="position"&&n.translate(i.x()-i.offsetX(),i.y()-i.offsetY())},t),n;n=this._cache.get(bo)||new ke.Transform,this.parent?this.parent.getAbsoluteTransform().copyInto(n):n.reset();var r=this.transformsEnabled();if(r==="all")n.multiply(this.getTransform());else if(r==="position"){const i=this.attrs.x||0,o=this.attrs.y||0,s=this.attrs.offsetX||0,a=this.attrs.offsetY||0;n.translate(i-s,o-a)}return n.dirty=!1,n}getAbsoluteScale(t){for(var n=this;n;)n._isUnderCache&&(t=n),n=n.getParent();const i=this.getAbsoluteTransform(t).decompose();return{x:i.scaleX,y:i.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(ss,this._getTransform)}_getTransform(){var t,n,r=this._cache.get(ss)||new ke.Transform;r.reset();var i=this.x(),o=this.y(),s=ya.Konva.getAngle(this.rotation()),a=(t=this.attrs.scaleX)!==null&&t!==void 0?t:1,l=(n=this.attrs.scaleY)!==null&&n!==void 0?n:1,u=this.attrs.skewX||0,c=this.attrs.skewY||0,d=this.attrs.offsetX||0,f=this.attrs.offsetY||0;return(i!==0||o!==0)&&r.translate(i,o),s!==0&&r.rotate(s),(u!==0||c!==0)&&r.skew(u,c),(a!==1||l!==1)&&r.scale(a,l),(d!==0||f!==0)&&r.translate(-1*d,-1*f),r.dirty=!1,r}clone(t){var n=ke.Util.cloneObject(this.attrs),r,i,o,s,a;for(r in t)n[r]=t[r];var l=new this.constructor(n);for(r in this.eventListeners)for(i=this.eventListeners[r],o=i.length,s=0;s<o;s++)a=i[s],a.name.indexOf(Wce)<0&&(l.eventListeners[r]||(l.eventListeners[r]=[]),l.eventListeners[r].push(a));return l}_toKonvaCanvas(t){t=t||{};var n=this.getClientRect(),r=this.getStage(),i=t.x!==void 0?t.x:Math.floor(n.x),o=t.y!==void 0?t.y:Math.floor(n.y),s=t.pixelRatio||1,a=new Dp.SceneCanvas({width:t.width||Math.ceil(n.width)||(r?r.width():0),height:t.height||Math.ceil(n.height)||(r?r.height():0),pixelRatio:s}),l=a.getContext();return t.imageSmoothingEnabled===!1&&(l._context.imageSmoothingEnabled=!1),l.save(),(i||o)&&l.translate(-1*i,-1*o),this.drawScene(a),l.restore(),a}toCanvas(t){return this._toKonvaCanvas(t)._canvas}toDataURL(t){t=t||{};var n=t.mimeType||null,r=t.quality||null,i=this._toKonvaCanvas(t).toDataURL(n,r);return t.callback&&t.callback(i),i}toImage(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,ke.Util._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():ya.Konva.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,s,a;for(o=0;o<i.length;o++)if(s=i[o].name,a=i[o].handler,(s!=="konva"||n==="konva")&&(!n||s===n)&&(!r||r===a)){if(i.splice(o,1),i.length===0){delete this.eventListeners[t];break}o--}}_fireChangeEvent(t,n,r){this._fire(t+Hce,{oldVal:n,newVal:r})}addName(t){if(!this.hasName(t)){var n=this.name(),r=n?n+" "+t:t;this.name(r)}return this}hasName(t){if(!t)return!1;const n=this.name();if(!n)return!1;var r=(n||"").split(/\s/g);return r.indexOf(t)!==-1}removeName(t){var n=(this.name()||"").split(/\s/g),r=n.indexOf(t);return r!==-1&&(n.splice(r,1),this.name(n.join(" "))),this}setAttr(t,n){var r=this[t6+ke.Util._capitalize(t)];return ke.Util._isFunction(r)?r.call(this,n):this._setAttr(t,n),this}_requestDraw(){if(ya.Konva.autoDrawEnabled){const t=this.getLayer()||this.getStage();t==null||t.batchDraw()}}_setAttr(t,n){var r=this.attrs[t];r===n&&!ke.Util.isObject(n)||(n==null?delete this.attrs[t]:this.attrs[t]=n,this._shouldFireChangeEvents&&this._fireChangeEvent(t,r,n),this._requestDraw())}_setComponentAttr(t,n,r){var i;r!==void 0&&(i=this.attrs[t],i||(this.attrs[t]=this.getAttr(t)),this.attrs[t][n]=r,this._fireChangeEvent(t,i,r))}_fireAndBubble(t,n,r){n&&this.nodeType===n6&&(n.target=this);var i=(t===JE||t===e6)&&(r&&(this===r||this.isAncestorOf&&this.isAncestorOf(r))||this.nodeType==="Stage"&&!r);if(!i){this._fire(t,n);var o=(t===JE||t===e6)&&r&&r.isAncestorOf&&r.isAncestorOf(this)&&!r.isAncestorOf(this.parent);(n&&!n.cancelBubble||!n)&&this.parent&&this.parent.isListening()&&!o&&(r&&r.parent?this._fireAndBubble.call(this.parent,t,n,r):this._fireAndBubble.call(this.parent,t,n))}}_getProtoListeners(t){var n,r,i;const o=(n=this._cache.get(Lp))!==null&&n!==void 0?n:{};let s=o==null?void 0:o[t];if(s===void 0){s=[];let a=Object.getPrototypeOf(this);for(;a;){const l=(i=(r=a.eventListeners)===null||r===void 0?void 0:r[t])!==null&&i!==void 0?i:[];s.push(...l),a=Object.getPrototypeOf(a)}o[t]=s,this._cache.set(Lp,o)}return s}_fire(t,n){n=n||{},n.currentTarget=this,n.type=t;const r=this._getProtoListeners(t);if(r)for(var i=0;i<r.length;i++)r[i].handler.call(this,n);const o=this.eventListeners[t];if(o)for(var i=0;i<o.length;i++)o[i].handler.call(this,n)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(t){var n=t?t.pointerId:void 0,r=this.getStage(),i=this.getAbsolutePosition(),o=r._getPointerById(n)||r._changedPointerPositions[0]||i;Br.DD._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-i.x,y:o.y-i.y},dragStatus:"ready",pointerId:n})}startDrag(t,n=!0){Br.DD._dragElements.has(this._id)||this._createDragElement(t);const r=Br.DD._dragElements.get(this._id);r.dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:t&&t.evt},n)}_setDragPosition(t,n){const r=this.getStage()._getPointerById(n.pointerId);if(r){var i={x:r.x-n.offset.x,y:r.y-n.offset.y},o=this.dragBoundFunc();if(o!==void 0){const s=o.call(this,i,t);s?i=s:ke.Util.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}(!this._lastPos||this._lastPos.x!==i.x||this._lastPos.y!==i.y)&&(this.setAbsolutePosition(i),this._requestDraw()),this._lastPos=i}}stopDrag(t){const n=Br.DD._dragElements.get(this._id);n&&(n.dragStatus="stopped"),Br.DD._endDragBefore(t),Br.DD._endDragAfter(t)}setDraggable(t){this._setAttr("draggable",t),this._dragChange()}isDragging(){const t=Br.DD._dragElements.get(this._id);return t?t.dragStatus==="dragging":!1}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",function(t){var n=t.evt.button!==void 0,r=!n||ya.Konva.dragButtons.indexOf(t.evt.button)>=0;if(r&&!this.isDragging()){var i=!1;Br.DD._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=Br.DD._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&Br.DD._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return ke.Util.haveIntersection(r,this.getClientRect())}static create(t,n){return ke.Util._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=y2.prototype.getClassName.call(t),i=t.children,o,s,a;n&&(t.attrs.container=n),ya.Konva[r]||(ke.Util.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=ya.Konva[r];if(o=new l(t.attrs),i)for(s=i.length,a=0;a<s;a++)o.add(y2._createNode(i[a]));return o}};wt.Node=Se;Se.prototype.nodeType="Node";Se.prototype._attrsAffectingSize=[];Se.prototype.eventListeners={};Se.prototype.on.call(Se.prototype,Xce,function(){if(this._batchingTransformChange){this._needClearTransformCache=!0;return}this._clearCache(ss),this._clearSelfAndDescendantCache(bo)});Se.prototype.on.call(Se.prototype,"visibleChange.konva",function(){this._clearSelfAndDescendantCache(m2)});Se.prototype.on.call(Se.prototype,"listeningChange.konva",function(){this._clearSelfAndDescendantCache(g2)});Se.prototype.on.call(Se.prototype,"opacityChange.konva",function(){this._clearSelfAndDescendantCache(bg)});const ot=Ah.Factory.addGetterSetter;ot(Se,"zIndex");ot(Se,"absolutePosition");ot(Se,"position");ot(Se,"x",0,(0,zt.getNumberValidator)());ot(Se,"y",0,(0,zt.getNumberValidator)());ot(Se,"globalCompositeOperation","source-over",(0,zt.getStringValidator)());ot(Se,"opacity",1,(0,zt.getNumberValidator)());ot(Se,"name","",(0,zt.getStringValidator)());ot(Se,"id","",(0,zt.getStringValidator)());ot(Se,"rotation",0,(0,zt.getNumberValidator)());Ah.Factory.addComponentsGetterSetter(Se,"scale",["x","y"]);ot(Se,"scaleX",1,(0,zt.getNumberValidator)());ot(Se,"scaleY",1,(0,zt.getNumberValidator)());Ah.Factory.addComponentsGetterSetter(Se,"skew",["x","y"]);ot(Se,"skewX",0,(0,zt.getNumberValidator)());ot(Se,"skewY",0,(0,zt.getNumberValidator)());Ah.Factory.addComponentsGetterSetter(Se,"offset",["x","y"]);ot(Se,"offsetX",0,(0,zt.getNumberValidator)());ot(Se,"offsetY",0,(0,zt.getNumberValidator)());ot(Se,"dragDistance",null,(0,zt.getNumberValidator)());ot(Se,"width",0,(0,zt.getNumberValidator)());ot(Se,"height",0,(0,zt.getNumberValidator)());ot(Se,"listening",!0,(0,zt.getBooleanValidator)());ot(Se,"preventDefault",!0,(0,zt.getBooleanValidator)());ot(Se,"filters",null,function(e){return this._filterUpToDate=!1,e});ot(Se,"visible",!0,(0,zt.getBooleanValidator)());ot(Se,"transformsEnabled","all",(0,zt.getStringValidator)());ot(Se,"size");ot(Se,"dragBoundFunc");ot(Se,"draggable",!1,(0,zt.getBooleanValidator)());Ah.Factory.backCompat(Se,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});var gl={};Object.defineProperty(gl,"__esModule",{value:!0});gl.Container=void 0;const yc=Te,Ub=wt,sv=de;class ml extends Ub.Node{constructor(){super(...arguments),this.children=[]}getChildren(t){if(!t)return this.children||[];const n=this.children||[];var r=[];return n.forEach(function(i){t(i)&&r.push(i)}),r}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(t.length===0)return this;if(t.length>1){for(var n=0;n<t.length;n++)this.add(t[n]);return this}const r=t[0];return r.getParent()?(r.moveTo(this),this):(this._validateAdd(r),r.index=this.getChildren().length,r.parent=this,r._clearCaches(),this.getChildren().push(r),this._fire("add",{child:r}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(t){return this._generalFind(t,!1)}findOne(t){var n=this._generalFind(t,!0);return n.length>0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=Ub.Node.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=Ub.Node.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(a){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.hit;if(a){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),s=this.clipWidth(),a=this.clipHeight(),l=this.clipFunc(),u=s&&a||l;const c=r===this;if(u){o.save();var d=this.getAbsoluteTransform(r),f=d.getMatrix();o.transform(f[0],f[1],f[2],f[3],f[4],f[5]),o.beginPath();let S;if(l)S=l.call(this,o,this);else{var h=this.clipX(),p=this.clipY();o.rect(h,p,s,a)}o.clip.apply(o,S),f=d.copy().invert().getMatrix(),o.transform(f[0],f[1],f[2],f[3],f[4],f[5])}var m=!c&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";m&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(S){S[t](n,r)}),m&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,s,a,l,u={x:1/0,y:1/0,width:0,height:0},c=this;(n=this.children)===null||n===void 0||n.forEach(function(m){if(m.visible()){var S=m.getClientRect({relativeTo:c,skipShadow:t.skipShadow,skipStroke:t.skipStroke});S.width===0&&S.height===0||(o===void 0?(o=S.x,s=S.y,a=S.x+S.width,l=S.y+S.height):(o=Math.min(o,S.x),s=Math.min(s,S.y),a=Math.max(a,S.x+S.width),l=Math.max(l,S.y+S.height)))}});for(var d=this.find("Shape"),f=!1,h=0;h<d.length;h++){var p=d[h];if(p._isVisible(this)){f=!0;break}}return f&&o!==void 0?u={x:o,y:s,width:a-o,height:l-s}:u={x:0,y:0,width:0,height:0},r?u:this._transformedRect(u,i)}}gl.Container=ml;yc.Factory.addComponentsGetterSetter(ml,"clip",["x","y","width","height"]);yc.Factory.addGetterSetter(ml,"clipX",void 0,(0,sv.getNumberValidator)());yc.Factory.addGetterSetter(ml,"clipY",void 0,(0,sv.getNumberValidator)());yc.Factory.addGetterSetter(ml,"clipWidth",void 0,(0,sv.getNumberValidator)());yc.Factory.addGetterSetter(ml,"clipHeight",void 0,(0,sv.getNumberValidator)());yc.Factory.addGetterSetter(ml,"clipFunc");var uN={},Tr={};Object.defineProperty(Tr,"__esModule",{value:!0});Tr.releaseCapture=Tr.setPointerCapture=Tr.hasPointerCapture=Tr.createEvent=Tr.getCapturedShape=void 0;const Qce=Pe,Ff=new Map,cN=Qce.Konva._global.PointerEvent!==void 0;function Zce(e){return Ff.get(e)}Tr.getCapturedShape=Zce;function _C(e){return{evt:e,pointerId:e.pointerId}}Tr.createEvent=_C;function Jce(e,t){return Ff.get(e)===t}Tr.hasPointerCapture=Jce;function ede(e,t){dN(e),t.getStage()&&(Ff.set(e,t),cN&&t._fire("gotpointercapture",_C(new PointerEvent("gotpointercapture"))))}Tr.setPointerCapture=ede;function dN(e,t){const n=Ff.get(e);if(!n)return;const r=n.getStage();r&&r.content,Ff.delete(e),cN&&n._fire("lostpointercapture",_C(new PointerEvent("lostpointercapture")))}Tr.releaseCapture=dN;(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Stage=e.stages=void 0;const t=Ot,n=Te,r=gl,i=Pe,o=_i,s=ov,a=Pe,l=Tr;var u="Stage",c="string",d="px",f="mouseout",h="mouseleave",p="mouseover",m="mouseenter",S="mousemove",v="mousedown",y="mouseup",g="pointermove",b="pointerdown",_="pointerup",w="pointercancel",x="lostpointercapture",T="pointerout",P="pointerleave",E="pointerover",A="pointerenter",$="contextmenu",I="touchstart",C="touchend",R="touchmove",M="touchcancel",N="wheel",O=5,D=[[m,"_pointerenter"],[v,"_pointerdown"],[S,"_pointermove"],[y,"_pointerup"],[h,"_pointerleave"],[I,"_pointerdown"],[R,"_pointermove"],[C,"_pointerup"],[M,"_pointercancel"],[p,"_pointerover"],[N,"_wheel"],[$,"_contextmenu"],[b,"_pointerdown"],[g,"_pointermove"],[_,"_pointerup"],[w,"_pointercancel"],[x,"_lostpointercapture"]];const L={mouse:{[T]:f,[P]:h,[E]:p,[A]:m,[g]:S,[b]:v,[_]:y,[w]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[T]:"touchout",[P]:"touchleave",[E]:"touchover",[A]:"touchenter",[g]:R,[b]:I,[_]:C,[w]:M,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[T]:T,[P]:P,[E]:E,[A]:A,[g]:g,[b]:b,[_]:_,[w]:w,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},j=Y=>Y.indexOf("pointer")>=0?"pointer":Y.indexOf("touch")>=0?"touch":"mouse",U=Y=>{const B=j(Y);if(B==="pointer")return i.Konva.pointerEventsEnabled&&L.pointer;if(B==="touch")return L.touch;if(B==="mouse")return L.mouse};function G(Y={}){return(Y.clipFunc||Y.clipWidth||Y.clipHeight)&&t.Util.warn("Stage does not support clipping. Please use clip for Layers or Groups."),Y}const W="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);";e.stages=[];class X extends r.Container{constructor(B){super(G(B)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),e.stages.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{G(this.attrs)}),this._checkVisibility()}_validateAdd(B){const H=B.getType()==="Layer",Q=B.getType()==="FastLayer";H||Q||t.Util.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const B=this.visible()?"":"none";this.content.style.display=B}setContainer(B){if(typeof B===c){if(B.charAt(0)==="."){var H=B.slice(1);B=document.getElementsByClassName(H)[0]}else{var Q;B.charAt(0)!=="#"?Q=B:Q=B.slice(1),B=document.getElementById(Q)}if(!B)throw"Can not find container in document with id "+Q}return this._setAttr("container",B),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),B.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var B=this.children,H=B.length,Q;for(Q=0;Q<H;Q++)B[Q].clear();return this}clone(B){return B||(B={}),B.container=typeof document<"u"&&document.createElement("div"),r.Container.prototype.clone.call(this,B)}destroy(){super.destroy();var B=this.content;B&&t.Util._isInDocument(B)&&this.container().removeChild(B);var H=e.stages.indexOf(this);return H>-1&&e.stages.splice(H,1),t.Util.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const B=this._pointerPositions[0]||this._changedPointerPositions[0];return B?{x:B.x,y:B.y}:(t.Util.warn(W),null)}_getPointerById(B){return this._pointerPositions.find(H=>H.id===B)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(B){B=B||{},B.x=B.x||0,B.y=B.y||0,B.width=B.width||this.width(),B.height=B.height||this.height();var H=new o.SceneCanvas({width:B.width,height:B.height,pixelRatio:B.pixelRatio||1}),Q=H.getContext()._context,J=this.children;return(B.x||B.y)&&Q.translate(-1*B.x,-1*B.y),J.forEach(function(ne){if(ne.isVisible()){var te=ne._toKonvaCanvas(B);Q.drawImage(te._canvas,B.x,B.y,te.getWidth()/te.getPixelRatio(),te.getHeight()/te.getPixelRatio())}}),H}getIntersection(B){if(!B)return null;var H=this.children,Q=H.length,J=Q-1,ne;for(ne=J;ne>=0;ne--){const te=H[ne].getIntersection(B);if(te)return te}return null}_resizeDOM(){var B=this.width(),H=this.height();this.content&&(this.content.style.width=B+d,this.content.style.height=H+d),this.bufferCanvas.setSize(B,H),this.bufferHitCanvas.setSize(B,H),this.children.forEach(Q=>{Q.setSize({width:B,height:H}),Q.draw()})}add(B,...H){if(arguments.length>1){for(var Q=0;Q<arguments.length;Q++)this.add(arguments[Q]);return this}super.add(B);var J=this.children.length;return J>O&&t.Util.warn("The stage has "+J+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),B.setSize({width:this.width(),height:this.height()}),B.draw(),i.Konva.isBrowser&&this.content.appendChild(B.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(B){return l.hasPointerCapture(B,this)}setPointerCapture(B){l.setPointerCapture(B,this)}releaseCapture(B){l.releaseCapture(B,this)}getLayers(){return this.children}_bindContentEvents(){i.Konva.isBrowser&&D.forEach(([B,H])=>{this.content.addEventListener(B,Q=>{this[H](Q)},{passive:!1})})}_pointerenter(B){this.setPointersPositions(B);const H=U(B.type);this._fire(H.pointerenter,{evt:B,target:this,currentTarget:this})}_pointerover(B){this.setPointersPositions(B);const H=U(B.type);this._fire(H.pointerover,{evt:B,target:this,currentTarget:this})}_getTargetShape(B){let H=this[B+"targetShape"];return H&&!H.getStage()&&(H=null),H}_pointerleave(B){const H=U(B.type),Q=j(B.type);if(H){this.setPointersPositions(B);var J=this._getTargetShape(Q),ne=!s.DD.isDragging||i.Konva.hitOnDragEnabled;J&&ne?(J._fireAndBubble(H.pointerout,{evt:B}),J._fireAndBubble(H.pointerleave,{evt:B}),this._fire(H.pointerleave,{evt:B,target:this,currentTarget:this}),this[Q+"targetShape"]=null):ne&&(this._fire(H.pointerleave,{evt:B,target:this,currentTarget:this}),this._fire(H.pointerout,{evt:B,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(B){const H=U(B.type),Q=j(B.type);if(H){this.setPointersPositions(B);var J=!1;this._changedPointerPositions.forEach(ne=>{var te=this.getIntersection(ne);if(s.DD.justDragged=!1,i.Konva["_"+Q+"ListenClick"]=!0,!(te&&te.isListening()))return;i.Konva.capturePointerEventsEnabled&&te.setPointerCapture(ne.id),this[Q+"ClickStartShape"]=te,te._fireAndBubble(H.pointerdown,{evt:B,pointerId:ne.id}),J=!0;const ve=B.type.indexOf("touch")>=0;te.preventDefault()&&B.cancelable&&ve&&B.preventDefault()}),J||this._fire(H.pointerdown,{evt:B,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(B){const H=U(B.type),Q=j(B.type);if(!H)return;s.DD.isDragging&&s.DD.node.preventDefault()&&B.cancelable&&B.preventDefault(),this.setPointersPositions(B);var J=!s.DD.isDragging||i.Konva.hitOnDragEnabled;if(!J)return;var ne={};let te=!1;var xe=this._getTargetShape(Q);this._changedPointerPositions.forEach(ve=>{const ce=l.getCapturedShape(ve.id)||this.getIntersection(ve),Ne=ve.id,se={evt:B,pointerId:Ne};var gt=xe!==ce;if(gt&&xe&&(xe._fireAndBubble(H.pointerout,Object.assign({},se),ce),xe._fireAndBubble(H.pointerleave,Object.assign({},se),ce)),ce){if(ne[ce._id])return;ne[ce._id]=!0}ce&&ce.isListening()?(te=!0,gt&&(ce._fireAndBubble(H.pointerover,Object.assign({},se),xe),ce._fireAndBubble(H.pointerenter,Object.assign({},se),xe),this[Q+"targetShape"]=ce),ce._fireAndBubble(H.pointermove,Object.assign({},se))):xe&&(this._fire(H.pointerover,{evt:B,target:this,currentTarget:this,pointerId:Ne}),this[Q+"targetShape"]=null)}),te||this._fire(H.pointermove,{evt:B,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(B){const H=U(B.type),Q=j(B.type);if(!H)return;this.setPointersPositions(B);const J=this[Q+"ClickStartShape"],ne=this[Q+"ClickEndShape"];var te={};let xe=!1;this._changedPointerPositions.forEach(ve=>{const ce=l.getCapturedShape(ve.id)||this.getIntersection(ve);if(ce){if(ce.releaseCapture(ve.id),te[ce._id])return;te[ce._id]=!0}const Ne=ve.id,se={evt:B,pointerId:Ne};let gt=!1;i.Konva["_"+Q+"InDblClickWindow"]?(gt=!0,clearTimeout(this[Q+"DblTimeout"])):s.DD.justDragged||(i.Konva["_"+Q+"InDblClickWindow"]=!0,clearTimeout(this[Q+"DblTimeout"])),this[Q+"DblTimeout"]=setTimeout(function(){i.Konva["_"+Q+"InDblClickWindow"]=!1},i.Konva.dblClickWindow),ce&&ce.isListening()?(xe=!0,this[Q+"ClickEndShape"]=ce,ce._fireAndBubble(H.pointerup,Object.assign({},se)),i.Konva["_"+Q+"ListenClick"]&&J&&J===ce&&(ce._fireAndBubble(H.pointerclick,Object.assign({},se)),gt&&ne&&ne===ce&&ce._fireAndBubble(H.pointerdblclick,Object.assign({},se)))):(this[Q+"ClickEndShape"]=null,i.Konva["_"+Q+"ListenClick"]&&this._fire(H.pointerclick,{evt:B,target:this,currentTarget:this,pointerId:Ne}),gt&&this._fire(H.pointerdblclick,{evt:B,target:this,currentTarget:this,pointerId:Ne}))}),xe||this._fire(H.pointerup,{evt:B,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),i.Konva["_"+Q+"ListenClick"]=!1,B.cancelable&&Q!=="touch"&&B.preventDefault()}_contextmenu(B){this.setPointersPositions(B);var H=this.getIntersection(this.getPointerPosition());H&&H.isListening()?H._fireAndBubble($,{evt:B}):this._fire($,{evt:B,target:this,currentTarget:this})}_wheel(B){this.setPointersPositions(B);var H=this.getIntersection(this.getPointerPosition());H&&H.isListening()?H._fireAndBubble(N,{evt:B}):this._fire(N,{evt:B,target:this,currentTarget:this})}_pointercancel(B){this.setPointersPositions(B);const H=l.getCapturedShape(B.pointerId)||this.getIntersection(this.getPointerPosition());H&&H._fireAndBubble(_,l.createEvent(B)),l.releaseCapture(B.pointerId)}_lostpointercapture(B){l.releaseCapture(B.pointerId)}setPointersPositions(B){var H=this._getContentPosition(),Q=null,J=null;B=B||window.event,B.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(B.touches,ne=>{this._pointerPositions.push({id:ne.identifier,x:(ne.clientX-H.left)/H.scaleX,y:(ne.clientY-H.top)/H.scaleY})}),Array.prototype.forEach.call(B.changedTouches||B.touches,ne=>{this._changedPointerPositions.push({id:ne.identifier,x:(ne.clientX-H.left)/H.scaleX,y:(ne.clientY-H.top)/H.scaleY})})):(Q=(B.clientX-H.left)/H.scaleX,J=(B.clientY-H.top)/H.scaleY,this.pointerPos={x:Q,y:J},this._pointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(B)}],this._changedPointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(B)}])}_setPointerPosition(B){t.Util.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(B)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var B=this.content.getBoundingClientRect();return{top:B.top,left:B.left,scaleX:B.width/this.content.clientWidth||1,scaleY:B.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new o.SceneCanvas({width:this.width(),height:this.height()}),this.bufferHitCanvas=new o.HitCanvas({pixelRatio:1,width:this.width(),height:this.height()}),!!i.Konva.isBrowser){var B=this.container();if(!B)throw"Stage has no container. A container is required.";B.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),B.appendChild(this.content),this._resizeDOM()}}cache(){return t.Util.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(B){B.batchDraw()}),this}}e.Stage=X,X.prototype.nodeType=u,(0,a._registerNode)(X),n.Factory.addGetterSetter(X,"container")})(uN);var kh={},sn={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Shape=e.shapes=void 0;const t=Pe,n=Ot,r=Te,i=wt,o=de,s=Pe,a=Tr;var l="hasShadow",u="shadowRGBA",c="patternImage",d="linearGradient",f="radialGradient";let h;function p(){return h||(h=n.Util.createCanvasElement().getContext("2d"),h)}e.shapes={};function m(P){const E=this.attrs.fillRule;E?P.fill(E):P.fill()}function S(P){P.stroke()}function v(P){P.fill()}function y(P){P.stroke()}function g(){this._clearCache(l)}function b(){this._clearCache(u)}function _(){this._clearCache(c)}function w(){this._clearCache(d)}function x(){this._clearCache(f)}class T extends i.Node{constructor(E){super(E);let A;for(;A=n.Util.getRandomColor(),!(A&&!(A in e.shapes)););this.colorKey=A,e.shapes[A]=this}getContext(){return n.Util.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return n.Util.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(l,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(c,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var E=p();const A=E.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(A&&A.setTransform){const $=new n.Transform;$.translate(this.fillPatternX(),this.fillPatternY()),$.rotate(t.Konva.getAngle(this.fillPatternRotation())),$.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),$.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const I=$.getMatrix(),C=typeof DOMMatrix>"u"?{a:I[0],b:I[1],c:I[2],d:I[3],e:I[4],f:I[5]}:new DOMMatrix(I);A.setTransform(C)}return A}}_getLinearGradient(){return this._getCache(d,this.__getLinearGradient)}__getLinearGradient(){var E=this.fillLinearGradientColorStops();if(E){for(var A=p(),$=this.fillLinearGradientStartPoint(),I=this.fillLinearGradientEndPoint(),C=A.createLinearGradient($.x,$.y,I.x,I.y),R=0;R<E.length;R+=2)C.addColorStop(E[R],E[R+1]);return C}}_getRadialGradient(){return this._getCache(f,this.__getRadialGradient)}__getRadialGradient(){var E=this.fillRadialGradientColorStops();if(E){for(var A=p(),$=this.fillRadialGradientStartPoint(),I=this.fillRadialGradientEndPoint(),C=A.createRadialGradient($.x,$.y,this.fillRadialGradientStartRadius(),I.x,I.y,this.fillRadialGradientEndRadius()),R=0;R<E.length;R+=2)C.addColorStop(E[R],E[R+1]);return C}}getShadowRGBA(){return this._getCache(u,this._getShadowRGBA)}_getShadowRGBA(){if(this.hasShadow()){var E=n.Util.colorToRGBA(this.shadowColor());if(E)return"rgba("+E.r+","+E.g+","+E.b+","+E.a*(this.shadowOpacity()||1)+")"}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const E=this.hitStrokeWidth();return E==="auto"?this.hasStroke():this.strokeEnabled()&&!!E}intersects(E){var A=this.getStage(),$=A.bufferHitCanvas,I;return $.getContext().clear(),this.drawHit($,null,!0),I=$.context.getImageData(Math.round(E.x),Math.round(E.y),1,1).data,I[3]>0}destroy(){return i.Node.prototype.destroy.call(this),delete e.shapes[this.colorKey],delete this.colorKey,this}_useBufferCanvas(E){var A;if(!this.getStage()||!((A=this.attrs.perfectDrawEnabled)!==null&&A!==void 0?A:!0))return!1;const I=E||this.hasFill(),C=this.hasStroke(),R=this.getAbsoluteOpacity()!==1;if(I&&C&&R)return!0;const M=this.hasShadow(),N=this.shadowForStrokeEnabled();return!!(I&&C&&M&&N)}setStrokeHitEnabled(E){n.Util.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),E?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var E=this.size();return{x:this._centroid?-E.width/2:0,y:this._centroid?-E.height/2:0,width:E.width,height:E.height}}getClientRect(E={}){const A=E.skipTransform,$=E.relativeTo,I=this.getSelfRect(),R=!E.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,M=I.width+R,N=I.height+R,O=!E.skipShadow&&this.hasShadow(),D=O?this.shadowOffsetX():0,L=O?this.shadowOffsetY():0,j=M+Math.abs(D),U=N+Math.abs(L),G=O&&this.shadowBlur()||0,W=j+G*2,X=U+G*2,Y={width:W,height:X,x:-(R/2+G)+Math.min(D,0)+I.x,y:-(R/2+G)+Math.min(L,0)+I.y};return A?Y:this._transformedRect(Y,$)}drawScene(E,A){var $=this.getLayer(),I=E||$.getCanvas(),C=I.getContext(),R=this._getCanvasCache(),M=this.getSceneFunc(),N=this.hasShadow(),O,D,L,j=I.isCache,U=A===this;if(!this.isVisible()&&!U)return this;if(R){C.save();var G=this.getAbsoluteTransform(A).getMatrix();return C.transform(G[0],G[1],G[2],G[3],G[4],G[5]),this._drawCachedSceneCanvas(C),C.restore(),this}if(!M)return this;if(C.save(),this._useBufferCanvas()&&!j){O=this.getStage(),D=O.bufferCanvas,L=D.getContext(),L.clear(),L.save(),L._applyLineJoin(this);var W=this.getAbsoluteTransform(A).getMatrix();L.transform(W[0],W[1],W[2],W[3],W[4],W[5]),M.call(this,L,this),L.restore();var X=D.pixelRatio;N&&C._applyShadow(this),C._applyOpacity(this),C._applyGlobalCompositeOperation(this),C.drawImage(D._canvas,0,0,D.width/X,D.height/X)}else{if(C._applyLineJoin(this),!U){var W=this.getAbsoluteTransform(A).getMatrix();C.transform(W[0],W[1],W[2],W[3],W[4],W[5]),C._applyOpacity(this),C._applyGlobalCompositeOperation(this)}N&&C._applyShadow(this),M.call(this,C,this)}return C.restore(),this}drawHit(E,A,$=!1){if(!this.shouldDrawHit(A,$))return this;var I=this.getLayer(),C=E||I.hitCanvas,R=C&&C.getContext(),M=this.hitFunc()||this.sceneFunc(),N=this._getCanvasCache(),O=N&&N.hit;if(this.colorKey||n.Util.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),O){R.save();var D=this.getAbsoluteTransform(A).getMatrix();return R.transform(D[0],D[1],D[2],D[3],D[4],D[5]),this._drawCachedHitCanvas(R),R.restore(),this}if(!M)return this;if(R.save(),R._applyLineJoin(this),!(this===A)){var j=this.getAbsoluteTransform(A).getMatrix();R.transform(j[0],j[1],j[2],j[3],j[4],j[5])}return M.call(this,R,this),R.restore(),this}drawHitFromCache(E=0){var A=this._getCanvasCache(),$=this._getCachedSceneCanvas(),I=A.hit,C=I.getContext(),R=I.getWidth(),M=I.getHeight(),N,O,D,L,j,U;C.clear(),C.drawImage($._canvas,0,0,R,M);try{for(N=C.getImageData(0,0,R,M),O=N.data,D=O.length,L=n.Util._hexToRgb(this.colorKey),j=0;j<D;j+=4)U=O[j+3],U>E?(O[j]=L.r,O[j+1]=L.g,O[j+2]=L.b,O[j+3]=255):O[j+3]=0;C.putImageData(N,0,0)}catch(G){n.Util.error("Unable to draw hit graph from cached scene canvas. "+G.message)}return this}hasPointerCapture(E){return a.hasPointerCapture(E,this)}setPointerCapture(E){a.setPointerCapture(E,this)}releaseCapture(E){a.releaseCapture(E,this)}}e.Shape=T,T.prototype._fillFunc=m,T.prototype._strokeFunc=S,T.prototype._fillFuncHit=v,T.prototype._strokeFuncHit=y,T.prototype._centroid=!1,T.prototype.nodeType="Shape",(0,s._registerNode)(T),T.prototype.eventListeners={},T.prototype.on.call(T.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",g),T.prototype.on.call(T.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",b),T.prototype.on.call(T.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",_),T.prototype.on.call(T.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",w),T.prototype.on.call(T.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",x),r.Factory.addGetterSetter(T,"stroke",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(T,"strokeWidth",2,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillAfterStrokeEnabled",!1),r.Factory.addGetterSetter(T,"hitStrokeWidth","auto",(0,o.getNumberOrAutoValidator)()),r.Factory.addGetterSetter(T,"strokeHitEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(T,"perfectDrawEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(T,"shadowForStrokeEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(T,"lineJoin"),r.Factory.addGetterSetter(T,"lineCap"),r.Factory.addGetterSetter(T,"sceneFunc"),r.Factory.addGetterSetter(T,"hitFunc"),r.Factory.addGetterSetter(T,"dash"),r.Factory.addGetterSetter(T,"dashOffset",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"shadowColor",void 0,(0,o.getStringValidator)()),r.Factory.addGetterSetter(T,"shadowBlur",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"shadowOpacity",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(T,"shadowOffset",["x","y"]),r.Factory.addGetterSetter(T,"shadowOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"shadowOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternImage"),r.Factory.addGetterSetter(T,"fill",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(T,"fillPatternX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillLinearGradientColorStops"),r.Factory.addGetterSetter(T,"strokeLinearGradientColorStops"),r.Factory.addGetterSetter(T,"fillRadialGradientStartRadius",0),r.Factory.addGetterSetter(T,"fillRadialGradientEndRadius",0),r.Factory.addGetterSetter(T,"fillRadialGradientColorStops"),r.Factory.addGetterSetter(T,"fillPatternRepeat","repeat"),r.Factory.addGetterSetter(T,"fillEnabled",!0),r.Factory.addGetterSetter(T,"strokeEnabled",!0),r.Factory.addGetterSetter(T,"shadowEnabled",!0),r.Factory.addGetterSetter(T,"dashEnabled",!0),r.Factory.addGetterSetter(T,"strokeScaleEnabled",!0),r.Factory.addGetterSetter(T,"fillPriority","color"),r.Factory.addComponentsGetterSetter(T,"fillPatternOffset",["x","y"]),r.Factory.addGetterSetter(T,"fillPatternOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(T,"fillPatternScale",["x","y"]),r.Factory.addGetterSetter(T,"fillPatternScaleX",1,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternScaleY",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(T,"fillLinearGradientStartPoint",["x","y"]),r.Factory.addComponentsGetterSetter(T,"strokeLinearGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillLinearGradientStartPointX",0),r.Factory.addGetterSetter(T,"strokeLinearGradientStartPointX",0),r.Factory.addGetterSetter(T,"fillLinearGradientStartPointY",0),r.Factory.addGetterSetter(T,"strokeLinearGradientStartPointY",0),r.Factory.addComponentsGetterSetter(T,"fillLinearGradientEndPoint",["x","y"]),r.Factory.addComponentsGetterSetter(T,"strokeLinearGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillLinearGradientEndPointX",0),r.Factory.addGetterSetter(T,"strokeLinearGradientEndPointX",0),r.Factory.addGetterSetter(T,"fillLinearGradientEndPointY",0),r.Factory.addGetterSetter(T,"strokeLinearGradientEndPointY",0),r.Factory.addComponentsGetterSetter(T,"fillRadialGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillRadialGradientStartPointX",0),r.Factory.addGetterSetter(T,"fillRadialGradientStartPointY",0),r.Factory.addComponentsGetterSetter(T,"fillRadialGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillRadialGradientEndPointX",0),r.Factory.addGetterSetter(T,"fillRadialGradientEndPointY",0),r.Factory.addGetterSetter(T,"fillPatternRotation",0),r.Factory.addGetterSetter(T,"fillRule",void 0,(0,o.getStringValidator)()),r.Factory.backCompat(T,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"})})(sn);Object.defineProperty(kh,"__esModule",{value:!0});kh.Layer=void 0;const go=Ot,Gb=gl,Ul=wt,wC=Te,i6=_i,tde=de,nde=sn,rde=Pe;var ide="#",ode="beforeDraw",sde="draw",fN=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],ade=fN.length;class vc extends Gb.Container{constructor(t){super(t),this.canvas=new i6.SceneCanvas,this.hitCanvas=new i6.HitCanvas({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[t+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){Ul.Node.prototype.moveToTop.call(this);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){var t=Ul.Node.prototype.moveUp.call(this);if(!t)return!1;var n=this.getStage();return!n||!n.content?!1:(n.content.removeChild(this.getNativeCanvasElement()),this.index<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[this.index+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(Ul.Node.prototype.moveDown.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(Ul.Node.prototype.moveToBottom.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var t=this.getNativeCanvasElement();return Ul.Node.prototype.remove.call(this),t&&t.parentNode&&go.Util._isInDocument(t)&&t.parentNode.removeChild(t),this}getStage(){return this.parent}setSize({width:t,height:n}){return this.canvas.setSize(t,n),this.hitCanvas.setSize(t,n),this._setSmoothEnabled(),this}_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&go.Util.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(t){return t=t||{},t.width=t.width||this.getWidth(),t.height=t.height||this.getHeight(),t.x=t.x!==void 0?t.x:this.x(),t.y=t.y!==void 0?t.y:this.y(),Ul.Node.prototype._toKonvaCanvas.call(this,t)}_checkVisibility(){this.visible()?this.canvas._canvas.style.display="block":this.canvas._canvas.style.display="none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){go.Util.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){go.Util.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,go.Util.requestAnimFrame(()=>{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i<ade;i++){const o=fN[i],s=this._getIntersection({x:t.x+o.x*n,y:t.y+o.y*n}),a=s.shape;if(a)return a;if(r=!!s.antialiased,!s.antialiased)break}if(r)n+=1;else return null}}_getIntersection(t){const n=this.hitCanvas.pixelRatio,r=this.hitCanvas.context.getImageData(Math.round(t.x*n),Math.round(t.y*n),1,1).data,i=r[3];if(i===255){const o=go.Util._rgbToHex(r[0],r[1],r[2]),s=nde.shapes[ide+o];return s?{shape:s}:{antialiased:!0}}else if(i>0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(ode,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),Gb.Container.prototype.drawScene.call(this,i,n),this._fire(sde,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),Gb.Container.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){go.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return go.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return go.Util.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}kh.Layer=vc;vc.prototype.nodeType="Layer";(0,rde._registerNode)(vc);wC.Factory.addGetterSetter(vc,"imageSmoothingEnabled",!0);wC.Factory.addGetterSetter(vc,"clearBeforeDraw",!0);wC.Factory.addGetterSetter(vc,"hitGraphEnabled",!0,(0,tde.getBooleanValidator)());var av={};Object.defineProperty(av,"__esModule",{value:!0});av.FastLayer=void 0;const lde=Ot,ude=kh,cde=Pe;class xC extends ude.Layer{constructor(t){super(t),this.listening(!1),lde.Util.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}av.FastLayer=xC;xC.prototype.nodeType="FastLayer";(0,cde._registerNode)(xC);var bc={};Object.defineProperty(bc,"__esModule",{value:!0});bc.Group=void 0;const dde=Ot,fde=gl,hde=Pe;class CC extends fde.Container{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&dde.Util.throw("You may only add groups and shapes to groups.")}}bc.Group=CC;CC.prototype.nodeType="Group";(0,hde._registerNode)(CC);var Sc={};Object.defineProperty(Sc,"__esModule",{value:!0});Sc.Animation=void 0;const Hb=Pe,o6=Ot;var qb=function(){return Hb.glob.performance&&Hb.glob.performance.now?function(){return Hb.glob.performance.now()}:function(){return new Date().getTime()}}();class Gi{constructor(t,n){this.id=Gi.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:qb(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;i<r;i++)if(n[i]._id===t._id)return!1;return this.layers.push(t),!0}isRunning(){var t=Gi,n=t.animations,r=n.length,i;for(i=0;i<r;i++)if(n[i].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=qb(),Gi._addAnimation(this),this}stop(){return Gi._removeAnimation(this),this}_updateFrameObject(t){this.frame.timeDiff=t-this.frame.lastTime,this.frame.lastTime=t,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(t){this.animations.push(t),this._handleAnimation()}static _removeAnimation(t){var n=t.id,r=this.animations,i=r.length,o;for(o=0;o<i;o++)if(r[o].id===n){this.animations.splice(o,1);break}}static _runFrames(){var t={},n=this.animations,r,i,o,s,a,l,u,c,d;for(s=0;s<n.length;s++)if(r=n[s],i=r.layers,o=r.func,r._updateFrameObject(qb()),l=i.length,o?d=o.call(r,r.frame)!==!1:d=!0,!!d)for(a=0;a<l;a++)u=i[a],u._id!==void 0&&(t[u._id]=u);for(c in t)t.hasOwnProperty(c)&&t[c].batchDraw()}static _animationLoop(){var t=Gi;t.animations.length?(t._runFrames(),o6.Util.requestAnimFrame(t._animationLoop)):t.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,o6.Util.requestAnimFrame(this._animationLoop))}}Sc.Animation=Gi;Gi.animations=[];Gi.animIdCounter=0;Gi.animRunning=!1;var hN={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Easings=e.Tween=void 0;const t=Ot,n=Sc,r=wt,i=Pe;var o={node:1,duration:1,easing:1,onFinish:1,yoyo:1},s=1,a=2,l=3,u=0,c=["fill","stroke","shadowColor"];class d{constructor(p,m,S,v,y,g,b){this.prop=p,this.propFunc=m,this.begin=v,this._pos=v,this.duration=g,this._change=0,this.prevPos=0,this.yoyo=b,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=S,this._change=y-this.begin,this.pause()}fire(p){var m=this[p];m&&m()}setTime(p){p>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():p<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=p,this.update())}getTime(){return this._time}setPosition(p){this.prevPos=this._pos,this.propFunc(p),this._pos=p}getPosition(p){return p===void 0&&(p=this._time),this.func(p,this.begin,this._change,this.duration)}play(){this.state=a,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=l,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(p){this.pause(),this._time=p,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var p=this.getTimer()-this._startTime;this.state===a?this.setTime(p):this.state===l&&this.setTime(this.duration-p)}pause(){this.state=s,this.fire("onPause")}getTimer(){return new Date().getTime()}}class f{constructor(p){var m=this,S=p.node,v=S._id,y,g=p.easing||e.Easings.Linear,b=!!p.yoyo,_;typeof p.duration>"u"?y=.3:p.duration===0?y=.001:y=p.duration,this.node=S,this._id=u++;var w=S.getLayer()||(S instanceof i.Konva.Stage?S.getLayers():null);w||t.Util.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new n.Animation(function(){m.tween.onEnterFrame()},w),this.tween=new d(_,function(x){m._tweenFunc(x)},g,0,1,y*1e3,b),this._addListeners(),f.attrs[v]||(f.attrs[v]={}),f.attrs[v][this._id]||(f.attrs[v][this._id]={}),f.tweens[v]||(f.tweens[v]={});for(_ in p)o[_]===void 0&&this._addAttr(_,p[_]);this.reset(),this.onFinish=p.onFinish,this.onReset=p.onReset,this.onUpdate=p.onUpdate}_addAttr(p,m){var S=this.node,v=S._id,y,g,b,_,w,x,T,P;if(b=f.tweens[v][p],b&&delete f.attrs[v][b][p],y=S.getAttr(p),t.Util._isArray(m))if(g=[],w=Math.max(m.length,y.length),p==="points"&&m.length!==y.length&&(m.length>y.length?(T=y,y=t.Util._prepareArrayForTween(y,m,S.closed())):(x=m,m=t.Util._prepareArrayForTween(m,y,S.closed()))),p.indexOf("fill")===0)for(_=0;_<w;_++)if(_%2===0)g.push(m[_]-y[_]);else{var E=t.Util.colorToRGBA(y[_]);P=t.Util.colorToRGBA(m[_]),y[_]=E,g.push({r:P.r-E.r,g:P.g-E.g,b:P.b-E.b,a:P.a-E.a})}else for(_=0;_<w;_++)g.push(m[_]-y[_]);else c.indexOf(p)!==-1?(y=t.Util.colorToRGBA(y),P=t.Util.colorToRGBA(m),g={r:P.r-y.r,g:P.g-y.g,b:P.b-y.b,a:P.a-y.a}):g=m-y;f.attrs[v][this._id][p]={start:y,diff:g,end:m,trueEnd:x,trueStart:T},f.tweens[v][p]=this._id}_tweenFunc(p){var m=this.node,S=f.attrs[m._id][this._id],v,y,g,b,_,w,x,T;for(v in S){if(y=S[v],g=y.start,b=y.diff,T=y.end,t.Util._isArray(g))if(_=[],x=Math.max(g.length,T.length),v.indexOf("fill")===0)for(w=0;w<x;w++)w%2===0?_.push((g[w]||0)+b[w]*p):_.push("rgba("+Math.round(g[w].r+b[w].r*p)+","+Math.round(g[w].g+b[w].g*p)+","+Math.round(g[w].b+b[w].b*p)+","+(g[w].a+b[w].a*p)+")");else for(w=0;w<x;w++)_.push((g[w]||0)+b[w]*p);else c.indexOf(v)!==-1?_="rgba("+Math.round(g.r+b.r*p)+","+Math.round(g.g+b.g*p)+","+Math.round(g.b+b.b*p)+","+(g.a+b.a*p)+")":_=g+b*p;m.setAttr(v,_)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueEnd&&p.setAttr("points",m.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueStart&&p.points(m.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(p){return this.tween.seek(p*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var p=this.node._id,m=this._id,S=f.tweens[p],v;this.pause();for(v in S)delete f.tweens[p][v];delete f.attrs[p][m]}}e.Tween=f,f.attrs={},f.tweens={},r.Node.prototype.to=function(h){var p=h.onFinish;h.node=this,h.onFinish=function(){this.destroy(),p&&p()};var m=new f(h);m.play()},e.Easings={BackEaseIn(h,p,m,S){var v=1.70158;return m*(h/=S)*h*((v+1)*h-v)+p},BackEaseOut(h,p,m,S){var v=1.70158;return m*((h=h/S-1)*h*((v+1)*h+v)+1)+p},BackEaseInOut(h,p,m,S){var v=1.70158;return(h/=S/2)<1?m/2*(h*h*(((v*=1.525)+1)*h-v))+p:m/2*((h-=2)*h*(((v*=1.525)+1)*h+v)+2)+p},ElasticEaseIn(h,p,m,S,v,y){var g=0;return h===0?p:(h/=S)===1?p+m:(y||(y=S*.3),!v||v<Math.abs(m)?(v=m,g=y/4):g=y/(2*Math.PI)*Math.asin(m/v),-(v*Math.pow(2,10*(h-=1))*Math.sin((h*S-g)*(2*Math.PI)/y))+p)},ElasticEaseOut(h,p,m,S,v,y){var g=0;return h===0?p:(h/=S)===1?p+m:(y||(y=S*.3),!v||v<Math.abs(m)?(v=m,g=y/4):g=y/(2*Math.PI)*Math.asin(m/v),v*Math.pow(2,-10*h)*Math.sin((h*S-g)*(2*Math.PI)/y)+m+p)},ElasticEaseInOut(h,p,m,S,v,y){var g=0;return h===0?p:(h/=S/2)===2?p+m:(y||(y=S*(.3*1.5)),!v||v<Math.abs(m)?(v=m,g=y/4):g=y/(2*Math.PI)*Math.asin(m/v),h<1?-.5*(v*Math.pow(2,10*(h-=1))*Math.sin((h*S-g)*(2*Math.PI)/y))+p:v*Math.pow(2,-10*(h-=1))*Math.sin((h*S-g)*(2*Math.PI)/y)*.5+m+p)},BounceEaseOut(h,p,m,S){return(h/=S)<1/2.75?m*(7.5625*h*h)+p:h<2/2.75?m*(7.5625*(h-=1.5/2.75)*h+.75)+p:h<2.5/2.75?m*(7.5625*(h-=2.25/2.75)*h+.9375)+p:m*(7.5625*(h-=2.625/2.75)*h+.984375)+p},BounceEaseIn(h,p,m,S){return m-e.Easings.BounceEaseOut(S-h,0,m,S)+p},BounceEaseInOut(h,p,m,S){return h<S/2?e.Easings.BounceEaseIn(h*2,0,m,S)*.5+p:e.Easings.BounceEaseOut(h*2-S,0,m,S)*.5+m*.5+p},EaseIn(h,p,m,S){return m*(h/=S)*h+p},EaseOut(h,p,m,S){return-m*(h/=S)*(h-2)+p},EaseInOut(h,p,m,S){return(h/=S/2)<1?m/2*h*h+p:-m/2*(--h*(h-2)-1)+p},StrongEaseIn(h,p,m,S){return m*(h/=S)*h*h*h*h+p},StrongEaseOut(h,p,m,S){return m*((h=h/S-1)*h*h*h*h+1)+p},StrongEaseInOut(h,p,m,S){return(h/=S/2)<1?m/2*h*h*h*h*h+p:m/2*((h-=2)*h*h*h*h+2)+p},Linear(h,p,m,S){return m*h/S+p}}})(hN);(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Konva=void 0;const t=Pe,n=Ot,r=wt,i=gl,o=uN,s=kh,a=av,l=bc,u=ov,c=sn,d=Sc,f=hN,h=Oo,p=_i;e.Konva=n.Util._assign(t.Konva,{Util:n.Util,Transform:n.Transform,Node:r.Node,Container:i.Container,Stage:o.Stage,stages:o.stages,Layer:s.Layer,FastLayer:a.FastLayer,Group:l.Group,DD:u.DD,Shape:c.Shape,shapes:c.shapes,Animation:d.Animation,Tween:f.Tween,Easings:f.Easings,Context:h.Context,Canvas:p.Canvas}),e.default=e.Konva})(oN);var lv={};Object.defineProperty(lv,"__esModule",{value:!0});lv.Arc=void 0;const uv=Te,pde=sn,s6=Pe,cv=de,gde=Pe;class Yo extends pde.Shape{_sceneFunc(t){var n=s6.Konva.getAngle(this.angle()),r=this.clockwise();t.beginPath(),t.arc(0,0,this.outerRadius(),0,n,r),t.arc(0,0,this.innerRadius(),n,0,!r),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}getSelfRect(){const t=this.innerRadius(),n=this.outerRadius(),r=this.clockwise(),i=s6.Konva.getAngle(r?360-this.angle():this.angle()),o=Math.cos(Math.min(i,Math.PI)),s=1,a=Math.sin(Math.min(Math.max(Math.PI,i),3*Math.PI/2)),l=Math.sin(Math.min(i,Math.PI/2)),u=o*(o>0?t:n),c=s*n,d=a*(a>0?t:n),f=l*(l>0?n:t);return{x:u,y:r?-1*f:d,width:c-u,height:f-d}}}lv.Arc=Yo;Yo.prototype._centroid=!0;Yo.prototype.className="Arc";Yo.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,gde._registerNode)(Yo);uv.Factory.addGetterSetter(Yo,"innerRadius",0,(0,cv.getNumberValidator)());uv.Factory.addGetterSetter(Yo,"outerRadius",0,(0,cv.getNumberValidator)());uv.Factory.addGetterSetter(Yo,"angle",0,(0,cv.getNumberValidator)());uv.Factory.addGetterSetter(Yo,"clockwise",!1,(0,cv.getBooleanValidator)());var dv={},Rh={};Object.defineProperty(Rh,"__esModule",{value:!0});Rh.Line=void 0;const fv=Te,mde=sn,pN=de,yde=Pe;function v2(e,t,n,r,i,o,s){var a=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),u=s*a/(a+l),c=s*l/(a+l),d=n-u*(i-e),f=r-u*(o-t),h=n+c*(i-e),p=r+c*(o-t);return[d,f,h,p]}function a6(e,t){var n=e.length,r=[],i,o;for(i=2;i<n-2;i+=2)o=v2(e[i-2],e[i-1],e[i],e[i+1],e[i+2],e[i+3],t),!isNaN(o[0])&&(r.push(o[0]),r.push(o[1]),r.push(e[i]),r.push(e[i+1]),r.push(o[2]),r.push(o[3]));return r}class ia extends mde.Shape{constructor(t){super(t),this.on("pointsChange.konva tensionChange.konva closedChange.konva bezierChange.konva",function(){this._clearCache("tensionPoints")})}_sceneFunc(t){var 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n=t[0],r=t[0],i=t[1],o=t[1],s,a,l=0;l<t.length/2;l++)s=t[l*2],a=t[l*2+1],isNaN(s)||(n=Math.min(n,s),r=Math.max(r,s)),isNaN(a)||(i=Math.min(i,a),o=Math.max(o,a));return{x:n,y:i,width:r-n,height:o-i}}getLength(){return this.pathLength}getPointAtLength(t){return Jt.getPointAtLengthOfDataArray(t,this.dataArray)}static getLineLength(t,n,r,i){return Math.sqrt((r-t)*(r-t)+(i-n)*(i-n))}static getPathLength(t){let n=0;for(var r=0;r<t.length;++r)n+=t[r].pathLength;return n}static getPointAtLengthOfDataArray(t,n){var r,i=0,o=n.length;if(!o)return null;for(;i<o&&t>n[i].pathLength;)t-=n[i].pathLength,++i;if(i===o)return r=n[i-1].points.slice(-2),{x:r[0],y:r[1]};if(t<.01)return r=n[i].points.slice(0,2),{x:r[0],y:r[1]};var s=n[i],a=s.points;switch(s.command){case"L":return Jt.getPointOnLine(t,s.start.x,s.start.y,a[0],a[1]);case"C":return Jt.getPointOnCubicBezier((0,Gl.t2length)(t,Jt.getPathLength(n),m=>(0,Gl.getCubicArcLength)([s.start.x,a[0],a[2],a[4]],[s.start.y,a[1],a[3],a[5]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return Jt.getPointOnQuadraticBezier((0,Gl.t2length)(t,Jt.getPathLength(n),m=>(0,Gl.getQuadraticArcLength)([s.start.x,a[0],a[2]],[s.start.y,a[1],a[3]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3]);case"A":var l=a[0],u=a[1],c=a[2],d=a[3],f=a[4],h=a[5],p=a[6];return f+=h*t/s.pathLength,Jt.getPointOnEllipticalArc(l,u,c,d,f,p)}return null}static getPointOnLine(t,n,r,i,o,s,a){s===void 0&&(s=n),a===void 0&&(a=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i<n&&(u*=-1);var c=l*u,d;if(i===n)d={x:s,y:a+c};else if((a-r)/(s-n+1e-8)===l)d={x:s+u,y:a+c};else{var f,h,p=this.getLineLength(n,r,i,o),m=(s-n)*(i-n)+(a-r)*(o-r);m=m/(p*p),f=n+m*(i-n),h=r+m*(o-r);var S=this.getLineLength(s,a,f,h),v=Math.sqrt(t*t-S*S);u=Math.sqrt(v*v/(1+l*l)),i<n&&(u*=-1),c=l*u,d={x:f+u,y:h+c}}return d}static getPointOnCubicBezier(t,n,r,i,o,s,a,l,u){function c(S){return S*S*S}function d(S){return 3*S*S*(1-S)}function f(S){return 3*S*(1-S)*(1-S)}function h(S){return(1-S)*(1-S)*(1-S)}var p=l*c(t)+s*d(t)+i*f(t)+n*h(t),m=u*c(t)+a*d(t)+o*f(t)+r*h(t);return{x:p,y:m}}static getPointOnQuadraticBezier(t,n,r,i,o,s,a){function l(h){return h*h}function u(h){return 2*h*(1-h)}function c(h){return(1-h)*(1-h)}var d=s*l(t)+i*u(t)+n*c(t),f=a*l(t)+o*u(t)+r*c(t);return{x:d,y:f}}static getPointOnEllipticalArc(t,n,r,i,o,s){var a=Math.cos(s),l=Math.sin(s),u={x:r*Math.cos(o),y:i*Math.sin(o)};return{x:t+(u.x*a-u.y*l),y:n+(u.x*l+u.y*a)}}static parsePathData(t){if(!t)return[];var n=t,r=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];n=n.replace(new RegExp(" ","g"),",");for(var i=0;i<r.length;i++)n=n.replace(new RegExp(r[i],"g"),"|"+r[i]);var o=n.split("|"),s=[],a=[],l=0,u=0,c=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi,d;for(i=1;i<o.length;i++){var f=o[i],h=f.charAt(0);for(f=f.slice(1),a.length=0;d=c.exec(f);)a.push(d[0]);for(var p=[],m=0,S=a.length;m<S;m++){if(a[m]==="00"){p.push(0,0);continue}var v=parseFloat(a[m]);isNaN(v)?p.push(0):p.push(v)}for(;p.length>0&&!isNaN(p[0]);){var y=null,g=[],b=l,_=u,w,x,T,P,E,A,$,I,C,R;switch(h){case"l":l+=p.shift(),u+=p.shift(),y="L",g.push(l,u);break;case"L":l=p.shift(),u=p.shift(),g.push(l,u);break;case"m":var M=p.shift(),N=p.shift();if(l+=M,u+=N,y="M",s.length>2&&s[s.length-1].command==="z"){for(var O=s.length-2;O>=0;O--)if(s[O].command==="M"){l=s[O].points[0]+M,u=s[O].points[1]+N;break}}g.push(l,u),h="l";break;case"M":l=p.shift(),u=p.shift(),y="M",g.push(l,u),h="L";break;case"h":l+=p.shift(),y="L",g.push(l,u);break;case"H":l=p.shift(),y="L",g.push(l,u);break;case"v":u+=p.shift(),y="L",g.push(l,u);break;case"V":u=p.shift(),y="L",g.push(l,u);break;case"C":g.push(p.shift(),p.shift(),p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"c":g.push(l+p.shift(),u+p.shift(),l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="C",g.push(l,u);break;case"S":x=l,T=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),T=u+(u-w.points[3])),g.push(x,T,p.shift(),p.shift()),l=p.shift(),u=p.shift(),y="C",g.push(l,u);break;case"s":x=l,T=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),T=u+(u-w.points[3])),g.push(x,T,l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="C",g.push(l,u);break;case"Q":g.push(p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"q":g.push(l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="Q",g.push(l,u);break;case"T":x=l,T=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),T=u+(u-w.points[1])),l=p.shift(),u=p.shift(),y="Q",g.push(x,T,l,u);break;case"t":x=l,T=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),T=u+(u-w.points[1])),l+=p.shift(),u+=p.shift(),y="Q",g.push(x,T,l,u);break;case"A":P=p.shift(),E=p.shift(),A=p.shift(),$=p.shift(),I=p.shift(),C=l,R=u,l=p.shift(),u=p.shift(),y="A",g=this.convertEndpointToCenterParameterization(C,R,l,u,$,I,P,E,A);break;case"a":P=p.shift(),E=p.shift(),A=p.shift(),$=p.shift(),I=p.shift(),C=l,R=u,l+=p.shift(),u+=p.shift(),y="A",g=this.convertEndpointToCenterParameterization(C,R,l,u,$,I,P,E,A);break}s.push({command:y||h,points:g,start:{x:b,y:_},pathLength:this.calcLength(b,_,y||h,g)})}(h==="z"||h==="Z")&&s.push({command:"z",points:[],start:void 0,pathLength:0})}return s}static calcLength(t,n,r,i){var o,s,a,l,u=Jt;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":return(0,Gl.getCubicArcLength)([t,i[0],i[2],i[4]],[n,i[1],i[3],i[5]],1);case"Q":return(0,Gl.getQuadraticArcLength)([t,i[0],i[2]],[n,i[1],i[3]],1);case"A":o=0;var c=i[4],d=i[5],f=i[4]+d,h=Math.PI/180;if(Math.abs(c-f)<h&&(h=Math.abs(c-f)),s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],c,0),d<0)for(l=c-h;l>f;l-=h)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;else for(l=c+h;l<f;l+=h)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;return a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],f,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),o}return 0}static convertEndpointToCenterParameterization(t,n,r,i,o,s,a,l,u){var c=u*(Math.PI/180),d=Math.cos(c)*(t-r)/2+Math.sin(c)*(n-i)/2,f=-1*Math.sin(c)*(t-r)/2+Math.cos(c)*(n-i)/2,h=d*d/(a*a)+f*f/(l*l);h>1&&(a*=Math.sqrt(h),l*=Math.sqrt(h));var p=Math.sqrt((a*a*(l*l)-a*a*(f*f)-l*l*(d*d))/(a*a*(f*f)+l*l*(d*d)));o===s&&(p*=-1),isNaN(p)&&(p=0);var m=p*a*f/l,S=p*-l*d/a,v=(t+r)/2+Math.cos(c)*m-Math.sin(c)*S,y=(n+i)/2+Math.sin(c)*m+Math.cos(c)*S,g=function(E){return Math.sqrt(E[0]*E[0]+E[1]*E[1])},b=function(E,A){return(E[0]*A[0]+E[1]*A[1])/(g(E)*g(A))},_=function(E,A){return(E[0]*A[1]<E[1]*A[0]?-1:1)*Math.acos(b(E,A))},w=_([1,0],[(d-m)/a,(f-S)/l]),x=[(d-m)/a,(f-S)/l],T=[(-1*d-m)/a,(-1*f-S)/l],P=_(x,T);return b(x,T)<=-1&&(P=Math.PI),b(x,T)>=1&&(P=0),s===0&&P>0&&(P=P-2*Math.PI),s===1&&P<0&&(P=P+2*Math.PI),[v,y,a,l,w,P,c,s]}}_c.Path=Jt;Jt.prototype.className="Path";Jt.prototype._attrsAffectingSize=["data"];(0,Sde._registerNode)(Jt);vde.Factory.addGetterSetter(Jt,"data");Object.defineProperty(dv,"__esModule",{value:!0});dv.Arrow=void 0;const hv=Te,_de=Rh,mN=de,wde=Pe,l6=_c;class yl extends _de.Line{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var s=this.pointerLength(),a=r.length,l,u;if(o){const f=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[a-2],r[a-1]],h=l6.Path.calcLength(i[i.length-4],i[i.length-3],"C",f),p=l6.Path.getPointOnQuadraticBezier(Math.min(1,1-s/h),f[0],f[1],f[2],f[3],f[4],f[5]);l=r[a-2]-p.x,u=r[a-1]-p.y}else l=r[a-2]-r[a-4],u=r[a-1]-r[a-3];var c=(Math.atan2(u,l)+n)%n,d=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[a-2],r[a-1]),t.rotate(c),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}dv.Arrow=yl;yl.prototype.className="Arrow";(0,wde._registerNode)(yl);hv.Factory.addGetterSetter(yl,"pointerLength",10,(0,mN.getNumberValidator)());hv.Factory.addGetterSetter(yl,"pointerWidth",10,(0,mN.getNumberValidator)());hv.Factory.addGetterSetter(yl,"pointerAtBeginning",!1);hv.Factory.addGetterSetter(yl,"pointerAtEnding",!0);var pv={};Object.defineProperty(pv,"__esModule",{value:!0});pv.Circle=void 0;const xde=Te,Cde=sn,Tde=de,Ede=Pe;let wc=class extends Cde.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}};pv.Circle=wc;wc.prototype._centroid=!0;wc.prototype.className="Circle";wc.prototype._attrsAffectingSize=["radius"];(0,Ede._registerNode)(wc);xde.Factory.addGetterSetter(wc,"radius",0,(0,Tde.getNumberValidator)());var gv={};Object.defineProperty(gv,"__esModule",{value:!0});gv.Ellipse=void 0;const TC=Te,Pde=sn,yN=de,Ade=Pe;class oa extends Pde.Shape{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}gv.Ellipse=oa;oa.prototype.className="Ellipse";oa.prototype._centroid=!0;oa.prototype._attrsAffectingSize=["radiusX","radiusY"];(0,Ade._registerNode)(oa);TC.Factory.addComponentsGetterSetter(oa,"radius",["x","y"]);TC.Factory.addGetterSetter(oa,"radiusX",0,(0,yN.getNumberValidator)());TC.Factory.addGetterSetter(oa,"radiusY",0,(0,yN.getNumberValidator)());var mv={};Object.defineProperty(mv,"__esModule",{value:!0});mv.Image=void 0;const Wb=Ot,vl=Te,kde=sn,Rde=Pe,Oh=de;let ao=class vN extends kde.Shape{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.cornerRadius(),o=this.attrs.image;let s;if(o){const a=this.attrs.cropWidth,l=this.attrs.cropHeight;a&&l?s=[o,this.cropX(),this.cropY(),a,l,0,0,n,r]:s=[o,0,0,n,r]}(this.hasFill()||this.hasStroke()||i)&&(t.beginPath(),i?Wb.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),o&&(i&&t.clip(),t.drawImage.apply(t,s))}_hitFunc(t){var n=this.width(),r=this.height(),i=this.cornerRadius();t.beginPath(),i?Wb.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=Wb.Util.createImageElement();i.onload=function(){var o=new vN({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}};mv.Image=ao;ao.prototype.className="Image";(0,Rde._registerNode)(ao);vl.Factory.addGetterSetter(ao,"cornerRadius",0,(0,Oh.getNumberOrArrayOfNumbersValidator)(4));vl.Factory.addGetterSetter(ao,"image");vl.Factory.addComponentsGetterSetter(ao,"crop",["x","y","width","height"]);vl.Factory.addGetterSetter(ao,"cropX",0,(0,Oh.getNumberValidator)());vl.Factory.addGetterSetter(ao,"cropY",0,(0,Oh.getNumberValidator)());vl.Factory.addGetterSetter(ao,"cropWidth",0,(0,Oh.getNumberValidator)());vl.Factory.addGetterSetter(ao,"cropHeight",0,(0,Oh.getNumberValidator)());var oc={};Object.defineProperty(oc,"__esModule",{value:!0});oc.Tag=oc.Label=void 0;const yv=Te,Ode=sn,Mde=bc,EC=de,bN=Pe;var SN=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],Ide="Change.konva",Nde="none",b2="up",S2="right",_2="down",w2="left",Dde=SN.length;class PC extends Mde.Group{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r<Dde;r++)t.on(SN[r]+Ide,i)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var t=this.getText(),n=this.getTag(),r,i,o,s,a,l,u;if(t&&n){switch(r=t.width(),i=t.height(),o=n.pointerDirection(),s=n.pointerWidth(),u=n.pointerHeight(),a=0,l=0,o){case b2:a=r/2,l=-1*u;break;case S2:a=r+s,l=i/2;break;case _2:a=r/2,l=i+u;break;case w2:a=-1*s,l=i/2;break}n.setAttrs({x:-1*a,y:-1*l,width:r,height:i}),t.setAttrs({x:-1*a,y:-1*l})}}}oc.Label=PC;PC.prototype.className="Label";(0,bN._registerNode)(PC);class bl extends Ode.Shape{_sceneFunc(t){var n=this.width(),r=this.height(),i=this.pointerDirection(),o=this.pointerWidth(),s=this.pointerHeight(),a=this.cornerRadius();let l=0,u=0,c=0,d=0;typeof a=="number"?l=u=c=d=Math.min(a,n/2,r/2):(l=Math.min(a[0]||0,n/2,r/2),u=Math.min(a[1]||0,n/2,r/2),d=Math.min(a[2]||0,n/2,r/2),c=Math.min(a[3]||0,n/2,r/2)),t.beginPath(),t.moveTo(l,0),i===b2&&(t.lineTo((n-o)/2,0),t.lineTo(n/2,-1*s),t.lineTo((n+o)/2,0)),t.lineTo(n-u,0),t.arc(n-u,u,u,Math.PI*3/2,0,!1),i===S2&&(t.lineTo(n,(r-s)/2),t.lineTo(n+o,r/2),t.lineTo(n,(r+s)/2)),t.lineTo(n,r-d),t.arc(n-d,r-d,d,0,Math.PI/2,!1),i===_2&&(t.lineTo((n+o)/2,r),t.lineTo(n/2,r+s),t.lineTo((n-o)/2,r)),t.lineTo(c,r),t.arc(c,r-c,c,Math.PI/2,Math.PI,!1),i===w2&&(t.lineTo(0,(r+s)/2),t.lineTo(-1*o,r/2),t.lineTo(0,(r-s)/2)),t.lineTo(0,l),t.arc(l,l,l,Math.PI,Math.PI*3/2,!1),t.closePath(),t.fillStrokeShape(this)}getSelfRect(){var t=0,n=0,r=this.pointerWidth(),i=this.pointerHeight(),o=this.pointerDirection(),s=this.width(),a=this.height();return o===b2?(n-=i,a+=i):o===_2?a+=i:o===w2?(t-=r*1.5,s+=r):o===S2&&(s+=r*1.5),{x:t,y:n,width:s,height:a}}}oc.Tag=bl;bl.prototype.className="Tag";(0,bN._registerNode)(bl);yv.Factory.addGetterSetter(bl,"pointerDirection",Nde);yv.Factory.addGetterSetter(bl,"pointerWidth",0,(0,EC.getNumberValidator)());yv.Factory.addGetterSetter(bl,"pointerHeight",0,(0,EC.getNumberValidator)());yv.Factory.addGetterSetter(bl,"cornerRadius",0,(0,EC.getNumberOrArrayOfNumbersValidator)(4));var Mh={};Object.defineProperty(Mh,"__esModule",{value:!0});Mh.Rect=void 0;const Lde=Te,$de=sn,Fde=Pe,Bde=Ot,jde=de;let vv=class extends $de.Shape{_sceneFunc(t){var n=this.cornerRadius(),r=this.width(),i=this.height();t.beginPath(),n?Bde.Util.drawRoundedRectPath(t,r,i,n):t.rect(0,0,r,i),t.closePath(),t.fillStrokeShape(this)}};Mh.Rect=vv;vv.prototype.className="Rect";(0,Fde._registerNode)(vv);Lde.Factory.addGetterSetter(vv,"cornerRadius",0,(0,jde.getNumberOrArrayOfNumbersValidator)(4));var bv={};Object.defineProperty(bv,"__esModule",{value:!0});bv.RegularPolygon=void 0;const _N=Te,Vde=sn,wN=de,zde=Pe;class Sl extends Vde.Shape{_sceneFunc(t){const n=this._getPoints();t.beginPath(),t.moveTo(n[0].x,n[0].y);for(var r=1;r<n.length;r++)t.lineTo(n[r].x,n[r].y);t.closePath(),t.fillStrokeShape(this)}_getPoints(){const t=this.attrs.sides,n=this.attrs.radius||0,r=[];for(var i=0;i<t;i++)r.push({x:n*Math.sin(i*2*Math.PI/t),y:-1*n*Math.cos(i*2*Math.PI/t)});return r}getSelfRect(){const t=this._getPoints();var n=t[0].x,r=t[0].y,i=t[0].x,o=t[0].y;return t.forEach(s=>{n=Math.min(n,s.x),r=Math.max(r,s.x),i=Math.min(i,s.y),o=Math.max(o,s.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}bv.RegularPolygon=Sl;Sl.prototype.className="RegularPolygon";Sl.prototype._centroid=!0;Sl.prototype._attrsAffectingSize=["radius"];(0,zde._registerNode)(Sl);_N.Factory.addGetterSetter(Sl,"radius",0,(0,wN.getNumberValidator)());_N.Factory.addGetterSetter(Sl,"sides",0,(0,wN.getNumberValidator)());var Sv={};Object.defineProperty(Sv,"__esModule",{value:!0});Sv.Ring=void 0;const xN=Te,Ude=sn,CN=de,Gde=Pe;var u6=Math.PI*2;class _l extends Ude.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,u6,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),u6,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}Sv.Ring=_l;_l.prototype.className="Ring";_l.prototype._centroid=!0;_l.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,Gde._registerNode)(_l);xN.Factory.addGetterSetter(_l,"innerRadius",0,(0,CN.getNumberValidator)());xN.Factory.addGetterSetter(_l,"outerRadius",0,(0,CN.getNumberValidator)());var _v={};Object.defineProperty(_v,"__esModule",{value:!0});_v.Sprite=void 0;const wl=Te,Hde=sn,qde=Sc,TN=de,Wde=Pe;class lo extends Hde.Shape{constructor(t){super(t),this._updated=!0,this.anim=new qde.Animation(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+0],l=o[i+1],u=o[i+2],c=o[i+3],d=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,c),t.closePath(),t.fillStrokeShape(this)),d)if(s){var f=s[n],h=r*2;t.drawImage(d,a,l,u,c,f[h+0],f[h+1],u,c)}else t.drawImage(d,a,l,u,c,0,0,u,c)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+2],l=o[i+3];if(t.beginPath(),s){var u=s[n],c=r*2;t.rect(u[c+0],u[c+1],a,l)}else t.rect(0,0,a,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t<o-1?this.frameIndex(t+1):this.frameIndex(0)}}_v.Sprite=lo;lo.prototype.className="Sprite";(0,Wde._registerNode)(lo);wl.Factory.addGetterSetter(lo,"animation");wl.Factory.addGetterSetter(lo,"animations");wl.Factory.addGetterSetter(lo,"frameOffsets");wl.Factory.addGetterSetter(lo,"image");wl.Factory.addGetterSetter(lo,"frameIndex",0,(0,TN.getNumberValidator)());wl.Factory.addGetterSetter(lo,"frameRate",17,(0,TN.getNumberValidator)());wl.Factory.backCompat(lo,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});var wv={};Object.defineProperty(wv,"__esModule",{value:!0});wv.Star=void 0;const AC=Te,Kde=sn,kC=de,Xde=Pe;class sa extends Kde.Shape{_sceneFunc(t){var n=this.innerRadius(),r=this.outerRadius(),i=this.numPoints();t.beginPath(),t.moveTo(0,0-r);for(var o=1;o<i*2;o++){var s=o%2===0?r:n,a=s*Math.sin(o*Math.PI/i),l=-1*s*Math.cos(o*Math.PI/i);t.lineTo(a,l)}t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}wv.Star=sa;sa.prototype.className="Star";sa.prototype._centroid=!0;sa.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,Xde._registerNode)(sa);AC.Factory.addGetterSetter(sa,"numPoints",5,(0,kC.getNumberValidator)());AC.Factory.addGetterSetter(sa,"innerRadius",0,(0,kC.getNumberValidator)());AC.Factory.addGetterSetter(sa,"outerRadius",0,(0,kC.getNumberValidator)());var ol={};Object.defineProperty(ol,"__esModule",{value:!0});ol.Text=ol.stringToArray=void 0;const x2=Ot,ir=Te,Yde=sn,aa=de,Qde=Pe;function EN(e){return Array.from(e)}ol.stringToArray=EN;var Hl="auto",Zde="center",Jc="justify",Jde="Change.konva",efe="2d",c6="-",PN="left",tfe="text",nfe="Text",rfe="top",ife="bottom",d6="middle",AN="normal",ofe="px ",$p=" ",sfe="right",afe="word",lfe="char",f6="none",Kb="…",kN=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],ufe=kN.length;function cfe(e){return e.split(",").map(t=>{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Fp;function Xb(){return Fp||(Fp=x2.Util.createCanvasElement().getContext(efe),Fp)}function dfe(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function ffe(e){e.setAttr("miterLimit",2),e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function hfe(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class Ut extends Yde.Shape{constructor(t){super(hfe(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n<ufe;n++)this.on(kN[n]+Jde,this._setTextData);this._setTextData()}_sceneFunc(t){var n=this.textArr,r=n.length;if(this.text()){var i=this.padding(),o=this.fontSize(),s=this.lineHeight()*o,a=this.verticalAlign(),l=0,u=this.align(),c=this.getWidth(),d=this.letterSpacing(),f=this.fill(),h=this.textDecoration(),p=h.indexOf("underline")!==-1,m=h.indexOf("line-through")!==-1,S,v=0,v=s/2,y=0,g=0;for(t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",d6),t.setAttr("textAlign",PN),a===d6?l=(this.getHeight()-r*s-i*2)/2:a===ife&&(l=this.getHeight()-r*s-i*2),t.translate(i,l+i),S=0;S<r;S++){var y=0,g=0,b=n[S],_=b.text,w=b.width,x=b.lastInParagraph,T,P,E;if(t.save(),u===sfe?y+=c-w-i*2:u===Zde&&(y+=(c-w-i*2)/2),p){t.save(),t.beginPath(),t.moveTo(y,v+g+Math.round(o/2)),T=_.split(" ").length-1,P=T===0,E=u===Jc&&!x?c-i*2:w,t.lineTo(y+Math.round(E),v+g+Math.round(o/2)),t.lineWidth=o/15;const M=this._getLinearGradient();t.strokeStyle=M||f,t.stroke(),t.restore()}if(m){t.save(),t.beginPath(),t.moveTo(y,v+g),T=_.split(" ").length-1,P=T===0,E=u===Jc&&x&&!P?c-i*2:w,t.lineTo(y+Math.round(E),v+g),t.lineWidth=o/15;const M=this._getLinearGradient();t.strokeStyle=M||f,t.stroke(),t.restore()}if(d!==0||u===Jc){T=_.split(" ").length-1;for(var A=EN(_),$=0;$<A.length;$++){var I=A[$];I===" "&&!x&&u===Jc&&(y+=(c-i*2-w)/T),this._partialTextX=y,this._partialTextY=v+g,this._partialText=I,t.fillStrokeShape(this),y+=this.measureSize(I).width+d}}else this._partialTextX=y,this._partialTextY=v+g,this._partialText=_,t.fillStrokeShape(this);t.restore(),r>1&&(v+=s)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=x2.Util._isString(t)?t:t==null?"":t+"";return this._setAttr(tfe,n),this}getWidth(){var t=this.attrs.width===Hl||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===Hl||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return x2.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=Xb(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+$p+this.fontVariant()+$p+(this.fontSize()+ofe)+cfe(this.fontFamily())}_addTextLine(t){this.align()===Jc&&(t=t.trim());var r=this._getTextWidth(t);return this.textArr.push({text:t,width:r,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return Xb().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(`
+`),n=+this.fontSize(),r=0,i=this.lineHeight()*n,o=this.attrs.width,s=this.attrs.height,a=o!==Hl&&o!==void 0,l=s!==Hl&&s!==void 0,u=this.padding(),c=o-u*2,d=s-u*2,f=0,h=this.wrap(),p=h!==f6,m=h!==lfe&&p,S=this.ellipsis();this.textArr=[],Xb().font=this._getContextFont();for(var v=S?this._getTextWidth(Kb):0,y=0,g=t.length;y<g;++y){var b=t[y],_=this._getTextWidth(b);if(a&&_>c)for(;b.length>0;){for(var w=0,x=b.length,T="",P=0;w<x;){var E=w+x>>>1,A=b.slice(0,E+1),$=this._getTextWidth(A)+v;$<=c?(w=E+1,T=A,P=$):x=E}if(T){if(m){var I,C=b[T.length],R=C===$p||C===c6;R&&P<=c?I=T.length:I=Math.max(T.lastIndexOf($p),T.lastIndexOf(c6))+1,I>0&&(w=I,T=T.slice(0,w),P=this._getTextWidth(T))}T=T.trimRight(),this._addTextLine(T),r=Math.max(r,P),f+=i;var M=this._shouldHandleEllipsis(f);if(M){this._tryToAddEllipsisToLastLine();break}if(b=b.slice(w),b=b.trimLeft(),b.length>0&&(_=this._getTextWidth(b),_<=c)){this._addTextLine(b),f+=i,r=Math.max(r,_);break}}else break}else this._addTextLine(b),f+=i,r=Math.max(r,_),this._shouldHandleEllipsis(f)&&y<g-1&&this._tryToAddEllipsisToLastLine();if(this.textArr[this.textArr.length-1]&&(this.textArr[this.textArr.length-1].lastInParagraph=!0),l&&f+i>d)break}this.textHeight=n,this.textWidth=r}_shouldHandleEllipsis(t){var n=+this.fontSize(),r=this.lineHeight()*n,i=this.attrs.height,o=i!==Hl&&i!==void 0,s=this.padding(),a=i-s*2,l=this.wrap(),u=l!==f6;return!u||o&&t+r>a}_tryToAddEllipsisToLastLine(){var t=this.attrs.width,n=t!==Hl&&t!==void 0,r=this.padding(),i=t-r*2,o=this.ellipsis(),s=this.textArr[this.textArr.length-1];if(!(!s||!o)){if(n){var a=this._getTextWidth(s.text+Kb)<i;a||(s.text=s.text.slice(0,s.text.length-3))}this.textArr.splice(this.textArr.length-1,1),this._addTextLine(s.text+Kb)}}getStrokeScaleEnabled(){return!0}}ol.Text=Ut;Ut.prototype._fillFunc=dfe;Ut.prototype._strokeFunc=ffe;Ut.prototype.className=nfe;Ut.prototype._attrsAffectingSize=["text","fontSize","padding","wrap","lineHeight","letterSpacing"];(0,Qde._registerNode)(Ut);ir.Factory.overWriteSetter(Ut,"width",(0,aa.getNumberOrAutoValidator)());ir.Factory.overWriteSetter(Ut,"height",(0,aa.getNumberOrAutoValidator)());ir.Factory.addGetterSetter(Ut,"fontFamily","Arial");ir.Factory.addGetterSetter(Ut,"fontSize",12,(0,aa.getNumberValidator)());ir.Factory.addGetterSetter(Ut,"fontStyle",AN);ir.Factory.addGetterSetter(Ut,"fontVariant",AN);ir.Factory.addGetterSetter(Ut,"padding",0,(0,aa.getNumberValidator)());ir.Factory.addGetterSetter(Ut,"align",PN);ir.Factory.addGetterSetter(Ut,"verticalAlign",rfe);ir.Factory.addGetterSetter(Ut,"lineHeight",1,(0,aa.getNumberValidator)());ir.Factory.addGetterSetter(Ut,"wrap",afe);ir.Factory.addGetterSetter(Ut,"ellipsis",!1,(0,aa.getBooleanValidator)());ir.Factory.addGetterSetter(Ut,"letterSpacing",0,(0,aa.getNumberValidator)());ir.Factory.addGetterSetter(Ut,"text","",(0,aa.getStringValidator)());ir.Factory.addGetterSetter(Ut,"textDecoration","");var xv={};Object.defineProperty(xv,"__esModule",{value:!0});xv.TextPath=void 0;const Yb=Ot,Ai=Te,pfe=sn,ed=_c,Qb=ol,RN=de,gfe=Pe;var mfe="",ON="normal";function MN(e){e.fillText(this.partialText,0,0)}function IN(e){e.strokeText(this.partialText,0,0)}class an extends pfe.Shape{constructor(t){super(t),this.dummyCanvas=Yb.Util.createCanvasElement(),this.dataArray=[],this._readDataAttribute(),this.on("dataChange.konva",function(){this._readDataAttribute(),this._setTextData()}),this.on("textChange.konva alignChange.konva letterSpacingChange.konva kerningFuncChange.konva fontSizeChange.konva fontFamilyChange.konva",this._setTextData),this._setTextData()}_getTextPathLength(){return ed.Path.getPathLength(this.dataArray)}_getPointAtLength(t){if(!this.attrs.data)return null;const n=this.pathLength;return t-1>n?null:ed.Path.getPointAtLengthOfDataArray(t,this.dataArray)}_readDataAttribute(){this.dataArray=ed.Path.parsePathData(this.attrs.data),this.pathLength=this._getTextPathLength()}_sceneFunc(t){t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",this.textBaseline()),t.setAttr("textAlign","left"),t.save();var n=this.textDecoration(),r=this.fill(),i=this.fontSize(),o=this.glyphInfo;n==="underline"&&t.beginPath();for(var s=0;s<o.length;s++){t.save();var a=o[s].p0;t.translate(a.x,a.y),t.rotate(o[s].rotation),this.partialText=o[s].text,t.fillStrokeShape(this),n==="underline"&&(s===0&&t.moveTo(0,i/2+1),t.lineTo(i,i/2+1)),t.restore()}n==="underline"&&(t.strokeStyle=r,t.lineWidth=i/20,t.stroke()),t.restore()}_hitFunc(t){t.beginPath();var n=this.glyphInfo;if(n.length>=1){var r=n[0].p0;t.moveTo(r.x,r.y)}for(var i=0;i<n.length;i++){var o=n[i].p1;t.lineTo(o.x,o.y)}t.setAttr("lineWidth",this.fontSize()),t.setAttr("strokeStyle",this.colorKey),t.stroke()}getTextWidth(){return this.textWidth}getTextHeight(){return Yb.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}setText(t){return Qb.Text.prototype.setText.call(this,t)}_getContextFont(){return Qb.Text.prototype._getContextFont.call(this)}_getTextSize(t){var n=this.dummyCanvas,r=n.getContext("2d");r.save(),r.font=this._getContextFont();var i=r.measureText(t);return r.restore(),{width:i.width,height:parseInt(`${this.fontSize()}`,10)}}_setTextData(){const{width:t,height:n}=this._getTextSize(this.attrs.text);if(this.textWidth=t,this.textHeight=n,this.glyphInfo=[],!this.attrs.data)return null;const r=this.letterSpacing(),i=this.align(),o=this.kerningFunc(),s=Math.max(this.textWidth+((this.attrs.text||"").length-1)*r,0);let a=0;i==="center"&&(a=Math.max(0,this.pathLength/2-s/2)),i==="right"&&(a=Math.max(0,this.pathLength-s));const l=(0,Qb.stringToArray)(this.text());let u=a;for(var c=0;c<l.length;c++){const d=this._getPointAtLength(u);if(!d)return;let f=this._getTextSize(l[c]).width+r;if(l[c]===" "&&i==="justify"){const y=this.text().split(" ").length-1;f+=(this.pathLength-s)/y}const h=this._getPointAtLength(u+f);if(!h)return;const p=ed.Path.getLineLength(d.x,d.y,h.x,h.y);let m=0;if(o)try{m=o(l[c-1],l[c])*this.fontSize()}catch{m=0}d.x+=m,h.x+=m,this.textWidth+=m;const S=ed.Path.getPointOnLine(m+p/2,d.x,d.y,h.x,h.y),v=Math.atan2(h.y-d.y,h.x-d.x);this.glyphInfo.push({transposeX:S.x,transposeY:S.y,text:l[c],rotation:v,p0:d,p1:h}),u+=f}}getSelfRect(){if(!this.glyphInfo.length)return{x:0,y:0,width:0,height:0};var t=[];this.glyphInfo.forEach(function(c){t.push(c.p0.x),t.push(c.p0.y),t.push(c.p1.x),t.push(c.p1.y)});for(var n=t[0]||0,r=t[0]||0,i=t[1]||0,o=t[1]||0,s,a,l=0;l<t.length/2;l++)s=t[l*2],a=t[l*2+1],n=Math.min(n,s),r=Math.max(r,s),i=Math.min(i,a),o=Math.max(o,a);var u=this.fontSize();return{x:n-u/2,y:i-u/2,width:r-n+u,height:o-i+u}}destroy(){return Yb.Util.releaseCanvas(this.dummyCanvas),super.destroy()}}xv.TextPath=an;an.prototype._fillFunc=MN;an.prototype._strokeFunc=IN;an.prototype._fillFuncHit=MN;an.prototype._strokeFuncHit=IN;an.prototype.className="TextPath";an.prototype._attrsAffectingSize=["text","fontSize","data"];(0,gfe._registerNode)(an);Ai.Factory.addGetterSetter(an,"data");Ai.Factory.addGetterSetter(an,"fontFamily","Arial");Ai.Factory.addGetterSetter(an,"fontSize",12,(0,RN.getNumberValidator)());Ai.Factory.addGetterSetter(an,"fontStyle",ON);Ai.Factory.addGetterSetter(an,"align","left");Ai.Factory.addGetterSetter(an,"letterSpacing",0,(0,RN.getNumberValidator)());Ai.Factory.addGetterSetter(an,"textBaseline","middle");Ai.Factory.addGetterSetter(an,"fontVariant",ON);Ai.Factory.addGetterSetter(an,"text",mfe);Ai.Factory.addGetterSetter(an,"textDecoration",null);Ai.Factory.addGetterSetter(an,"kerningFunc",null);var Cv={};Object.defineProperty(Cv,"__esModule",{value:!0});Cv.Transformer=void 0;const Je=Ot,Ze=Te,h6=wt,yfe=sn,vfe=Mh,p6=bc,si=Pe,la=de,bfe=Pe;var NN="tr-konva",Sfe=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange","anchorStyleFuncChange"].map(e=>e+`.${NN}`).join(" "),g6="nodesRect",_fe=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],wfe={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const xfe="ontouchstart"in si.Konva._global;function Cfe(e,t){if(e==="rotater")return"crosshair";t+=Je.Util.degToRad(wfe[e]||0);var n=(Je.Util.radToDeg(t)%360+360)%360;return Je.Util._inRange(n,315+22.5,360)||Je.Util._inRange(n,0,22.5)?"ns-resize":Je.Util._inRange(n,45-22.5,45+22.5)?"nesw-resize":Je.Util._inRange(n,90-22.5,90+22.5)?"ew-resize":Je.Util._inRange(n,135-22.5,135+22.5)?"nwse-resize":Je.Util._inRange(n,180-22.5,180+22.5)?"ns-resize":Je.Util._inRange(n,225-22.5,225+22.5)?"nesw-resize":Je.Util._inRange(n,270-22.5,270+22.5)?"ew-resize":Je.Util._inRange(n,315-22.5,315+22.5)?"nwse-resize":(Je.Util.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var Qm=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],m6=1e8;function Tfe(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function DN(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function Efe(e,t){const n=Tfe(e);return DN(e,t,n)}function Pfe(e,t,n){let r=t;for(let i=0;i<e.length;i++){const o=si.Konva.getAngle(e[i]),s=Math.abs(o-t)%(Math.PI*2);Math.min(s,Math.PI*2-s)<n&&(r=o)}return r}class ze extends p6.Group{constructor(t){super(t),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(Sfe,this.update),this.getNode()&&this.update()}attachTo(t){return this.setNode(t),this}setNode(t){return Je.Util.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([t])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return NN+this._id}setNodes(t=[]){this._nodes&&this._nodes.length&&this.detach();const n=t.filter(i=>i.isAncestorOf(this)?(Je.Util.error("Konva.Transformer cannot be an a child of the node you are trying to attach"),!1):!0);this._nodes=t=n,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(i=>{const o=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},s=i._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");i.on(s,o),i.on(_fe.map(a=>a+`.${this._getEventNamespace()}`).join(" "),o),i.on(`absoluteTransformChange.${this._getEventNamespace()}`,o),this._proxyDrag(i)}),this._resetTransformCache();var r=!!this.findOne(".top-left");return r&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,s=i.y-n.y;this.nodes().forEach(a=>{if(a===t||a.isDragging())return;const l=a.getAbsolutePosition();a.setAbsolutePosition({x:l.x+o,y:l.y+s}),a.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(g6),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(g6,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),s=t.getAbsolutePosition(r),a=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=(si.Konva.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),c={x:s.x+a*Math.cos(u)+l*Math.sin(-u),y:s.y+l*Math.cos(u)+a*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return DN(c,-si.Konva.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-m6,y:-m6,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const c=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var d=[{x:c.x,y:c.y},{x:c.x+c.width,y:c.y},{x:c.x+c.width,y:c.y+c.height},{x:c.x,y:c.y+c.height}],f=u.getAbsoluteTransform();d.forEach(function(h){var p=f.point(h);n.push(p)})});const r=new Je.Transform;r.rotate(-si.Konva.getAngle(this.rotation()));var i,o,s,a;n.forEach(function(u){var c=r.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:s-i,height:a-o,rotation:si.Konva.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),Qm.forEach((function(t){this._createAnchor(t)}).bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new vfe.Rect({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:xfe?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=si.Konva.getAngle(this.rotation()),o=Cfe(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new yfe.Shape({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*Je.Util._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var s=t.target.getAbsolutePosition(),a=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-s.x,y:a.y-s.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),s=o.getStage();s.setPointersPositions(t);const a=s.getPointerPosition();let l={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const c=o.getAbsolutePosition();if(!(u.x===c.x&&u.y===c.y)){if(this._movingAnchorName==="rotater"){var d=this._getNodeRect();n=o.x()-d.width/2,r=-o.y()+d.height/2;let I=Math.atan2(-r,n)+Math.PI/2;d.height<0&&(I-=Math.PI);var f=si.Konva.getAngle(this.rotation());const C=f+I,R=si.Konva.getAngle(this.rotationSnapTolerance()),N=Pfe(this.rotationSnaps(),C,R)-d.rotation,O=Efe(d,N);this._fitNodesInto(O,t);return}var h=this.shiftBehavior(),p;h==="inverted"?p=this.keepRatio()&&!t.shiftKey:h==="none"?p=this.keepRatio():p=this.keepRatio()||t.shiftKey;var g=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-left").x()>m.x?-1:1,v=this.findOne(".top-left").y()>m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".top-left").x(m.x-n),this.findOne(".top-left").y(m.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-m.x,2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-right").x()<m.x?-1:1,v=this.findOne(".top-right").y()>m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".top-right").x(m.x+n),this.findOne(".top-right").y(m.y-r)}var y=o.position();this.findOne(".top-left").y(y.y),this.findOne(".bottom-right").x(y.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(o.y()-m.y,2));var S=m.x<o.x()?-1:1,v=o.y()<m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,o.x(m.x-n),o.y(m.y+r)}y=o.position(),this.findOne(".top-left").x(y.x),this.findOne(".bottom-right").y(y.y)}else if(this._movingAnchorName==="bottom-center")this.findOne(".bottom-right").y(o.y());else if(this._movingAnchorName==="bottom-right"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};i=Math.sqrt(Math.pow(o.x()-m.x,2)+Math.pow(o.y()-m.y,2));var S=this.findOne(".bottom-right").x()<m.x?-1:1,v=this.findOne(".bottom-right").y()<m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".bottom-right").x(m.x+n),this.findOne(".bottom-right").y(m.y+r)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));var g=this.centeredScaling()||t.altKey;if(g){var b=this.findOne(".top-left"),_=this.findOne(".bottom-right"),w=b.x(),x=b.y(),T=this.getWidth()-_.x(),P=this.getHeight()-_.y();_.move({x:-w,y:-x}),b.move({x:T,y:P})}var E=this.findOne(".top-left").getAbsolutePosition();n=E.x,r=E.y;var A=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),$=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:n,y:r,width:A,height:$,rotation:si.Konva.getAngle(this.rotation())},t)}}_handleMouseUp(t){this._removeEvents(t)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(t){if(this._transforming){this._transforming=!1,typeof window<"u"&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var n=this.getNode();this._fire("transformend",{evt:t,target:n}),n&&this._nodes.forEach(r=>{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(Je.Util._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(Je.Util._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var s=new Je.Transform;if(s.rotate(si.Konva.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const d=s.point({x:-this.padding()*2,y:0});if(t.x+=d.x,t.y+=d.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const d=s.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const d=s.point({x:0,y:-this.padding()*2});if(t.x+=d.x,t.y+=d.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const d=s.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const d=this.boundBoxFunc()(r,t);d?t=d:Je.Util.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const a=1e7,l=new Je.Transform;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/a,r.height/a);const u=new Je.Transform;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/a,t.height/a);const c=u.multiply(l.invert());this._nodes.forEach(d=>{var f;const h=d.getParent().getAbsoluteTransform(),p=d.getTransform().copy();p.translate(d.offsetX(),d.offsetY());const m=new Je.Transform;m.multiply(h.copy().invert()).multiply(c).multiply(h).multiply(p);const S=m.decompose();d.setAttrs(S),this._fire("transform",{evt:n,target:d}),d._fire("transform",{evt:n,target:d}),(f=d.getLayer())===null||f===void 0||f.batchDraw()}),this.rotation(Je.Util._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(Je.Util._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),s=this.resizeEnabled(),a=this.padding(),l=this.anchorSize();const u=this.find("._anchor");u.forEach(d=>{d.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+a,offsetY:l/2+a,visible:s&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+a,visible:s&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-a,offsetY:l/2+a,visible:s&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+a,visible:s&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-a,visible:s&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-a,visible:s&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*Je.Util._sign(i)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0});const c=this.anchorStyleFunc();c&&u.forEach(d=>{c(d)}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),p6.Group.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return h6.Node.prototype.toObject.call(this)}clone(t){var n=h6.Node.prototype.clone.call(this,t);return n}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}Cv.Transformer=ze;function Afe(e){return e instanceof Array||Je.Util.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){Qm.indexOf(t)===-1&&Je.Util.warn("Unknown anchor name: "+t+". Available names are: "+Qm.join(", "))}),e||[]}ze.prototype.className="Transformer";(0,bfe._registerNode)(ze);Ze.Factory.addGetterSetter(ze,"enabledAnchors",Qm,Afe);Ze.Factory.addGetterSetter(ze,"flipEnabled",!0,(0,la.getBooleanValidator)());Ze.Factory.addGetterSetter(ze,"resizeEnabled",!0);Ze.Factory.addGetterSetter(ze,"anchorSize",10,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"rotateEnabled",!0);Ze.Factory.addGetterSetter(ze,"rotationSnaps",[]);Ze.Factory.addGetterSetter(ze,"rotateAnchorOffset",50,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"rotationSnapTolerance",5,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"borderEnabled",!0);Ze.Factory.addGetterSetter(ze,"anchorStroke","rgb(0, 161, 255)");Ze.Factory.addGetterSetter(ze,"anchorStrokeWidth",1,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"anchorFill","white");Ze.Factory.addGetterSetter(ze,"anchorCornerRadius",0,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"borderStroke","rgb(0, 161, 255)");Ze.Factory.addGetterSetter(ze,"borderStrokeWidth",1,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"borderDash");Ze.Factory.addGetterSetter(ze,"keepRatio",!0);Ze.Factory.addGetterSetter(ze,"shiftBehavior","default");Ze.Factory.addGetterSetter(ze,"centeredScaling",!1);Ze.Factory.addGetterSetter(ze,"ignoreStroke",!1);Ze.Factory.addGetterSetter(ze,"padding",0,(0,la.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"node");Ze.Factory.addGetterSetter(ze,"nodes");Ze.Factory.addGetterSetter(ze,"boundBoxFunc");Ze.Factory.addGetterSetter(ze,"anchorDragBoundFunc");Ze.Factory.addGetterSetter(ze,"anchorStyleFunc");Ze.Factory.addGetterSetter(ze,"shouldOverdrawWholeArea",!1);Ze.Factory.addGetterSetter(ze,"useSingleNodeRotation",!0);Ze.Factory.backCompat(ze,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});var Tv={};Object.defineProperty(Tv,"__esModule",{value:!0});Tv.Wedge=void 0;const Ev=Te,kfe=sn,Rfe=Pe,LN=de,Ofe=Pe;class Qo extends kfe.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,Rfe.Konva.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}Tv.Wedge=Qo;Qo.prototype.className="Wedge";Qo.prototype._centroid=!0;Qo.prototype._attrsAffectingSize=["radius"];(0,Ofe._registerNode)(Qo);Ev.Factory.addGetterSetter(Qo,"radius",0,(0,LN.getNumberValidator)());Ev.Factory.addGetterSetter(Qo,"angle",0,(0,LN.getNumberValidator)());Ev.Factory.addGetterSetter(Qo,"clockwise",!1);Ev.Factory.backCompat(Qo,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});var Pv={};Object.defineProperty(Pv,"__esModule",{value:!0});Pv.Blur=void 0;const y6=Te,Mfe=wt,Ife=de;function v6(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var Nfe=[512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,289,287,285,282,280,278,275,273,271,269,267,265,263,261,259],Dfe=[9,11,12,13,13,14,14,15,15,15,15,16,16,16,16,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24];function Lfe(e,t){var n=e.data,r=e.width,i=e.height,o,s,a,l,u,c,d,f,h,p,m,S,v,y,g,b,_,w,x,T,P,E,A,$,I=t+t+1,C=r-1,R=i-1,M=t+1,N=M*(M+1)/2,O=new v6,D=null,L=O,j=null,U=null,G=Nfe[t],W=Dfe[t];for(a=1;a<I;a++)L=L.next=new v6,a===M&&(D=L);for(L.next=O,d=c=0,s=0;s<i;s++){for(b=_=w=x=f=h=p=m=0,S=M*(T=n[c]),v=M*(P=n[c+1]),y=M*(E=n[c+2]),g=M*(A=n[c+3]),f+=N*T,h+=N*P,p+=N*E,m+=N*A,L=O,a=0;a<M;a++)L.r=T,L.g=P,L.b=E,L.a=A,L=L.next;for(a=1;a<M;a++)l=c+((C<a?C:a)<<2),f+=(L.r=T=n[l])*($=M-a),h+=(L.g=P=n[l+1])*$,p+=(L.b=E=n[l+2])*$,m+=(L.a=A=n[l+3])*$,b+=T,_+=P,w+=E,x+=A,L=L.next;for(j=O,U=D,o=0;o<r;o++)n[c+3]=A=m*G>>W,A!==0?(A=255/A,n[c]=(f*G>>W)*A,n[c+1]=(h*G>>W)*A,n[c+2]=(p*G>>W)*A):n[c]=n[c+1]=n[c+2]=0,f-=S,h-=v,p-=y,m-=g,S-=j.r,v-=j.g,y-=j.b,g-=j.a,l=d+((l=o+t+1)<C?l:C)<<2,b+=j.r=n[l],_+=j.g=n[l+1],w+=j.b=n[l+2],x+=j.a=n[l+3],f+=b,h+=_,p+=w,m+=x,j=j.next,S+=T=U.r,v+=P=U.g,y+=E=U.b,g+=A=U.a,b-=T,_-=P,w-=E,x-=A,U=U.next,c+=4;d+=r}for(o=0;o<r;o++){for(_=w=x=b=h=p=m=f=0,c=o<<2,S=M*(T=n[c]),v=M*(P=n[c+1]),y=M*(E=n[c+2]),g=M*(A=n[c+3]),f+=N*T,h+=N*P,p+=N*E,m+=N*A,L=O,a=0;a<M;a++)L.r=T,L.g=P,L.b=E,L.a=A,L=L.next;for(u=r,a=1;a<=t;a++)c=u+o<<2,f+=(L.r=T=n[c])*($=M-a),h+=(L.g=P=n[c+1])*$,p+=(L.b=E=n[c+2])*$,m+=(L.a=A=n[c+3])*$,b+=T,_+=P,w+=E,x+=A,L=L.next,a<R&&(u+=r);for(c=o,j=O,U=D,s=0;s<i;s++)l=c<<2,n[l+3]=A=m*G>>W,A>0?(A=255/A,n[l]=(f*G>>W)*A,n[l+1]=(h*G>>W)*A,n[l+2]=(p*G>>W)*A):n[l]=n[l+1]=n[l+2]=0,f-=S,h-=v,p-=y,m-=g,S-=j.r,v-=j.g,y-=j.b,g-=j.a,l=o+((l=s+M)<R?l:R)*r<<2,f+=b+=j.r=n[l],h+=_+=j.g=n[l+1],p+=w+=j.b=n[l+2],m+=x+=j.a=n[l+3],j=j.next,S+=T=U.r,v+=P=U.g,y+=E=U.b,g+=A=U.a,b-=T,_-=P,w-=E,x-=A,U=U.next,c+=r}}const $fe=function(t){var n=Math.round(this.blurRadius());n>0&&Lfe(t,n)};Pv.Blur=$fe;y6.Factory.addGetterSetter(Mfe.Node,"blurRadius",0,(0,Ife.getNumberValidator)(),y6.Factory.afterSetFilter);var Av={};Object.defineProperty(Av,"__esModule",{value:!0});Av.Brighten=void 0;const b6=Te,Ffe=wt,Bfe=de,jfe=function(e){var t=this.brightness()*255,n=e.data,r=n.length,i;for(i=0;i<r;i+=4)n[i]+=t,n[i+1]+=t,n[i+2]+=t};Av.Brighten=jfe;b6.Factory.addGetterSetter(Ffe.Node,"brightness",0,(0,Bfe.getNumberValidator)(),b6.Factory.afterSetFilter);var kv={};Object.defineProperty(kv,"__esModule",{value:!0});kv.Contrast=void 0;const S6=Te,Vfe=wt,zfe=de,Ufe=function(e){var t=Math.pow((this.contrast()+100)/100,2),n=e.data,r=n.length,i=150,o=150,s=150,a;for(a=0;a<r;a+=4)i=n[a],o=n[a+1],s=n[a+2],i/=255,i-=.5,i*=t,i+=.5,i*=255,o/=255,o-=.5,o*=t,o+=.5,o*=255,s/=255,s-=.5,s*=t,s+=.5,s*=255,i=i<0?0:i>255?255:i,o=o<0?0:o>255?255:o,s=s<0?0:s>255?255:s,n[a]=i,n[a+1]=o,n[a+2]=s};kv.Contrast=Ufe;S6.Factory.addGetterSetter(Vfe.Node,"contrast",0,(0,zfe.getNumberValidator)(),S6.Factory.afterSetFilter);var Rv={};Object.defineProperty(Rv,"__esModule",{value:!0});Rv.Emboss=void 0;const Ws=Te,Ov=wt,Gfe=Ot,$N=de,Hfe=function(e){var t=this.embossStrength()*10,n=this.embossWhiteLevel()*255,r=this.embossDirection(),i=this.embossBlend(),o=0,s=0,a=e.data,l=e.width,u=e.height,c=l*4,d=u;switch(r){case"top-left":o=-1,s=-1;break;case"top":o=-1,s=0;break;case"top-right":o=-1,s=1;break;case"right":o=0,s=1;break;case"bottom-right":o=1,s=1;break;case"bottom":o=1,s=0;break;case"bottom-left":o=1,s=-1;break;case"left":o=0,s=-1;break;default:Gfe.Util.error("Unknown emboss direction: "+r)}do{var f=(d-1)*c,h=o;d+h<1&&(h=0),d+h>u&&(h=0);var p=(d-1+h)*l*4,m=l;do{var S=f+(m-1)*4,v=s;m+v<1&&(v=0),m+v>l&&(v=0);var y=p+(m-1+v)*4,g=a[S]-a[y],b=a[S+1]-a[y+1],_=a[S+2]-a[y+2],w=g,x=w>0?w:-w,T=b>0?b:-b,P=_>0?_:-_;if(T>x&&(w=b),P>x&&(w=_),w*=t,i){var E=a[S]+w,A=a[S+1]+w,$=a[S+2]+w;a[S]=E>255?255:E<0?0:E,a[S+1]=A>255?255:A<0?0:A,a[S+2]=$>255?255:$<0?0:$}else{var I=n-w;I<0?I=0:I>255&&(I=255),a[S]=a[S+1]=a[S+2]=I}}while(--m)}while(--d)};Rv.Emboss=Hfe;Ws.Factory.addGetterSetter(Ov.Node,"embossStrength",.5,(0,$N.getNumberValidator)(),Ws.Factory.afterSetFilter);Ws.Factory.addGetterSetter(Ov.Node,"embossWhiteLevel",.5,(0,$N.getNumberValidator)(),Ws.Factory.afterSetFilter);Ws.Factory.addGetterSetter(Ov.Node,"embossDirection","top-left",null,Ws.Factory.afterSetFilter);Ws.Factory.addGetterSetter(Ov.Node,"embossBlend",!1,null,Ws.Factory.afterSetFilter);var Mv={};Object.defineProperty(Mv,"__esModule",{value:!0});Mv.Enhance=void 0;const _6=Te,qfe=wt,Wfe=de;function Zb(e,t,n,r,i){var o=n-t,s=i-r,a;return o===0?r+s/2:s===0?r:(a=(e-t)/o,a=s*a+r,a)}const Kfe=function(e){var t=e.data,n=t.length,r=t[0],i=r,o,s=t[1],a=s,l,u=t[2],c=u,d,f,h=this.enhance();if(h!==0){for(f=0;f<n;f+=4)o=t[f+0],o<r?r=o:o>i&&(i=o),l=t[f+1],l<s?s=l:l>a&&(a=l),d=t[f+2],d<u?u=d:d>c&&(c=d);i===r&&(i=255,r=0),a===s&&(a=255,s=0),c===u&&(c=255,u=0);var p,m,S,v,y,g,b,_,w;for(h>0?(m=i+h*(255-i),S=r-h*(r-0),y=a+h*(255-a),g=s-h*(s-0),_=c+h*(255-c),w=u-h*(u-0)):(p=(i+r)*.5,m=i+h*(i-p),S=r+h*(r-p),v=(a+s)*.5,y=a+h*(a-v),g=s+h*(s-v),b=(c+u)*.5,_=c+h*(c-b),w=u+h*(u-b)),f=0;f<n;f+=4)t[f+0]=Zb(t[f+0],r,i,S,m),t[f+1]=Zb(t[f+1],s,a,g,y),t[f+2]=Zb(t[f+2],u,c,w,_)}};Mv.Enhance=Kfe;_6.Factory.addGetterSetter(qfe.Node,"enhance",0,(0,Wfe.getNumberValidator)(),_6.Factory.afterSetFilter);var Iv={};Object.defineProperty(Iv,"__esModule",{value:!0});Iv.Grayscale=void 0;const Xfe=function(e){var t=e.data,n=t.length,r,i;for(r=0;r<n;r+=4)i=.34*t[r]+.5*t[r+1]+.16*t[r+2],t[r]=i,t[r+1]=i,t[r+2]=i};Iv.Grayscale=Xfe;var Nv={};Object.defineProperty(Nv,"__esModule",{value:!0});Nv.HSL=void 0;const sc=Te,RC=wt,OC=de;sc.Factory.addGetterSetter(RC.Node,"hue",0,(0,OC.getNumberValidator)(),sc.Factory.afterSetFilter);sc.Factory.addGetterSetter(RC.Node,"saturation",0,(0,OC.getNumberValidator)(),sc.Factory.afterSetFilter);sc.Factory.addGetterSetter(RC.Node,"luminance",0,(0,OC.getNumberValidator)(),sc.Factory.afterSetFilter);const Yfe=function(e){var t=e.data,n=t.length,r=1,i=Math.pow(2,this.saturation()),o=Math.abs(this.hue()+360)%360,s=this.luminance()*127,a,l=r*i*Math.cos(o*Math.PI/180),u=r*i*Math.sin(o*Math.PI/180),c=.299*r+.701*l+.167*u,d=.587*r-.587*l+.33*u,f=.114*r-.114*l-.497*u,h=.299*r-.299*l-.328*u,p=.587*r+.413*l+.035*u,m=.114*r-.114*l+.293*u,S=.299*r-.3*l+1.25*u,v=.587*r-.586*l-1.05*u,y=.114*r+.886*l-.2*u,g,b,_,w;for(a=0;a<n;a+=4)g=t[a+0],b=t[a+1],_=t[a+2],w=t[a+3],t[a+0]=c*g+d*b+f*_+s,t[a+1]=h*g+p*b+m*_+s,t[a+2]=S*g+v*b+y*_+s,t[a+3]=w};Nv.HSL=Yfe;var Dv={};Object.defineProperty(Dv,"__esModule",{value:!0});Dv.HSV=void 0;const ac=Te,MC=wt,IC=de,Qfe=function(e){var t=e.data,n=t.length,r=Math.pow(2,this.value()),i=Math.pow(2,this.saturation()),o=Math.abs(this.hue()+360)%360,s,a=r*i*Math.cos(o*Math.PI/180),l=r*i*Math.sin(o*Math.PI/180),u=.299*r+.701*a+.167*l,c=.587*r-.587*a+.33*l,d=.114*r-.114*a-.497*l,f=.299*r-.299*a-.328*l,h=.587*r+.413*a+.035*l,p=.114*r-.114*a+.293*l,m=.299*r-.3*a+1.25*l,S=.587*r-.586*a-1.05*l,v=.114*r+.886*a-.2*l,y,g,b,_;for(s=0;s<n;s+=4)y=t[s+0],g=t[s+1],b=t[s+2],_=t[s+3],t[s+0]=u*y+c*g+d*b,t[s+1]=f*y+h*g+p*b,t[s+2]=m*y+S*g+v*b,t[s+3]=_};Dv.HSV=Qfe;ac.Factory.addGetterSetter(MC.Node,"hue",0,(0,IC.getNumberValidator)(),ac.Factory.afterSetFilter);ac.Factory.addGetterSetter(MC.Node,"saturation",0,(0,IC.getNumberValidator)(),ac.Factory.afterSetFilter);ac.Factory.addGetterSetter(MC.Node,"value",0,(0,IC.getNumberValidator)(),ac.Factory.afterSetFilter);var Lv={};Object.defineProperty(Lv,"__esModule",{value:!0});Lv.Invert=void 0;const Zfe=function(e){var t=e.data,n=t.length,r;for(r=0;r<n;r+=4)t[r]=255-t[r],t[r+1]=255-t[r+1],t[r+2]=255-t[r+2]};Lv.Invert=Zfe;var $v={};Object.defineProperty($v,"__esModule",{value:!0});$v.Kaleidoscope=void 0;const Zm=Te,FN=wt,w6=Ot,BN=de;var Jfe=function(e,t,n){var r=e.data,i=t.data,o=e.width,s=e.height,a=n.polarCenterX||o/2,l=n.polarCenterY||s/2,u,c,d,f=0,h=0,p=0,m=0,S,v=Math.sqrt(a*a+l*l);c=o-a,d=s-l,S=Math.sqrt(c*c+d*d),v=S>v?S:v;var y=s,g=o,b,_,w=360/g*Math.PI/180,x,T;for(_=0;_<g;_+=1)for(x=Math.sin(_*w),T=Math.cos(_*w),b=0;b<y;b+=1)c=Math.floor(a+v*b/y*T),d=Math.floor(l+v*b/y*x),u=(d*o+c)*4,f=r[u+0],h=r[u+1],p=r[u+2],m=r[u+3],u=(_+b*o)*4,i[u+0]=f,i[u+1]=h,i[u+2]=p,i[u+3]=m},ehe=function(e,t,n){var r=e.data,i=t.data,o=e.width,s=e.height,a=n.polarCenterX||o/2,l=n.polarCenterY||s/2,u,c,d,f,h,p=0,m=0,S=0,v=0,y,g=Math.sqrt(a*a+l*l);c=o-a,d=s-l,y=Math.sqrt(c*c+d*d),g=y>g?y:g;var b=s,_=o,w,x,T=n.polarRotation||0,P,E;for(c=0;c<o;c+=1)for(d=0;d<s;d+=1)f=c-a,h=d-l,w=Math.sqrt(f*f+h*h)*b/g,x=(Math.atan2(h,f)*180/Math.PI+360+T)%360,x=x*_/360,P=Math.floor(x),E=Math.floor(w),u=(E*o+P)*4,p=r[u+0],m=r[u+1],S=r[u+2],v=r[u+3],u=(d*o+c)*4,i[u+0]=p,i[u+1]=m,i[u+2]=S,i[u+3]=v};const the=function(e){var t=e.width,n=e.height,r,i,o,s,a,l,u,c,d,f,h=Math.round(this.kaleidoscopePower()),p=Math.round(this.kaleidoscopeAngle()),m=Math.floor(t*(p%360)/360);if(!(h<1)){var S=w6.Util.createCanvasElement();S.width=t,S.height=n;var v=S.getContext("2d").getImageData(0,0,t,n);w6.Util.releaseCanvas(S),Jfe(e,v,{polarCenterX:t/2,polarCenterY:n/2});for(var y=t/Math.pow(2,h);y<=8;)y=y*2,h-=1;y=Math.ceil(y);var g=y,b=0,_=g,w=1;for(m+y>t&&(b=g,_=0,w=-1),i=0;i<n;i+=1)for(r=b;r!==_;r+=w)o=Math.round(r+m)%t,d=(t*i+o)*4,a=v.data[d+0],l=v.data[d+1],u=v.data[d+2],c=v.data[d+3],f=(t*i+r)*4,v.data[f+0]=a,v.data[f+1]=l,v.data[f+2]=u,v.data[f+3]=c;for(i=0;i<n;i+=1)for(g=Math.floor(y),s=0;s<h;s+=1){for(r=0;r<g+1;r+=1)d=(t*i+r)*4,a=v.data[d+0],l=v.data[d+1],u=v.data[d+2],c=v.data[d+3],f=(t*i+g*2-r-1)*4,v.data[f+0]=a,v.data[f+1]=l,v.data[f+2]=u,v.data[f+3]=c;g*=2}ehe(v,e,{polarRotation:0})}};$v.Kaleidoscope=the;Zm.Factory.addGetterSetter(FN.Node,"kaleidoscopePower",2,(0,BN.getNumberValidator)(),Zm.Factory.afterSetFilter);Zm.Factory.addGetterSetter(FN.Node,"kaleidoscopeAngle",0,(0,BN.getNumberValidator)(),Zm.Factory.afterSetFilter);var Fv={};Object.defineProperty(Fv,"__esModule",{value:!0});Fv.Mask=void 0;const x6=Te,nhe=wt,rhe=de;function Bp(e,t,n){var r=(n*e.width+t)*4,i=[];return i.push(e.data[r++],e.data[r++],e.data[r++],e.data[r++]),i}function td(e,t){return Math.sqrt(Math.pow(e[0]-t[0],2)+Math.pow(e[1]-t[1],2)+Math.pow(e[2]-t[2],2))}function ihe(e){for(var t=[0,0,0],n=0;n<e.length;n++)t[0]+=e[n][0],t[1]+=e[n][1],t[2]+=e[n][2];return t[0]/=e.length,t[1]/=e.length,t[2]/=e.length,t}function ohe(e,t){var n=Bp(e,0,0),r=Bp(e,e.width-1,0),i=Bp(e,0,e.height-1),o=Bp(e,e.width-1,e.height-1),s=t||10;if(td(n,r)<s&&td(r,o)<s&&td(o,i)<s&&td(i,n)<s){for(var a=ihe([r,n,o,i]),l=[],u=0;u<e.width*e.height;u++){var c=td(a,[e.data[u*4],e.data[u*4+1],e.data[u*4+2]]);l[u]=c<s?0:255}return l}}function she(e,t){for(var n=0;n<e.width*e.height;n++)e.data[4*n+3]=t[n]}function ahe(e,t,n){for(var r=[1,1,1,1,0,1,1,1,1],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a<n;a++)for(var l=0;l<t;l++){for(var u=a*t+l,c=0,d=0;d<i;d++)for(var f=0;f<i;f++){var h=a+d-o,p=l+f-o;if(h>=0&&h<n&&p>=0&&p<t){var m=h*t+p,S=r[d*i+f];c+=e[m]*S}}s[u]=c===255*8?255:0}return s}function lhe(e,t,n){for(var r=[1,1,1,1,1,1,1,1,1],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a<n;a++)for(var l=0;l<t;l++){for(var u=a*t+l,c=0,d=0;d<i;d++)for(var f=0;f<i;f++){var h=a+d-o,p=l+f-o;if(h>=0&&h<n&&p>=0&&p<t){var m=h*t+p,S=r[d*i+f];c+=e[m]*S}}s[u]=c>=255*4?255:0}return s}function uhe(e,t,n){for(var r=[.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a<n;a++)for(var l=0;l<t;l++){for(var u=a*t+l,c=0,d=0;d<i;d++)for(var f=0;f<i;f++){var h=a+d-o,p=l+f-o;if(h>=0&&h<n&&p>=0&&p<t){var m=h*t+p,S=r[d*i+f];c+=e[m]*S}}s[u]=c}return s}const che=function(e){var t=this.threshold(),n=ohe(e,t);return n&&(n=ahe(n,e.width,e.height),n=lhe(n,e.width,e.height),n=uhe(n,e.width,e.height),she(e,n)),e};Fv.Mask=che;x6.Factory.addGetterSetter(nhe.Node,"threshold",0,(0,rhe.getNumberValidator)(),x6.Factory.afterSetFilter);var Bv={};Object.defineProperty(Bv,"__esModule",{value:!0});Bv.Noise=void 0;const C6=Te,dhe=wt,fhe=de,hhe=function(e){var t=this.noise()*255,n=e.data,r=n.length,i=t/2,o;for(o=0;o<r;o+=4)n[o+0]+=i-2*i*Math.random(),n[o+1]+=i-2*i*Math.random(),n[o+2]+=i-2*i*Math.random()};Bv.Noise=hhe;C6.Factory.addGetterSetter(dhe.Node,"noise",.2,(0,fhe.getNumberValidator)(),C6.Factory.afterSetFilter);var jv={};Object.defineProperty(jv,"__esModule",{value:!0});jv.Pixelate=void 0;const T6=Te,phe=Ot,ghe=wt,mhe=de,yhe=function(e){var t=Math.ceil(this.pixelSize()),n=e.width,r=e.height,i,o,s,a,l,u,c,d=Math.ceil(n/t),f=Math.ceil(r/t),h,p,m,S,v,y,g,b=e.data;if(t<=0){phe.Util.error("pixelSize value can not be <= 0");return}for(v=0;v<d;v+=1)for(y=0;y<f;y+=1){for(a=0,l=0,u=0,c=0,h=v*t,p=h+t,m=y*t,S=m+t,g=0,i=h;i<p;i+=1)if(!(i>=n))for(o=m;o<S;o+=1)o>=r||(s=(n*o+i)*4,a+=b[s+0],l+=b[s+1],u+=b[s+2],c+=b[s+3],g+=1);for(a=a/g,l=l/g,u=u/g,c=c/g,i=h;i<p;i+=1)if(!(i>=n))for(o=m;o<S;o+=1)o>=r||(s=(n*o+i)*4,b[s+0]=a,b[s+1]=l,b[s+2]=u,b[s+3]=c)}};jv.Pixelate=yhe;T6.Factory.addGetterSetter(ghe.Node,"pixelSize",8,(0,mhe.getNumberValidator)(),T6.Factory.afterSetFilter);var Vv={};Object.defineProperty(Vv,"__esModule",{value:!0});Vv.Posterize=void 0;const E6=Te,vhe=wt,bhe=de,She=function(e){var t=Math.round(this.levels()*254)+1,n=e.data,r=n.length,i=255/t,o;for(o=0;o<r;o+=1)n[o]=Math.floor(n[o]/i)*i};Vv.Posterize=She;E6.Factory.addGetterSetter(vhe.Node,"levels",.5,(0,bhe.getNumberValidator)(),E6.Factory.afterSetFilter);var zv={};Object.defineProperty(zv,"__esModule",{value:!0});zv.RGB=void 0;const Jm=Te,NC=wt,_he=de,whe=function(e){var t=e.data,n=t.length,r=this.red(),i=this.green(),o=this.blue(),s,a;for(s=0;s<n;s+=4)a=(.34*t[s]+.5*t[s+1]+.16*t[s+2])/255,t[s]=a*r,t[s+1]=a*i,t[s+2]=a*o,t[s+3]=t[s+3]};zv.RGB=whe;Jm.Factory.addGetterSetter(NC.Node,"red",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Jm.Factory.addGetterSetter(NC.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Jm.Factory.addGetterSetter(NC.Node,"blue",0,_he.RGBComponent,Jm.Factory.afterSetFilter);var Uv={};Object.defineProperty(Uv,"__esModule",{value:!0});Uv.RGBA=void 0;const Bf=Te,Gv=wt,xhe=de,Che=function(e){var t=e.data,n=t.length,r=this.red(),i=this.green(),o=this.blue(),s=this.alpha(),a,l;for(a=0;a<n;a+=4)l=1-s,t[a]=r*s+t[a]*l,t[a+1]=i*s+t[a+1]*l,t[a+2]=o*s+t[a+2]*l};Uv.RGBA=Che;Bf.Factory.addGetterSetter(Gv.Node,"red",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Bf.Factory.addGetterSetter(Gv.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Bf.Factory.addGetterSetter(Gv.Node,"blue",0,xhe.RGBComponent,Bf.Factory.afterSetFilter);Bf.Factory.addGetterSetter(Gv.Node,"alpha",1,function(e){return this._filterUpToDate=!1,e>1?1:e<0?0:e});var Hv={};Object.defineProperty(Hv,"__esModule",{value:!0});Hv.Sepia=void 0;const The=function(e){var t=e.data,n=t.length,r,i,o,s;for(r=0;r<n;r+=4)i=t[r+0],o=t[r+1],s=t[r+2],t[r+0]=Math.min(255,i*.393+o*.769+s*.189),t[r+1]=Math.min(255,i*.349+o*.686+s*.168),t[r+2]=Math.min(255,i*.272+o*.534+s*.131)};Hv.Sepia=The;var qv={};Object.defineProperty(qv,"__esModule",{value:!0});qv.Solarize=void 0;const Ehe=function(e){var t=e.data,n=e.width,r=e.height,i=n*4,o=r;do{var s=(o-1)*i,a=n;do{var l=s+(a-1)*4,u=t[l],c=t[l+1],d=t[l+2];u>127&&(u=255-u),c>127&&(c=255-c),d>127&&(d=255-d),t[l]=u,t[l+1]=c,t[l+2]=d}while(--a)}while(--o)};qv.Solarize=Ehe;var Wv={};Object.defineProperty(Wv,"__esModule",{value:!0});Wv.Threshold=void 0;const P6=Te,Phe=wt,Ahe=de,khe=function(e){var t=this.threshold()*255,n=e.data,r=n.length,i;for(i=0;i<r;i+=1)n[i]=n[i]<t?0:255};Wv.Threshold=khe;P6.Factory.addGetterSetter(Phe.Node,"threshold",.5,(0,Ahe.getNumberValidator)(),P6.Factory.afterSetFilter);Object.defineProperty(nv,"__esModule",{value:!0});nv.Konva=void 0;const A6=oN,Rhe=lv,Ohe=dv,Mhe=pv,Ihe=gv,Nhe=mv,k6=oc,Dhe=Rh,Lhe=_c,$he=Mh,Fhe=bv,Bhe=Sv,jhe=_v,Vhe=wv,zhe=ol,Uhe=xv,Ghe=Cv,Hhe=Tv,qhe=Pv,Whe=Av,Khe=kv,Xhe=Rv,Yhe=Mv,Qhe=Iv,Zhe=Nv,Jhe=Dv,epe=Lv,tpe=$v,npe=Fv,rpe=Bv,ipe=jv,ope=Vv,spe=zv,ape=Uv,lpe=Hv,upe=qv,cpe=Wv;nv.Konva=A6.Konva.Util._assign(A6.Konva,{Arc:Rhe.Arc,Arrow:Ohe.Arrow,Circle:Mhe.Circle,Ellipse:Ihe.Ellipse,Image:Nhe.Image,Label:k6.Label,Tag:k6.Tag,Line:Dhe.Line,Path:Lhe.Path,Rect:$he.Rect,RegularPolygon:Fhe.RegularPolygon,Ring:Bhe.Ring,Sprite:jhe.Sprite,Star:Vhe.Star,Text:zhe.Text,TextPath:Uhe.TextPath,Transformer:Ghe.Transformer,Wedge:Hhe.Wedge,Filters:{Blur:qhe.Blur,Brighten:Whe.Brighten,Contrast:Khe.Contrast,Emboss:Xhe.Emboss,Enhance:Yhe.Enhance,Grayscale:Qhe.Grayscale,HSL:Zhe.HSL,HSV:Jhe.HSV,Invert:epe.Invert,Kaleidoscope:tpe.Kaleidoscope,Mask:npe.Mask,Noise:rpe.Noise,Pixelate:ipe.Pixelate,Posterize:ope.Posterize,RGB:spe.RGB,RGBA:ape.RGBA,Sepia:lpe.Sepia,Solarize:upe.Solarize,Threshold:cpe.Threshold}});var dpe=SC.exports;Object.defineProperty(dpe,"__esModule",{value:!0});const fpe=nv;SC.exports=fpe.Konva;var hpe=SC.exports;const nd=al(hpe),ppe=async(e,t,n)=>{const{width:r,height:i}=t,o=document.createElement("div"),s=new nd.Stage({container:o,width:r,height:i}),a=new nd.Layer,l=new nd.Layer;return a.add(new nd.Rect({...t,fill:n?"black":"white"})),e.forEach(u=>l.add(new nd.Line({points:u.points,stroke:n?"white":"black",strokeWidth:u.strokeWidth*2,tension:0,lineCap:"round",lineJoin:"round",shadowForStrokeEnabled:!1,globalCompositeOperation:u.tool==="brush"?"source-over":"destination-out"}))),s.add(a),s.add(l),o.remove(),s},gpe=async(e,t,n)=>new Promise((r,i)=>{const o=document.createElement("canvas");o.width=t,o.height=n;const s=o.getContext("2d"),a=new Image;if(!s){o.remove(),i("Unable to get context");return}a.onload=function(){s.drawImage(a,0,0),o.remove(),r(s.getImageData(0,0,t,n))},a.src=e}),R6=async(e,t)=>{const n=e.toDataURL(t);return await gpe(n,t.width,t.height)},mpe=async(e,t,n,r,i)=>{const o=fe("canvas"),s=tv(),a=Ple();if(!s||!a){o.error("Unable to find canvas / stage");return}const l={...t,...n},u=s.clone();u.scale({x:1,y:1});const c=u.getAbsolutePosition(),d={x:l.x+c.x,y:l.y+c.y,width:l.width,height:l.height},f=await Km(u,d),h=await R6(u,d),p=await ppe(r?e.objects.filter(IO):[],l,i),m=await Km(p,l),S=await R6(p,l);return{baseBlob:f,baseImageData:h,maskBlob:m,maskImageData:S}},ype=e=>{let t=!0,n=!1;const r=e.length;let i=3;for(i;i<r;i+=4)if(e[i]===255?t=!1:n=!0,!t&&n)return{isFullyTransparent:t,isPartiallyTransparent:n};return{isFullyTransparent:t,isPartiallyTransparent:n}},vpe=e=>{const t=e.length;let n=0;for(n;n<t;)if(e[n++]===0&&e[n++]===0&&e[n++]===0&&e[n++]===255)return!0;return!1},bpe=(e,t)=>{const{isPartiallyTransparent:n,isFullyTransparent:r}=ype(e.data),i=vpe(t.data);return n?r?"txt2img":"outpaint":i?"inpaint":"img2img"},Spe=e=>oO(e,n=>n.isEnabled&&(!!n.processedControlImage||n.processorType==="none"&&!!n.controlImage)),Kv=(e,t,n)=>{const{isEnabled:r,controlNets:i}=e.controlNet,o=Spe(i),s=t.nodes[Ke];if(r&&o.length&&o.length){const a={id:Op,type:"collect",is_intermediate:!0};t.nodes[Op]=a,t.edges.push({source:{node_id:Op,field:"collection"},destination:{node_id:n,field:"control"}}),o.forEach(l=>{const{controlNetId:u,controlImage:c,processedControlImage:d,beginStepPct:f,endStepPct:h,controlMode:p,resizeMode:m,model:S,processorType:v,weight:y}=l,g={id:`control_net_${u}`,type:"controlnet",is_intermediate:!0,begin_step_percent:f,end_step_percent:h,control_mode:p,resize_mode:m,control_model:S,control_weight:y};if(d&&v!=="none")g.image={image_name:d};else if(c)g.image={image_name:c};else return;if(t.nodes[g.id]=g,s){const b=_0(g,["id","type"]);s.controlnets.push(b)}t.edges.push({source:{node_id:g.id,field:"control"},destination:{node_id:Op,field:"item"}})})}},xc=(e,t)=>{const{positivePrompt:n,iterations:r,seed:i,shouldRandomizeSeed:o}=e.generation,{combinatorial:s,isEnabled:a,maxPrompts:l}=e.dynamicPrompts,u=t.nodes[Ke];if(a){rY(t.nodes[Be],"prompt");const c={id:zb,type:"dynamic_prompt",is_intermediate:!0,max_prompts:s?l:r,combinatorial:s,prompt:n},d={id:lr,type:"iterate",is_intermediate:!0};if(t.nodes[zb]=c,t.nodes[lr]=d,t.edges.push({source:{node_id:zb,field:"prompt_collection"},destination:{node_id:lr,field:"collection"}},{source:{node_id:lr,field:"item"},destination:{node_id:Be,field:"prompt"}}),u&&t.edges.push({source:{node_id:lr,field:"item"},destination:{node_id:Ke,field:"positive_prompt"}}),o){const 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Pve={type:"spring",stiffness:500,damping:25,restSpeed:10},Ave=e=>({type:"spring",stiffness:550,damping:e===0?2*Math.sqrt(550):30,restSpeed:10}),kve={type:"keyframes",duration:.8},Rve={type:"keyframes",ease:[.25,.1,.35,1],duration:.3},Ove=(e,{keyframes:t})=>t.length>2?kve:El.has(e)?e.startsWith("scale")?Ave(t[1]):Pve:Rve,$2=(e,t)=>e==="zIndex"?!1:!!(typeof t=="number"||Array.isArray(t)||typeof t=="string"&&(Xs.test(t)||t==="0")&&!t.startsWith("url(")),Mve=new Set(["brightness","contrast","saturate","opacity"]);function Ive(e){const[t,n]=e.slice(0,-1).split("(");if(t==="drop-shadow")return e;const[r]=n.match(i1)||[];if(!r)return e;const i=n.replace(r,"");let o=Mve.has(t)?1:0;return r!==n&&(o*=100),t+"("+o+i+")"}const Nve=/([a-z-]*)\(.*?\)/g,F2={...Xs,getAnimatableNone:e=>{const t=e.match(Nve);return t?t.map(Ive).join(" 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f=e.getValue(d),h=a[d];if(!f||h===void 0||c&&Yve(c,d))continue;const p={delay:n,elapsed:0,...o};if(window.HandoffAppearAnimations&&!f.hasAnimated){const S=e.getProps()[N0e];S&&(p.elapsed=window.HandoffAppearAnimations(S,d,f,_t))}f.start(o3(d,f,h,e.shouldReduceMotion&&El.has(d)?{type:!1}:p));const m=f.animation;dy(l)&&(l.add(d),m.then(()=>l.remove(d))),u.push(m)}return s&&Promise.all(u).then(()=>{s&&qve(e,s)}),u}function B2(e,t,n={}){const r=s1(e,t,n.custom);let{transition:i=e.getDefaultTransition()||{}}=r||{};n.transitionOverride&&(i=n.transitionOverride);const o=r?()=>Promise.all(vL(e,r,n)):()=>Promise.resolve(),s=e.variantChildren&&e.variantChildren.size?(l=0)=>{const{delayChildren:u=0,staggerChildren:c,staggerDirection:d}=i;return Qve(e,t,u+l,c,d,n)}:()=>Promise.resolve(),{when:a}=i;if(a){const[l,u]=a==="beforeChildren"?[o,s]:[s,o];return l().then(()=>u())}else return Promise.all([o(),s(n.delay)])}function Qve(e,t,n=0,r=0,i=1,o){const 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p={},m=1/0;for(let v=0;v<t1e;v++){const y=e1e[v],g=n[y],b=c[y]!==void 0?c[y]:d[y],_=Gf(b),w=y===u?g.isActive:null;w===!1&&(m=v);let x=b===d[y]&&b!==c[y]&&_;if(x&&r&&e.manuallyAnimateOnMount&&(x=!1),g.protectedKeys={...p},!g.isActive&&w===null||!b&&!g.prevProp||n1(b)||typeof b=="boolean")continue;const T=i1e(g.prevProp,b);let P=T||y===u&&g.isActive&&!x&&_||v>m&&_;const E=Array.isArray(b)?b:[b];let A=E.reduce(i,{});w===!1&&(A={});const{prevResolvedValues:$={}}=g,I={...$,...A},C=R=>{P=!0,h.delete(R),g.needsAnimating[R]=!0};for(const R in I){const M=A[R],N=$[R];p.hasOwnProperty(R)||(M!==N?ay(M)&&ay(N)?!jD(M,N)||T?C(R):g.protectedKeys[R]=!0:M!==void 0?C(R):h.add(R):M!==void 0&&h.has(R)?C(R):g.protectedKeys[R]=!0)}g.prevProp=b,g.prevResolvedValues=A,g.isActive&&(p={...p,...A}),r&&e.blockInitialAnimation&&(P=!1),P&&!x&&f.push(...E.map(R=>({animation:R,options:{type:y,...l}})))}if(h.size){const v={};h.forEach(y=>{const g=e.getBaseTarget(y);g!==void 0&&(v[y]=g)}),f.push({animation:v})}let 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P1e{constructor(t){this.openGlobalLock=null,this.isDragging=!1,this.currentDirection=null,this.originPoint={x:0,y:0},this.constraints=!1,this.hasMutatedConstraints=!1,this.elastic=Wt(),this.visualElement=t}start(t,{snapToCursor:n=!1}={}){const{presenceContext:r}=this.visualElement;if(r&&r.isPresent===!1)return;const i=l=>{this.stopAnimation(),n&&this.snapToCursor(o1(l,"page").point)},o=(l,u)=>{const{drag:c,dragPropagation:d,onDragStart:f}=this.getProps();if(c&&!d&&(this.openGlobalLock&&this.openGlobalLock(),this.openGlobalLock=$D(c),!this.openGlobalLock))return;this.isDragging=!0,this.currentDirection=null,this.resolveConstraints(),this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!0,this.visualElement.projection.target=void 0),Li(p=>{let m=this.getAxisMotionValue(p).get()||0;if(Ji.test(m)){const{projection:S}=this.visualElement;if(S&&S.layout){const v=S.layout.layoutBox[p];v&&(m=Mr(v)*(parseFloat(m)/100))}}this.originPoint[p]=m}),f&&_t.update(()=>f(l,u),!1,!0);const{animationState:h}=this.visualElement;h&&h.setActive("whileDrag",!0)},s=(l,u)=>{const{dragPropagation:c,dragDirectionLock:d,onDirectionLock:f,onDrag:h}=this.getProps();if(!c&&!this.openGlobalLock)return;const{offset:p}=u;if(d&&this.currentDirection===null){this.currentDirection=A1e(p),this.currentDirection!==null&&f&&f(this.currentDirection);return}this.updateAxis("x",u.point,p),this.updateAxis("y",u.point,p),this.visualElement.render(),h&&h(l,u)},a=(l,u)=>this.stop(l,u);this.panSession=new bL(t,{onSessionStart:i,onStart:o,onMove:s,onSessionEnd:a},{transformPagePoint:this.visualElement.getTransformPagePoint()})}stop(t,n){const r=this.isDragging;if(this.cancel(),!r)return;const{velocity:i}=n;this.startAnimation(i);const{onDragEnd:o}=this.getProps();o&&_t.update(()=>o(t,n))}cancel(){this.isDragging=!1;const{projection:t,animationState:n}=this.visualElement;t&&(t.isAnimationBlocked=!1),this.panSession&&this.panSession.end(),this.panSession=void 0;const{dragPropagation:r}=this.getProps();!r&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),n&&n.setActive("whileDrag",!1)}updateAxis(t,n,r){const{drag:i}=this.getProps();if(!r||!qp(t,i,this.currentDirection))return;const o=this.getAxisMotionValue(t);let s=this.originPoint[t]+r[t];this.constraints&&this.constraints[t]&&(s=p1e(s,this.constraints[t],this.elastic[t])),o.set(s)}resolveConstraints(){const{dragConstraints:t,dragElastic:n}=this.getProps(),{layout:r}=this.visualElement.projection||{},i=this.constraints;t&&hu(t)?this.constraints||(this.constraints=this.resolveRefConstraints()):t&&r?this.constraints=g1e(r.layoutBox,t):this.constraints=!1,this.elastic=b1e(n),i!==this.constraints&&r&&this.constraints&&!this.hasMutatedConstraints&&Li(o=>{this.getAxisMotionValue(o)&&(this.constraints[o]=v1e(r.layoutBox[o],this.constraints[o]))})}resolveRefConstraints(){const{dragConstraints:t,onMeasureDragConstraints:n}=this.getProps();if(!t||!hu(t))return!1;const r=t.current,{projection:i}=this.visualElement;if(!i||!i.layout)return!1;const o=T1e(r,i.root,this.visualElement.getTransformPagePoint());let s=m1e(i.layout.layoutBox,o);if(n){const a=n(S1e(s));this.hasMutatedConstraints=!!a,a&&(s=_L(a))}return s}startAnimation(t){const{drag:n,dragMomentum:r,dragElastic:i,dragTransition:o,dragSnapToOrigin:s,onDragTransitionEnd:a}=this.getProps(),l=this.constraints||{},u=Li(c=>{if(!qp(c,n,this.currentDirection))return;let d=l&&l[c]||{};s&&(d={min:0,max:0});const f=i?200:1e6,h=i?40:1e7,p={type:"inertia",velocity:r?t[c]:0,bounceStiffness:f,bounceDamping:h,timeConstant:750,restDelta:1,restSpeed:10,...o,...d};return this.startAxisValueAnimation(c,p)});return Promise.all(u).then(a)}startAxisValueAnimation(t,n){const r=this.getAxisMotionValue(t);return r.start(o3(t,r,0,n))}stopAnimation(){Li(t=>this.getAxisMotionValue(t).stop())}getAxisMotionValue(t){const n="_drag"+t.toUpperCase(),r=this.visualElement.getProps(),i=r[n];return i||this.visualElement.getValue(t,(r.initial?r.initial[t]:void 0)||0)}snapToCursor(t){Li(n=>{const{drag:r}=this.getProps();if(!qp(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:s,max:a}=i.layout.layoutBox[n];o.set(t[n]-At(s,a,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!hu(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};Li(s=>{const a=this.getAxisMotionValue(s);if(a){const l=a.get();i[s]=y1e({min:l,max:l},this.constraints[s])}});const{transformTemplate:o}=this.visualElement.getProps();this.visualElement.current.style.transform=o?o({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),Li(s=>{if(!qp(s,t,null))return;const a=this.getAxisMotionValue(s),{min:l,max:u}=this.constraints[s];a.set(At(l,u,i[s]))})}addListeners(){if(!this.visualElement.current)return;E1e.set(this.visualElement,this);const t=this.visualElement.current,n=Io(t,"pointerdown",l=>{const{drag:u,dragListener:c=!0}=this.getProps();u&&c&&this.start(l)}),r=()=>{const{dragConstraints:l}=this.getProps();hu(l)&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,o=i.addEventListener("measure",r);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),r();const s=Ao(window,"resize",()=>this.scalePositionWithinConstraints()),a=i.addEventListener("didUpdate",({delta:l,hasLayoutChanged:u})=>{this.isDragging&&u&&(Li(c=>{const d=this.getAxisMotionValue(c);d&&(this.originPoint[c]+=l[c].translate,d.set(d.get()+l[c].translate))}),this.visualElement.render())});return()=>{s(),n(),o(),a&&a()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:o=!1,dragElastic:s=V2,dragMomentum:a=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:o,dragElastic:s,dragMomentum:a}}}function qp(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function A1e(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class k1e extends ua{constructor(t){super(t),this.removeGroupControls=Qt,this.removeListeners=Qt,this.controls=new P1e(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||Qt}unmount(){this.removeGroupControls(),this.removeListeners()}}const RP=e=>(t,n)=>{e&&_t.update(()=>e(t,n))};class R1e extends ua{constructor(){super(...arguments),this.removePointerDownListener=Qt}onPointerDown(t){this.session=new bL(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:RP(t),onStart:RP(n),onMove:r,onEnd:(o,s)=>{delete this.session,i&&_t.update(()=>i(o,s))}}}mount(){this.removePointerDownListener=Io(this.node.current,"pointerdown",t=>this.onPointerDown(t))}update(){this.session&&this.session.updateHandlers(this.createPanHandlers())}unmount(){this.removePointerDownListener(),this.session&&this.session.end()}}function O1e(){const e=k.useContext(Lh);if(e===null)return[!0,null];const{isPresent:t,onExitComplete:n,register:r}=e,i=k.useId();return k.useEffect(()=>r(i),[]),!t&&n?[!1,()=>n&&n(i)]:[!0]}function A4e(){return M1e(k.useContext(Lh))}function M1e(e){return e===null?!0:e.isPresent}const Pg={hasAnimatedSinceResize:!0,hasEverUpdated:!1};function OP(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const od={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(me.test(e))e=parseFloat(e);else return e;const n=OP(e,t.target.x),r=OP(e,t.target.y);return`${n}% ${r}%`}},I1e={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=Xs.parse(e);if(i.length>5)return r;const o=Xs.createTransformer(e),s=typeof i[0]!="number"?1:0,a=n.x.scale*t.x,l=n.y.scale*t.y;i[0+s]/=a,i[1+s]/=l;const u=At(a,l,.5);return typeof i[2+s]=="number"&&(i[2+s]/=u),typeof i[3+s]=="number"&&(i[3+s]/=u),o(i)}};class N1e extends Xe.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;Vye(D1e),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),Pg.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,s=r.projection;return s&&(s.isPresent=o,i||t.layoutDependency!==n||n===void 0?s.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?s.promote():s.relegate()||_t.postRender(()=>{const a=s.getStack();(!a||!a.members.length)&&this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),queueMicrotask(()=>{!t.currentAnimation&&t.isLead()&&this.safeToRemove()}))}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n&&n.group&&n.group.remove(i),r&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t&&t()}render(){return null}}function TL(e){const[t,n]=O1e(),r=k.useContext(HC);return Xe.createElement(N1e,{...e,layoutGroup:r,switchLayoutGroup:k.useContext(SD),isPresent:t,safeToRemove:n})}const D1e={borderRadius:{...od,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:od,borderTopRightRadius:od,borderBottomLeftRadius:od,borderBottomRightRadius:od,boxShadow:I1e},EL=["TopLeft","TopRight","BottomLeft","BottomRight"],L1e=EL.length,MP=e=>typeof e=="string"?parseFloat(e):e,IP=e=>typeof e=="number"||me.test(e);function $1e(e,t,n,r,i,o){i?(e.opacity=At(0,n.opacity!==void 0?n.opacity:1,F1e(r)),e.opacityExit=At(t.opacity!==void 0?t.opacity:1,0,B1e(r))):o&&(e.opacity=At(t.opacity!==void 0?t.opacity:1,n.opacity!==void 0?n.opacity:1,r));for(let s=0;s<L1e;s++){const a=`border${EL[s]}Radius`;let l=NP(t,a),u=NP(n,a);if(l===void 0&&u===void 0)continue;l||(l=0),u||(u=0),l===0||u===0||IP(l)===IP(u)?(e[a]=Math.max(At(MP(l),MP(u),r),0),(Ji.test(u)||Ji.test(l))&&(e[a]+="%")):e[a]=u}(t.rotate||n.rotate)&&(e.rotate=At(t.rotate||0,n.rotate||0,r))}function NP(e,t){return e[t]!==void 0?e[t]:e.borderRadius}const F1e=PL(0,.5,t3),B1e=PL(.5,.95,Qt);function PL(e,t,n){return r=>r<e?0:r>t?1:n(qf(e,t,r))}function DP(e,t){e.min=t.min,e.max=t.max}function Vr(e,t){DP(e.x,t.x),DP(e.y,t.y)}function LP(e,t,n,r,i){return e-=t,e=fy(e,1/n,r),i!==void 0&&(e=fy(e,1/i,r)),e}function j1e(e,t=0,n=1,r=.5,i,o=e,s=e){if(Ji.test(t)&&(t=parseFloat(t),t=At(s.min,s.max,t/100)-s.min),typeof t!="number")return;let a=At(o.min,o.max,r);e===o&&(a-=t),e.min=LP(e.min,t,n,a,i),e.max=LP(e.max,t,n,a,i)}function $P(e,t,[n,r,i],o,s){j1e(e,t[n],t[r],t[i],t.scale,o,s)}const V1e=["x","scaleX","originX"],z1e=["y","scaleY","originY"];function FP(e,t,n,r){$P(e.x,t,V1e,n?n.x:void 0,r?r.x:void 0),$P(e.y,t,z1e,n?n.y:void 0,r?r.y:void 0)}function BP(e){return e.translate===0&&e.scale===1}function AL(e){return BP(e.x)&&BP(e.y)}function G2(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function jP(e){return Mr(e.x)/Mr(e.y)}class U1e{constructor(){this.members=[]}add(t){s3(this.members,t),t.scheduleRender()}remove(t){if(a3(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function VP(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:c}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),c&&(r+=`rotateY(${c}deg) `)}const s=e.x.scale*t.x,a=e.y.scale*t.y;return(s!==1||a!==1)&&(r+=`scale(${s}, ${a})`),r||"none"}const G1e=(e,t)=>e.depth-t.depth;class H1e{constructor(){this.children=[],this.isDirty=!1}add(t){s3(this.children,t),this.isDirty=!0}remove(t){a3(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(G1e),this.isDirty=!1,this.children.forEach(t)}}function q1e(e,t){const n=performance.now(),r=({timestamp:i})=>{const o=i-n;o>=t&&(Uo(r),e(o-t))};return _t.read(r,!0),()=>Uo(r)}function W1e(e){window.MotionDebug&&window.MotionDebug.record(e)}function K1e(e){return e instanceof SVGElement&&e.tagName!=="svg"}function X1e(e,t,n){const r=_r(e)?e:uc(e);return r.start(o3("",r,t,n)),r.animation}const zP=["","X","Y","Z"],UP=1e3;let Y1e=0;const Ta={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function kL({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(s={},a=t==null?void 0:t()){this.id=Y1e++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{Ta.totalNodes=Ta.resolvedTargetDeltas=Ta.recalculatedProjection=0,this.nodes.forEach(J1e),this.nodes.forEach(ibe),this.nodes.forEach(obe),this.nodes.forEach(ebe),W1e(Ta)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=s,this.root=a?a.root||a:this,this.path=a?[...a.path,a]:[],this.parent=a,this.depth=a?a.depth+1:0;for(let l=0;l<this.path.length;l++)this.path[l].shouldResetTransform=!0;this.root===this&&(this.nodes=new H1e)}addEventListener(s,a){return this.eventHandlers.has(s)||this.eventHandlers.set(s,new l3),this.eventHandlers.get(s).add(a)}notifyListeners(s,...a){const l=this.eventHandlers.get(s);l&&l.notify(...a)}hasListeners(s){return this.eventHandlers.has(s)}mount(s,a=this.root.hasTreeAnimated){if(this.instance)return;this.isSVG=K1e(s),this.instance=s;const{layoutId:l,layout:u,visualElement:c}=this.options;if(c&&!c.current&&c.mount(s),this.root.nodes.add(this),this.parent&&this.parent.children.add(this),a&&(u||l)&&(this.isLayoutDirty=!0),e){let d;const f=()=>this.root.updateBlockedByResize=!1;e(s,()=>{this.root.updateBlockedByResize=!0,d&&d(),d=q1e(f,250),Pg.hasAnimatedSinceResize&&(Pg.hasAnimatedSinceResize=!1,this.nodes.forEach(HP))})}l&&this.root.registerSharedNode(l,this),this.options.animate!==!1&&c&&(l||u)&&this.addEventListener("didUpdate",({delta:d,hasLayoutChanged:f,hasRelativeTargetChanged:h,layout:p})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const m=this.options.transition||c.getDefaultTransition()||cbe,{onLayoutAnimationStart:S,onLayoutAnimationComplete:v}=c.getProps(),y=!this.targetLayout||!G2(this.targetLayout,p)||h,g=!f&&h;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||g||f&&(y||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(d,g);const b={...gL(m,"layout"),onPlay:S,onComplete:v};(c.shouldReduceMotion||this.options.layoutRoot)&&(b.delay=0,b.type=!1),this.startAnimation(b)}else f||HP(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=p})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const s=this.getStack();s&&s.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,Uo(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(sbe),this.animationId++)}getTransformTemplate(){const{visualElement:s}=this.options;return s&&s.getProps().transformTemplate}willUpdate(s=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let c=0;c<this.path.length;c++){const d=this.path[c];d.shouldResetTransform=!0,d.updateScroll("snapshot"),d.options.layoutRoot&&d.willUpdate(!1)}const{layoutId:a,layout:l}=this.options;if(a===void 0&&!l)return;const u=this.getTransformTemplate();this.prevTransformTemplateValue=u?u(this.latestValues,""):void 0,this.updateSnapshot(),s&&this.notifyListeners("willUpdate")}update(){if(this.updateScheduled=!1,this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(GP);return}this.isUpdating||this.nodes.forEach(nbe),this.isUpdating=!1,this.nodes.forEach(rbe),this.nodes.forEach(Q1e),this.nodes.forEach(Z1e),this.clearAllSnapshots();const a=performance.now();$n.delta=Ks(0,1e3/60,a-$n.timestamp),$n.timestamp=a,$n.isProcessing=!0,nS.update.process($n),nS.preRender.process($n),nS.render.process($n),$n.isProcessing=!1}didUpdate(){this.updateScheduled||(this.updateScheduled=!0,queueMicrotask(()=>this.update()))}clearAllSnapshots(){this.nodes.forEach(tbe),this.sharedNodes.forEach(abe)}scheduleUpdateProjection(){_t.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){_t.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l<this.path.length;l++)this.path[l].updateScroll();const s=this.layout;this.layout=this.measure(!1),this.layoutCorrected=Wt(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox);const{visualElement:a}=this.options;a&&a.notify("LayoutMeasure",this.layout.layoutBox,s?s.layoutBox:void 0)}updateScroll(s="measure"){let a=!!(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===s&&(a=!1),a&&(this.scroll={animationId:this.root.animationId,phase:s,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){if(!i)return;const s=this.isLayoutDirty||this.shouldResetTransform,a=this.projectionDelta&&!AL(this.projectionDelta),l=this.getTransformTemplate(),u=l?l(this.latestValues,""):void 0,c=u!==this.prevTransformTemplateValue;s&&(a||Ca(this.latestValues)||c)&&(i(this.instance,u),this.shouldResetTransform=!1,this.scheduleRender())}measure(s=!0){const a=this.measurePageBox();let l=this.removeElementScroll(a);return s&&(l=this.removeTransform(l)),dbe(l),{animationId:this.root.animationId,measuredBox:a,layoutBox:l,latestValues:{},source:this.id}}measurePageBox(){const{visualElement:s}=this.options;if(!s)return Wt();const a=s.measureViewportBox(),{scroll:l}=this.root;return l&&(fs(a.x,l.offset.x),fs(a.y,l.offset.y)),a}removeElementScroll(s){const a=Wt();Vr(a,s);for(let l=0;l<this.path.length;l++){const u=this.path[l],{scroll:c,options:d}=u;if(u!==this.root&&c&&d.layoutScroll){if(c.isRoot){Vr(a,s);const{scroll:f}=this.root;f&&(fs(a.x,-f.offset.x),fs(a.y,-f.offset.y))}fs(a.x,c.offset.x),fs(a.y,c.offset.y)}}return a}applyTransform(s,a=!1){const l=Wt();Vr(l,s);for(let u=0;u<this.path.length;u++){const c=this.path[u];!a&&c.options.layoutScroll&&c.scroll&&c!==c.root&&mu(l,{x:-c.scroll.offset.x,y:-c.scroll.offset.y}),Ca(c.latestValues)&&mu(l,c.latestValues)}return Ca(this.latestValues)&&mu(l,this.latestValues),l}removeTransform(s){const a=Wt();Vr(a,s);for(let l=0;l<this.path.length;l++){const u=this.path[l];if(!u.instance||!Ca(u.latestValues))continue;z2(u.latestValues)&&u.updateSnapshot();const c=Wt(),d=u.measurePageBox();Vr(c,d),FP(a,u.latestValues,u.snapshot?u.snapshot.layoutBox:void 0,c)}return Ca(this.latestValues)&&FP(a,this.latestValues),a}setTargetDelta(s){this.targetDelta=s,this.root.scheduleUpdateProjection(),this.isProjectionDirty=!0}setOptions(s){this.options={...this.options,...s,crossfade:s.crossfade!==void 0?s.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}forceRelativeParentToResolveTarget(){this.relativeParent&&this.relativeParent.resolvedRelativeTargetAt!==$n.timestamp&&this.relativeParent.resolveTargetDelta(!0)}resolveTargetDelta(s=!1){var a;const l=this.getLead();this.isProjectionDirty||(this.isProjectionDirty=l.isProjectionDirty),this.isTransformDirty||(this.isTransformDirty=l.isTransformDirty),this.isSharedProjectionDirty||(this.isSharedProjectionDirty=l.isSharedProjectionDirty);const u=!!this.resumingFrom||this!==l;if(!(s||u&&this.isSharedProjectionDirty||this.isProjectionDirty||!((a=this.parent)===null||a===void 0)&&a.isProjectionDirty||this.attemptToResolveRelativeTarget))return;const{layout:d,layoutId:f}=this.options;if(!(!this.layout||!(d||f))){if(this.resolvedRelativeTargetAt=$n.timestamp,!this.targetDelta&&!this.relativeTarget){const h=this.getClosestProjectingParent();h&&h.layout&&this.animationProgress!==1?(this.relativeParent=h,this.forceRelativeParentToResolveTarget(),this.relativeTarget=Wt(),this.relativeTargetOrigin=Wt(),Nd(this.relativeTargetOrigin,this.layout.layoutBox,h.layout.layoutBox),Vr(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}if(!(!this.relativeTarget&&!this.targetDelta)){if(this.target||(this.target=Wt(),this.targetWithTransforms=Wt()),this.relativeTarget&&this.relativeTargetOrigin&&this.relativeParent&&this.relativeParent.target?(this.forceRelativeParentToResolveTarget(),h1e(this.target,this.relativeTarget,this.relativeParent.target)):this.targetDelta?(this.resumingFrom?this.target=this.applyTransform(this.layout.layoutBox):Vr(this.target,this.layout.layoutBox),xL(this.target,this.targetDelta)):Vr(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget){this.attemptToResolveRelativeTarget=!1;const h=this.getClosestProjectingParent();h&&!!h.resumingFrom==!!this.resumingFrom&&!h.options.layoutScroll&&h.target&&this.animationProgress!==1?(this.relativeParent=h,this.forceRelativeParentToResolveTarget(),this.relativeTarget=Wt(),this.relativeTargetOrigin=Wt(),Nd(this.relativeTargetOrigin,this.target,h.target),Vr(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}Ta.resolvedTargetDeltas++}}}getClosestProjectingParent(){if(!(!this.parent||z2(this.parent.latestValues)||wL(this.parent.latestValues)))return this.parent.isProjecting()?this.parent:this.parent.getClosestProjectingParent()}isProjecting(){return!!((this.relativeTarget||this.targetDelta||this.options.layoutRoot)&&this.layout)}calcProjection(){var s;const a=this.getLead(),l=!!this.resumingFrom||this!==a;let u=!0;if((this.isProjectionDirty||!((s=this.parent)===null||s===void 0)&&s.isProjectionDirty)&&(u=!1),l&&(this.isSharedProjectionDirty||this.isTransformDirty)&&(u=!1),this.resolvedRelativeTargetAt===$n.timestamp&&(u=!1),u)return;const{layout:c,layoutId:d}=this.options;if(this.isTreeAnimating=!!(this.parent&&this.parent.isTreeAnimating||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(c||d))return;Vr(this.layoutCorrected,this.layout.layoutBox);const f=this.treeScale.x,h=this.treeScale.y;w1e(this.layoutCorrected,this.treeScale,this.path,l),a.layout&&!a.target&&(this.treeScale.x!==1||this.treeScale.y!==1)&&(a.target=a.layout.layoutBox);const{target:p}=a;if(!p){this.projectionTransform&&(this.projectionDelta=gu(),this.projectionTransform="none",this.scheduleRender());return}this.projectionDelta||(this.projectionDelta=gu(),this.projectionDeltaWithTransform=gu());const m=this.projectionTransform;Id(this.projectionDelta,this.layoutCorrected,p,this.latestValues),this.projectionTransform=VP(this.projectionDelta,this.treeScale),(this.projectionTransform!==m||this.treeScale.x!==f||this.treeScale.y!==h)&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",p)),Ta.recalculatedProjection++}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(s=!0){if(this.options.scheduleRender&&this.options.scheduleRender(),s){const a=this.getStack();a&&a.scheduleRender()}this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(s,a=!1){const l=this.snapshot,u=l?l.latestValues:{},c={...this.latestValues},d=gu();(!this.relativeParent||!this.relativeParent.options.layoutRoot)&&(this.relativeTarget=this.relativeTargetOrigin=void 0),this.attemptToResolveRelativeTarget=!a;const f=Wt(),h=l?l.source:void 0,p=this.layout?this.layout.source:void 0,m=h!==p,S=this.getStack(),v=!S||S.members.length<=1,y=!!(m&&!v&&this.options.crossfade===!0&&!this.path.some(ube));this.animationProgress=0;let g;this.mixTargetDelta=b=>{const _=b/1e3;qP(d.x,s.x,_),qP(d.y,s.y,_),this.setTargetDelta(d),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(Nd(f,this.layout.layoutBox,this.relativeParent.layout.layoutBox),lbe(this.relativeTarget,this.relativeTargetOrigin,f,_),g&&G2(this.relativeTarget,g)&&(this.isProjectionDirty=!1),g||(g=Wt()),Vr(g,this.relativeTarget)),m&&(this.animationValues=c,$1e(c,u,this.latestValues,_,y,v)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=_},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(s){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(Uo(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=_t.update(()=>{Pg.hasAnimatedSinceResize=!0,this.currentAnimation=X1e(0,UP,{...s,onUpdate:a=>{this.mixTargetDelta(a),s.onUpdate&&s.onUpdate(a)},onComplete:()=>{s.onComplete&&s.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const s=this.getStack();s&&s.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(UP),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const s=this.getLead();let{targetWithTransforms:a,target:l,layout:u,latestValues:c}=s;if(!(!a||!l||!u)){if(this!==s&&this.layout&&u&&RL(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||Wt();const d=Mr(this.layout.layoutBox.x);l.x.min=s.target.x.min,l.x.max=l.x.min+d;const f=Mr(this.layout.layoutBox.y);l.y.min=s.target.y.min,l.y.max=l.y.min+f}Vr(a,l),mu(a,c),Id(this.projectionDeltaWithTransform,this.layoutCorrected,a,c)}}registerSharedNode(s,a){this.sharedNodes.has(s)||this.sharedNodes.set(s,new U1e),this.sharedNodes.get(s).add(a);const u=a.options.initialPromotionConfig;a.promote({transition:u?u.transition:void 0,preserveFollowOpacity:u&&u.shouldPreserveFollowOpacity?u.shouldPreserveFollowOpacity(a):void 0})}isLead(){const s=this.getStack();return s?s.lead===this:!0}getLead(){var s;const{layoutId:a}=this.options;return a?((s=this.getStack())===null||s===void 0?void 0:s.lead)||this:this}getPrevLead(){var s;const{layoutId:a}=this.options;return a?(s=this.getStack())===null||s===void 0?void 0:s.prevLead:void 0}getStack(){const{layoutId:s}=this.options;if(s)return this.root.sharedNodes.get(s)}promote({needsReset:s,transition:a,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),s&&(this.projectionDelta=void 0,this.needsReset=!0),a&&this.setOptions({transition:a})}relegate(){const s=this.getStack();return s?s.relegate(this):!1}resetRotation(){const{visualElement:s}=this.options;if(!s)return;let a=!1;const{latestValues:l}=s;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(a=!0),!a)return;const u={};for(let c=0;c<zP.length;c++){const d="rotate"+zP[c];l[d]&&(u[d]=l[d],s.setStaticValue(d,0))}s.render();for(const c in u)s.setStaticValue(c,u[c]);s.scheduleRender()}getProjectionStyles(s={}){var a,l;const u={};if(!this.instance||this.isSVG)return u;if(this.isVisible)u.visibility="";else return{visibility:"hidden"};const c=this.getTransformTemplate();if(this.needsReset)return this.needsReset=!1,u.opacity="",u.pointerEvents=Eg(s.pointerEvents)||"",u.transform=c?c(this.latestValues,""):"none",u;const d=this.getLead();if(!this.projectionDelta||!this.layout||!d.target){const m={};return this.options.layoutId&&(m.opacity=this.latestValues.opacity!==void 0?this.latestValues.opacity:1,m.pointerEvents=Eg(s.pointerEvents)||""),this.hasProjected&&!Ca(this.latestValues)&&(m.transform=c?c({},""):"none",this.hasProjected=!1),m}const f=d.animationValues||d.latestValues;this.applyTransformsToTarget(),u.transform=VP(this.projectionDeltaWithTransform,this.treeScale,f),c&&(u.transform=c(f,u.transform));const{x:h,y:p}=this.projectionDelta;u.transformOrigin=`${h.origin*100}% ${p.origin*100}% 0`,d.animationValues?u.opacity=d===this?(l=(a=f.opacity)!==null&&a!==void 0?a:this.latestValues.opacity)!==null&&l!==void 0?l:1:this.preserveOpacity?this.latestValues.opacity:f.opacityExit:u.opacity=d===this?f.opacity!==void 0?f.opacity:"":f.opacityExit!==void 0?f.opacityExit:0;for(const m in oy){if(f[m]===void 0)continue;const{correct:S,applyTo:v}=oy[m],y=u.transform==="none"?f[m]:S(f[m],d);if(v){const g=v.length;for(let b=0;b<g;b++)u[v[b]]=y}else u[m]=y}return this.options.layoutId&&(u.pointerEvents=d===this?Eg(s.pointerEvents)||"":"none"),u}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(s=>{var a;return(a=s.currentAnimation)===null||a===void 0?void 0:a.stop()}),this.root.nodes.forEach(GP),this.root.sharedNodes.clear()}}}function Q1e(e){e.updateLayout()}function Z1e(e){var t;const n=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&n&&e.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:i}=e.layout,{animationType:o}=e.options,s=n.source!==e.layout.source;o==="size"?Li(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Mr(f);f.min=r[d].min,f.max=f.min+h}):RL(o,n.layoutBox,r)&&Li(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Mr(r[d]);f.max=f.min+h,e.relativeTarget&&!e.currentAnimation&&(e.isProjectionDirty=!0,e.relativeTarget[d].max=e.relativeTarget[d].min+h)});const a=gu();Id(a,r,n.layoutBox);const l=gu();s?Id(l,e.applyTransform(i,!0),n.measuredBox):Id(l,r,n.layoutBox);const u=!AL(a);let c=!1;if(!e.resumeFrom){const d=e.getClosestProjectingParent();if(d&&!d.resumeFrom){const{snapshot:f,layout:h}=d;if(f&&h){const p=Wt();Nd(p,n.layoutBox,f.layoutBox);const m=Wt();Nd(m,r,h.layoutBox),G2(p,m)||(c=!0),d.options.layoutRoot&&(e.relativeTarget=m,e.relativeTargetOrigin=p,e.relativeParent=d)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:l,layoutDelta:a,hasLayoutChanged:u,hasRelativeTargetChanged:c})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function J1e(e){Ta.totalNodes++,e.parent&&(e.isProjecting()||(e.isProjectionDirty=e.parent.isProjectionDirty),e.isSharedProjectionDirty||(e.isSharedProjectionDirty=!!(e.isProjectionDirty||e.parent.isProjectionDirty||e.parent.isSharedProjectionDirty)),e.isTransformDirty||(e.isTransformDirty=e.parent.isTransformDirty))}function ebe(e){e.isProjectionDirty=e.isSharedProjectionDirty=e.isTransformDirty=!1}function tbe(e){e.clearSnapshot()}function GP(e){e.clearMeasurements()}function nbe(e){e.isLayoutDirty=!1}function rbe(e){const{visualElement:t}=e.options;t&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function HP(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0,e.isProjectionDirty=!0}function ibe(e){e.resolveTargetDelta()}function obe(e){e.calcProjection()}function sbe(e){e.resetRotation()}function abe(e){e.removeLeadSnapshot()}function qP(e,t,n){e.translate=At(t.translate,0,n),e.scale=At(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function WP(e,t,n,r){e.min=At(t.min,n.min,r),e.max=At(t.max,n.max,r)}function lbe(e,t,n,r){WP(e.x,t.x,n.x,r),WP(e.y,t.y,n.y,r)}function ube(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const cbe={duration:.45,ease:[.4,0,.1,1]};function KP(e){e.min=Math.round(e.min*2)/2,e.max=Math.round(e.max*2)/2}function dbe(e){KP(e.x),KP(e.y)}function RL(e,t,n){return e==="position"||e==="preserve-aspect"&&!j2(jP(t),jP(n),.2)}const fbe=kL({attachResizeListener:(e,t)=>Ao(e,"resize",t),measureScroll:()=>({x:document.documentElement.scrollLeft||document.body.scrollLeft,y:document.documentElement.scrollTop||document.body.scrollTop}),checkIsScrollRoot:()=>!0}),pS={current:void 0},OL=kL({measureScroll:e=>({x:e.scrollLeft,y:e.scrollTop}),defaultParent:()=>{if(!pS.current){const e=new fbe({});e.mount(window),e.setOptions({layoutScroll:!0}),pS.current=e}return pS.current},resetTransform:(e,t)=>{e.style.transform=t!==void 0?t:"none"},checkIsScrollRoot:e=>window.getComputedStyle(e).position==="fixed"}),hbe={pan:{Feature:R1e},drag:{Feature:k1e,ProjectionNode:OL,MeasureLayout:TL}},pbe=/var\((--[a-zA-Z0-9-_]+),? ?([a-zA-Z0-9 ()%#.,-]+)?\)/;function gbe(e){const t=pbe.exec(e);if(!t)return[,];const[,n,r]=t;return[n,r]}function H2(e,t,n=1){const[r,i]=gbe(e);if(!r)return;const o=window.getComputedStyle(t).getPropertyValue(r);return o?o.trim():I2(i)?H2(i,t,n+1):i}function mbe(e,{...t},n){const r=e.current;if(!(r instanceof Element))return{target:t,transitionEnd:n};n&&(n={...n}),e.values.forEach(i=>{const o=i.get();if(!I2(o))return;const s=H2(o,r);s&&i.set(s)});for(const i in t){const o=t[i];if(!I2(o))continue;const s=H2(o,r);s&&(t[i]=s,n||(n={}),n[i]===void 0&&(n[i]=o))}return{target:t,transitionEnd:n}}const ybe=new Set(["width","height","top","left","right","bottom","x","y","translateX","translateY"]),ML=e=>ybe.has(e),vbe=e=>Object.keys(e).some(ML),XP=e=>e===Pl||e===me,YP=(e,t)=>parseFloat(e.split(", ")[t]),QP=(e,t)=>(n,{transform:r})=>{if(r==="none"||!r)return 0;const i=r.match(/^matrix3d\((.+)\)$/);if(i)return YP(i[1],t);{const o=r.match(/^matrix\((.+)\)$/);return o?YP(o[1],e):0}},bbe=new Set(["x","y","z"]),Sbe=$h.filter(e=>!bbe.has(e));function _be(e){const t=[];return Sbe.forEach(n=>{const r=e.getValue(n);r!==void 0&&(t.push([n,r.get()]),r.set(n.startsWith("scale")?1:0))}),t.length&&e.render(),t}const cc={width:({x:e},{paddingLeft:t="0",paddingRight:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),height:({y:e},{paddingTop:t="0",paddingBottom:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),top:(e,{top:t})=>parseFloat(t),left:(e,{left:t})=>parseFloat(t),bottom:({y:e},{top:t})=>parseFloat(t)+(e.max-e.min),right:({x:e},{left:t})=>parseFloat(t)+(e.max-e.min),x:QP(4,13),y:QP(5,14)};cc.translateX=cc.x;cc.translateY=cc.y;const wbe=(e,t,n)=>{const r=t.measureViewportBox(),i=t.current,o=getComputedStyle(i),{display:s}=o,a={};s==="none"&&t.setStaticValue("display",e.display||"block"),n.forEach(u=>{a[u]=cc[u](r,o)}),t.render();const l=t.measureViewportBox();return n.forEach(u=>{const c=t.getValue(u);c&&c.jump(a[u]),e[u]=cc[u](l,o)}),e},xbe=(e,t,n={},r={})=>{t={...t},r={...r};const i=Object.keys(t).filter(ML);let o=[],s=!1;const a=[];if(i.forEach(l=>{const u=e.getValue(l);if(!e.hasValue(l))return;let c=n[l],d=id(c);const f=t[l];let h;if(ay(f)){const p=f.length,m=f[0]===null?1:0;c=f[m],d=id(c);for(let S=m;S<p&&f[S]!==null;S++)h?e3(id(f[S])===h):h=id(f[S])}else h=id(f);if(d!==h)if(XP(d)&&XP(h)){const p=u.get();typeof p=="string"&&u.set(parseFloat(p)),typeof f=="string"?t[l]=parseFloat(f):Array.isArray(f)&&h===me&&(t[l]=f.map(parseFloat))}else d!=null&&d.transform&&(h!=null&&h.transform)&&(c===0||f===0)?c===0?u.set(h.transform(c)):t[l]=d.transform(f):(s||(o=_be(e),s=!0),a.push(l),r[l]=r[l]!==void 0?r[l]:t[l],u.jump(f))}),a.length){const l=a.indexOf("height")>=0?window.pageYOffset:null,u=wbe(t,e,a);return o.length&&o.forEach(([c,d])=>{e.getValue(c).set(d)}),e.render(),t1&&l!==null&&window.scrollTo({top:l}),{target:u,transitionEnd:r}}else return{target:t,transitionEnd:r}};function Cbe(e,t,n,r){return vbe(t)?xbe(e,t,n,r):{target:t,transitionEnd:r}}const Tbe=(e,t,n,r)=>{const i=mbe(e,t,r);return t=i.target,r=i.transitionEnd,Cbe(e,t,n,r)},q2={current:null},IL={current:!1};function Ebe(){if(IL.current=!0,!!t1)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>q2.current=e.matches;e.addListener(t),t()}else q2.current=!1}function Pbe(e,t,n){const{willChange:r}=t;for(const i in t){const o=t[i],s=n[i];if(_r(o))e.addValue(i,o),dy(r)&&r.add(i);else if(_r(s))e.addValue(i,uc(o,{owner:e})),dy(r)&&r.remove(i);else if(s!==o)if(e.hasValue(i)){const a=e.getValue(i);!a.hasAnimated&&a.set(o)}else{const a=e.getStaticValue(i);e.addValue(i,uc(a!==void 0?a:o,{owner:e}))}}for(const i in n)t[i]===void 0&&e.removeValue(i);return t}const ZP=new WeakMap,NL=Object.keys(Hf),Abe=NL.length,JP=["AnimationStart","AnimationComplete","Update","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"],kbe=GC.length;class Rbe{constructor({parent:t,props:n,presenceContext:r,reducedMotionConfig:i,visualState:o},s={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.features={},this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{this.current&&(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>_t.render(this.render,!1,!0);const{latestValues:a,renderState:l}=o;this.latestValues=a,this.baseTarget={...a},this.initialValues=n.initial?{...a}:{},this.renderState=l,this.parent=t,this.props=n,this.presenceContext=r,this.depth=t?t.depth+1:0,this.reducedMotionConfig=i,this.options=s,this.isControllingVariants=r1(n),this.isVariantNode=bD(n),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=!!(t&&t.current);const{willChange:u,...c}=this.scrapeMotionValuesFromProps(n,{});for(const d in c){const f=c[d];a[d]!==void 0&&_r(f)&&(f.set(a[d],!1),dy(u)&&u.add(d))}}scrapeMotionValuesFromProps(t,n){return{}}mount(t){this.current=t,ZP.set(t,this),this.projection&&!this.projection.instance&&this.projection.mount(t),this.parent&&this.isVariantNode&&!this.isControllingVariants&&(this.removeFromVariantTree=this.parent.addVariantChild(this)),this.values.forEach((n,r)=>this.bindToMotionValue(r,n)),IL.current||Ebe(),this.shouldReduceMotion=this.reducedMotionConfig==="never"?!1:this.reducedMotionConfig==="always"?!0:q2.current,this.parent&&this.parent.children.add(this),this.update(this.props,this.presenceContext)}unmount(){ZP.delete(this.current),this.projection&&this.projection.unmount(),Uo(this.notifyUpdate),Uo(this.render),this.valueSubscriptions.forEach(t=>t()),this.removeFromVariantTree&&this.removeFromVariantTree(),this.parent&&this.parent.children.delete(this);for(const t in this.events)this.events[t].clear();for(const t in this.features)this.features[t].unmount();this.current=null}bindToMotionValue(t,n){const r=El.has(t),i=n.on("change",s=>{this.latestValues[t]=s,this.props.onUpdate&&_t.update(this.notifyUpdate,!1,!0),r&&this.projection&&(this.projection.isTransformDirty=!0)}),o=n.on("renderRequest",this.scheduleRender);this.valueSubscriptions.set(t,()=>{i(),o()})}sortNodePosition(t){return!this.current||!this.sortInstanceNodePosition||this.type!==t.type?0:this.sortInstanceNodePosition(this.current,t.current)}loadFeatures({children:t,...n},r,i,o){let s,a;for(let l=0;l<Abe;l++){const u=NL[l],{isEnabled:c,Feature:d,ProjectionNode:f,MeasureLayout:h}=Hf[u];f&&(s=f),c(n)&&(!this.features[u]&&d&&(this.features[u]=new d(this)),h&&(a=h))}if(!this.projection&&s){this.projection=new s(this.latestValues,this.parent&&this.parent.projection);const{layoutId:l,layout:u,drag:c,dragConstraints:d,layoutScroll:f,layoutRoot:h}=n;this.projection.setOptions({layoutId:l,layout:u,alwaysMeasureLayout:!!c||d&&hu(d),visualElement:this,scheduleRender:()=>this.scheduleRender(),animationType:typeof u=="string"?u:"both",initialPromotionConfig:o,layoutScroll:f,layoutRoot:h})}return a}updateFeatures(){for(const t in this.features){const n=this.features[t];n.isMounted?n.update():(n.mount(),n.isMounted=!0)}}triggerBuild(){this.build(this.renderState,this.latestValues,this.options,this.props)}measureViewportBox(){return this.current?this.measureInstanceViewportBox(this.current,this.props):Wt()}getStaticValue(t){return this.latestValues[t]}setStaticValue(t,n){this.latestValues[t]=n}makeTargetAnimatable(t,n=!0){return this.makeTargetAnimatableFromInstance(t,this.props,n)}update(t,n){(t.transformTemplate||this.props.transformTemplate)&&this.scheduleRender(),this.prevProps=this.props,this.props=t,this.prevPresenceContext=this.presenceContext,this.presenceContext=n;for(let r=0;r<JP.length;r++){const i=JP[r];this.propEventSubscriptions[i]&&(this.propEventSubscriptions[i](),delete this.propEventSubscriptions[i]);const o=t["on"+i];o&&(this.propEventSubscriptions[i]=this.on(i,o))}this.prevMotionValues=Pbe(this,this.scrapeMotionValuesFromProps(t,this.prevProps),this.prevMotionValues),this.handleChildMotionValue&&this.handleChildMotionValue()}getProps(){return this.props}getVariant(t){return this.props.variants?this.props.variants[t]:void 0}getDefaultTransition(){return this.props.transition}getTransformPagePoint(){return this.props.transformPagePoint}getClosestVariantNode(){return this.isVariantNode?this:this.parent?this.parent.getClosestVariantNode():void 0}getVariantContext(t=!1){if(t)return this.parent?this.parent.getVariantContext():void 0;if(!this.isControllingVariants){const r=this.parent?this.parent.getVariantContext()||{}:{};return this.props.initial!==void 0&&(r.initial=this.props.initial),r}const n={};for(let r=0;r<kbe;r++){const i=GC[r],o=this.props[i];(Gf(o)||o===!1)&&(n[i]=o)}return n}addVariantChild(t){const n=this.getClosestVariantNode();if(n)return n.variantChildren&&n.variantChildren.add(t),()=>n.variantChildren.delete(t)}addValue(t,n){n!==this.values.get(t)&&(this.removeValue(t),this.bindToMotionValue(t,n)),this.values.set(t,n),this.latestValues[t]=n.get()}removeValue(t){this.values.delete(t);const n=this.valueSubscriptions.get(t);n&&(n(),this.valueSubscriptions.delete(t)),delete this.latestValues[t],this.removeValueFromRenderState(t,this.renderState)}hasValue(t){return this.values.has(t)}getValue(t,n){if(this.props.values&&this.props.values[t])return this.props.values[t];let r=this.values.get(t);return r===void 0&&n!==void 0&&(r=uc(n,{owner:this}),this.addValue(t,r)),r}readValue(t){var n;return this.latestValues[t]!==void 0||!this.current?this.latestValues[t]:(n=this.getBaseTargetFromProps(this.props,t))!==null&&n!==void 0?n:this.readValueFromInstance(this.current,t,this.options)}setBaseTarget(t,n){this.baseTarget[t]=n}getBaseTarget(t){var n;const{initial:r}=this.props,i=typeof r=="string"||typeof r=="object"?(n=JC(this.props,r))===null||n===void 0?void 0:n[t]:void 0;if(r&&i!==void 0)return i;const o=this.getBaseTargetFromProps(this.props,t);return o!==void 0&&!_r(o)?o:this.initialValues[t]!==void 0&&i===void 0?void 0:this.baseTarget[t]}on(t,n){return this.events[t]||(this.events[t]=new l3),this.events[t].add(n)}notify(t,...n){this.events[t]&&this.events[t].notify(...n)}}class DL extends Rbe{sortInstanceNodePosition(t,n){return t.compareDocumentPosition(n)&2?1:-1}getBaseTargetFromProps(t,n){return t.style?t.style[n]:void 0}removeValueFromRenderState(t,{vars:n,style:r}){delete n[t],delete r[t]}makeTargetAnimatableFromInstance({transition:t,transitionEnd:n,...r},{transformValues:i},o){let s=Xve(r,t||{},this);if(i&&(n&&(n=i(n)),r&&(r=i(r)),s&&(s=i(s))),o){Wve(this,r,s);const a=Tbe(this,r,s,n);n=a.transitionEnd,r=a.target}return{transition:t,transitionEnd:n,...r}}}function Obe(e){return window.getComputedStyle(e)}class Mbe extends DL{readValueFromInstance(t,n){if(El.has(n)){const r=i3(n);return r&&r.default||0}else{const r=Obe(t),i=(xD(n)?r.getPropertyValue(n):r[n])||0;return typeof i=="string"?i.trim():i}}measureInstanceViewportBox(t,{transformPagePoint:n}){return CL(t,n)}build(t,n,r,i){WC(t,n,r,i.transformTemplate)}scrapeMotionValuesFromProps(t,n){return ZC(t,n)}handleChildMotionValue(){this.childSubscription&&(this.childSubscription(),delete this.childSubscription);const{children:t}=this.props;_r(t)&&(this.childSubscription=t.on("change",n=>{this.current&&(this.current.textContent=`${n}`)}))}renderInstance(t,n,r,i){kD(t,n,r,i)}}class Ibe extends DL{constructor(){super(...arguments),this.isSVGTag=!1}getBaseTargetFromProps(t,n){return t[n]}readValueFromInstance(t,n){if(El.has(n)){const r=i3(n);return r&&r.default||0}return n=RD.has(n)?n:QC(n),t.getAttribute(n)}measureInstanceViewportBox(){return Wt()}scrapeMotionValuesFromProps(t,n){return MD(t,n)}build(t,n,r,i){XC(t,n,r,this.isSVGTag,i.transformTemplate)}renderInstance(t,n,r,i){OD(t,n,r,i)}mount(t){this.isSVGTag=YC(t.tagName),super.mount(t)}}const Nbe=(e,t)=>qC(e)?new Ibe(t,{enableHardwareAcceleration:!1}):new Mbe(t,{enableHardwareAcceleration:!0}),Dbe={layout:{ProjectionNode:OL,MeasureLayout:TL}},Lbe={...u1e,...R0e,...hbe,...Dbe},$be=Bye((e,t)=>p0e(e,t,Lbe,Nbe));function LL(){const e=k.useRef(!1);return iy(()=>(e.current=!0,()=>{e.current=!1}),[]),e}function Fbe(){const e=LL(),[t,n]=k.useState(0),r=k.useCallback(()=>{e.current&&n(t+1)},[t]);return[k.useCallback(()=>_t.postRender(r),[r]),t]}class Bbe extends k.Component{getSnapshotBeforeUpdate(t){const n=this.props.childRef.current;if(n&&t.isPresent&&!this.props.isPresent){const r=this.props.sizeRef.current;r.height=n.offsetHeight||0,r.width=n.offsetWidth||0,r.top=n.offsetTop,r.left=n.offsetLeft}return null}componentDidUpdate(){}render(){return this.props.children}}function jbe({children:e,isPresent:t}){const n=k.useId(),r=k.useRef(null),i=k.useRef({width:0,height:0,top:0,left:0});return k.useInsertionEffect(()=>{const{width:o,height:s,top:a,left:l}=i.current;if(t||!r.current||!o||!s)return;r.current.dataset.motionPopId=n;const u=document.createElement("style");return document.head.appendChild(u),u.sheet&&u.sheet.insertRule(`
+          [data-motion-pop-id="${n}"] {
+            position: absolute !important;
+            width: ${o}px !important;
+            height: ${s}px !important;
+            top: ${a}px !important;
+            left: ${l}px !important;
+          }
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p3{constructor(t){super(t),this.supportsBinary=!t.forceBase64}get name(){return"websocket"}doOpen(){if(!this.check())return;const t=this.uri(),n=this.opts.protocols,r=p8?{}:ZL(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(r.headers=this.opts.extraHeaders);try{this.ws=h8&&!p8?n?new Xp(t,n):new Xp(t):new Xp(t,n,r)}catch(i){return this.emitReserved("error",i)}this.ws.binaryType=this.socket.binaryType||KSe,this.addEventListeners()}addEventListeners(){this.ws.onopen=()=>{this.opts.autoUnref&&this.ws._socket.unref(),this.onOpen()},this.ws.onclose=t=>this.onClose({description:"websocket connection closed",context:t}),this.ws.onmessage=t=>this.onData(t.data),this.ws.onerror=t=>this.onError("websocket error",t)}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;f3(r,this.supportsBinary,o=>{const s={};try{h8&&this.ws.send(o)}catch{}i&&g3(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})}}doClose(){typeof this.ws<"u"&&(this.ws.close(),this.ws=null)}uri(){const t=this.opts.secure?"wss":"ws",n=this.query||{};return this.opts.timestampRequests&&(n[this.opts.timestampParam]=e$()),this.supportsBinary||(n.b64=1),this.createUri(t,n)}check(){return!!Xp}}function YSe(e,t){return e.type==="message"&&typeof e.data!="string"&&t[0]>=48&&t[0]<=54}class QSe extends p3{get name(){return"webtransport"}doOpen(){typeof WebTransport=="function"&&(this.transport=new WebTransport(this.createUri("https"),this.opts.transportOptions[this.name]),this.transport.closed.then(()=>{this.onClose()}).catch(t=>{this.onError("webtransport error",t)}),this.transport.ready.then(()=>{this.transport.createBidirectionalStream().then(t=>{const n=t.readable.getReader();this.writer=t.writable.getWriter();let r;const i=()=>{n.read().then(({done:s,value:a})=>{s||(!r&&a.byteLength===1&&a[0]===54?r=!0:(this.onPacket(ISe(a,r,"arraybuffer")),r=!1),i())}).catch(s=>{})};i();const o=this.query.sid?`0{"sid":"${this.query.sid}"}`:"0";this.writer.write(new TextEncoder().encode(o)).then(()=>this.onOpen())})}))}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;PSe(r,o=>{YSe(r,o)&&this.writer.write(Uint8Array.of(54)),this.writer.write(o).then(()=>{i&&g3(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})})}}doClose(){var t;(t=this.transport)===null||t===void 0||t.close()}}const ZSe={websocket:XSe,webtransport:QSe,polling:WSe},JSe=/^(?:(?![^:@\/?#]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@\/?#]*)(?::([^:@\/?#]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,e_e=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function tw(e){const t=e,n=e.indexOf("["),r=e.indexOf("]");n!=-1&&r!=-1&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));let i=JSe.exec(e||""),o={},s=14;for(;s--;)o[e_e[s]]=i[s]||"";return n!=-1&&r!=-1&&(o.source=t,o.host=o.host.substring(1,o.host.length-1).replace(/;/g,":"),o.authority=o.authority.replace("[","").replace("]","").replace(/;/g,":"),o.ipv6uri=!0),o.pathNames=t_e(o,o.path),o.queryKey=n_e(o,o.query),o}function t_e(e,t){const n=/\/{2,9}/g,r=t.replace(n,"/").split("/");return(t.slice(0,1)=="/"||t.length===0)&&r.splice(0,1),t.slice(-1)=="/"&&r.splice(r.length-1,1),r}function n_e(e,t){const n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,function(r,i,o){i&&(n[i]=o)}),n}let r$=class Zl extends Zt{constructor(t,n={}){super(),this.writeBuffer=[],t&&typeof t=="object"&&(n=t,t=null),t?(t=tw(t),n.hostname=t.host,n.secure=t.protocol==="https"||t.protocol==="wss",n.port=t.port,t.query&&(n.query=t.query)):n.host&&(n.hostname=tw(n.host).host),a1(this,n),this.secure=n.secure!=null?n.secure:typeof location<"u"&&location.protocol==="https:",n.hostname&&!n.port&&(n.port=this.secure?"443":"80"),this.hostname=n.hostname||(typeof location<"u"?location.hostname:"localhost"),this.port=n.port||(typeof location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket","webtransport"],this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!1},n),this.opts.path=this.opts.path.replace(/\/$/,"")+(this.opts.addTrailingSlash?"/":""),typeof this.opts.query=="string"&&(this.opts.query=VSe(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=QL,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port},this.opts.transportOptions[t]);return new ZSe[t](r)}open(){let t;if(this.opts.rememberUpgrade&&Zl.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch{this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;Zl.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",d=>{if(!r)if(d.type==="pong"&&d.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;Zl.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(c(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const f=new Error("probe error");f.transport=n.name,this.emitReserved("upgradeError",f)}}))};function o(){r||(r=!0,c(),n.close(),n=null)}const s=d=>{const f=new Error("probe error: "+d);f.transport=n.name,o(),this.emitReserved("upgradeError",f)};function a(){s("transport closed")}function l(){s("socket closed")}function u(d){n&&d.name!==n.name&&o()}const c=()=>{n.removeListener("open",i),n.removeListener("error",s),n.removeListener("close",a),this.off("close",l),this.off("upgrading",u)};n.once("open",i),n.once("error",s),n.once("close",a),this.once("close",l),this.once("upgrading",u),this.upgrades.indexOf("webtransport")!==-1&&t!=="webtransport"?this.setTimeoutFn(()=>{r||n.open()},200):n.open()}onOpen(){if(this.readyState="open",Zl.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade){let t=0;const n=this.upgrades.length;for(;t<n;t++)this.probe(this.upgrades[t])}}onPacket(t){if(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")switch(this.emitReserved("packet",t),this.emitReserved("heartbeat"),t.type){case"open":this.onHandshake(JSON.parse(t.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const n=new Error("server error");n.code=t.data,this.onError(n);break;case"message":this.emitReserved("data",t.data),this.emitReserved("message",t.data);break}}onHandshake(t){this.emitReserved("handshake",t),this.id=t.sid,this.transport.query.sid=t.sid,this.upgrades=this.filterUpgrades(t.upgrades),this.pingInterval=t.pingInterval,this.pingTimeout=t.pingTimeout,this.maxPayload=t.maxPayload,this.onOpen(),this.readyState!=="closed"&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn(()=>{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const t=this.getWritablePackets();this.transport.send(t),this.prevBufferLen=t.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r<this.writeBuffer.length;r++){const i=this.writeBuffer[r].data;if(i&&(n+=FSe(i)),r>0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(t,n,r){return this.sendPacket("message",t,n,r),this}send(t,n,r){return this.sendPacket("message",t,n,r),this}sendPacket(t,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const o={type:t,data:n,options:r};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),i&&this.once("flush",i),this.flush()}close(){const t=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),t()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():t()}):this.upgrading?r():t()),this}onError(t){Zl.priorWebsocketSuccess=!1,this.emitReserved("error",t),this.onClose("transport error",t)}onClose(t,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",t,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(t){const n=[];let r=0;const i=t.length;for(;r<i;r++)~this.transports.indexOf(t[r])&&n.push(t[r]);return n}};r$.protocol=QL;function r_e(e,t="",n){let r=e;n=n||typeof location<"u"&&location,e==null&&(e=n.protocol+"//"+n.host),typeof e=="string"&&(e.charAt(0)==="/"&&(e.charAt(1)==="/"?e=n.protocol+e:e=n.host+e),/^(https?|wss?):\/\//.test(e)||(typeof n<"u"?e=n.protocol+"//"+e:e="https://"+e),r=tw(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";const o=r.host.indexOf(":")!==-1?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}const i_e=typeof ArrayBuffer=="function",o_e=e=>typeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,i$=Object.prototype.toString,s_e=typeof Blob=="function"||typeof Blob<"u"&&i$.call(Blob)==="[object BlobConstructor]",a_e=typeof File=="function"||typeof File<"u"&&i$.call(File)==="[object FileConstructor]";function m3(e){return i_e&&(e instanceof ArrayBuffer||o_e(e))||s_e&&e instanceof Blob||a_e&&e instanceof File}function Rg(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n<r;n++)if(Rg(e[n]))return!0;return!1}if(m3(e))return!0;if(e.toJSON&&typeof e.toJSON=="function"&&arguments.length===1)return Rg(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&Rg(e[n]))return!0;return!1}function l_e(e){const t=[],n=e.data,r=e;return r.data=nw(n,t),r.attachments=t.length,{packet:r,buffers:t}}function nw(e,t){if(!e)return e;if(m3(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}else if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=nw(e[r],t);return n}else if(typeof e=="object"&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=nw(e[r],t));return n}return e}function u_e(e,t){return e.data=rw(e.data,t),delete e.attachments,e}function rw(e,t){if(!e)return e;if(e&&e._placeholder===!0){if(typeof e.num=="number"&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}else if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=rw(e[n],t);else if(typeof e=="object")for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=rw(e[n],t));return e}const c_e=["connect","connect_error","disconnect","disconnecting","newListener","removeListener"],d_e=5;var Ie;(function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"})(Ie||(Ie={}));class f_e{constructor(t){this.replacer=t}encode(t){return(t.type===Ie.EVENT||t.type===Ie.ACK)&&Rg(t)?this.encodeAsBinary({type:t.type===Ie.EVENT?Ie.BINARY_EVENT:Ie.BINARY_ACK,nsp:t.nsp,data:t.data,id:t.id}):[this.encodeAsString(t)]}encodeAsString(t){let n=""+t.type;return(t.type===Ie.BINARY_EVENT||t.type===Ie.BINARY_ACK)&&(n+=t.attachments+"-"),t.nsp&&t.nsp!=="/"&&(n+=t.nsp+","),t.id!=null&&(n+=t.id),t.data!=null&&(n+=JSON.stringify(t.data,this.replacer)),n}encodeAsBinary(t){const n=l_e(t),r=this.encodeAsString(n.packet),i=n.buffers;return i.unshift(r),i}}function g8(e){return Object.prototype.toString.call(e)==="[object Object]"}class y3 extends Zt{constructor(t){super(),this.reviver=t}add(t){let n;if(typeof t=="string"){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");n=this.decodeString(t);const r=n.type===Ie.BINARY_EVENT;r||n.type===Ie.BINARY_ACK?(n.type=r?Ie.EVENT:Ie.ACK,this.reconstructor=new h_e(n),n.attachments===0&&super.emitReserved("decoded",n)):super.emitReserved("decoded",n)}else if(m3(t)||t.base64)if(this.reconstructor)n=this.reconstructor.takeBinaryData(t),n&&(this.reconstructor=null,super.emitReserved("decoded",n));else throw new Error("got binary data when not reconstructing a packet");else throw new Error("Unknown type: "+t)}decodeString(t){let n=0;const r={type:Number(t.charAt(0))};if(Ie[r.type]===void 0)throw new Error("unknown packet type "+r.type);if(r.type===Ie.BINARY_EVENT||r.type===Ie.BINARY_ACK){const o=n+1;for(;t.charAt(++n)!=="-"&&n!=t.length;);const s=t.substring(o,n);if(s!=Number(s)||t.charAt(n)!=="-")throw new Error("Illegal attachments");r.attachments=Number(s)}if(t.charAt(n+1)==="/"){const o=n+1;for(;++n&&!(t.charAt(n)===","||n===t.length););r.nsp=t.substring(o,n)}else r.nsp="/";const i=t.charAt(n+1);if(i!==""&&Number(i)==i){const o=n+1;for(;++n;){const s=t.charAt(n);if(s==null||Number(s)!=s){--n;break}if(n===t.length)break}r.id=Number(t.substring(o,n+1))}if(t.charAt(++n)){const o=this.tryParse(t.substr(n));if(y3.isPayloadValid(r.type,o))r.data=o;else throw new Error("invalid payload")}return r}tryParse(t){try{return JSON.parse(t,this.reviver)}catch{return!1}}static isPayloadValid(t,n){switch(t){case Ie.CONNECT:return g8(n);case Ie.DISCONNECT:return n===void 0;case Ie.CONNECT_ERROR:return typeof n=="string"||g8(n);case Ie.EVENT:case Ie.BINARY_EVENT:return Array.isArray(n)&&(typeof n[0]=="number"||typeof n[0]=="string"&&c_e.indexOf(n[0])===-1);case Ie.ACK:case Ie.BINARY_ACK:return Array.isArray(n)}}destroy(){this.reconstructor&&(this.reconstructor.finishedReconstruction(),this.reconstructor=null)}}class h_e{constructor(t){this.packet=t,this.buffers=[],this.reconPack=t}takeBinaryData(t){if(this.buffers.push(t),this.buffers.length===this.reconPack.attachments){const n=u_e(this.reconPack,this.buffers);return this.finishedReconstruction(),n}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const p_e=Object.freeze(Object.defineProperty({__proto__:null,Decoder:y3,Encoder:f_e,get PacketType(){return Ie},protocol:d_e},Symbol.toStringTag,{value:"Module"}));function ui(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const g_e=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class o$ extends Zt{constructor(t,n,r){super(),this.connected=!1,this.recovered=!1,this.receiveBuffer=[],this.sendBuffer=[],this._queue=[],this._queueSeq=0,this.ids=0,this.acks={},this.flags={},this.io=t,this.nsp=n,r&&r.auth&&(this.auth=r.auth),this._opts=Object.assign({},r),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const t=this.io;this.subs=[ui(t,"open",this.onopen.bind(this)),ui(t,"packet",this.onpacket.bind(this)),ui(t,"error",this.onerror.bind(this)),ui(t,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected?this:(this.subEvents(),this.io._reconnecting||this.io.open(),this.io._readyState==="open"&&this.onopen(),this)}open(){return this.connect()}send(...t){return t.unshift("message"),this.emit.apply(this,t),this}emit(t,...n){if(g_e.hasOwnProperty(t))throw new Error('"'+t.toString()+'" is a reserved event name');if(n.unshift(t),this._opts.retries&&!this.flags.fromQueue&&!this.flags.volatile)return this._addToQueue(n),this;const r={type:Ie.EVENT,data:n};if(r.options={},r.options.compress=this.flags.compress!==!1,typeof n[n.length-1]=="function"){const s=this.ids++,a=n.pop();this._registerAckCallback(s,a),r.id=s}const i=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!i||!this.connected)||(this.connected?(this.notifyOutgoingListeners(r),this.packet(r)):this.sendBuffer.push(r)),this.flags={},this}_registerAckCallback(t,n){var r;const i=(r=this.flags.timeout)!==null&&r!==void 0?r:this._opts.ackTimeout;if(i===void 0){this.acks[t]=n;return}const o=this.io.setTimeoutFn(()=>{delete this.acks[t];for(let s=0;s<this.sendBuffer.length;s++)this.sendBuffer[s].id===t&&this.sendBuffer.splice(s,1);n.call(this,new Error("operation has timed out"))},i);this.acks[t]=(...s)=>{this.io.clearTimeoutFn(o),n.apply(this,[null,...s])}}emitWithAck(t,...n){const r=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((i,o)=>{n.push((s,a)=>r?s?o(s):i(a):i(s)),this.emit(t,...n)})}_addToQueue(t){let n;typeof t[t.length-1]=="function"&&(n=t.pop());const r={id:this._queueSeq++,tryCount:0,pending:!1,args:t,flags:Object.assign({fromQueue:!0},this.flags)};t.push((i,...o)=>r!==this._queue[0]?void 0:(i!==null?r.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(i)):(this._queue.shift(),n&&n(null,...o)),r.pending=!1,this._drainQueue())),this._queue.push(r),this._drainQueue()}_drainQueue(t=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!t||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this._sendConnectPacket(t)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(t){this.packet({type:Ie.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},t):t})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case Ie.CONNECT:t.data&&t.data.sid?this.onconnect(t.data.sid,t.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case Ie.EVENT:case Ie.BINARY_EVENT:this.onevent(t);break;case Ie.ACK:case Ie.BINARY_ACK:this.onack(t);break;case Ie.DISCONNECT:this.ondisconnect();break;case Ie.CONNECT_ERROR:this.destroy();const r=new Error(t.data.message);r.data=t.data.data,this.emitReserved("connect_error",r);break}}onevent(t){const n=t.data||[];t.id!=null&&n.push(this.ack(t.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(t){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,t)}super.emit.apply(this,t),this._pid&&t.length&&typeof t[t.length-1]=="string"&&(this._lastOffset=t[t.length-1])}ack(t){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:Ie.ACK,id:t,data:i}))}}onack(t){const n=this.acks[t.id];typeof n=="function"&&(n.apply(this,t.data),delete this.acks[t.id])}onconnect(t,n){this.id=t,this.recovered=n&&this._pid===n,this._pid=n,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}emitBuffered(){this.receiveBuffer.forEach(t=>this.emitEvent(t)),this.receiveBuffer=[],this.sendBuffer.forEach(t=>{this.notifyOutgoingListeners(t),this.packet(t)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(t=>t()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:Ie.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(t){return this.flags.compress=t,this}get volatile(){return this.flags.volatile=!0,this}timeout(t){return this.flags.timeout=t,this}onAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(t),this}prependAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(t),this}offAny(t){if(!this._anyListeners)return this;if(t){const n=this._anyListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(t),this}prependAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(t),this}offAnyOutgoing(t){if(!this._anyOutgoingListeners)return this;if(t){const n=this._anyOutgoingListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(t){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const n=this._anyOutgoingListeners.slice();for(const r of n)r.apply(this,t.data)}}}function Tc(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}Tc.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=Math.floor(t*10)&1?e+n:e-n}return Math.min(e,this.max)|0};Tc.prototype.reset=function(){this.attempts=0};Tc.prototype.setMin=function(e){this.ms=e};Tc.prototype.setMax=function(e){this.max=e};Tc.prototype.setJitter=function(e){this.jitter=e};class iw extends Zt{constructor(t,n){var r;super(),this.nsps={},this.subs=[],t&&typeof t=="object"&&(n=t,t=void 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n)if(this.nsps[r].active)return;this._close()}_packet(t){const n=this.encoder.encode(t);for(let r=0;r<n.length;r++)this.engine.write(n[r],t.options)}cleanup(){this.subs.forEach(t=>t()),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(t,n){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",t,n),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const n=this.backoff.duration();this._reconnecting=!0;const r=this.setTimeoutFn(()=>{t.skipReconnect||(this.emitReserved("reconnect_attempt",t.backoff.attempts),!t.skipReconnect&&t.open(i=>{i?(t._reconnecting=!1,t.reconnect(),this.emitReserved("reconnect_error",i)):t.onreconnect()}))},n);this.opts.autoUnref&&r.unref(),this.subs.push(()=>{this.clearTimeoutFn(r)})}}onreconnect(){const t=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",t)}}const sd={};function Og(e,t){typeof e=="object"&&(t=e,e=void 0),t=t||{};const n=r_e(e,t.path||"/socket.io"),r=n.source,i=n.id,o=n.path,s=sd[i]&&o in sd[i].nsps,a=t.forceNew||t["force new connection"]||t.multiplex===!1||s;let l;return a?l=new iw(r,t):(sd[i]||(sd[i]=new iw(r,t)),l=sd[i]),n.query&&!t.query&&(t.query=n.queryKey),l.socket(n.path,t)}Object.assign(Og,{Manager:iw,Socket:o$,io:Og,connect:Og});const m8=()=>{let e=!1,t=`ws://${window.location.host}`;const n={timeout:6e4,path:"/ws/socket.io",autoConnect:!1};if(["nodes","package"].includes("production")){const 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nn.UnregisterDroppable:{const{id:n,key:r}=t,i=e.droppable.containers.get(n);if(!i||r!==i.key)return e;const o=new $d(e.droppable.containers);return o.delete(n),{...e,droppable:{...e.droppable,containers:o}}}default:return e}}function A2e(e){let{disabled:t}=e;const{active:n,activatorEvent:r,draggableNodes:i}=k.useContext(Gh),o=my(r),s=my(n==null?void 0:n.id);return k.useEffect(()=>{if(!t&&!r&&o&&s!=null){if(!S3(o)||document.activeElement===o.target)return;const a=i.get(s);if(!a)return;const{activatorNode:l,node:u}=a;if(!l.current&&!u.current)return;requestAnimationFrame(()=>{for(const c of[l.current,u.current]){if(!c)continue;const d=S_e(c);if(d){d.focus();break}}})}},[r,t,i,s,o]),null}function E$(e,t){let{transform:n,...r}=t;return e!=null&&e.length?e.reduce((i,o)=>o({transform:i,...r}),n):n}function k2e(e){return k.useMemo(()=>({draggable:{...Mg.draggable,...e==null?void 0:e.draggable},droppable:{...Mg.droppable,...e==null?void 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_n({active:C.current,activeNode:Mn,event:Gn,options:ln,context:te,onStart(Hn){const wn=C.current;if(wn==null)return;const co=T.get(wn);if(!co)return;const{onDragStart:Zo}=D.current,Jo={active:{id:wn,data:co.data,rect:$}};zi.unstable_batchedUpdates(()=>{Zo==null||Zo(Jo),_(hs.Initializing),v({type:nn.DragStart,initialCoordinates:Hn,active:wn}),y({type:"onDragStart",event:Jo})})},onMove(Hn){v({type:nn.DragMove,coordinates:Hn})},onEnd:Ri(nn.DragEnd),onCancel:Ri(nn.DragCancel)});zi.unstable_batchedUpdates(()=>{M(ar),O(Ct.nativeEvent)});function Ri(Hn){return async function(){const{active:co,collisions:Zo,over:Jo,scrollAdjustedTranslate:Al}=te.current;let fo=null;if(co&&Al){const{cancelDrop:qn}=D.current;fo={activatorEvent:Gn,active:co,collisions:Zo,delta:Al,over:Jo},Hn===nn.DragEnd&&typeof qn=="function"&&await Promise.resolve(qn(fo))&&(Hn=nn.DragCancel)}C.current=null,zi.unstable_batchedUpdates(()=>{v({type:Hn}),_(hs.Uninitialized),wr(null),M(null),O(null);const qn=Hn===nn.DragEnd?"onDragEnd":"onDragCancel";if(fo){const fa=D.current[qn];fa==null||fa(fo),y({type:qn,event:fo})}})}}},[T]),Sn=k.useCallback((Ct,nt)=>(_n,ln)=>{const Mn=_n.nativeEvent,Gn=T.get(ln);if(C.current!==null||!Gn||Mn.dndKit||Mn.defaultPrevented)return;const ar={active:Gn};Ct(_n,nt.options,ar)===!0&&(Mn.dndKit={capturedBy:nt.sensor},C.current=ln,uo(_n,nt))},[T,uo]),ii=d2e(c,Sn);b2e(c),io(()=>{J&&b===hs.Initializing&&_(hs.Initialized)},[J,b]),k.useEffect(()=>{const{onDragMove:Ct}=D.current,{active:nt,activatorEvent:_n,collisions:ln,over:Mn}=te.current;if(!nt||!_n)return;const Gn={active:nt,activatorEvent:_n,collisions:ln,delta:{x:On.x,y:On.y},over:Mn};zi.unstable_batchedUpdates(()=>{Ct==null||Ct(Gn),y({type:"onDragMove",event:Gn})})},[On.x,On.y]),k.useEffect(()=>{const{active:Ct,activatorEvent:nt,collisions:_n,droppableContainers:ln,scrollAdjustedTranslate:Mn}=te.current;if(!Ct||C.current==null||!nt||!Mn)return;const{onDragOver:Gn}=D.current,ar=ln.get(Ht),Ri=ar&&ar.rect.current?{id:ar.id,rect:ar.rect.current,data:ar.data,disabled:ar.disabled}:null,Hn={active:Ct,activatorEvent:nt,collisions:_n,delta:{x:Mn.x,y:Mn.y},over:Ri};zi.unstable_batchedUpdates(()=>{wr(Ri),Gn==null||Gn(Hn),y({type:"onDragOver",event:Hn})})},[Ht]),io(()=>{te.current={activatorEvent:N,active:I,activeNode:Y,collisionRect:bn,collisions:Un,droppableRects:G,draggableNodes:T,draggingNode:ce,draggingNodeRect:Ne,droppableContainers:E,over:xt,scrollableAncestors:Mt,scrollAdjustedTranslate:On},$.current={initial:Ne,translated:bn}},[I,Y,Un,bn,T,ce,Ne,G,E,xt,Mt,On]),a2e({...H,delta:P,draggingRect:bn,pointerCoordinates:Gt,scrollableAncestors:Mt,scrollableAncestorRects:ut});const ca=k.useMemo(()=>({active:I,activeNode:Y,activeNodeRect:J,activatorEvent:N,collisions:Un,containerNodeRect:ne,dragOverlay:ve,draggableNodes:T,droppableContainers:E,droppableRects:G,over:xt,measureDroppableContainers:W,scrollableAncestors:Mt,scrollableAncestorRects:ut,measuringConfiguration:U,measuringScheduled:X,windowRect:vn}),[I,Y,J,N,Un,ne,ve,T,E,G,xt,W,Mt,ut,U,X,vn]),ki=k.useMemo(()=>({activatorEvent:N,activators:ii,active:I,activeNodeRect:J,ariaDescribedById:{draggable:L},dispatch:v,draggableNodes:T,over:xt,measureDroppableContainers:W}),[N,ii,I,J,v,L,T,xt,W]);return Xe.createElement(a$.Provider,{value:g},Xe.createElement(Gh.Provider,{value:ki},Xe.createElement(T$.Provider,{value:ca},Xe.createElement(f1.Provider,{value:ri},u)),Xe.createElement(A2e,{disabled:(a==null?void 0:a.restoreFocus)===!1})),Xe.createElement(R_e,{...a,hiddenTextDescribedById:L}));function da(){const Ct=(R==null?void 0:R.autoScrollEnabled)===!1,nt=typeof l=="object"?l.enabled===!1:l===!1,_n=w&&!Ct&&!nt;return typeof l=="object"?{...l,enabled:_n}:{enabled:_n}}}),M2e=k.createContext(null),P8="button",I2e="Droppable";function N2e(e){let{id:t,data:n,disabled:r=!1,attributes:i}=e;const o=c1(I2e),{activators:s,activatorEvent:a,active:l,activeNodeRect:u,ariaDescribedById:c,draggableNodes:d,over:f}=k.useContext(Gh),{role:h=P8,roleDescription:p="draggable",tabIndex:m=0}=i??{},S=(l==null?void 0:l.id)===t,v=k.useContext(S?f1:M2e),[y,g]=gy(),[b,_]=gy(),w=S2e(s,t),x=Xf(n);io(()=>(d.set(t,{id:t,key:o,node:y,activatorNode:b,data:x}),()=>{const P=d.get(t);P&&P.key===o&&d.delete(t)}),[d,t]);const T=k.useMemo(()=>({role:h,tabIndex:m,"aria-disabled":r,"aria-pressed":S&&h===P8?!0:void 0,"aria-roledescription":p,"aria-describedby":c.draggable}),[r,h,m,S,p,c.draggable]);return{active:l,activatorEvent:a,activeNodeRect:u,attributes:T,isDragging:S,listeners:r?void 0:w,node:y,over:f,setNodeRef:g,setActivatorNodeRef:_,transform:v}}function D2e(){return k.useContext(T$)}const L2e="Droppable",$2e={timeout:25};function F2e(e){let{data:t,disabled:n=!1,id:r,resizeObserverConfig:i}=e;const o=c1(L2e),{active:s,dispatch:a,over:l,measureDroppableContainers:u}=k.useContext(Gh),c=k.useRef({disabled:n}),d=k.useRef(!1),f=k.useRef(null),h=k.useRef(null),{disabled:p,updateMeasurementsFor:m,timeout:S}={...$2e,...i},v=Xf(m??r),y=k.useCallback(()=>{if(!d.current){d.current=!0;return}h.current!=null&&clearTimeout(h.current),h.current=setTimeout(()=>{u(Array.isArray(v.current)?v.current:[v.current]),h.current=null},S)},[S]),g=d1({callback:y,disabled:p||!s}),b=k.useCallback((T,P)=>{g&&(P&&(g.unobserve(P),d.current=!1),T&&g.observe(T))},[g]),[_,w]=gy(b),x=Xf(t);return k.useEffect(()=>{!g||!_.current||(g.disconnect(),d.current=!1,g.observe(_.current))},[_,g]),io(()=>(a({type:nn.RegisterDroppable,element:{id:r,key:o,disabled:n,node:_,rect:f,data:x}}),()=>a({type:nn.UnregisterDroppable,key:o,id:r})),[r]),k.useEffect(()=>{n!==c.current.disabled&&(a({type:nn.SetDroppableDisabled,id:r,key:o,disabled:n}),c.current.disabled=n)},[r,o,n,a]),{active:s,rect:f,isOver:(l==null?void 0:l.id)===r,node:_,over:l,setNodeRef:w}}function B2e(e){let{animation:t,children:n}=e;const[r,i]=k.useState(null),[o,s]=k.useState(null),a=my(n);return!n&&!r&&a&&i(a),io(()=>{if(!o)return;const l=r==null?void 0:r.key,u=r==null?void 0:r.props.id;if(l==null||u==null){i(null);return}Promise.resolve(t(u,o)).then(()=>{i(null)})},[t,r,o]),Xe.createElement(Xe.Fragment,null,n,r?k.cloneElement(r,{ref:s}):null)}const j2e={x:0,y:0,scaleX:1,scaleY:1};function V2e(e){let{children:t}=e;return Xe.createElement(Gh.Provider,{value:C$},Xe.createElement(f1.Provider,{value:j2e},t))}const 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deleted file mode 100644
index cf22438196..0000000000
--- a/invokeai/frontend/web/dist/assets/index-e2437518.js
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r=this.events[t].add(n);return t==="change"?()=>{r(),kt.read(()=>{this.events.change.getSize()||this.stop()})}:r}clearListeners(){for(const t in this.events)this.events[t].clear()}attach(t,n){this.passiveEffect=t,this.stopPassiveEffect=n}set(t,n=!0){!n||!this.passiveEffect?this.updateAndNotify(t,n):this.passiveEffect(t,this.updateAndNotify)}setWithVelocity(t,n,r){this.set(n),this.prev=t,this.timeDelta=r}jump(t){this.updateAndNotify(t),this.prev=t,this.stop(),this.stopPassiveEffect&&this.stopPassiveEffect()}get(){return this.current}getPrevious(){return this.prev}getVelocity(){return this.canTrackVelocity?FM(parseFloat(this.current)-parseFloat(this.prev),this.timeDelta):0}start(t){return this.stop(),new 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f=e.getValue(d),h=a[d];if(!f||h===void 0||c&&Gre(c,d))continue;const p={delay:n,elapsed:0,...o};if(window.HandoffAppearAnimations&&!f.hasAnimated){const S=e.getProps()[kne];S&&(p.elapsed=window.HandoffAppearAnimations(S,d,f,kt))}f.start(R5(d,f,h,e.shouldReduceMotion&&Rl.has(d)?{type:!1}:p));const m=f.animation;xy(l)&&(l.add(d),m.then(()=>l.remove(d))),u.push(m)}return s&&Promise.all(u).then(()=>{s&&jre(e,s)}),u}function dw(e,t,n={}){const r=Mv(e,t,n.custom);let{transition:i=e.getDefaultTransition()||{}}=r||{};n.transitionOverride&&(i=n.transitionOverride);const o=r?()=>Promise.all(qM(e,r,n)):()=>Promise.resolve(),s=e.variantChildren&&e.variantChildren.size?(l=0)=>{const{delayChildren:u=0,staggerChildren:c,staggerDirection:d}=i;return Hre(e,t,u+l,c,d,n)}:()=>Promise.resolve(),{when:a}=i;if(a){const[l,u]=a==="beforeChildren"?[o,s]:[s,o];return l().then(()=>u())}else return Promise.all([o(),s(n.delay)])}function Hre(e,t,n=0,r=0,i=1,o){const s=[],a=(e.variantChildren.size-1)*r,l=i===1?(u=0)=>u*r:(u=0)=>a-u*r;return Array.from(e.variantChildren).sort(qre).forEach((u,c)=>{u.notify("AnimationStart",t),s.push(dw(u,t,{...o,delay:n+l(c)}).then(()=>u.notify("AnimationComplete",t)))}),Promise.all(s)}function qre(e,t){return e.sortNodePosition(t)}function Wre(e,t,n={}){e.notify("AnimationStart",t);let r;if(Array.isArray(t)){const i=t.map(o=>dw(e,o,n));r=Promise.all(i)}else if(typeof t=="string")r=dw(e,t,n);else{const i=typeof t=="function"?Mv(e,t,n.custom):t;r=Promise.all(qM(e,i,n))}return r.then(()=>e.notify("AnimationComplete",t))}const Kre=[...g5].reverse(),Yre=g5.length;function Xre(e){return t=>Promise.all(t.map(({animation:n,options:r})=>Wre(e,n,r)))}function Qre(e){let t=Xre(e);const n=Jre();let r=!0;const i=(l,u)=>{const c=Mv(e,u);if(c){const{transition:d,transitionEnd:f,...h}=c;l={...l,...h,...f}}return l};function o(l){t=l(e)}function s(l,u){const c=e.getProps(),d=e.getVariantContext(!0)||{},f=[],h=new Set;let p={},m=1/0;for(let v=0;v<Yre;v++){const y=Kre[v],g=n[y],b=c[y]!==void 0?c[y]:d[y],_=eh(b),w=y===u?g.isActive:null;w===!1&&(m=v);let x=b===d[y]&&b!==c[y]&&_;if(x&&r&&e.manuallyAnimateOnMount&&(x=!1),g.protectedKeys={...p},!g.isActive&&w===null||!b&&!g.prevProp||Av(b)||typeof b=="boolean")continue;const C=Zre(g.prevProp,b);let k=C||y===u&&g.isActive&&!x&&_||v>m&&_;const P=Array.isArray(b)?b:[b];let A=P.reduce(i,{});w===!1&&(A={});const{prevResolvedValues:$={}}=g,N={...$,...A},T=O=>{k=!0,h.delete(O),g.needsAnimating[O]=!0};for(const O in N){const I=A[O],M=$[O];p.hasOwnProperty(O)||(I!==M?by(I)&&by(M)?!pM(I,M)||C?T(O):g.protectedKeys[O]=!0:I!==void 0?T(O):h.add(O):I!==void 0&&h.has(O)?T(O):g.protectedKeys[O]=!0)}g.prevProp=b,g.prevResolvedValues=A,g.isActive&&(p={...p,...A}),r&&e.blockInitialAnimation&&(k=!1),k&&!x&&f.push(...P.map(O=>({animation:O,options:{type:y,...l}})))}if(h.size){const v={};h.forEach(y=>{const g=e.getBaseTarget(y);g!==void 0&&(v[y]=g)}),f.push({animation:v})}let S=!!f.length;return r&&c.initial===!1&&!e.manuallyAnimateOnMount&&(S=!1),r=!1,S?t(f):Promise.resolve()}function a(l,u,c){var d;if(n[l].isActive===u)return Promise.resolve();(d=e.variantChildren)===null||d===void 0||d.forEach(h=>{var p;return(p=h.animationState)===null||p===void 0?void 0:p.setActive(l,u)}),n[l].isActive=u;const f=s(c,l);for(const h in n)n[h].protectedKeys={};return f}return{animateChanges:s,setActive:a,setAnimateFunction:o,getState:()=>n}}function Zre(e,t){return typeof t=="string"?t!==e:Array.isArray(t)?!pM(t,e):!1}function La(e=!1){return{isActive:e,protectedKeys:{},needsAnimating:{},prevResolvedValues:{}}}function Jre(){return{animate:La(!0),whileInView:La(),whileHover:La(),whileTap:La(),whileDrag:La(),whileFocus:La(),exit:La()}}class eie extends Ea{constructor(t){super(t),t.animationState||(t.animationState=Qre(t))}updateAnimationControlsSubscription(){const{animate:t}=this.node.getProps();this.unmount(),Av(t)&&(this.unmount=t.subscribe(this.node))}mount(){this.updateAnimationControlsSubscription()}update(){const{animate:t}=this.node.getProps(),{animate:n}=this.node.prevProps||{};t!==n&&this.updateAnimationControlsSubscription()}unmount(){}}let tie=0;class nie extends Ea{constructor(){super(...arguments),this.id=tie++}update(){if(!this.node.presenceContext)return;const{isPresent:t,onExitComplete:n,custom:r}=this.node.presenceContext,{isPresent:i}=this.node.prevPresenceContext||{};if(!this.node.animationState||t===i)return;const o=this.node.animationState.setActive("exit",!t,{custom:r??this.node.getProps().custom});n&&!t&&o.then(()=>n(this.id))}mount(){const{register:t}=this.node.presenceContext||{};t&&(this.unmount=t(this.id))}unmount(){}}const rie={animation:{Feature:eie},exit:{Feature:nie}},yP=(e,t)=>Math.abs(e-t);function iie(e,t){const n=yP(e.x,t.x),r=yP(e.y,t.y);return Math.sqrt(n**2+r**2)}class WM{constructor(t,n,{transformPagePoint:r}={}){if(this.startEvent=null,this.lastMoveEvent=null,this.lastMoveEventInfo=null,this.handlers={},this.updatePoint=()=>{if(!(this.lastMoveEvent&&this.lastMoveEventInfo))return;const u=jS(this.lastMoveEventInfo,this.history),c=this.startEvent!==null,d=iie(u.offset,{x:0,y:0})>=3;if(!c&&!d)return;const{point:f}=u,{timestamp:h}=Xn;this.history.push({...f,timestamp:h});const{onStart:p,onMove:m}=this.handlers;c||(p&&p(this.lastMoveEvent,u),this.startEvent=this.lastMoveEvent),m&&m(this.lastMoveEvent,u)},this.handlePointerMove=(u,c)=>{this.lastMoveEvent=u,this.lastMoveEventInfo=BS(c,this.transformPagePoint),kt.update(this.updatePoint,!0)},this.handlePointerUp=(u,c)=>{if(this.end(),!(this.lastMoveEvent&&this.lastMoveEventInfo))return;const{onEnd:d,onSessionEnd:f}=this.handlers,h=jS(u.type==="pointercancel"?this.lastMoveEventInfo:BS(c,this.transformPagePoint),this.history);this.startEvent&&d&&d(u,h),f&&f(u,h)},!uM(t))return;this.handlers=n,this.transformPagePoint=r;const i=Iv(t),o=BS(i,this.transformPagePoint),{point:s}=o,{timestamp:a}=Xn;this.history=[{...s,timestamp:a}];const{onSessionStart:l}=n;l&&l(t,jS(o,this.history)),this.removeListeners=ea(es(window,"pointermove",this.handlePointerMove),es(window,"pointerup",this.handlePointerUp),es(window,"pointercancel",this.handlePointerUp))}updateHandlers(t){this.handlers=t}end(){this.removeListeners&&this.removeListeners(),cs(this.updatePoint)}}function BS(e,t){return t?{point:t(e.point)}:e}function vP(e,t){return{x:e.x-t.x,y:e.y-t.y}}function jS({point:e},t){return{point:e,delta:vP(e,KM(t)),offset:vP(e,oie(t)),velocity:sie(t,.1)}}function oie(e){return e[0]}function KM(e){return e[e.length-1]}function sie(e,t){if(e.length<2)return{x:0,y:0};let n=e.length-1,r=null;const i=KM(e);for(;n>=0&&(r=e[n],!(i.timestamp-r.timestamp>ta(t)));)n--;if(!r)return{x:0,y:0};const o=ts(i.timestamp-r.timestamp);if(o===0)return{x:0,y:0};const s={x:(i.x-r.x)/o,y:(i.y-r.y)/o};return s.x===1/0&&(s.x=0),s.y===1/0&&(s.y=0),s}function Hr(e){return e.max-e.min}function fw(e,t=0,n=.01){return Math.abs(e-t)<=n}function bP(e,t,n,r=.5){e.origin=r,e.originPoint=Dt(t.min,t.max,e.origin),e.scale=Hr(n)/Hr(t),(fw(e.scale,1,1e-4)||isNaN(e.scale))&&(e.scale=1),e.translate=Dt(n.min,n.max,e.origin)-e.originPoint,(fw(e.translate)||isNaN(e.translate))&&(e.translate=0)}function df(e,t,n,r){bP(e.x,t.x,n.x,r?r.originX:void 0),bP(e.y,t.y,n.y,r?r.originY:void 0)}function SP(e,t,n){e.min=n.min+t.min,e.max=e.min+Hr(t)}function aie(e,t,n){SP(e.x,t.x,n.x),SP(e.y,t.y,n.y)}function _P(e,t,n){e.min=t.min-n.min,e.max=e.min+Hr(t)}function ff(e,t,n){_P(e.x,t.x,n.x),_P(e.y,t.y,n.y)}function lie(e,{min:t,max:n},r){return t!==void 0&&e<t?e=r?Dt(t,e,r.min):Math.max(e,t):n!==void 0&&e>n&&(e=r?Dt(n,e,r.max):Math.min(e,n)),e}function wP(e,t,n){return{min:t!==void 0?e.min+t:void 0,max:n!==void 0?e.max+n-(e.max-e.min):void 0}}function uie(e,{top:t,left:n,bottom:r,right:i}){return{x:wP(e.x,n,i),y:wP(e.y,t,r)}}function xP(e,t){let n=t.min-e.min,r=t.max-e.max;return t.max-t.min<e.max-e.min&&([n,r]=[r,n]),{min:n,max:r}}function cie(e,t){return{x:xP(e.x,t.x),y:xP(e.y,t.y)}}function die(e,t){let n=.5;const r=Hr(e),i=Hr(t);return i>r?n=nh(t.min,t.max-r,e.min):r>i&&(n=nh(e.min,e.max-i,t.min)),fa(0,1,n)}function fie(e,t){const n={};return t.min!==void 0&&(n.min=t.min-e.min),t.max!==void 0&&(n.max=t.max-e.min),n}const hw=.35;function hie(e=hw){return e===!1?e=0:e===!0&&(e=hw),{x:CP(e,"left","right"),y:CP(e,"top","bottom")}}function CP(e,t,n){return{min:TP(e,t),max:TP(e,n)}}function TP(e,t){return typeof e=="number"?e:e[t]||0}const EP=()=>({translate:0,scale:1,origin:0,originPoint:0}),Mu=()=>({x:EP(),y:EP()}),PP=()=>({min:0,max:0}),en=()=>({x:PP(),y:PP()});function Zi(e){return[e("x"),e("y")]}function YM({top:e,left:t,right:n,bottom:r}){return{x:{min:t,max:n},y:{min:e,max:r}}}function pie({x:e,y:t}){return{top:t.min,right:e.max,bottom:t.max,left:e.min}}function gie(e,t){if(!t)return e;const n=t({x:e.left,y:e.top}),r=t({x:e.right,y:e.bottom});return{top:n.y,left:n.x,bottom:r.y,right:r.x}}function VS(e){return e===void 0||e===1}function pw({scale:e,scaleX:t,scaleY:n}){return!VS(e)||!VS(t)||!VS(n)}function Ga(e){return pw(e)||XM(e)||e.z||e.rotate||e.rotateX||e.rotateY}function XM(e){return kP(e.x)||kP(e.y)}function kP(e){return e&&e!=="0%"}function Cy(e,t,n){const r=e-n,i=t*r;return n+i}function AP(e,t,n,r,i){return i!==void 0&&(e=Cy(e,i,r)),Cy(e,n,r)+t}function gw(e,t=0,n=1,r,i){e.min=AP(e.min,t,n,r,i),e.max=AP(e.max,t,n,r,i)}function QM(e,{x:t,y:n}){gw(e.x,t.translate,t.scale,t.originPoint),gw(e.y,n.translate,n.scale,n.originPoint)}function mie(e,t,n,r=!1){const i=n.length;if(!i)return;t.x=t.y=1;let o,s;for(let a=0;a<i;a++){o=n[a],s=o.projectionDelta;const l=o.instance;l&&l.style&&l.style.display==="contents"||(r&&o.options.layoutScroll&&o.scroll&&o!==o.root&&Nu(e,{x:-o.scroll.offset.x,y:-o.scroll.offset.y}),s&&(t.x*=s.x.scale,t.y*=s.y.scale,QM(e,s)),r&&Ga(o.latestValues)&&Nu(e,o.latestValues))}t.x=OP(t.x),t.y=OP(t.y)}function OP(e){return Number.isInteger(e)||e>1.0000000000001||e<.999999999999?e:1}function Ns(e,t){e.min=e.min+t,e.max=e.max+t}function RP(e,t,[n,r,i]){const o=t[i]!==void 0?t[i]:.5,s=Dt(e.min,e.max,o);gw(e,t[n],t[r],s,t.scale)}const yie=["x","scaleX","originX"],vie=["y","scaleY","originY"];function Nu(e,t){RP(e.x,t,yie),RP(e.y,t,vie)}function ZM(e,t){return YM(gie(e.getBoundingClientRect(),t))}function bie(e,t,n){const r=ZM(e,n),{scroll:i}=t;return i&&(Ns(r.x,i.offset.x),Ns(r.y,i.offset.y)),r}const Sie=new WeakMap;class _ie{constructor(t){this.openGlobalLock=null,this.isDragging=!1,this.currentDirection=null,this.originPoint={x:0,y:0},this.constraints=!1,this.hasMutatedConstraints=!1,this.elastic=en(),this.visualElement=t}start(t,{snapToCursor:n=!1}={}){const{presenceContext:r}=this.visualElement;if(r&&r.isPresent===!1)return;const i=l=>{this.stopAnimation(),n&&this.snapToCursor(Iv(l,"page").point)},o=(l,u)=>{const{drag:c,dragPropagation:d,onDragStart:f}=this.getProps();if(c&&!d&&(this.openGlobalLock&&this.openGlobalLock(),this.openGlobalLock=dM(c),!this.openGlobalLock))return;this.isDragging=!0,this.currentDirection=null,this.resolveConstraints(),this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!0,this.visualElement.projection.target=void 0),Zi(p=>{let m=this.getAxisMotionValue(p).get()||0;if(bo.test(m)){const{projection:S}=this.visualElement;if(S&&S.layout){const v=S.layout.layoutBox[p];v&&(m=Hr(v)*(parseFloat(m)/100))}}this.originPoint[p]=m}),f&&kt.update(()=>f(l,u),!1,!0);const{animationState:h}=this.visualElement;h&&h.setActive("whileDrag",!0)},s=(l,u)=>{const{dragPropagation:c,dragDirectionLock:d,onDirectionLock:f,onDrag:h}=this.getProps();if(!c&&!this.openGlobalLock)return;const{offset:p}=u;if(d&&this.currentDirection===null){this.currentDirection=wie(p),this.currentDirection!==null&&f&&f(this.currentDirection);return}this.updateAxis("x",u.point,p),this.updateAxis("y",u.point,p),this.visualElement.render(),h&&h(l,u)},a=(l,u)=>this.stop(l,u);this.panSession=new WM(t,{onSessionStart:i,onStart:o,onMove:s,onSessionEnd:a},{transformPagePoint:this.visualElement.getTransformPagePoint()})}stop(t,n){const r=this.isDragging;if(this.cancel(),!r)return;const{velocity:i}=n;this.startAnimation(i);const{onDragEnd:o}=this.getProps();o&&kt.update(()=>o(t,n))}cancel(){this.isDragging=!1;const{projection:t,animationState:n}=this.visualElement;t&&(t.isAnimationBlocked=!1),this.panSession&&this.panSession.end(),this.panSession=void 0;const{dragPropagation:r}=this.getProps();!r&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),n&&n.setActive("whileDrag",!1)}updateAxis(t,n,r){const{drag:i}=this.getProps();if(!r||!ag(t,i,this.currentDirection))return;const o=this.getAxisMotionValue(t);let s=this.originPoint[t]+r[t];this.constraints&&this.constraints[t]&&(s=lie(s,this.constraints[t],this.elastic[t])),o.set(s)}resolveConstraints(){const{dragConstraints:t,dragElastic:n}=this.getProps(),{layout:r}=this.visualElement.projection||{},i=this.constraints;t&&Ru(t)?this.constraints||(this.constraints=this.resolveRefConstraints()):t&&r?this.constraints=uie(r.layoutBox,t):this.constraints=!1,this.elastic=hie(n),i!==this.constraints&&r&&this.constraints&&!this.hasMutatedConstraints&&Zi(o=>{this.getAxisMotionValue(o)&&(this.constraints[o]=fie(r.layoutBox[o],this.constraints[o]))})}resolveRefConstraints(){const{dragConstraints:t,onMeasureDragConstraints:n}=this.getProps();if(!t||!Ru(t))return!1;const r=t.current,{projection:i}=this.visualElement;if(!i||!i.layout)return!1;const o=bie(r,i.root,this.visualElement.getTransformPagePoint());let s=cie(i.layout.layoutBox,o);if(n){const a=n(pie(s));this.hasMutatedConstraints=!!a,a&&(s=YM(a))}return s}startAnimation(t){const{drag:n,dragMomentum:r,dragElastic:i,dragTransition:o,dragSnapToOrigin:s,onDragTransitionEnd:a}=this.getProps(),l=this.constraints||{},u=Zi(c=>{if(!ag(c,n,this.currentDirection))return;let d=l&&l[c]||{};s&&(d={min:0,max:0});const f=i?200:1e6,h=i?40:1e7,p={type:"inertia",velocity:r?t[c]:0,bounceStiffness:f,bounceDamping:h,timeConstant:750,restDelta:1,restSpeed:10,...o,...d};return this.startAxisValueAnimation(c,p)});return Promise.all(u).then(a)}startAxisValueAnimation(t,n){const r=this.getAxisMotionValue(t);return r.start(R5(t,r,0,n))}stopAnimation(){Zi(t=>this.getAxisMotionValue(t).stop())}getAxisMotionValue(t){const n="_drag"+t.toUpperCase(),r=this.visualElement.getProps(),i=r[n];return i||this.visualElement.getValue(t,(r.initial?r.initial[t]:void 0)||0)}snapToCursor(t){Zi(n=>{const{drag:r}=this.getProps();if(!ag(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:s,max:a}=i.layout.layoutBox[n];o.set(t[n]-Dt(s,a,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!Ru(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};Zi(s=>{const a=this.getAxisMotionValue(s);if(a){const l=a.get();i[s]=die({min:l,max:l},this.constraints[s])}});const{transformTemplate:o}=this.visualElement.getProps();this.visualElement.current.style.transform=o?o({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),Zi(s=>{if(!ag(s,t,null))return;const a=this.getAxisMotionValue(s),{min:l,max:u}=this.constraints[s];a.set(Dt(l,u,i[s]))})}addListeners(){if(!this.visualElement.current)return;Sie.set(this.visualElement,this);const t=this.visualElement.current,n=es(t,"pointerdown",l=>{const{drag:u,dragListener:c=!0}=this.getProps();u&&c&&this.start(l)}),r=()=>{const{dragConstraints:l}=this.getProps();Ru(l)&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,o=i.addEventListener("measure",r);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),r();const s=Qo(window,"resize",()=>this.scalePositionWithinConstraints()),a=i.addEventListener("didUpdate",({delta:l,hasLayoutChanged:u})=>{this.isDragging&&u&&(Zi(c=>{const d=this.getAxisMotionValue(c);d&&(this.originPoint[c]+=l[c].translate,d.set(d.get()+l[c].translate))}),this.visualElement.render())});return()=>{s(),n(),o(),a&&a()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:o=!1,dragElastic:s=hw,dragMomentum:a=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:o,dragElastic:s,dragMomentum:a}}}function ag(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function wie(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class xie extends Ea{constructor(t){super(t),this.removeGroupControls=on,this.removeListeners=on,this.controls=new _ie(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||on}unmount(){this.removeGroupControls(),this.removeListeners()}}const IP=e=>(t,n)=>{e&&kt.update(()=>e(t,n))};class Cie extends Ea{constructor(){super(...arguments),this.removePointerDownListener=on}onPointerDown(t){this.session=new WM(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:IP(t),onStart:IP(n),onMove:r,onEnd:(o,s)=>{delete this.session,i&&kt.update(()=>i(o,s))}}}mount(){this.removePointerDownListener=es(this.node.current,"pointerdown",t=>this.onPointerDown(t))}update(){this.session&&this.session.updateHandlers(this.createPanHandlers())}unmount(){this.removePointerDownListener(),this.session&&this.session.end()}}function Tie(){const e=E.useContext(rp);if(e===null)return[!0,null];const{isPresent:t,onExitComplete:n,register:r}=e,i=E.useId();return E.useEffect(()=>r(i),[]),!t&&n?[!1,()=>n&&n(i)]:[!0]}function Eie(){return Pie(E.useContext(rp))}function Pie(e){return e===null?!0:e.isPresent}const om={hasAnimatedSinceResize:!0,hasEverUpdated:!1};function MP(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const bd={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(we.test(e))e=parseFloat(e);else return e;const n=MP(e,t.target.x),r=MP(e,t.target.y);return`${n}% ${r}%`}},kie={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=ha.parse(e);if(i.length>5)return r;const o=ha.createTransformer(e),s=typeof i[0]!="number"?1:0,a=n.x.scale*t.x,l=n.y.scale*t.y;i[0+s]/=a,i[1+s]/=l;const u=Dt(a,l,.5);return typeof i[2+s]=="number"&&(i[2+s]/=u),typeof i[3+s]=="number"&&(i[3+s]/=u),o(i)}};class Aie extends ze.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;Dte(Oie),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),om.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,s=r.projection;return s&&(s.isPresent=o,i||t.layoutDependency!==n||n===void 0?s.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?s.promote():s.relegate()||kt.postRender(()=>{const a=s.getStack();(!a||!a.members.length)&&this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),queueMicrotask(()=>{!t.currentAnimation&&t.isLead()&&this.safeToRemove()}))}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n&&n.group&&n.group.remove(i),r&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t&&t()}render(){return null}}function JM(e){const[t,n]=Tie(),r=E.useContext(y5);return ze.createElement(Aie,{...e,layoutGroup:r,switchLayoutGroup:E.useContext(K7),isPresent:t,safeToRemove:n})}const Oie={borderRadius:{...bd,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:bd,borderTopRightRadius:bd,borderBottomLeftRadius:bd,borderBottomRightRadius:bd,boxShadow:kie},eN=["TopLeft","TopRight","BottomLeft","BottomRight"],Rie=eN.length,NP=e=>typeof e=="string"?parseFloat(e):e,DP=e=>typeof e=="number"||we.test(e);function Iie(e,t,n,r,i,o){i?(e.opacity=Dt(0,n.opacity!==void 0?n.opacity:1,Mie(r)),e.opacityExit=Dt(t.opacity!==void 0?t.opacity:1,0,Nie(r))):o&&(e.opacity=Dt(t.opacity!==void 0?t.opacity:1,n.opacity!==void 0?n.opacity:1,r));for(let s=0;s<Rie;s++){const a=`border${eN[s]}Radius`;let l=LP(t,a),u=LP(n,a);if(l===void 0&&u===void 0)continue;l||(l=0),u||(u=0),l===0||u===0||DP(l)===DP(u)?(e[a]=Math.max(Dt(NP(l),NP(u),r),0),(bo.test(u)||bo.test(l))&&(e[a]+="%")):e[a]=u}(t.rotate||n.rotate)&&(e.rotate=Dt(t.rotate||0,n.rotate||0,r))}function LP(e,t){return e[t]!==void 0?e[t]:e.borderRadius}const Mie=tN(0,.5,P5),Nie=tN(.5,.95,on);function tN(e,t,n){return r=>r<e?0:r>t?1:n(nh(e,t,r))}function $P(e,t){e.min=t.min,e.max=t.max}function ei(e,t){$P(e.x,t.x),$P(e.y,t.y)}function FP(e,t,n,r,i){return e-=t,e=Cy(e,1/n,r),i!==void 0&&(e=Cy(e,1/i,r)),e}function Die(e,t=0,n=1,r=.5,i,o=e,s=e){if(bo.test(t)&&(t=parseFloat(t),t=Dt(s.min,s.max,t/100)-s.min),typeof t!="number")return;let a=Dt(o.min,o.max,r);e===o&&(a-=t),e.min=FP(e.min,t,n,a,i),e.max=FP(e.max,t,n,a,i)}function BP(e,t,[n,r,i],o,s){Die(e,t[n],t[r],t[i],t.scale,o,s)}const Lie=["x","scaleX","originX"],$ie=["y","scaleY","originY"];function jP(e,t,n,r){BP(e.x,t,Lie,n?n.x:void 0,r?r.x:void 0),BP(e.y,t,$ie,n?n.y:void 0,r?r.y:void 0)}function VP(e){return e.translate===0&&e.scale===1}function nN(e){return VP(e.x)&&VP(e.y)}function mw(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function zP(e){return Hr(e.x)/Hr(e.y)}class Fie{constructor(){this.members=[]}add(t){I5(this.members,t),t.scheduleRender()}remove(t){if(M5(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function UP(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:c}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),c&&(r+=`rotateY(${c}deg) `)}const s=e.x.scale*t.x,a=e.y.scale*t.y;return(s!==1||a!==1)&&(r+=`scale(${s}, ${a})`),r||"none"}const Bie=(e,t)=>e.depth-t.depth;class jie{constructor(){this.children=[],this.isDirty=!1}add(t){I5(this.children,t),this.isDirty=!0}remove(t){M5(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(Bie),this.isDirty=!1,this.children.forEach(t)}}function Vie(e,t){const n=performance.now(),r=({timestamp:i})=>{const o=i-n;o>=t&&(cs(r),e(o-t))};return kt.read(r,!0),()=>cs(r)}function zie(e){window.MotionDebug&&window.MotionDebug.record(e)}function Uie(e){return e instanceof SVGElement&&e.tagName!=="svg"}function Gie(e,t,n){const r=Nr(e)?e:_c(e);return r.start(R5("",r,t,n)),r.animation}const GP=["","X","Y","Z"],HP=1e3;let Hie=0;const Ha={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function rN({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(s={},a=t==null?void 0:t()){this.id=Hie++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{Ha.totalNodes=Ha.resolvedTargetDeltas=Ha.recalculatedProjection=0,this.nodes.forEach(Kie),this.nodes.forEach(Jie),this.nodes.forEach(eoe),this.nodes.forEach(Yie),zie(Ha)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=s,this.root=a?a.root||a:this,this.path=a?[...a.path,a]:[],this.parent=a,this.depth=a?a.depth+1:0;for(let l=0;l<this.path.length;l++)this.path[l].shouldResetTransform=!0;this.root===this&&(this.nodes=new jie)}addEventListener(s,a){return this.eventHandlers.has(s)||this.eventHandlers.set(s,new N5),this.eventHandlers.get(s).add(a)}notifyListeners(s,...a){const l=this.eventHandlers.get(s);l&&l.notify(...a)}hasListeners(s){return this.eventHandlers.has(s)}mount(s,a=this.root.hasTreeAnimated){if(this.instance)return;this.isSVG=Uie(s),this.instance=s;const{layoutId:l,layout:u,visualElement:c}=this.options;if(c&&!c.current&&c.mount(s),this.root.nodes.add(this),this.parent&&this.parent.children.add(this),a&&(u||l)&&(this.isLayoutDirty=!0),e){let d;const f=()=>this.root.updateBlockedByResize=!1;e(s,()=>{this.root.updateBlockedByResize=!0,d&&d(),d=Vie(f,250),om.hasAnimatedSinceResize&&(om.hasAnimatedSinceResize=!1,this.nodes.forEach(WP))})}l&&this.root.registerSharedNode(l,this),this.options.animate!==!1&&c&&(l||u)&&this.addEventListener("didUpdate",({delta:d,hasLayoutChanged:f,hasRelativeTargetChanged:h,layout:p})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const m=this.options.transition||c.getDefaultTransition()||ooe,{onLayoutAnimationStart:S,onLayoutAnimationComplete:v}=c.getProps(),y=!this.targetLayout||!mw(this.targetLayout,p)||h,g=!f&&h;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||g||f&&(y||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(d,g);const b={...UM(m,"layout"),onPlay:S,onComplete:v};(c.shouldReduceMotion||this.options.layoutRoot)&&(b.delay=0,b.type=!1),this.startAnimation(b)}else f||WP(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=p})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const s=this.getStack();s&&s.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,cs(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(toe),this.animationId++)}getTransformTemplate(){const{visualElement:s}=this.options;return s&&s.getProps().transformTemplate}willUpdate(s=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let c=0;c<this.path.length;c++){const d=this.path[c];d.shouldResetTransform=!0,d.updateScroll("snapshot"),d.options.layoutRoot&&d.willUpdate(!1)}const{layoutId:a,layout:l}=this.options;if(a===void 0&&!l)return;const u=this.getTransformTemplate();this.prevTransformTemplateValue=u?u(this.latestValues,""):void 0,this.updateSnapshot(),s&&this.notifyListeners("willUpdate")}update(){if(this.updateScheduled=!1,this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(qP);return}this.isUpdating||this.nodes.forEach(Qie),this.isUpdating=!1,this.nodes.forEach(Zie),this.nodes.forEach(qie),this.nodes.forEach(Wie),this.clearAllSnapshots();const a=performance.now();Xn.delta=fa(0,1e3/60,a-Xn.timestamp),Xn.timestamp=a,Xn.isProcessing=!0,OS.update.process(Xn),OS.preRender.process(Xn),OS.render.process(Xn),Xn.isProcessing=!1}didUpdate(){this.updateScheduled||(this.updateScheduled=!0,queueMicrotask(()=>this.update()))}clearAllSnapshots(){this.nodes.forEach(Xie),this.sharedNodes.forEach(noe)}scheduleUpdateProjection(){kt.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){kt.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l<this.path.length;l++)this.path[l].updateScroll();const s=this.layout;this.layout=this.measure(!1),this.layoutCorrected=en(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox);const{visualElement:a}=this.options;a&&a.notify("LayoutMeasure",this.layout.layoutBox,s?s.layoutBox:void 0)}updateScroll(s="measure"){let a=!!(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===s&&(a=!1),a&&(this.scroll={animationId:this.root.animationId,phase:s,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){if(!i)return;const s=this.isLayoutDirty||this.shouldResetTransform,a=this.projectionDelta&&!nN(this.projectionDelta),l=this.getTransformTemplate(),u=l?l(this.latestValues,""):void 0,c=u!==this.prevTransformTemplateValue;s&&(a||Ga(this.latestValues)||c)&&(i(this.instance,u),this.shouldResetTransform=!1,this.scheduleRender())}measure(s=!0){const a=this.measurePageBox();let l=this.removeElementScroll(a);return s&&(l=this.removeTransform(l)),soe(l),{animationId:this.root.animationId,measuredBox:a,layoutBox:l,latestValues:{},source:this.id}}measurePageBox(){const{visualElement:s}=this.options;if(!s)return en();const a=s.measureViewportBox(),{scroll:l}=this.root;return l&&(Ns(a.x,l.offset.x),Ns(a.y,l.offset.y)),a}removeElementScroll(s){const a=en();ei(a,s);for(let l=0;l<this.path.length;l++){const u=this.path[l],{scroll:c,options:d}=u;if(u!==this.root&&c&&d.layoutScroll){if(c.isRoot){ei(a,s);const{scroll:f}=this.root;f&&(Ns(a.x,-f.offset.x),Ns(a.y,-f.offset.y))}Ns(a.x,c.offset.x),Ns(a.y,c.offset.y)}}return a}applyTransform(s,a=!1){const l=en();ei(l,s);for(let u=0;u<this.path.length;u++){const c=this.path[u];!a&&c.options.layoutScroll&&c.scroll&&c!==c.root&&Nu(l,{x:-c.scroll.offset.x,y:-c.scroll.offset.y}),Ga(c.latestValues)&&Nu(l,c.latestValues)}return Ga(this.latestValues)&&Nu(l,this.latestValues),l}removeTransform(s){const a=en();ei(a,s);for(let l=0;l<this.path.length;l++){const u=this.path[l];if(!u.instance||!Ga(u.latestValues))continue;pw(u.latestValues)&&u.updateSnapshot();const c=en(),d=u.measurePageBox();ei(c,d),jP(a,u.latestValues,u.snapshot?u.snapshot.layoutBox:void 0,c)}return Ga(this.latestValues)&&jP(a,this.latestValues),a}setTargetDelta(s){this.targetDelta=s,this.root.scheduleUpdateProjection(),this.isProjectionDirty=!0}setOptions(s){this.options={...this.options,...s,crossfade:s.crossfade!==void 0?s.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}forceRelativeParentToResolveTarget(){this.relativeParent&&this.relativeParent.resolvedRelativeTargetAt!==Xn.timestamp&&this.relativeParent.resolveTargetDelta(!0)}resolveTargetDelta(s=!1){var a;const l=this.getLead();this.isProjectionDirty||(this.isProjectionDirty=l.isProjectionDirty),this.isTransformDirty||(this.isTransformDirty=l.isTransformDirty),this.isSharedProjectionDirty||(this.isSharedProjectionDirty=l.isSharedProjectionDirty);const u=!!this.resumingFrom||this!==l;if(!(s||u&&this.isSharedProjectionDirty||this.isProjectionDirty||!((a=this.parent)===null||a===void 0)&&a.isProjectionDirty||this.attemptToResolveRelativeTarget))return;const{layout:d,layoutId:f}=this.options;if(!(!this.layout||!(d||f))){if(this.resolvedRelativeTargetAt=Xn.timestamp,!this.targetDelta&&!this.relativeTarget){const h=this.getClosestProjectingParent();h&&h.layout&&this.animationProgress!==1?(this.relativeParent=h,this.forceRelativeParentToResolveTarget(),this.relativeTarget=en(),this.relativeTargetOrigin=en(),ff(this.relativeTargetOrigin,this.layout.layoutBox,h.layout.layoutBox),ei(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}if(!(!this.relativeTarget&&!this.targetDelta)){if(this.target||(this.target=en(),this.targetWithTransforms=en()),this.relativeTarget&&this.relativeTargetOrigin&&this.relativeParent&&this.relativeParent.target?(this.forceRelativeParentToResolveTarget(),aie(this.target,this.relativeTarget,this.relativeParent.target)):this.targetDelta?(this.resumingFrom?this.target=this.applyTransform(this.layout.layoutBox):ei(this.target,this.layout.layoutBox),QM(this.target,this.targetDelta)):ei(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget){this.attemptToResolveRelativeTarget=!1;const h=this.getClosestProjectingParent();h&&!!h.resumingFrom==!!this.resumingFrom&&!h.options.layoutScroll&&h.target&&this.animationProgress!==1?(this.relativeParent=h,this.forceRelativeParentToResolveTarget(),this.relativeTarget=en(),this.relativeTargetOrigin=en(),ff(this.relativeTargetOrigin,this.target,h.target),ei(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}Ha.resolvedTargetDeltas++}}}getClosestProjectingParent(){if(!(!this.parent||pw(this.parent.latestValues)||XM(this.parent.latestValues)))return this.parent.isProjecting()?this.parent:this.parent.getClosestProjectingParent()}isProjecting(){return!!((this.relativeTarget||this.targetDelta||this.options.layoutRoot)&&this.layout)}calcProjection(){var s;const a=this.getLead(),l=!!this.resumingFrom||this!==a;let u=!0;if((this.isProjectionDirty||!((s=this.parent)===null||s===void 0)&&s.isProjectionDirty)&&(u=!1),l&&(this.isSharedProjectionDirty||this.isTransformDirty)&&(u=!1),this.resolvedRelativeTargetAt===Xn.timestamp&&(u=!1),u)return;const{layout:c,layoutId:d}=this.options;if(this.isTreeAnimating=!!(this.parent&&this.parent.isTreeAnimating||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(c||d))return;ei(this.layoutCorrected,this.layout.layoutBox);const f=this.treeScale.x,h=this.treeScale.y;mie(this.layoutCorrected,this.treeScale,this.path,l),a.layout&&!a.target&&(this.treeScale.x!==1||this.treeScale.y!==1)&&(a.target=a.layout.layoutBox);const{target:p}=a;if(!p){this.projectionTransform&&(this.projectionDelta=Mu(),this.projectionTransform="none",this.scheduleRender());return}this.projectionDelta||(this.projectionDelta=Mu(),this.projectionDeltaWithTransform=Mu());const m=this.projectionTransform;df(this.projectionDelta,this.layoutCorrected,p,this.latestValues),this.projectionTransform=UP(this.projectionDelta,this.treeScale),(this.projectionTransform!==m||this.treeScale.x!==f||this.treeScale.y!==h)&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",p)),Ha.recalculatedProjection++}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(s=!0){if(this.options.scheduleRender&&this.options.scheduleRender(),s){const a=this.getStack();a&&a.scheduleRender()}this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(s,a=!1){const l=this.snapshot,u=l?l.latestValues:{},c={...this.latestValues},d=Mu();(!this.relativeParent||!this.relativeParent.options.layoutRoot)&&(this.relativeTarget=this.relativeTargetOrigin=void 0),this.attemptToResolveRelativeTarget=!a;const f=en(),h=l?l.source:void 0,p=this.layout?this.layout.source:void 0,m=h!==p,S=this.getStack(),v=!S||S.members.length<=1,y=!!(m&&!v&&this.options.crossfade===!0&&!this.path.some(ioe));this.animationProgress=0;let g;this.mixTargetDelta=b=>{const _=b/1e3;KP(d.x,s.x,_),KP(d.y,s.y,_),this.setTargetDelta(d),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(ff(f,this.layout.layoutBox,this.relativeParent.layout.layoutBox),roe(this.relativeTarget,this.relativeTargetOrigin,f,_),g&&mw(this.relativeTarget,g)&&(this.isProjectionDirty=!1),g||(g=en()),ei(g,this.relativeTarget)),m&&(this.animationValues=c,Iie(c,u,this.latestValues,_,y,v)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=_},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(s){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(cs(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=kt.update(()=>{om.hasAnimatedSinceResize=!0,this.currentAnimation=Gie(0,HP,{...s,onUpdate:a=>{this.mixTargetDelta(a),s.onUpdate&&s.onUpdate(a)},onComplete:()=>{s.onComplete&&s.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const s=this.getStack();s&&s.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(HP),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const s=this.getLead();let{targetWithTransforms:a,target:l,layout:u,latestValues:c}=s;if(!(!a||!l||!u)){if(this!==s&&this.layout&&u&&iN(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||en();const d=Hr(this.layout.layoutBox.x);l.x.min=s.target.x.min,l.x.max=l.x.min+d;const f=Hr(this.layout.layoutBox.y);l.y.min=s.target.y.min,l.y.max=l.y.min+f}ei(a,l),Nu(a,c),df(this.projectionDeltaWithTransform,this.layoutCorrected,a,c)}}registerSharedNode(s,a){this.sharedNodes.has(s)||this.sharedNodes.set(s,new Fie),this.sharedNodes.get(s).add(a);const u=a.options.initialPromotionConfig;a.promote({transition:u?u.transition:void 0,preserveFollowOpacity:u&&u.shouldPreserveFollowOpacity?u.shouldPreserveFollowOpacity(a):void 0})}isLead(){const s=this.getStack();return s?s.lead===this:!0}getLead(){var s;const{layoutId:a}=this.options;return a?((s=this.getStack())===null||s===void 0?void 0:s.lead)||this:this}getPrevLead(){var s;const{layoutId:a}=this.options;return a?(s=this.getStack())===null||s===void 0?void 0:s.prevLead:void 0}getStack(){const{layoutId:s}=this.options;if(s)return this.root.sharedNodes.get(s)}promote({needsReset:s,transition:a,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),s&&(this.projectionDelta=void 0,this.needsReset=!0),a&&this.setOptions({transition:a})}relegate(){const s=this.getStack();return s?s.relegate(this):!1}resetRotation(){const{visualElement:s}=this.options;if(!s)return;let a=!1;const{latestValues:l}=s;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(a=!0),!a)return;const u={};for(let c=0;c<GP.length;c++){const d="rotate"+GP[c];l[d]&&(u[d]=l[d],s.setStaticValue(d,0))}s.render();for(const c in u)s.setStaticValue(c,u[c]);s.scheduleRender()}getProjectionStyles(s={}){var a,l;const u={};if(!this.instance||this.isSVG)return u;if(this.isVisible)u.visibility="";else return{visibility:"hidden"};const c=this.getTransformTemplate();if(this.needsReset)return this.needsReset=!1,u.opacity="",u.pointerEvents=im(s.pointerEvents)||"",u.transform=c?c(this.latestValues,""):"none",u;const d=this.getLead();if(!this.projectionDelta||!this.layout||!d.target){const m={};return this.options.layoutId&&(m.opacity=this.latestValues.opacity!==void 0?this.latestValues.opacity:1,m.pointerEvents=im(s.pointerEvents)||""),this.hasProjected&&!Ga(this.latestValues)&&(m.transform=c?c({},""):"none",this.hasProjected=!1),m}const f=d.animationValues||d.latestValues;this.applyTransformsToTarget(),u.transform=UP(this.projectionDeltaWithTransform,this.treeScale,f),c&&(u.transform=c(f,u.transform));const{x:h,y:p}=this.projectionDelta;u.transformOrigin=`${h.origin*100}% ${p.origin*100}% 0`,d.animationValues?u.opacity=d===this?(l=(a=f.opacity)!==null&&a!==void 0?a:this.latestValues.opacity)!==null&&l!==void 0?l:1:this.preserveOpacity?this.latestValues.opacity:f.opacityExit:u.opacity=d===this?f.opacity!==void 0?f.opacity:"":f.opacityExit!==void 0?f.opacityExit:0;for(const m in yy){if(f[m]===void 0)continue;const{correct:S,applyTo:v}=yy[m],y=u.transform==="none"?f[m]:S(f[m],d);if(v){const g=v.length;for(let b=0;b<g;b++)u[v[b]]=y}else u[m]=y}return this.options.layoutId&&(u.pointerEvents=d===this?im(s.pointerEvents)||"":"none"),u}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(s=>{var a;return(a=s.currentAnimation)===null||a===void 0?void 0:a.stop()}),this.root.nodes.forEach(qP),this.root.sharedNodes.clear()}}}function qie(e){e.updateLayout()}function Wie(e){var t;const n=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&n&&e.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:i}=e.layout,{animationType:o}=e.options,s=n.source!==e.layout.source;o==="size"?Zi(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Hr(f);f.min=r[d].min,f.max=f.min+h}):iN(o,n.layoutBox,r)&&Zi(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Hr(r[d]);f.max=f.min+h,e.relativeTarget&&!e.currentAnimation&&(e.isProjectionDirty=!0,e.relativeTarget[d].max=e.relativeTarget[d].min+h)});const a=Mu();df(a,r,n.layoutBox);const l=Mu();s?df(l,e.applyTransform(i,!0),n.measuredBox):df(l,r,n.layoutBox);const u=!nN(a);let c=!1;if(!e.resumeFrom){const d=e.getClosestProjectingParent();if(d&&!d.resumeFrom){const{snapshot:f,layout:h}=d;if(f&&h){const p=en();ff(p,n.layoutBox,f.layoutBox);const m=en();ff(m,r,h.layoutBox),mw(p,m)||(c=!0),d.options.layoutRoot&&(e.relativeTarget=m,e.relativeTargetOrigin=p,e.relativeParent=d)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:l,layoutDelta:a,hasLayoutChanged:u,hasRelativeTargetChanged:c})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function Kie(e){Ha.totalNodes++,e.parent&&(e.isProjecting()||(e.isProjectionDirty=e.parent.isProjectionDirty),e.isSharedProjectionDirty||(e.isSharedProjectionDirty=!!(e.isProjectionDirty||e.parent.isProjectionDirty||e.parent.isSharedProjectionDirty)),e.isTransformDirty||(e.isTransformDirty=e.parent.isTransformDirty))}function Yie(e){e.isProjectionDirty=e.isSharedProjectionDirty=e.isTransformDirty=!1}function Xie(e){e.clearSnapshot()}function qP(e){e.clearMeasurements()}function Qie(e){e.isLayoutDirty=!1}function Zie(e){const{visualElement:t}=e.options;t&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function WP(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0,e.isProjectionDirty=!0}function Jie(e){e.resolveTargetDelta()}function eoe(e){e.calcProjection()}function toe(e){e.resetRotation()}function noe(e){e.removeLeadSnapshot()}function KP(e,t,n){e.translate=Dt(t.translate,0,n),e.scale=Dt(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function YP(e,t,n,r){e.min=Dt(t.min,n.min,r),e.max=Dt(t.max,n.max,r)}function roe(e,t,n,r){YP(e.x,t.x,n.x,r),YP(e.y,t.y,n.y,r)}function ioe(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const ooe={duration:.45,ease:[.4,0,.1,1]};function XP(e){e.min=Math.round(e.min*2)/2,e.max=Math.round(e.max*2)/2}function soe(e){XP(e.x),XP(e.y)}function iN(e,t,n){return e==="position"||e==="preserve-aspect"&&!fw(zP(t),zP(n),.2)}const aoe=rN({attachResizeListener:(e,t)=>Qo(e,"resize",t),measureScroll:()=>({x:document.documentElement.scrollLeft||document.body.scrollLeft,y:document.documentElement.scrollTop||document.body.scrollTop}),checkIsScrollRoot:()=>!0}),zS={current:void 0},oN=rN({measureScroll:e=>({x:e.scrollLeft,y:e.scrollTop}),defaultParent:()=>{if(!zS.current){const e=new aoe({});e.mount(window),e.setOptions({layoutScroll:!0}),zS.current=e}return zS.current},resetTransform:(e,t)=>{e.style.transform=t!==void 0?t:"none"},checkIsScrollRoot:e=>window.getComputedStyle(e).position==="fixed"}),loe={pan:{Feature:Cie},drag:{Feature:xie,ProjectionNode:oN,MeasureLayout:JM}},uoe=/var\((--[a-zA-Z0-9-_]+),? ?([a-zA-Z0-9 ()%#.,-]+)?\)/;function coe(e){const t=uoe.exec(e);if(!t)return[,];const[,n,r]=t;return[n,r]}function yw(e,t,n=1){const[r,i]=coe(e);if(!r)return;const o=window.getComputedStyle(t).getPropertyValue(r);return o?o.trim():ow(i)?yw(i,t,n+1):i}function doe(e,{...t},n){const r=e.current;if(!(r instanceof Element))return{target:t,transitionEnd:n};n&&(n={...n}),e.values.forEach(i=>{const o=i.get();if(!ow(o))return;const s=yw(o,r);s&&i.set(s)});for(const i in t){const o=t[i];if(!ow(o))continue;const s=yw(o,r);s&&(t[i]=s,n||(n={}),n[i]===void 0&&(n[i]=o))}return{target:t,transitionEnd:n}}const foe=new Set(["width","height","top","left","right","bottom","x","y","translateX","translateY"]),sN=e=>foe.has(e),hoe=e=>Object.keys(e).some(sN),QP=e=>e===Il||e===we,ZP=(e,t)=>parseFloat(e.split(", ")[t]),JP=(e,t)=>(n,{transform:r})=>{if(r==="none"||!r)return 0;const i=r.match(/^matrix3d\((.+)\)$/);if(i)return ZP(i[1],t);{const o=r.match(/^matrix\((.+)\)$/);return o?ZP(o[1],e):0}},poe=new Set(["x","y","z"]),goe=ip.filter(e=>!poe.has(e));function moe(e){const t=[];return goe.forEach(n=>{const r=e.getValue(n);r!==void 0&&(t.push([n,r.get()]),r.set(n.startsWith("scale")?1:0))}),t.length&&e.render(),t}const wc={width:({x:e},{paddingLeft:t="0",paddingRight:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),height:({y:e},{paddingTop:t="0",paddingBottom:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),top:(e,{top:t})=>parseFloat(t),left:(e,{left:t})=>parseFloat(t),bottom:({y:e},{top:t})=>parseFloat(t)+(e.max-e.min),right:({x:e},{left:t})=>parseFloat(t)+(e.max-e.min),x:JP(4,13),y:JP(5,14)};wc.translateX=wc.x;wc.translateY=wc.y;const yoe=(e,t,n)=>{const r=t.measureViewportBox(),i=t.current,o=getComputedStyle(i),{display:s}=o,a={};s==="none"&&t.setStaticValue("display",e.display||"block"),n.forEach(u=>{a[u]=wc[u](r,o)}),t.render();const l=t.measureViewportBox();return n.forEach(u=>{const c=t.getValue(u);c&&c.jump(a[u]),e[u]=wc[u](l,o)}),e},voe=(e,t,n={},r={})=>{t={...t},r={...r};const i=Object.keys(t).filter(sN);let o=[],s=!1;const a=[];if(i.forEach(l=>{const u=e.getValue(l);if(!e.hasValue(l))return;let c=n[l],d=vd(c);const f=t[l];let h;if(by(f)){const p=f.length,m=f[0]===null?1:0;c=f[m],d=vd(c);for(let S=m;S<p&&f[S]!==null;S++)h?E5(vd(f[S])===h):h=vd(f[S])}else h=vd(f);if(d!==h)if(QP(d)&&QP(h)){const p=u.get();typeof p=="string"&&u.set(parseFloat(p)),typeof f=="string"?t[l]=parseFloat(f):Array.isArray(f)&&h===we&&(t[l]=f.map(parseFloat))}else d!=null&&d.transform&&(h!=null&&h.transform)&&(c===0||f===0)?c===0?u.set(h.transform(c)):t[l]=d.transform(f):(s||(o=moe(e),s=!0),a.push(l),r[l]=r[l]!==void 0?r[l]:t[l],u.jump(f))}),a.length){const l=a.indexOf("height")>=0?window.pageYOffset:null,u=yoe(t,e,a);return o.length&&o.forEach(([c,d])=>{e.getValue(c).set(d)}),e.render(),kv&&l!==null&&window.scrollTo({top:l}),{target:u,transitionEnd:r}}else return{target:t,transitionEnd:r}};function boe(e,t,n,r){return hoe(t)?voe(e,t,n,r):{target:t,transitionEnd:r}}const Soe=(e,t,n,r)=>{const i=doe(e,t,r);return t=i.target,r=i.transitionEnd,boe(e,t,n,r)},vw={current:null},aN={current:!1};function _oe(){if(aN.current=!0,!!kv)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>vw.current=e.matches;e.addListener(t),t()}else vw.current=!1}function woe(e,t,n){const{willChange:r}=t;for(const i in t){const o=t[i],s=n[i];if(Nr(o))e.addValue(i,o),xy(r)&&r.add(i);else if(Nr(s))e.addValue(i,_c(o,{owner:e})),xy(r)&&r.remove(i);else if(s!==o)if(e.hasValue(i)){const a=e.getValue(i);!a.hasAnimated&&a.set(o)}else{const a=e.getStaticValue(i);e.addValue(i,_c(a!==void 0?a:o,{owner:e}))}}for(const i in n)t[i]===void 0&&e.removeValue(i);return t}const e6=new WeakMap,lN=Object.keys(th),xoe=lN.length,t6=["AnimationStart","AnimationComplete","Update","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"],Coe=m5.length;class Toe{constructor({parent:t,props:n,presenceContext:r,reducedMotionConfig:i,visualState:o},s={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.features={},this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{this.current&&(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>kt.render(this.render,!1,!0);const{latestValues:a,renderState:l}=o;this.latestValues=a,this.baseTarget={...a},this.initialValues=n.initial?{...a}:{},this.renderState=l,this.parent=t,this.props=n,this.presenceContext=r,this.depth=t?t.depth+1:0,this.reducedMotionConfig=i,this.options=s,this.isControllingVariants=Ov(n),this.isVariantNode=W7(n),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=!!(t&&t.current);const{willChange:u,...c}=this.scrapeMotionValuesFromProps(n,{});for(const d in c){const f=c[d];a[d]!==void 0&&Nr(f)&&(f.set(a[d],!1),xy(u)&&u.add(d))}}scrapeMotionValuesFromProps(t,n){return{}}mount(t){this.current=t,e6.set(t,this),this.projection&&!this.projection.instance&&this.projection.mount(t),this.parent&&this.isVariantNode&&!this.isControllingVariants&&(this.removeFromVariantTree=this.parent.addVariantChild(this)),this.values.forEach((n,r)=>this.bindToMotionValue(r,n)),aN.current||_oe(),this.shouldReduceMotion=this.reducedMotionConfig==="never"?!1:this.reducedMotionConfig==="always"?!0:vw.current,this.parent&&this.parent.children.add(this),this.update(this.props,this.presenceContext)}unmount(){e6.delete(this.current),this.projection&&this.projection.unmount(),cs(this.notifyUpdate),cs(this.render),this.valueSubscriptions.forEach(t=>t()),this.removeFromVariantTree&&this.removeFromVariantTree(),this.parent&&this.parent.children.delete(this);for(const t in this.events)this.events[t].clear();for(const t in this.features)this.features[t].unmount();this.current=null}bindToMotionValue(t,n){const r=Rl.has(t),i=n.on("change",s=>{this.latestValues[t]=s,this.props.onUpdate&&kt.update(this.notifyUpdate,!1,!0),r&&this.projection&&(this.projection.isTransformDirty=!0)}),o=n.on("renderRequest",this.scheduleRender);this.valueSubscriptions.set(t,()=>{i(),o()})}sortNodePosition(t){return!this.current||!this.sortInstanceNodePosition||this.type!==t.type?0:this.sortInstanceNodePosition(this.current,t.current)}loadFeatures({children:t,...n},r,i,o){let s,a;for(let l=0;l<xoe;l++){const u=lN[l],{isEnabled:c,Feature:d,ProjectionNode:f,MeasureLayout:h}=th[u];f&&(s=f),c(n)&&(!this.features[u]&&d&&(this.features[u]=new d(this)),h&&(a=h))}if(!this.projection&&s){this.projection=new 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Array.isArray(e)?le.array:e===null?le.null:e.then&&typeof e.then=="function"&&e.catch&&typeof e.catch=="function"?le.promise:typeof Map<"u"&&e instanceof Map?le.map:typeof Set<"u"&&e instanceof Set?le.set:typeof Date<"u"&&e instanceof Date?le.date:le.object;default:return le.unknown}},re=Qe.arrayToEnum(["invalid_type","invalid_literal","custom","invalid_union","invalid_union_discriminator","invalid_enum_value","unrecognized_keys","invalid_arguments","invalid_return_type","invalid_date","invalid_string","too_small","too_big","invalid_intersection_types","not_multiple_of","not_finite"]),due=e=>JSON.stringify(e,null,2).replace(/"([^"]+)":/g,"$1:");class $i extends Error{constructor(t){super(),this.issues=[],this.addIssue=r=>{this.issues=[...this.issues,r]},this.addIssues=(r=[])=>{this.issues=[...this.issues,...r]};const n=new.target.prototype;Object.setPrototypeOf?Object.setPrototypeOf(this,n):this.__proto__=n,this.name="ZodError",this.issues=t}get errors(){return this.issues}format(t){const n=t||function(o){return o.message},r={_errors:[]},i=o=>{for(const s of o.issues)if(s.code==="invalid_union")s.unionErrors.map(i);else if(s.code==="invalid_return_type")i(s.returnTypeError);else if(s.code==="invalid_arguments")i(s.argumentsError);else if(s.path.length===0)r._errors.push(n(s));else{let a=r,l=0;for(;l<s.path.length;){const u=s.path[l];l===s.path.length-1?(a[u]=a[u]||{_errors:[]},a[u]._errors.push(n(s))):a[u]=a[u]||{_errors:[]},a=a[u],l++}}};return i(this),r}toString(){return this.message}get message(){return JSON.stringify(this.issues,Qe.jsonStringifyReplacer,2)}get isEmpty(){return this.issues.length===0}flatten(t=n=>n.message){const n={},r=[];for(const i of this.issues)i.path.length>0?(n[i.path[0]]=n[i.path[0]]||[],n[i.path[0]].push(t(i))):r.push(t(i));return{formErrors:r,fieldErrors:n}}get formErrors(){return this.flatten()}}$i.create=e=>new $i(e);const oh=(e,t)=>{let n;switch(e.code){case re.invalid_type:e.received===le.undefined?n="Required":n=`Expected ${e.expected}, received ${e.received}`;break;case re.invalid_literal:n=`Invalid literal value, expected ${JSON.stringify(e.expected,Qe.jsonStringifyReplacer)}`;break;case re.unrecognized_keys:n=`Unrecognized key(s) in object: ${Qe.joinValues(e.keys,", ")}`;break;case re.invalid_union:n="Invalid input";break;case re.invalid_union_discriminator:n=`Invalid discriminator value. Expected ${Qe.joinValues(e.options)}`;break;case re.invalid_enum_value:n=`Invalid enum value. Expected ${Qe.joinValues(e.options)}, received '${e.received}'`;break;case re.invalid_arguments:n="Invalid function arguments";break;case re.invalid_return_type:n="Invalid function return type";break;case re.invalid_date:n="Invalid date";break;case re.invalid_string:typeof e.validation=="object"?"includes"in e.validation?(n=`Invalid input: must include "${e.validation.includes}"`,typeof e.validation.position=="number"&&(n=`${n} at one or more positions greater than or equal to ${e.validation.position}`)):"startsWith"in e.validation?n=`Invalid input: must start with "${e.validation.startsWith}"`:"endsWith"in e.validation?n=`Invalid input: must end with "${e.validation.endsWith}"`:Qe.assertNever(e.validation):e.validation!=="regex"?n=`Invalid ${e.validation}`:n="Invalid";break;case re.too_small:e.type==="array"?n=`Array must contain ${e.exact?"exactly":e.inclusive?"at least":"more than"} ${e.minimum} element(s)`:e.type==="string"?n=`String must contain ${e.exact?"exactly":e.inclusive?"at least":"over"} ${e.minimum} character(s)`:e.type==="number"?n=`Number must be ${e.exact?"exactly equal to ":e.inclusive?"greater than or equal to ":"greater than "}${e.minimum}`:e.type==="date"?n=`Date must be ${e.exact?"exactly equal to ":e.inclusive?"greater than or equal to ":"greater than "}${new Date(Number(e.minimum))}`:n="Invalid input";break;case re.too_big:e.type==="array"?n=`Array must contain ${e.exact?"exactly":e.inclusive?"at most":"less than"} ${e.maximum} element(s)`:e.type==="string"?n=`String must contain ${e.exact?"exactly":e.inclusive?"at most":"under"} ${e.maximum} character(s)`:e.type==="number"?n=`Number must be ${e.exact?"exactly":e.inclusive?"less than or equal to":"less than"} ${e.maximum}`:e.type==="bigint"?n=`BigInt must be ${e.exact?"exactly":e.inclusive?"less than or equal to":"less than"} ${e.maximum}`:e.type==="date"?n=`Date must be ${e.exact?"exactly":e.inclusive?"smaller than or equal to":"smaller than"} ${new Date(Number(e.maximum))}`:n="Invalid input";break;case re.custom:n="Invalid input";break;case re.invalid_intersection_types:n="Intersection results could not be merged";break;case re.not_multiple_of:n=`Number must be a multiple of ${e.multipleOf}`;break;case re.not_finite:n="Number must be finite";break;default:n=t.defaultError,Qe.assertNever(e)}return{message:n}};let cD=oh;function fue(e){cD=e}function Ey(){return cD}const Py=e=>{const{data:t,path:n,errorMaps:r,issueData:i}=e,o=[...n,...i.path||[]],s={...i,path:o};let a="";const l=r.filter(u=>!!u).slice().reverse();for(const u of l)a=u(s,{data:t,defaultError:a}).message;return{...i,path:o,message:i.message||a}},hue=[];function ce(e,t){const n=Py({issueData:t,data:e.data,path:e.path,errorMaps:[e.common.contextualErrorMap,e.schemaErrorMap,Ey(),oh].filter(r=>!!r)});e.common.issues.push(n)}class er{constructor(){this.value="valid"}dirty(){this.value==="valid"&&(this.value="dirty")}abort(){this.value!=="aborted"&&(this.value="aborted")}static mergeArray(t,n){const r=[];for(const i of n){if(i.status==="aborted")return Ce;i.status==="dirty"&&t.dirty(),r.push(i.value)}return{status:t.value,value:r}}static async mergeObjectAsync(t,n){const r=[];for(const i of n)r.push({key:await i.key,value:await i.value});return er.mergeObjectSync(t,r)}static mergeObjectSync(t,n){const r={};for(const i of n){const{key:o,value:s}=i;if(o.status==="aborted"||s.status==="aborted")return Ce;o.status==="dirty"&&t.dirty(),s.status==="dirty"&&t.dirty(),(typeof s.value<"u"||i.alwaysSet)&&(r[o.value]=s.value)}return{status:t.value,value:r}}}const Ce=Object.freeze({status:"aborted"}),dD=e=>({status:"dirty",value:e}),pr=e=>({status:"valid",value:e}),Ow=e=>e.status==="aborted",Rw=e=>e.status==="dirty",ky=e=>e.status==="valid",Ay=e=>typeof Promise<"u"&&e instanceof Promise;var be;(function(e){e.errToObj=t=>typeof t=="string"?{message:t}:t||{},e.toString=t=>typeof t=="string"?t:t==null?void 0:t.message})(be||(be={}));class xo{constructor(t,n,r,i){this._cachedPath=[],this.parent=t,this.data=n,this._path=r,this._key=i}get path(){return this._cachedPath.length||(this._key instanceof Array?this._cachedPath.push(...this._path,...this._key):this._cachedPath.push(...this._path,this._key)),this._cachedPath}}const O6=(e,t)=>{if(ky(t))return{success:!0,data:t.value};if(!e.common.issues.length)throw new Error("Validation failed but no issues detected.");return{success:!1,get error(){if(this._error)return this._error;const n=new $i(e.common.issues);return this._error=n,this._error}}};function Oe(e){if(!e)return{};const{errorMap:t,invalid_type_error:n,required_error:r,description:i}=e;if(t&&(n||r))throw new Error(`Can't use "invalid_type_error" or "required_error" in conjunction with custom error map.`);return t?{errorMap:t,description:i}:{errorMap:(s,a)=>s.code!=="invalid_type"?{message:a.defaultError}:typeof a.data>"u"?{message:r??a.defaultError}:{message:n??a.defaultError},description:i}}class Re{constructor(t){this.spa=this.safeParseAsync,this._def=t,this.parse=this.parse.bind(this),this.safeParse=this.safeParse.bind(this),this.parseAsync=this.parseAsync.bind(this),this.safeParseAsync=this.safeParseAsync.bind(this),this.spa=this.spa.bind(this),this.refine=this.refine.bind(this),this.refinement=this.refinement.bind(this),this.superRefine=this.superRefine.bind(this),this.optional=this.optional.bind(this),this.nullable=this.nullable.bind(this),this.nullish=this.nullish.bind(this),this.array=this.array.bind(this),this.promise=this.promise.bind(this),this.or=this.or.bind(this),this.and=this.and.bind(this),this.transform=this.transform.bind(this),this.brand=this.brand.bind(this),this.default=this.default.bind(this),this.catch=this.catch.bind(this),this.describe=this.describe.bind(this),this.pipe=this.pipe.bind(this),this.isNullable=this.isNullable.bind(this),this.isOptional=this.isOptional.bind(this)}get description(){return this._def.description}_getType(t){return js(t.data)}_getOrReturnCtx(t,n){return n||{common:t.parent.common,data:t.data,parsedType:js(t.data),schemaErrorMap:this._def.errorMap,path:t.path,parent:t.parent}}_processInputParams(t){return{status:new er,ctx:{common:t.parent.common,data:t.data,parsedType:js(t.data),schemaErrorMap:this._def.errorMap,path:t.path,parent:t.parent}}}_parseSync(t){const n=this._parse(t);if(Ay(n))throw new Error("Synchronous parse encountered promise.");return n}_parseAsync(t){const n=this._parse(t);return Promise.resolve(n)}parse(t,n){const r=this.safeParse(t,n);if(r.success)return r.data;throw r.error}safeParse(t,n){var r;const i={common:{issues:[],async:(r=n==null?void 0:n.async)!==null&&r!==void 0?r:!1,contextualErrorMap:n==null?void 0:n.errorMap},path:(n==null?void 0:n.path)||[],schemaErrorMap:this._def.errorMap,parent:null,data:t,parsedType:js(t)},o=this._parseSync({data:t,path:i.path,parent:i});return O6(i,o)}async parseAsync(t,n){const r=await this.safeParseAsync(t,n);if(r.success)return r.data;throw r.error}async safeParseAsync(t,n){const r={common:{issues:[],contextualErrorMap:n==null?void 0:n.errorMap,async:!0},path:(n==null?void 0:n.path)||[],schemaErrorMap:this._def.errorMap,parent:null,data:t,parsedType:js(t)},i=this._parse({data:t,path:r.path,parent:r}),o=await(Ay(i)?i:Promise.resolve(i));return O6(r,o)}refine(t,n){const r=i=>typeof n=="string"||typeof n>"u"?{message:n}:typeof n=="function"?n(i):n;return this._refinement((i,o)=>{const s=t(i),a=()=>o.addIssue({code:re.custom,...r(i)});return typeof Promise<"u"&&s instanceof Promise?s.then(l=>l?!0:(a(),!1)):s?!0:(a(),!1)})}refinement(t,n){return this._refinement((r,i)=>t(r)?!0:(i.addIssue(typeof n=="function"?n(r,i):n),!1))}_refinement(t){return new Ui({schema:this,typeName:xe.ZodEffects,effect:{type:"refinement",refinement:t}})}superRefine(t){return this._refinement(t)}optional(){return ns.create(this,this._def)}nullable(){return vl.create(this,this._def)}nullish(){return this.nullable().optional()}array(){return Fi.create(this,this._def)}promise(){return kc.create(this,this._def)}or(t){return uh.create([this,t],this._def)}and(t){return ch.create(this,t,this._def)}transform(t){return new Ui({...Oe(this._def),schema:this,typeName:xe.ZodEffects,effect:{type:"transform",transform:t}})}default(t){const n=typeof t=="function"?t:()=>t;return new gh({...Oe(this._def),innerType:this,defaultValue:n,typeName:xe.ZodDefault})}brand(){return new hD({typeName:xe.ZodBranded,type:this,...Oe(this._def)})}catch(t){const n=typeof t=="function"?t:()=>t;return new My({...Oe(this._def),innerType:this,catchValue:n,typeName:xe.ZodCatch})}describe(t){const n=this.constructor;return new n({...this._def,description:t})}pipe(t){return dp.create(this,t)}isOptional(){return this.safeParse(void 0).success}isNullable(){return this.safeParse(null).success}}const pue=/^c[^\s-]{8,}$/i,gue=/^[a-z][a-z0-9]*$/,mue=/[0-9A-HJKMNP-TV-Z]{26}/,yue=/^([a-f0-9]{8}-[a-f0-9]{4}-[1-5][a-f0-9]{3}-[a-f0-9]{4}-[a-f0-9]{12}|00000000-0000-0000-0000-000000000000)$/i,vue=/^(([^<>()[\]\\.,;:\s@\"]+(\.[^<>()[\]\\.,;:\s@\"]+)*)|(\".+\"))@((\[(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\])|(\[IPv6:(([a-f0-9]{1,4}:){7}|::([a-f0-9]{1,4}:){0,6}|([a-f0-9]{1,4}:){1}:([a-f0-9]{1,4}:){0,5}|([a-f0-9]{1,4}:){2}:([a-f0-9]{1,4}:){0,4}|([a-f0-9]{1,4}:){3}:([a-f0-9]{1,4}:){0,3}|([a-f0-9]{1,4}:){4}:([a-f0-9]{1,4}:){0,2}|([a-f0-9]{1,4}:){5}:([a-f0-9]{1,4}:){0,1})([a-f0-9]{1,4}|(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2})))\])|([A-Za-z0-9]([A-Za-z0-9-]*[A-Za-z0-9])*(\.[A-Za-z]{2,})+))$/,bue=/^(\p{Extended_Pictographic}|\p{Emoji_Component})+$/u,Sue=/^(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))$/,_ue=/^(([a-f0-9]{1,4}:){7}|::([a-f0-9]{1,4}:){0,6}|([a-f0-9]{1,4}:){1}:([a-f0-9]{1,4}:){0,5}|([a-f0-9]{1,4}:){2}:([a-f0-9]{1,4}:){0,4}|([a-f0-9]{1,4}:){3}:([a-f0-9]{1,4}:){0,3}|([a-f0-9]{1,4}:){4}:([a-f0-9]{1,4}:){0,2}|([a-f0-9]{1,4}:){5}:([a-f0-9]{1,4}:){0,1})([a-f0-9]{1,4}|(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2})))$/,wue=e=>e.precision?e.offset?new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}(([+-]\\d{2}(:?\\d{2})?)|Z)$`):new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}Z$`):e.precision===0?e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"):e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?Z$");function xue(e,t){return!!((t==="v4"||!t)&&Sue.test(e)||(t==="v6"||!t)&&_ue.test(e))}class Ii extends Re{constructor(){super(...arguments),this._regex=(t,n,r)=>this.refinement(i=>t.test(i),{validation:n,code:re.invalid_string,...be.errToObj(r)}),this.nonempty=t=>this.min(1,be.errToObj(t)),this.trim=()=>new Ii({...this._def,checks:[...this._def.checks,{kind:"trim"}]}),this.toLowerCase=()=>new Ii({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]}),this.toUpperCase=()=>new Ii({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}_parse(t){if(this._def.coerce&&(t.data=String(t.data)),this._getType(t)!==le.string){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.string,received:o.parsedType}),Ce}const r=new er;let i;for(const o of this._def.checks)if(o.kind==="min")t.data.length<o.value&&(i=this._getOrReturnCtx(t,i),ce(i,{code:re.too_small,minimum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="max")t.data.length>o.value&&(i=this._getOrReturnCtx(t,i),ce(i,{code:re.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="length"){const s=t.data.length>o.value,a=t.data.length<o.value;(s||a)&&(i=this._getOrReturnCtx(t,i),s?ce(i,{code:re.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!0,message:o.message}):a&&ce(i,{code:re.too_small,minimum:o.value,type:"string",inclusive:!0,exact:!0,message:o.message}),r.dirty())}else if(o.kind==="email")vue.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"email",code:re.invalid_string,message:o.message}),r.dirty());else if(o.kind==="emoji")bue.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"emoji",code:re.invalid_string,message:o.message}),r.dirty());else if(o.kind==="uuid")yue.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"uuid",code:re.invalid_string,message:o.message}),r.dirty());else if(o.kind==="cuid")pue.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"cuid",code:re.invalid_string,message:o.message}),r.dirty());else if(o.kind==="cuid2")gue.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"cuid2",code:re.invalid_string,message:o.message}),r.dirty());else if(o.kind==="ulid")mue.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"ulid",code:re.invalid_string,message:o.message}),r.dirty());else if(o.kind==="url")try{new URL(t.data)}catch{i=this._getOrReturnCtx(t,i),ce(i,{validation:"url",code:re.invalid_string,message:o.message}),r.dirty()}else o.kind==="regex"?(o.regex.lastIndex=0,o.regex.test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"regex",code:re.invalid_string,message:o.message}),r.dirty())):o.kind==="trim"?t.data=t.data.trim():o.kind==="includes"?t.data.includes(o.value,o.position)||(i=this._getOrReturnCtx(t,i),ce(i,{code:re.invalid_string,validation:{includes:o.value,position:o.position},message:o.message}),r.dirty()):o.kind==="toLowerCase"?t.data=t.data.toLowerCase():o.kind==="toUpperCase"?t.data=t.data.toUpperCase():o.kind==="startsWith"?t.data.startsWith(o.value)||(i=this._getOrReturnCtx(t,i),ce(i,{code:re.invalid_string,validation:{startsWith:o.value},message:o.message}),r.dirty()):o.kind==="endsWith"?t.data.endsWith(o.value)||(i=this._getOrReturnCtx(t,i),ce(i,{code:re.invalid_string,validation:{endsWith:o.value},message:o.message}),r.dirty()):o.kind==="datetime"?wue(o).test(t.data)||(i=this._getOrReturnCtx(t,i),ce(i,{code:re.invalid_string,validation:"datetime",message:o.message}),r.dirty()):o.kind==="ip"?xue(t.data,o.version)||(i=this._getOrReturnCtx(t,i),ce(i,{validation:"ip",code:re.invalid_string,message:o.message}),r.dirty()):Qe.assertNever(o);return{status:r.value,value:t.data}}_addCheck(t){return new Ii({...this._def,checks:[...this._def.checks,t]})}email(t){return this._addCheck({kind:"email",...be.errToObj(t)})}url(t){return this._addCheck({kind:"url",...be.errToObj(t)})}emoji(t){return this._addCheck({kind:"emoji",...be.errToObj(t)})}uuid(t){return this._addCheck({kind:"uuid",...be.errToObj(t)})}cuid(t){return this._addCheck({kind:"cuid",...be.errToObj(t)})}cuid2(t){return this._addCheck({kind:"cuid2",...be.errToObj(t)})}ulid(t){return this._addCheck({kind:"ulid",...be.errToObj(t)})}ip(t){return this._addCheck({kind:"ip",...be.errToObj(t)})}datetime(t){var n;return typeof t=="string"?this._addCheck({kind:"datetime",precision:null,offset:!1,message:t}):this._addCheck({kind:"datetime",precision:typeof(t==null?void 0:t.precision)>"u"?null:t==null?void 0:t.precision,offset:(n=t==null?void 0:t.offset)!==null&&n!==void 0?n:!1,...be.errToObj(t==null?void 0:t.message)})}regex(t,n){return this._addCheck({kind:"regex",regex:t,...be.errToObj(n)})}includes(t,n){return this._addCheck({kind:"includes",value:t,position:n==null?void 0:n.position,...be.errToObj(n==null?void 0:n.message)})}startsWith(t,n){return this._addCheck({kind:"startsWith",value:t,...be.errToObj(n)})}endsWith(t,n){return this._addCheck({kind:"endsWith",value:t,...be.errToObj(n)})}min(t,n){return this._addCheck({kind:"min",value:t,...be.errToObj(n)})}max(t,n){return this._addCheck({kind:"max",value:t,...be.errToObj(n)})}length(t,n){return this._addCheck({kind:"length",value:t,...be.errToObj(n)})}get isDatetime(){return!!this._def.checks.find(t=>t.kind==="datetime")}get isEmail(){return!!this._def.checks.find(t=>t.kind==="email")}get isURL(){return!!this._def.checks.find(t=>t.kind==="url")}get isEmoji(){return!!this._def.checks.find(t=>t.kind==="emoji")}get isUUID(){return!!this._def.checks.find(t=>t.kind==="uuid")}get isCUID(){return!!this._def.checks.find(t=>t.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(t=>t.kind==="cuid2")}get isULID(){return!!this._def.checks.find(t=>t.kind==="ulid")}get isIP(){return!!this._def.checks.find(t=>t.kind==="ip")}get minLength(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxLength(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}}Ii.create=e=>{var t;return new Ii({checks:[],typeName:xe.ZodString,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Oe(e)})};function Cue(e,t){const n=(e.toString().split(".")[1]||"").length,r=(t.toString().split(".")[1]||"").length,i=n>r?n:r,o=parseInt(e.toFixed(i).replace(".","")),s=parseInt(t.toFixed(i).replace(".",""));return o%s/Math.pow(10,i)}class pa extends Re{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(t){if(this._def.coerce&&(t.data=Number(t.data)),this._getType(t)!==le.number){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.number,received:o.parsedType}),Ce}let r;const i=new er;for(const o of this._def.checks)o.kind==="int"?Qe.isInteger(t.data)||(r=this._getOrReturnCtx(t,r),ce(r,{code:re.invalid_type,expected:"integer",received:"float",message:o.message}),i.dirty()):o.kind==="min"?(o.inclusive?t.data<o.value:t.data<=o.value)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.too_small,minimum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="max"?(o.inclusive?t.data>o.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.too_big,maximum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="multipleOf"?Cue(t.data,o.value)!==0&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):o.kind==="finite"?Number.isFinite(t.data)||(r=this._getOrReturnCtx(t,r),ce(r,{code:re.not_finite,message:o.message}),i.dirty()):Qe.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,be.toString(n))}gt(t,n){return this.setLimit("min",t,!1,be.toString(n))}lte(t,n){return this.setLimit("max",t,!0,be.toString(n))}lt(t,n){return this.setLimit("max",t,!1,be.toString(n))}setLimit(t,n,r,i){return new pa({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:be.toString(i)}]})}_addCheck(t){return new pa({...this._def,checks:[...this._def.checks,t]})}int(t){return this._addCheck({kind:"int",message:be.toString(t)})}positive(t){return this._addCheck({kind:"min",value:0,inclusive:!1,message:be.toString(t)})}negative(t){return this._addCheck({kind:"max",value:0,inclusive:!1,message:be.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:0,inclusive:!0,message:be.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:0,inclusive:!0,message:be.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:be.toString(n)})}finite(t){return this._addCheck({kind:"finite",message:be.toString(t)})}safe(t){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:be.toString(t)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:be.toString(t)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}get isInt(){return!!this._def.checks.find(t=>t.kind==="int"||t.kind==="multipleOf"&&Qe.isInteger(t.value))}get isFinite(){let t=null,n=null;for(const r of this._def.checks){if(r.kind==="finite"||r.kind==="int"||r.kind==="multipleOf")return!0;r.kind==="min"?(n===null||r.value>n)&&(n=r.value):r.kind==="max"&&(t===null||r.value<t)&&(t=r.value)}return Number.isFinite(n)&&Number.isFinite(t)}}pa.create=e=>new pa({checks:[],typeName:xe.ZodNumber,coerce:(e==null?void 0:e.coerce)||!1,...Oe(e)});class ga extends Re{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(t){if(this._def.coerce&&(t.data=BigInt(t.data)),this._getType(t)!==le.bigint){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.bigint,received:o.parsedType}),Ce}let r;const i=new er;for(const o of this._def.checks)o.kind==="min"?(o.inclusive?t.data<o.value:t.data<=o.value)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.too_small,type:"bigint",minimum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="max"?(o.inclusive?t.data>o.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.too_big,type:"bigint",maximum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="multipleOf"?t.data%o.value!==BigInt(0)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):Qe.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,be.toString(n))}gt(t,n){return this.setLimit("min",t,!1,be.toString(n))}lte(t,n){return this.setLimit("max",t,!0,be.toString(n))}lt(t,n){return this.setLimit("max",t,!1,be.toString(n))}setLimit(t,n,r,i){return new ga({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:be.toString(i)}]})}_addCheck(t){return new ga({...this._def,checks:[...this._def.checks,t]})}positive(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:be.toString(t)})}negative(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:be.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:be.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:be.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:be.toString(n)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}}ga.create=e=>{var t;return new ga({checks:[],typeName:xe.ZodBigInt,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Oe(e)})};class sh extends Re{_parse(t){if(this._def.coerce&&(t.data=!!t.data),this._getType(t)!==le.boolean){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.boolean,received:r.parsedType}),Ce}return pr(t.data)}}sh.create=e=>new sh({typeName:xe.ZodBoolean,coerce:(e==null?void 0:e.coerce)||!1,...Oe(e)});class ml extends Re{_parse(t){if(this._def.coerce&&(t.data=new Date(t.data)),this._getType(t)!==le.date){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.date,received:o.parsedType}),Ce}if(isNaN(t.data.getTime())){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_date}),Ce}const r=new er;let i;for(const o of this._def.checks)o.kind==="min"?t.data.getTime()<o.value&&(i=this._getOrReturnCtx(t,i),ce(i,{code:re.too_small,message:o.message,inclusive:!0,exact:!1,minimum:o.value,type:"date"}),r.dirty()):o.kind==="max"?t.data.getTime()>o.value&&(i=this._getOrReturnCtx(t,i),ce(i,{code:re.too_big,message:o.message,inclusive:!0,exact:!1,maximum:o.value,type:"date"}),r.dirty()):Qe.assertNever(o);return{status:r.value,value:new Date(t.data.getTime())}}_addCheck(t){return new ml({...this._def,checks:[...this._def.checks,t]})}min(t,n){return this._addCheck({kind:"min",value:t.getTime(),message:be.toString(n)})}max(t,n){return this._addCheck({kind:"max",value:t.getTime(),message:be.toString(n)})}get minDate(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t!=null?new Date(t):null}get maxDate(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t!=null?new Date(t):null}}ml.create=e=>new ml({checks:[],coerce:(e==null?void 0:e.coerce)||!1,typeName:xe.ZodDate,...Oe(e)});class Oy extends Re{_parse(t){if(this._getType(t)!==le.symbol){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.symbol,received:r.parsedType}),Ce}return pr(t.data)}}Oy.create=e=>new Oy({typeName:xe.ZodSymbol,...Oe(e)});class ah extends Re{_parse(t){if(this._getType(t)!==le.undefined){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.undefined,received:r.parsedType}),Ce}return pr(t.data)}}ah.create=e=>new ah({typeName:xe.ZodUndefined,...Oe(e)});class lh extends Re{_parse(t){if(this._getType(t)!==le.null){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.null,received:r.parsedType}),Ce}return pr(t.data)}}lh.create=e=>new lh({typeName:xe.ZodNull,...Oe(e)});class Pc extends Re{constructor(){super(...arguments),this._any=!0}_parse(t){return pr(t.data)}}Pc.create=e=>new Pc({typeName:xe.ZodAny,...Oe(e)});class ol extends Re{constructor(){super(...arguments),this._unknown=!0}_parse(t){return pr(t.data)}}ol.create=e=>new ol({typeName:xe.ZodUnknown,...Oe(e)});class fs extends Re{_parse(t){const n=this._getOrReturnCtx(t);return ce(n,{code:re.invalid_type,expected:le.never,received:n.parsedType}),Ce}}fs.create=e=>new fs({typeName:xe.ZodNever,...Oe(e)});class Ry extends Re{_parse(t){if(this._getType(t)!==le.undefined){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.void,received:r.parsedType}),Ce}return pr(t.data)}}Ry.create=e=>new Ry({typeName:xe.ZodVoid,...Oe(e)});class Fi extends Re{_parse(t){const{ctx:n,status:r}=this._processInputParams(t),i=this._def;if(n.parsedType!==le.array)return ce(n,{code:re.invalid_type,expected:le.array,received:n.parsedType}),Ce;if(i.exactLength!==null){const s=n.data.length>i.exactLength.value,a=n.data.length<i.exactLength.value;(s||a)&&(ce(n,{code:s?re.too_big:re.too_small,minimum:a?i.exactLength.value:void 0,maximum:s?i.exactLength.value:void 0,type:"array",inclusive:!0,exact:!0,message:i.exactLength.message}),r.dirty())}if(i.minLength!==null&&n.data.length<i.minLength.value&&(ce(n,{code:re.too_small,minimum:i.minLength.value,type:"array",inclusive:!0,exact:!1,message:i.minLength.message}),r.dirty()),i.maxLength!==null&&n.data.length>i.maxLength.value&&(ce(n,{code:re.too_big,maximum:i.maxLength.value,type:"array",inclusive:!0,exact:!1,message:i.maxLength.message}),r.dirty()),n.common.async)return Promise.all([...n.data].map((s,a)=>i.type._parseAsync(new xo(n,s,n.path,a)))).then(s=>er.mergeArray(r,s));const o=[...n.data].map((s,a)=>i.type._parseSync(new xo(n,s,n.path,a)));return er.mergeArray(r,o)}get element(){return this._def.type}min(t,n){return new Fi({...this._def,minLength:{value:t,message:be.toString(n)}})}max(t,n){return new Fi({...this._def,maxLength:{value:t,message:be.toString(n)}})}length(t,n){return new Fi({...this._def,exactLength:{value:t,message:be.toString(n)}})}nonempty(t){return this.min(1,t)}}Fi.create=(e,t)=>new Fi({type:e,minLength:null,maxLength:null,exactLength:null,typeName:xe.ZodArray,...Oe(t)});function fu(e){if(e instanceof It){const t={};for(const n in e.shape){const r=e.shape[n];t[n]=ns.create(fu(r))}return new It({...e._def,shape:()=>t})}else return e instanceof Fi?new Fi({...e._def,type:fu(e.element)}):e instanceof ns?ns.create(fu(e.unwrap())):e instanceof vl?vl.create(fu(e.unwrap())):e instanceof Co?Co.create(e.items.map(t=>fu(t))):e}class It extends Re{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const t=this._def.shape(),n=Qe.objectKeys(t);return this._cached={shape:t,keys:n}}_parse(t){if(this._getType(t)!==le.object){const u=this._getOrReturnCtx(t);return ce(u,{code:re.invalid_type,expected:le.object,received:u.parsedType}),Ce}const{status:r,ctx:i}=this._processInputParams(t),{shape:o,keys:s}=this._getCached(),a=[];if(!(this._def.catchall instanceof fs&&this._def.unknownKeys==="strip"))for(const u in i.data)s.includes(u)||a.push(u);const l=[];for(const u of s){const c=o[u],d=i.data[u];l.push({key:{status:"valid",value:u},value:c._parse(new xo(i,d,i.path,u)),alwaysSet:u in i.data})}if(this._def.catchall instanceof fs){const u=this._def.unknownKeys;if(u==="passthrough")for(const c of a)l.push({key:{status:"valid",value:c},value:{status:"valid",value:i.data[c]}});else if(u==="strict")a.length>0&&(ce(i,{code:re.unrecognized_keys,keys:a}),r.dirty());else if(u!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const u=this._def.catchall;for(const c of a){const d=i.data[c];l.push({key:{status:"valid",value:c},value:u._parse(new xo(i,d,i.path,c)),alwaysSet:c in i.data})}}return i.common.async?Promise.resolve().then(async()=>{const u=[];for(const c of l){const d=await c.key;u.push({key:d,value:await c.value,alwaysSet:c.alwaysSet})}return u}).then(u=>er.mergeObjectSync(r,u)):er.mergeObjectSync(r,l)}get shape(){return this._def.shape()}strict(t){return be.errToObj,new It({...this._def,unknownKeys:"strict",...t!==void 0?{errorMap:(n,r)=>{var i,o,s,a;const l=(s=(o=(i=this._def).errorMap)===null||o===void 0?void 0:o.call(i,n,r).message)!==null&&s!==void 0?s:r.defaultError;return n.code==="unrecognized_keys"?{message:(a=be.errToObj(t).message)!==null&&a!==void 0?a:l}:{message:l}}}:{}})}strip(){return new It({...this._def,unknownKeys:"strip"})}passthrough(){return new It({...this._def,unknownKeys:"passthrough"})}extend(t){return new It({...this._def,shape:()=>({...this._def.shape(),...t})})}merge(t){return new It({unknownKeys:t._def.unknownKeys,catchall:t._def.catchall,shape:()=>({...this._def.shape(),...t._def.shape()}),typeName:xe.ZodObject})}setKey(t,n){return this.augment({[t]:n})}catchall(t){return new It({...this._def,catchall:t})}pick(t){const n={};return Qe.objectKeys(t).forEach(r=>{t[r]&&this.shape[r]&&(n[r]=this.shape[r])}),new It({...this._def,shape:()=>n})}omit(t){const n={};return Qe.objectKeys(this.shape).forEach(r=>{t[r]||(n[r]=this.shape[r])}),new It({...this._def,shape:()=>n})}deepPartial(){return fu(this)}partial(t){const n={};return Qe.objectKeys(this.shape).forEach(r=>{const i=this.shape[r];t&&!t[r]?n[r]=i:n[r]=i.optional()}),new It({...this._def,shape:()=>n})}required(t){const n={};return Qe.objectKeys(this.shape).forEach(r=>{if(t&&!t[r])n[r]=this.shape[r];else{let o=this.shape[r];for(;o instanceof ns;)o=o._def.innerType;n[r]=o}}),new It({...this._def,shape:()=>n})}keyof(){return fD(Qe.objectKeys(this.shape))}}It.create=(e,t)=>new It({shape:()=>e,unknownKeys:"strip",catchall:fs.create(),typeName:xe.ZodObject,...Oe(t)});It.strictCreate=(e,t)=>new It({shape:()=>e,unknownKeys:"strict",catchall:fs.create(),typeName:xe.ZodObject,...Oe(t)});It.lazycreate=(e,t)=>new It({shape:e,unknownKeys:"strip",catchall:fs.create(),typeName:xe.ZodObject,...Oe(t)});class uh extends Re{_parse(t){const{ctx:n}=this._processInputParams(t),r=this._def.options;function i(o){for(const a of o)if(a.result.status==="valid")return a.result;for(const a of o)if(a.result.status==="dirty")return n.common.issues.push(...a.ctx.common.issues),a.result;const s=o.map(a=>new $i(a.ctx.common.issues));return ce(n,{code:re.invalid_union,unionErrors:s}),Ce}if(n.common.async)return Promise.all(r.map(async o=>{const s={...n,common:{...n.common,issues:[]},parent:null};return{result:await o._parseAsync({data:n.data,path:n.path,parent:s}),ctx:s}})).then(i);{let o;const s=[];for(const l of r){const u={...n,common:{...n.common,issues:[]},parent:null},c=l._parseSync({data:n.data,path:n.path,parent:u});if(c.status==="valid")return c;c.status==="dirty"&&!o&&(o={result:c,ctx:u}),u.common.issues.length&&s.push(u.common.issues)}if(o)return n.common.issues.push(...o.ctx.common.issues),o.result;const a=s.map(l=>new $i(l));return ce(n,{code:re.invalid_union,unionErrors:a}),Ce}}get options(){return this._def.options}}uh.create=(e,t)=>new uh({options:e,typeName:xe.ZodUnion,...Oe(t)});const fm=e=>e instanceof fh?fm(e.schema):e instanceof Ui?fm(e.innerType()):e instanceof hh?[e.value]:e instanceof ma?e.options:e instanceof ph?Object.keys(e.enum):e instanceof gh?fm(e._def.innerType):e instanceof ah?[void 0]:e instanceof lh?[null]:null;class jv extends Re{_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==le.object)return ce(n,{code:re.invalid_type,expected:le.object,received:n.parsedType}),Ce;const r=this.discriminator,i=n.data[r],o=this.optionsMap.get(i);return o?n.common.async?o._parseAsync({data:n.data,path:n.path,parent:n}):o._parseSync({data:n.data,path:n.path,parent:n}):(ce(n,{code:re.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[r]}),Ce)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(t,n,r){const i=new Map;for(const o of n){const s=fm(o.shape[t]);if(!s)throw new Error(`A discriminator value for key \`${t}\` could not be extracted from all schema options`);for(const a of s){if(i.has(a))throw new Error(`Discriminator property ${String(t)} has duplicate value ${String(a)}`);i.set(a,o)}}return new jv({typeName:xe.ZodDiscriminatedUnion,discriminator:t,options:n,optionsMap:i,...Oe(r)})}}function Iw(e,t){const n=js(e),r=js(t);if(e===t)return{valid:!0,data:e};if(n===le.object&&r===le.object){const i=Qe.objectKeys(t),o=Qe.objectKeys(e).filter(a=>i.indexOf(a)!==-1),s={...e,...t};for(const a of o){const l=Iw(e[a],t[a]);if(!l.valid)return{valid:!1};s[a]=l.data}return{valid:!0,data:s}}else if(n===le.array&&r===le.array){if(e.length!==t.length)return{valid:!1};const i=[];for(let o=0;o<e.length;o++){const s=e[o],a=t[o],l=Iw(s,a);if(!l.valid)return{valid:!1};i.push(l.data)}return{valid:!0,data:i}}else return n===le.date&&r===le.date&&+e==+t?{valid:!0,data:e}:{valid:!1}}class ch extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=(o,s)=>{if(Ow(o)||Ow(s))return Ce;const a=Iw(o.value,s.value);return a.valid?((Rw(o)||Rw(s))&&n.dirty(),{status:n.value,value:a.data}):(ce(r,{code:re.invalid_intersection_types}),Ce)};return r.common.async?Promise.all([this._def.left._parseAsync({data:r.data,path:r.path,parent:r}),this._def.right._parseAsync({data:r.data,path:r.path,parent:r})]).then(([o,s])=>i(o,s)):i(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}ch.create=(e,t,n)=>new ch({left:e,right:t,typeName:xe.ZodIntersection,...Oe(n)});class Co extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.array)return ce(r,{code:re.invalid_type,expected:le.array,received:r.parsedType}),Ce;if(r.data.length<this._def.items.length)return ce(r,{code:re.too_small,minimum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),Ce;!this._def.rest&&r.data.length>this._def.items.length&&(ce(r,{code:re.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),n.dirty());const o=[...r.data].map((s,a)=>{const l=this._def.items[a]||this._def.rest;return l?l._parse(new xo(r,s,r.path,a)):null}).filter(s=>!!s);return r.common.async?Promise.all(o).then(s=>er.mergeArray(n,s)):er.mergeArray(n,o)}get items(){return this._def.items}rest(t){return new Co({...this._def,rest:t})}}Co.create=(e,t)=>{if(!Array.isArray(e))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new Co({items:e,typeName:xe.ZodTuple,rest:null,...Oe(t)})};class dh extends Re{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.object)return ce(r,{code:re.invalid_type,expected:le.object,received:r.parsedType}),Ce;const i=[],o=this._def.keyType,s=this._def.valueType;for(const a in r.data)i.push({key:o._parse(new xo(r,a,r.path,a)),value:s._parse(new xo(r,r.data[a],r.path,a))});return r.common.async?er.mergeObjectAsync(n,i):er.mergeObjectSync(n,i)}get element(){return this._def.valueType}static create(t,n,r){return n instanceof Re?new dh({keyType:t,valueType:n,typeName:xe.ZodRecord,...Oe(r)}):new dh({keyType:Ii.create(),valueType:t,typeName:xe.ZodRecord,...Oe(n)})}}class Iy extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.map)return ce(r,{code:re.invalid_type,expected:le.map,received:r.parsedType}),Ce;const i=this._def.keyType,o=this._def.valueType,s=[...r.data.entries()].map(([a,l],u)=>({key:i._parse(new xo(r,a,r.path,[u,"key"])),value:o._parse(new xo(r,l,r.path,[u,"value"]))}));if(r.common.async){const a=new Map;return Promise.resolve().then(async()=>{for(const l of s){const u=await l.key,c=await l.value;if(u.status==="aborted"||c.status==="aborted")return Ce;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}})}else{const a=new Map;for(const l of s){const u=l.key,c=l.value;if(u.status==="aborted"||c.status==="aborted")return Ce;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}}}}Iy.create=(e,t,n)=>new Iy({valueType:t,keyType:e,typeName:xe.ZodMap,...Oe(n)});class yl extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.set)return ce(r,{code:re.invalid_type,expected:le.set,received:r.parsedType}),Ce;const i=this._def;i.minSize!==null&&r.data.size<i.minSize.value&&(ce(r,{code:re.too_small,minimum:i.minSize.value,type:"set",inclusive:!0,exact:!1,message:i.minSize.message}),n.dirty()),i.maxSize!==null&&r.data.size>i.maxSize.value&&(ce(r,{code:re.too_big,maximum:i.maxSize.value,type:"set",inclusive:!0,exact:!1,message:i.maxSize.message}),n.dirty());const o=this._def.valueType;function s(l){const u=new Set;for(const c of l){if(c.status==="aborted")return Ce;c.status==="dirty"&&n.dirty(),u.add(c.value)}return{status:n.value,value:u}}const a=[...r.data.values()].map((l,u)=>o._parse(new xo(r,l,r.path,u)));return r.common.async?Promise.all(a).then(l=>s(l)):s(a)}min(t,n){return new yl({...this._def,minSize:{value:t,message:be.toString(n)}})}max(t,n){return new yl({...this._def,maxSize:{value:t,message:be.toString(n)}})}size(t,n){return this.min(t,n).max(t,n)}nonempty(t){return this.min(1,t)}}yl.create=(e,t)=>new yl({valueType:e,minSize:null,maxSize:null,typeName:xe.ZodSet,...Oe(t)});class Qu extends Re{constructor(){super(...arguments),this.validate=this.implement}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==le.function)return ce(n,{code:re.invalid_type,expected:le.function,received:n.parsedType}),Ce;function r(a,l){return Py({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,Ey(),oh].filter(u=>!!u),issueData:{code:re.invalid_arguments,argumentsError:l}})}function i(a,l){return Py({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,Ey(),oh].filter(u=>!!u),issueData:{code:re.invalid_return_type,returnTypeError:l}})}const o={errorMap:n.common.contextualErrorMap},s=n.data;return this._def.returns instanceof kc?pr(async(...a)=>{const l=new $i([]),u=await this._def.args.parseAsync(a,o).catch(f=>{throw l.addIssue(r(a,f)),l}),c=await s(...u);return await this._def.returns._def.type.parseAsync(c,o).catch(f=>{throw l.addIssue(i(c,f)),l})}):pr((...a)=>{const l=this._def.args.safeParse(a,o);if(!l.success)throw new $i([r(a,l.error)]);const u=s(...l.data),c=this._def.returns.safeParse(u,o);if(!c.success)throw new $i([i(u,c.error)]);return c.data})}parameters(){return this._def.args}returnType(){return this._def.returns}args(...t){return new Qu({...this._def,args:Co.create(t).rest(ol.create())})}returns(t){return new Qu({...this._def,returns:t})}implement(t){return this.parse(t)}strictImplement(t){return this.parse(t)}static create(t,n,r){return new Qu({args:t||Co.create([]).rest(ol.create()),returns:n||ol.create(),typeName:xe.ZodFunction,...Oe(r)})}}class fh extends Re{get schema(){return this._def.getter()}_parse(t){const{ctx:n}=this._processInputParams(t);return this._def.getter()._parse({data:n.data,path:n.path,parent:n})}}fh.create=(e,t)=>new fh({getter:e,typeName:xe.ZodLazy,...Oe(t)});class hh extends Re{_parse(t){if(t.data!==this._def.value){const n=this._getOrReturnCtx(t);return ce(n,{received:n.data,code:re.invalid_literal,expected:this._def.value}),Ce}return{status:"valid",value:t.data}}get value(){return this._def.value}}hh.create=(e,t)=>new hh({value:e,typeName:xe.ZodLiteral,...Oe(t)});function fD(e,t){return new ma({values:e,typeName:xe.ZodEnum,...Oe(t)})}class ma extends Re{_parse(t){if(typeof t.data!="string"){const n=this._getOrReturnCtx(t),r=this._def.values;return ce(n,{expected:Qe.joinValues(r),received:n.parsedType,code:re.invalid_type}),Ce}if(this._def.values.indexOf(t.data)===-1){const n=this._getOrReturnCtx(t),r=this._def.values;return ce(n,{received:n.data,code:re.invalid_enum_value,options:r}),Ce}return pr(t.data)}get options(){return this._def.values}get enum(){const t={};for(const n of this._def.values)t[n]=n;return t}get Values(){const t={};for(const n of this._def.values)t[n]=n;return t}get Enum(){const t={};for(const n of this._def.values)t[n]=n;return t}extract(t){return ma.create(t)}exclude(t){return ma.create(this.options.filter(n=>!t.includes(n)))}}ma.create=fD;class ph extends Re{_parse(t){const n=Qe.getValidEnumValues(this._def.values),r=this._getOrReturnCtx(t);if(r.parsedType!==le.string&&r.parsedType!==le.number){const i=Qe.objectValues(n);return ce(r,{expected:Qe.joinValues(i),received:r.parsedType,code:re.invalid_type}),Ce}if(n.indexOf(t.data)===-1){const i=Qe.objectValues(n);return ce(r,{received:r.data,code:re.invalid_enum_value,options:i}),Ce}return pr(t.data)}get enum(){return this._def.values}}ph.create=(e,t)=>new ph({values:e,typeName:xe.ZodNativeEnum,...Oe(t)});class kc extends Re{unwrap(){return this._def.type}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==le.promise&&n.common.async===!1)return ce(n,{code:re.invalid_type,expected:le.promise,received:n.parsedType}),Ce;const r=n.parsedType===le.promise?n.data:Promise.resolve(n.data);return pr(r.then(i=>this._def.type.parseAsync(i,{path:n.path,errorMap:n.common.contextualErrorMap})))}}kc.create=(e,t)=>new kc({type:e,typeName:xe.ZodPromise,...Oe(t)});class Ui extends Re{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===xe.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=this._def.effect||null;if(i.type==="preprocess"){const s=i.transform(r.data);return r.common.async?Promise.resolve(s).then(a=>this._def.schema._parseAsync({data:a,path:r.path,parent:r})):this._def.schema._parseSync({data:s,path:r.path,parent:r})}const o={addIssue:s=>{ce(r,s),s.fatal?n.abort():n.dirty()},get path(){return r.path}};if(o.addIssue=o.addIssue.bind(o),i.type==="refinement"){const s=a=>{const l=i.refinement(a,o);if(r.common.async)return Promise.resolve(l);if(l instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return a};if(r.common.async===!1){const a=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});return a.status==="aborted"?Ce:(a.status==="dirty"&&n.dirty(),s(a.value),{status:n.value,value:a.value})}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(a=>a.status==="aborted"?Ce:(a.status==="dirty"&&n.dirty(),s(a.value).then(()=>({status:n.value,value:a.value}))))}if(i.type==="transform")if(r.common.async===!1){const s=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});if(!ky(s))return s;const a=i.transform(s.value,o);if(a instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:n.value,value:a}}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(s=>ky(s)?Promise.resolve(i.transform(s.value,o)).then(a=>({status:n.value,value:a})):s);Qe.assertNever(i)}}Ui.create=(e,t,n)=>new Ui({schema:e,typeName:xe.ZodEffects,effect:t,...Oe(n)});Ui.createWithPreprocess=(e,t,n)=>new Ui({schema:t,effect:{type:"preprocess",transform:e},typeName:xe.ZodEffects,...Oe(n)});class ns extends Re{_parse(t){return this._getType(t)===le.undefined?pr(void 0):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}ns.create=(e,t)=>new ns({innerType:e,typeName:xe.ZodOptional,...Oe(t)});class vl extends Re{_parse(t){return this._getType(t)===le.null?pr(null):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}vl.create=(e,t)=>new vl({innerType:e,typeName:xe.ZodNullable,...Oe(t)});class gh extends Re{_parse(t){const{ctx:n}=this._processInputParams(t);let r=n.data;return n.parsedType===le.undefined&&(r=this._def.defaultValue()),this._def.innerType._parse({data:r,path:n.path,parent:n})}removeDefault(){return this._def.innerType}}gh.create=(e,t)=>new gh({innerType:e,typeName:xe.ZodDefault,defaultValue:typeof t.default=="function"?t.default:()=>t.default,...Oe(t)});class My extends Re{_parse(t){const{ctx:n}=this._processInputParams(t),r={...n,common:{...n.common,issues:[]}},i=this._def.innerType._parse({data:r.data,path:r.path,parent:{...r}});return Ay(i)?i.then(o=>({status:"valid",value:o.status==="valid"?o.value:this._def.catchValue({get error(){return new $i(r.common.issues)},input:r.data})})):{status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new $i(r.common.issues)},input:r.data})}}removeCatch(){return this._def.innerType}}My.create=(e,t)=>new My({innerType:e,typeName:xe.ZodCatch,catchValue:typeof t.catch=="function"?t.catch:()=>t.catch,...Oe(t)});class Ny extends Re{_parse(t){if(this._getType(t)!==le.nan){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.nan,received:r.parsedType}),Ce}return{status:"valid",value:t.data}}}Ny.create=e=>new Ny({typeName:xe.ZodNaN,...Oe(e)});const Tue=Symbol("zod_brand");class hD extends Re{_parse(t){const{ctx:n}=this._processInputParams(t),r=n.data;return this._def.type._parse({data:r,path:n.path,parent:n})}unwrap(){return this._def.type}}class dp extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.common.async)return(async()=>{const o=await this._def.in._parseAsync({data:r.data,path:r.path,parent:r});return o.status==="aborted"?Ce:o.status==="dirty"?(n.dirty(),dD(o.value)):this._def.out._parseAsync({data:o.value,path:r.path,parent:r})})();{const i=this._def.in._parseSync({data:r.data,path:r.path,parent:r});return i.status==="aborted"?Ce:i.status==="dirty"?(n.dirty(),{status:"dirty",value:i.value}):this._def.out._parseSync({data:i.value,path:r.path,parent:r})}}static create(t,n){return new dp({in:t,out:n,typeName:xe.ZodPipeline})}}const pD=(e,t={},n)=>e?Pc.create().superRefine((r,i)=>{var o,s;if(!e(r)){const a=typeof t=="function"?t(r):typeof t=="string"?{message:t}:t,l=(s=(o=a.fatal)!==null&&o!==void 0?o:n)!==null&&s!==void 0?s:!0,u=typeof a=="string"?{message:a}:a;i.addIssue({code:"custom",...u,fatal:l})}}):Pc.create(),Eue={object:It.lazycreate};var xe;(function(e){e.ZodString="ZodString",e.ZodNumber="ZodNumber",e.ZodNaN="ZodNaN",e.ZodBigInt="ZodBigInt",e.ZodBoolean="ZodBoolean",e.ZodDate="ZodDate",e.ZodSymbol="ZodSymbol",e.ZodUndefined="ZodUndefined",e.ZodNull="ZodNull",e.ZodAny="ZodAny",e.ZodUnknown="ZodUnknown",e.ZodNever="ZodNever",e.ZodVoid="ZodVoid",e.ZodArray="ZodArray",e.ZodObject="ZodObject",e.ZodUnion="ZodUnion",e.ZodDiscriminatedUnion="ZodDiscriminatedUnion",e.ZodIntersection="ZodIntersection",e.ZodTuple="ZodTuple",e.ZodRecord="ZodRecord",e.ZodMap="ZodMap",e.ZodSet="ZodSet",e.ZodFunction="ZodFunction",e.ZodLazy="ZodLazy",e.ZodLiteral="ZodLiteral",e.ZodEnum="ZodEnum",e.ZodEffects="ZodEffects",e.ZodNativeEnum="ZodNativeEnum",e.ZodOptional="ZodOptional",e.ZodNullable="ZodNullable",e.ZodDefault="ZodDefault",e.ZodCatch="ZodCatch",e.ZodPromise="ZodPromise",e.ZodBranded="ZodBranded",e.ZodPipeline="ZodPipeline"})(xe||(xe={}));const Pue=(e,t={message:`Input not instance of ${e.name}`})=>pD(n=>n instanceof e,t),gD=Ii.create,mD=pa.create,kue=Ny.create,Aue=ga.create,yD=sh.create,Oue=ml.create,Rue=Oy.create,Iue=ah.create,Mue=lh.create,Nue=Pc.create,Due=ol.create,Lue=fs.create,$ue=Ry.create,Fue=Fi.create,Bue=It.create,jue=It.strictCreate,Vue=uh.create,zue=jv.create,Uue=ch.create,Gue=Co.create,Hue=dh.create,que=Iy.create,Wue=yl.create,Kue=Qu.create,Yue=fh.create,Xue=hh.create,Que=ma.create,Zue=ph.create,Jue=kc.create,R6=Ui.create,ece=ns.create,tce=vl.create,nce=Ui.createWithPreprocess,rce=dp.create,ice=()=>gD().optional(),oce=()=>mD().optional(),sce=()=>yD().optional(),ace={string:e=>Ii.create({...e,coerce:!0}),number:e=>pa.create({...e,coerce:!0}),boolean:e=>sh.create({...e,coerce:!0}),bigint:e=>ga.create({...e,coerce:!0}),date:e=>ml.create({...e,coerce:!0})},lce=Ce;var Pn=Object.freeze({__proto__:null,defaultErrorMap:oh,setErrorMap:fue,getErrorMap:Ey,makeIssue:Py,EMPTY_PATH:hue,addIssueToContext:ce,ParseStatus:er,INVALID:Ce,DIRTY:dD,OK:pr,isAborted:Ow,isDirty:Rw,isValid:ky,isAsync:Ay,get 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t=e.reducerPath,n=e.context,r=e.context.endpointDefinitions,i=e.mutationThunk,o=e.api,s=e.assertTagType,a=e.refetchQuery,l=o.internalActions.removeQueryResult,u=Hi(Ca(i),Wh(i)),c=function(f,h){u(f)&&d(uL(f,"invalidatesTags",r,s),h),o.util.invalidateTags.match(f)&&d(lL(f.payload,void 0,void 0,void 0,void 0,s),h)};function d(f,h){var p=h.getState(),m=p[t],S=o.util.selectInvalidatedBy(p,f);n.batch(function(){for(var v,y=Array.from(S.values()),g=0,b=y;g<b.length;g++){var _=b[g].queryCacheKey,w=m.queries[_],x=(v=m.subscriptions[_])!=null?v:{};w&&(Object.keys(x).length===0?h.dispatch(l({queryCacheKey:_})):w.status!==Et.uninitialized&&h.dispatch(a(w,_)))}})}return c},khe=function(e){var 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g_=(e,t)=>n=>{n.target===t.current&&(e==null||e(n))},vve=e=>({userSelectionActive:e.userSelectionActive,elementsSelectable:e.elementsSelectable,dragging:e.paneDragging}),cF=E.memo(({isSelecting:e,selectionMode:t=Th.Full,panOnDrag:n,onSelectionStart:r,onSelectionEnd:i,onPaneClick:o,onPaneContextMenu:s,onPaneScroll:a,onPaneMouseEnter:l,onPaneMouseMove:u,onPaneMouseLeave:c,children:d})=>{const f=E.useRef(null),h=tr(),p=E.useRef(0),m=E.useRef(0),S=E.useRef(),{userSelectionActive:v,elementsSelectable:y,dragging:g}=Kt(vve,Dr),b=()=>{h.setState({userSelectionActive:!1,userSelectionRect:null}),p.current=0,m.current=0},_=N=>{o==null||o(N),h.getState().resetSelectedElements(),h.setState({nodesSelectionActive:!1})},w=N=>{if(Array.isArray(n)&&(n!=null&&n.includes(2))){N.preventDefault();return}s==null||s(N)},x=a?N=>a(N):void 0,C=N=>{const{resetSelectedElements:T,domNode:O}=h.getState();if(S.current=O==null?void 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wve=e=>e.nodesSelectionActive,dF=({children:e,onPaneClick:t,onPaneMouseEnter:n,onPaneMouseMove:r,onPaneMouseLeave:i,onPaneContextMenu:o,onPaneScroll:s,deleteKeyCode:a,onMove:l,onMoveStart:u,onMoveEnd:c,selectionKeyCode:d,selectionOnDrag:f,selectionMode:h,onSelectionStart:p,onSelectionEnd:m,multiSelectionKeyCode:S,panActivationKeyCode:v,zoomActivationKeyCode:y,elementsSelectable:g,zoomOnScroll:b,zoomOnPinch:_,panOnScroll:w,panOnScrollSpeed:x,panOnScrollMode:C,zoomOnDoubleClick:k,panOnDrag:P,defaultViewport:A,translateExtent:$,minZoom:N,maxZoom:T,preventScrolling:O,onSelectionContextMenu:I,noWheelClassName:M,noPanClassName:R,disableKeyboardA11y:D})=>{const L=Kt(wve),V=Eh(d),U=Eh(v)||P,K=V||f&&U!==!0;return 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n.count!="string",x=i0.hasDefaultValue(n),C=w?this.pluralResolver.getSuffix(u,n.count,n):"",k=n.ordinal&&w?this.pluralResolver.getSuffix(u,n.count,{ordinal:!1}):"",P=n[`defaultValue${C}`]||n[`defaultValue${k}`]||n.defaultValue;!this.isValidLookup(f)&&x&&(b=!0,f=P),this.isValidLookup(f)||(_=!0,f=s);const $=(n.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey)&&_?void 0:f,N=x&&P!==f&&this.options.updateMissing;if(_||b||N){if(this.logger.log(N?"updateKey":"missingKey",u,l,s,N?P:f),o){const M=this.resolve(s,{...n,keySeparator:!1});M&&M.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}let T=[];const O=this.languageUtils.getFallbackCodes(this.options.fallbackLng,n.lng||this.language);if(this.options.saveMissingTo==="fallback"&&O&&O[0])for(let M=0;M<O.length;M++)T.push(O[M]);else this.options.saveMissingTo==="all"?T=this.languageUtils.toResolveHierarchy(n.lng||this.language):T.push(n.lng||this.language);const I=(M,R,D)=>{const L=x&&D!==f?D:$;this.options.missingKeyHandler?this.options.missingKeyHandler(M,l,R,L,N,n):this.backendConnector&&this.backendConnector.saveMissing&&this.backendConnector.saveMissing(M,l,R,L,N,n),this.emit("missingKey",M,l,R,f)};this.options.saveMissing&&(this.options.saveMissingPlurals&&w?T.forEach(M=>{this.pluralResolver.getSuffixes(M,n).forEach(R=>{I([M],s+R,n[`defaultValue${R}`]||P)})}):I(T,s,P))}f=this.extendTranslation(f,t,n,d,r),_&&f===s&&this.options.appendNamespaceToMissingKey&&(f=`${l}:${s}`),(_||b)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?f=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${l}:${s}`:s,b?f:void 0):f=this.options.parseMissingKeyHandler(f))}return i?(d.res=f,d):f}extendTranslation(t,n,r,i,o){var s=this;if(this.i18nFormat&&this.i18nFormat.parse)t=this.i18nFormat.parse(t,{...this.options.interpolation.defaultVariables,...r},i.usedLng,i.usedNS,i.usedKey,{resolved:i});else if(!r.skipInterpolation){r.interpolation&&this.interpolator.init({...r,interpolation:{...this.options.interpolation,...r.interpolation}});const u=typeof t=="string"&&(r&&r.interpolation&&r.interpolation.skipOnVariables!==void 0?r.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);let c;if(u){const f=t.match(this.interpolator.nestingRegexp);c=f&&f.length}let d=r.replace&&typeof r.replace!="string"?r.replace:r;if(this.options.interpolation.defaultVariables&&(d={...this.options.interpolation.defaultVariables,...d}),t=this.interpolator.interpolate(t,d,r.lng||this.language,r),u){const f=t.match(this.interpolator.nestingRegexp),h=f&&f.length;c<h&&(r.nest=!1)}!r.lng&&this.options.compatibilityAPI!=="v1"&&i&&i.res&&(r.lng=i.usedLng),r.nest!==!1&&(t=this.interpolator.nest(t,function(){for(var f=arguments.length,h=new Array(f),p=0;p<f;p++)h[p]=arguments[p];return o&&o[0]===h[0]&&!r.context?(s.logger.warn(`It seems you are nesting recursively key: ${h[0]} in key: ${n[0]}`),null):s.translate(...h,n)},r)),r.interpolation&&this.interpolator.reset()}const a=r.postProcess||this.options.postProcess,l=typeof a=="string"?[a]:a;return t!=null&&l&&l.length&&r.applyPostProcessor!==!1&&(t=TF.handle(l,t,n,this.options&&this.options.postProcessPassResolved?{i18nResolved:i,...r}:r,this)),t}resolve(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},r,i,o,s,a;return typeof t=="string"&&(t=[t]),t.forEach(l=>{if(this.isValidLookup(r))return;const u=this.extractFromKey(l,n),c=u.key;i=c;let d=u.namespaces;this.options.fallbackNS&&(d=d.concat(this.options.fallbackNS));const f=n.count!==void 0&&typeof n.count!="string",h=f&&!n.ordinal&&n.count===0&&this.pluralResolver.shouldUseIntlApi(),p=n.context!==void 0&&(typeof n.context=="string"||typeof n.context=="number")&&n.context!=="",m=n.lngs?n.lngs:this.languageUtils.toResolveHierarchy(n.lng||this.language,n.fallbackLng);d.forEach(S=>{this.isValidLookup(r)||(a=S,!ck[`${m[0]}-${S}`]&&this.utils&&this.utils.hasLoadedNamespace&&!this.utils.hasLoadedNamespace(a)&&(ck[`${m[0]}-${S}`]=!0,this.logger.warn(`key "${i}" for languages "${m.join(", ")}" won't get resolved as namespace "${a}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(v=>{if(this.isValidLookup(r))return;s=v;const y=[c];if(this.i18nFormat&&this.i18nFormat.addLookupKeys)this.i18nFormat.addLookupKeys(y,c,v,S,n);else{let b;f&&(b=this.pluralResolver.getSuffix(v,n.count,n));const _=`${this.options.pluralSeparator}zero`,w=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(f&&(y.push(c+b),n.ordinal&&b.indexOf(w)===0&&y.push(c+b.replace(w,this.options.pluralSeparator)),h&&y.push(c+_)),p){const x=`${c}${this.options.contextSeparator}${n.context}`;y.push(x),f&&(y.push(x+b),n.ordinal&&b.indexOf(w)===0&&y.push(x+b.replace(w,this.options.pluralSeparator)),h&&y.push(x+_))}}let g;for(;g=y.pop();)this.isValidLookup(r)||(o=g,r=this.getResource(v,S,g,n))}))})}),{res:r,usedKey:i,exactUsedKey:o,usedLng:s,usedNS:a}}isValidLookup(t){return t!==void 0&&!(!this.options.returnNull&&t===null)&&!(!this.options.returnEmptyString&&t==="")}getResource(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(t,n,r,i):this.resourceStore.getResource(t,n,r,i)}static hasDefaultValue(t){const n="defaultValue";for(const r in t)if(Object.prototype.hasOwnProperty.call(t,r)&&n===r.substring(0,n.length)&&t[r]!==void 0)return!0;return!1}}function m_(e){return e.charAt(0).toUpperCase()+e.slice(1)}class dk{constructor(t){this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=ho.create("languageUtils")}getScriptPartFromCode(t){if(t=r0(t),!t||t.indexOf("-")<0)return null;const n=t.split("-");return n.length===2||(n.pop(),n[n.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(n.join("-"))}getLanguagePartFromCode(t){if(t=r0(t),!t||t.indexOf("-")<0)return t;const n=t.split("-");return this.formatLanguageCode(n[0])}formatLanguageCode(t){if(typeof t=="string"&&t.indexOf("-")>-1){const n=["hans","hant","latn","cyrl","cans","mong","arab"];let r=t.split("-");return this.options.lowerCaseLng?r=r.map(i=>i.toLowerCase()):r.length===2?(r[0]=r[0].toLowerCase(),r[1]=r[1].toUpperCase(),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=m_(r[1].toLowerCase()))):r.length===3&&(r[0]=r[0].toLowerCase(),r[1].length===2&&(r[1]=r[1].toUpperCase()),r[0]!=="sgn"&&r[2].length===2&&(r[2]=r[2].toUpperCase()),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=m_(r[1].toLowerCase())),n.indexOf(r[2].toLowerCase())>-1&&(r[2]=m_(r[2].toLowerCase()))),r.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?t.toLowerCase():t}isSupportedCode(t){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(t=this.getLanguagePartFromCode(t)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(t)>-1}getBestMatchFromCodes(t){if(!t)return null;let n;return t.forEach(r=>{if(n)return;const i=this.formatLanguageCode(r);(!this.options.supportedLngs||this.isSupportedCode(i))&&(n=i)}),!n&&this.options.supportedLngs&&t.forEach(r=>{if(n)return;const i=this.getLanguagePartFromCode(r);if(this.isSupportedCode(i))return n=i;n=this.options.supportedLngs.find(o=>{if(o===i)return o;if(!(o.indexOf("-")<0&&i.indexOf("-")<0)&&o.indexOf(i)===0)return o})}),n||(n=this.getFallbackCodes(this.options.fallbackLng)[0]),n}getFallbackCodes(t,n){if(!t)return[];if(typeof t=="function"&&(t=t(n)),typeof t=="string"&&(t=[t]),Object.prototype.toString.apply(t)==="[object Array]")return t;if(!n)return t.default||[];let r=t[n];return r||(r=t[this.getScriptPartFromCode(n)]),r||(r=t[this.formatLanguageCode(n)]),r||(r=t[this.getLanguagePartFromCode(n)]),r||(r=t.default),r||[]}toResolveHierarchy(t,n){const r=this.getFallbackCodes(n||this.options.fallbackLng||[],t),i=[],o=s=>{s&&(this.isSupportedCode(s)?i.push(s):this.logger.warn(`rejecting language code not found in supportedLngs: ${s}`))};return typeof t=="string"&&(t.indexOf("-")>-1||t.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&o(this.formatLanguageCode(t)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&o(this.getScriptPartFromCode(t)),this.options.load!=="currentOnly"&&o(this.getLanguagePartFromCode(t))):typeof t=="string"&&o(this.formatLanguageCode(t)),r.forEach(s=>{i.indexOf(s)<0&&o(this.formatLanguageCode(s))}),i}}let d1e=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],f1e={1:function(e){return+(e>1)},2:function(e){return+(e!=1)},3:function(e){return 0},4:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},5:function(e){return e==0?0:e==1?1:e==2?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5},6:function(e){return e==1?0:e>=2&&e<=4?1:2},7:function(e){return e==1?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},8:function(e){return e==1?0:e==2?1:e!=8&&e!=11?2:3},9:function(e){return+(e>=2)},10:function(e){return e==1?0:e==2?1:e<7?2:e<11?3:4},11:function(e){return e==1||e==11?0:e==2||e==12?1:e>2&&e<20?2:3},12:function(e){return+(e%10!=1||e%100==11)},13:function(e){return+(e!==0)},14:function(e){return e==1?0:e==2?1:e==3?2:3},15:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2},16:function(e){return e%10==1&&e%100!=11?0:e!==0?1:2},17:function(e){return e==1||e%10==1&&e%100!=11?0:1},18:function(e){return e==0?0:e==1?1:2},19:function(e){return e==1?0:e==0||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3},20:function(e){return e==1?0:e==0||e%100>0&&e%100<20?1:2},21:function(e){return e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0},22:function(e){return e==1?0:e==2?1:(e<0||e>10)&&e%10==0?2:3}};const h1e=["v1","v2","v3"],p1e=["v4"],fk={zero:0,one:1,two:2,few:3,many:4,other:5};function g1e(){const e={};return d1e.forEach(t=>{t.lngs.forEach(n=>{e[n]={numbers:t.nr,plurals:f1e[t.fc]}})}),e}class m1e{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.languageUtils=t,this.options=n,this.logger=ho.create("pluralResolver"),(!this.options.compatibilityJSON||p1e.includes(this.options.compatibilityJSON))&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=g1e()}addRule(t,n){this.rules[t]=n}getRule(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(r0(t),{type:n.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[t]||this.rules[this.languageUtils.getLanguagePartFromCode(t)]}needsPlural(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return this.shouldUseIntlApi()?r&&r.resolvedOptions().pluralCategories.length>1:r&&r.numbers.length>1}getPluralFormsOfKey(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(t,r).map(i=>`${n}${i}`)}getSuffixes(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return r?this.shouldUseIntlApi()?r.resolvedOptions().pluralCategories.sort((i,o)=>fk[i]-fk[o]).map(i=>`${this.options.prepend}${n.ordinal?`ordinal${this.options.prepend}`:""}${i}`):r.numbers.map(i=>this.getSuffix(t,i,n)):[]}getSuffix(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};const i=this.getRule(t,r);return i?this.shouldUseIntlApi()?`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${i.select(n)}`:this.getSuffixRetroCompatible(i,n):(this.logger.warn(`no plural rule found for: ${t}`),"")}getSuffixRetroCompatible(t,n){const r=t.noAbs?t.plurals(n):t.plurals(Math.abs(n));let i=t.numbers[r];this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1&&(i===2?i="plural":i===1&&(i=""));const o=()=>this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString();return this.options.compatibilityJSON==="v1"?i===1?"":typeof i=="number"?`_plural_${i.toString()}`:o():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1?o():this.options.prepend&&r.toString()?this.options.prepend+r.toString():r.toString()}shouldUseIntlApi(){return!h1e.includes(this.options.compatibilityJSON)}}function hk(e,t,n){let r=arguments.length>3&&arguments[3]!==void 0?arguments[3]:".",i=arguments.length>4&&arguments[4]!==void 0?arguments[4]:!0,o=s1e(e,t,n);return!o&&i&&typeof n=="string"&&(o=n0(e,n,r),o===void 0&&(o=n0(t,n,r))),o}class y1e{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=ho.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||(n=>n),this.init(t)}init(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};t.interpolation||(t.interpolation={escapeValue:!0});const n=t.interpolation;this.escape=n.escape!==void 0?n.escape:l1e,this.escapeValue=n.escapeValue!==void 0?n.escapeValue:!0,this.useRawValueToEscape=n.useRawValueToEscape!==void 0?n.useRawValueToEscape:!1,this.prefix=n.prefix?ou(n.prefix):n.prefixEscaped||"{{",this.suffix=n.suffix?ou(n.suffix):n.suffixEscaped||"}}",this.formatSeparator=n.formatSeparator?n.formatSeparator:n.formatSeparator||",",this.unescapePrefix=n.unescapeSuffix?"":n.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":n.unescapeSuffix||"",this.nestingPrefix=n.nestingPrefix?ou(n.nestingPrefix):n.nestingPrefixEscaped||ou("$t("),this.nestingSuffix=n.nestingSuffix?ou(n.nestingSuffix):n.nestingSuffixEscaped||ou(")"),this.nestingOptionsSeparator=n.nestingOptionsSeparator?n.nestingOptionsSeparator:n.nestingOptionsSeparator||",",this.maxReplaces=n.maxReplaces?n.maxReplaces:1e3,this.alwaysFormat=n.alwaysFormat!==void 0?n.alwaysFormat:!1,this.resetRegExp()}reset(){this.options&&this.init(this.options)}resetRegExp(){const t=`${this.prefix}(.+?)${this.suffix}`;this.regexp=new RegExp(t,"g");const n=`${this.prefix}${this.unescapePrefix}(.+?)${this.unescapeSuffix}${this.suffix}`;this.regexpUnescape=new RegExp(n,"g");const r=`${this.nestingPrefix}(.+?)${this.nestingSuffix}`;this.nestingRegexp=new RegExp(r,"g")}interpolate(t,n,r,i){let o,s,a;const l=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function u(p){return p.replace(/\$/g,"$$$$")}const c=p=>{if(p.indexOf(this.formatSeparator)<0){const y=hk(n,l,p,this.options.keySeparator,this.options.ignoreJSONStructure);return this.alwaysFormat?this.format(y,void 0,r,{...i,...n,interpolationkey:p}):y}const m=p.split(this.formatSeparator),S=m.shift().trim(),v=m.join(this.formatSeparator).trim();return this.format(hk(n,l,S,this.options.keySeparator,this.options.ignoreJSONStructure),v,r,{...i,...n,interpolationkey:S})};this.resetRegExp();const d=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,f=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 0?i.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables;return[{regex:this.regexpUnescape,safeValue:p=>u(p)},{regex:this.regexp,safeValue:p=>this.escapeValue?u(this.escape(p)):u(p)}].forEach(p=>{for(a=0;o=p.regex.exec(t);){const m=o[1].trim();if(s=c(m),s===void 0)if(typeof d=="function"){const v=d(t,o,i);s=typeof v=="string"?v:""}else if(i&&Object.prototype.hasOwnProperty.call(i,m))s="";else if(f){s=o[0];continue}else this.logger.warn(`missed to pass in variable ${m} for interpolating ${t}`),s="";else typeof s!="string"&&!this.useRawValueToEscape&&(s=ak(s));const S=p.safeValue(s);if(t=t.replace(o[0],S),f?(p.regex.lastIndex+=s.length,p.regex.lastIndex-=o[0].length):p.regex.lastIndex=0,a++,a>=this.maxReplaces)break}}),t}nest(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i,o,s;function a(l,u){const c=this.nestingOptionsSeparator;if(l.indexOf(c)<0)return l;const d=l.split(new RegExp(`${c}[ ]*{`));let f=`{${d[1]}`;l=d[0],f=this.interpolate(f,s);const h=f.match(/'/g),p=f.match(/"/g);(h&&h.length%2===0&&!p||p.length%2!==0)&&(f=f.replace(/'/g,'"'));try{s=JSON.parse(f),u&&(s={...u,...s})}catch(m){return this.logger.warn(`failed parsing options string in nesting for key ${l}`,m),`${l}${c}${f}`}return delete s.defaultValue,l}for(;i=this.nestingRegexp.exec(t);){let l=[];s={...r},s=s.replace&&typeof s.replace!="string"?s.replace:s,s.applyPostProcessor=!1,delete s.defaultValue;let u=!1;if(i[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(i[1])){const c=i[1].split(this.formatSeparator).map(d=>d.trim());i[1]=c.shift(),l=c,u=!0}if(o=n(a.call(this,i[1].trim(),s),s),o&&i[0]===t&&typeof o!="string")return o;typeof o!="string"&&(o=ak(o)),o||(this.logger.warn(`missed to resolve ${i[1]} for nesting ${t}`),o=""),u&&(o=l.reduce((c,d)=>this.format(c,d,r.lng,{...r,interpolationkey:i[1].trim()}),o.trim())),t=t.replace(i[0],o),this.regexp.lastIndex=0}return t}}function v1e(e){let t=e.toLowerCase().trim();const n={};if(e.indexOf("(")>-1){const r=e.split("(");t=r[0].toLowerCase().trim();const i=r[1].substring(0,r[1].length-1);t==="currency"&&i.indexOf(":")<0?n.currency||(n.currency=i.trim()):t==="relativetime"&&i.indexOf(":")<0?n.range||(n.range=i.trim()):i.split(";").forEach(s=>{if(!s)return;const[a,...l]=s.split(":"),u=l.join(":").trim().replace(/^'+|'+$/g,"");n[a.trim()]||(n[a.trim()]=u),u==="false"&&(n[a.trim()]=!1),u==="true"&&(n[a.trim()]=!0),isNaN(u)||(n[a.trim()]=parseInt(u,10))})}return{formatName:t,formatOptions:n}}function su(e){const t={};return function(r,i,o){const s=i+JSON.stringify(o);let a=t[s];return a||(a=e(r0(i),o),t[s]=a),a(r)}}class b1e{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=ho.create("formatter"),this.options=t,this.formats={number:su((n,r)=>{const i=new Intl.NumberFormat(n,{...r});return o=>i.format(o)}),currency:su((n,r)=>{const i=new Intl.NumberFormat(n,{...r,style:"currency"});return 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pk(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!1,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){let n={};if(typeof t[1]=="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),typeof t[2]=="object"||typeof t[3]=="object"){const r=t[3]||t[2];Object.keys(r).forEach(i=>{n[i]=r[i]})}return n},interpolation:{escapeValue:!0,format:(e,t,n,r)=>e,prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function gk(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function Eg(){}function w1e(e){Object.getOwnPropertyNames(Object.getPrototypeOf(e)).forEach(n=>{typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}class kh extends v1{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;if(super(),this.options=gk(t),this.services={},this.logger=ho,this.modules={external:[]},w1e(this),n&&!this.isInitialized&&!t.isClone){if(!this.options.initImmediate)return this.init(t,n),this;setTimeout(()=>{this.init(t,n)},0)}}init(){var t=this;let n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},r=arguments.length>1?arguments[1]:void 0;typeof n=="function"&&(r=n,n={}),!n.defaultNS&&n.defaultNS!==!1&&n.ns&&(typeof n.ns=="string"?n.defaultNS=n.ns:n.ns.indexOf("translation")<0&&(n.defaultNS=n.ns[0]));const i=pk();this.options={...i,...this.options,...gk(n)},this.options.compatibilityAPI!=="v1"&&(this.options.interpolation={...i.interpolation,...this.options.interpolation}),n.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=n.keySeparator),n.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=n.nsSeparator);function o(c){return c?typeof c=="function"?new c:c:null}if(!this.options.isClone){this.modules.logger?ho.init(o(this.modules.logger),this.options):ho.init(null,this.options);let c;this.modules.formatter?c=this.modules.formatter:typeof Intl<"u"&&(c=b1e);const d=new dk(this.options);this.store=new uk(this.options.resources,this.options);const f=this.services;f.logger=ho,f.resourceStore=this.store,f.languageUtils=d,f.pluralResolver=new m1e(d,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),c&&(!this.options.interpolation.format||this.options.interpolation.format===i.interpolation.format)&&(f.formatter=o(c),f.formatter.init(f,this.options),this.options.interpolation.format=f.formatter.format.bind(f.formatter)),f.interpolator=new y1e(this.options),f.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},f.backendConnector=new _1e(o(this.modules.backend),f.resourceStore,f,this.options),f.backendConnector.on("*",function(h){for(var p=arguments.length,m=new 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Please check the object you are passing to i18next.use()");if(!t.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return t.type==="backend"&&(this.modules.backend=t),(t.type==="logger"||t.log&&t.warn&&t.error)&&(this.modules.logger=t),t.type==="languageDetector"&&(this.modules.languageDetector=t),t.type==="i18nFormat"&&(this.modules.i18nFormat=t),t.type==="postProcessor"&&TF.addPostProcessor(t),t.type==="formatter"&&(this.modules.formatter=t),t.type==="3rdParty"&&this.modules.external.push(t),this}setResolvedLanguage(t){if(!(!t||!this.languages)&&!(["cimode","dev"].indexOf(t)>-1))for(let n=0;n<this.languages.length;n++){const r=this.languages[n];if(!(["cimode","dev"].indexOf(r)>-1)&&this.store.hasLanguageSomeTranslations(r)){this.resolvedLanguage=r;break}}}changeLanguage(t,n){var r=this;this.isLanguageChangingTo=t;const i=Ad();this.emit("languageChanging",t);const o=l=>{this.language=l,this.languages=this.services.languageUtils.toResolveHierarchy(l),this.resolvedLanguage=void 0,this.setResolvedLanguage(l)},s=(l,u)=>{u?(o(u),this.translator.changeLanguage(u),this.isLanguageChangingTo=void 0,this.emit("languageChanged",u),this.logger.log("languageChanged",u)):this.isLanguageChangingTo=void 0,i.resolve(function(){return r.t(...arguments)}),n&&n(l,function(){return r.t(...arguments)})},a=l=>{!t&&!l&&this.services.languageDetector&&(l=[]);const u=typeof l=="string"?l:this.services.languageUtils.getBestMatchFromCodes(l);u&&(this.language||o(u),this.translator.language||this.translator.changeLanguage(u),this.services.languageDetector&&this.services.languageDetector.cacheUserLanguage&&this.services.languageDetector.cacheUserLanguage(u)),this.loadResources(u,c=>{s(c,u)})};return!t&&this.services.languageDetector&&!this.services.languageDetector.async?a(this.services.languageDetector.detect()):!t&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(a):this.services.languageDetector.detect(a):a(t),i}getFixedT(t,n,r){var i=this;const o=function(s,a){let l;if(typeof a!="object"){for(var u=arguments.length,c=new Array(u>2?u-2:0),d=2;d<u;d++)c[d-2]=arguments[d];l=i.options.overloadTranslationOptionHandler([s,a].concat(c))}else l={...a};l.lng=l.lng||o.lng,l.lngs=l.lngs||o.lngs,l.ns=l.ns||o.ns,l.keyPrefix=l.keyPrefix||r||o.keyPrefix;const f=i.options.keySeparator||".";let h;return l.keyPrefix&&Array.isArray(s)?h=s.map(p=>`${l.keyPrefix}${f}${p}`):h=l.keyPrefix?`${l.keyPrefix}${f}${s}`:s,i.t(h,l)};return typeof t=="string"?o.lng=t:o.lngs=t,o.ns=n,o.keyPrefix=r,o}t(){return this.translator&&this.translator.translate(...arguments)}exists(){return this.translator&&this.translator.exists(...arguments)}setDefaultNamespace(t){this.options.defaultNS=t}hasLoadedNamespace(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;const r=n.lng||this.resolvedLanguage||this.languages[0],i=this.options?this.options.fallbackLng:!1,o=this.languages[this.languages.length-1];if(r.toLowerCase()==="cimode")return!0;const s=(a,l)=>{const u=this.services.backendConnector.state[`${a}|${l}`];return u===-1||u===2};if(n.precheck){const a=n.precheck(this,s);if(a!==void 0)return a}return!!(this.hasResourceBundle(r,t)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||s(r,t)&&(!i||s(o,t)))}loadNamespaces(t,n){const r=Ad();return this.options.ns?(typeof t=="string"&&(t=[t]),t.forEach(i=>{this.options.ns.indexOf(i)<0&&this.options.ns.push(i)}),this.loadResources(i=>{r.resolve(),n&&n(i)}),r):(n&&n(),Promise.resolve())}loadLanguages(t,n){const r=Ad();typeof t=="string"&&(t=[t]);const i=this.options.preload||[],o=t.filter(s=>i.indexOf(s)<0);return o.length?(this.options.preload=i.concat(o),this.loadResources(s=>{r.resolve(),n&&n(s)}),r):(n&&n(),Promise.resolve())}dir(t){if(t||(t=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!t)return"rtl";const n=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],r=this.services&&this.services.languageUtils||new dk(pk());return n.indexOf(r.getLanguagePartFromCode(t))>-1||t.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}static createInstance(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;return new kh(t,n)}cloneInstance(){let t=arguments.length>0&&arguments[0]!==void 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Please use public Util.degToRad instead."),e.Util.degToRad(g)},_radToDeg(g){return e.Util.warn("Util._radToDeg is removed. Please use public Util.radToDeg instead."),e.Util.radToDeg(g)},_getRotation(g){return t.Konva.angleDeg?e.Util.radToDeg(g):g},_capitalize(g){return g.charAt(0).toUpperCase()+g.slice(1)},throw(g){throw new Error(h+g)},error(g){console.error(h+g)},warn(g){t.Konva.showWarnings&&console.warn(f+g)},each(g,b){for(var _ in g)b(_,g[_])},_inRange(g,b,_){return b<=g&&g<_},_getProjectionToSegment(g,b,_,w,x,C){var k,P,A,$=(g-_)*(g-_)+(b-w)*(b-w);if($==0)k=g,P=b,A=(x-_)*(x-_)+(C-w)*(C-w);else{var N=((x-g)*(_-g)+(C-b)*(w-b))/$;N<0?(k=g,P=b,A=(g-x)*(g-x)+(b-C)*(b-C)):N>1?(k=_,P=w,A=(_-x)*(_-x)+(w-C)*(w-C)):(k=g+N*(_-g),P=b+N*(w-b),A=(k-x)*(k-x)+(P-C)*(P-C))}return[k,P,A]},_getProjectionToLine(g,b,_){var w=e.Util.cloneObject(g),x=Number.MAX_VALUE;return b.forEach(function(C,k){if(!(!_&&k===b.length-1)){var P=b[(k+1)%b.length],A=e.Util._getProjectionToSegment(C.x,C.y,P.x,P.y,g.x,g.y),$=A[0],N=A[1],T=A[2];T<x&&(w.x=$,w.y=N,x=T)}}),w},_prepareArrayForTween(g,b,_){var w,x=[],C=[];if(g.length>b.length){var k=b;b=g,g=k}for(w=0;w<g.length;w+=2)x.push({x:g[w],y:g[w+1]});for(w=0;w<b.length;w+=2)C.push({x:b[w],y:b[w+1]});var P=[];return C.forEach(function(A){var $=e.Util._getProjectionToLine(A,x,_);P.push($.x),P.push($.y)}),P},_prepareToStringify(g){var b;g.visitedByCircularReferenceRemoval=!0;for(var _ in g)if(g.hasOwnProperty(_)&&g[_]&&typeof g[_]=="object"){if(b=Object.getOwnPropertyDescriptor(g,_),g[_].visitedByCircularReferenceRemoval||e.Util._isElement(g[_]))if(b.configurable)delete g[_];else return null;else if(e.Util._prepareToStringify(g[_])===null)if(b.configurable)delete g[_];else return null}return delete g.visitedByCircularReferenceRemoval,g},_assign(g,b){for(var _ in b)g[_]=b[_];return g},_getFirstPointerId(g){return g.touches?g.changedTouches[0].identifier:g.pointerId||999},releaseCanvas(...g){t.Konva.releaseCanvasOnDestroy&&g.forEach(b=>{b.width=0,b.height=0})},drawRoundedRectPath(g,b,_,w){let x=0,C=0,k=0,P=0;typeof w=="number"?x=C=k=P=Math.min(w,b/2,_/2):(x=Math.min(w[0]||0,b/2,_/2),C=Math.min(w[1]||0,b/2,_/2),P=Math.min(w[2]||0,b/2,_/2),k=Math.min(w[3]||0,b/2,_/2)),g.moveTo(x,0),g.lineTo(b-C,0),g.arc(b-C,C,C,Math.PI*3/2,0,!1),g.lineTo(b,_-P),g.arc(b-P,_-P,P,0,Math.PI/2,!1),g.lineTo(k,_),g.arc(k,_-k,k,Math.PI/2,Math.PI,!1),g.lineTo(0,x),g.arc(x,x,x,Math.PI,Math.PI*3/2,!1)}}})(Bt);var At={},Ie={},fe={};Object.defineProperty(fe,"__esModule",{value:!0});fe.getComponentValidator=fe.getBooleanValidator=fe.getNumberArrayValidator=fe.getFunctionValidator=fe.getStringOrGradientValidator=fe.getStringValidator=fe.getNumberOrAutoValidator=fe.getNumberOrArrayOfNumbersValidator=fe.getNumberValidator=fe.alphaComponent=fe.RGBComponent=void 0;const ys=Le,Ut=Bt;function vs(e){return Ut.Util._isString(e)?'"'+e+'"':Object.prototype.toString.call(e)==="[object Number]"||Ut.Util._isBoolean(e)?e:Object.prototype.toString.call(e)}function OSe(e){return e>255?255:e<0?0:Math.round(e)}fe.RGBComponent=OSe;function RSe(e){return e>1?1:e<1e-4?1e-4:e}fe.alphaComponent=RSe;function ISe(){if(ys.Konva.isUnminified)return function(e,t){return Ut.Util._isNumber(e)||Ut.Util.warn(vs(e)+' is a not valid value for "'+t+'" attribute. 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n=t._getLinearGradient();n&&(this.setAttr("fillStyle",n),t._fillFunc(this))}_fillRadialGradient(t){const n=t._getRadialGradient();n&&(this.setAttr("fillStyle",n),t._fillFunc(this))}_fill(t){const n=t.fill(),r=t.getFillPriority();if(n&&r==="color"){this._fillColor(t);return}const i=t.getFillPatternImage();if(i&&r==="pattern"){this._fillPattern(t);return}const o=t.getFillLinearGradientColorStops();if(o&&r==="linear-gradient"){this._fillLinearGradient(t);return}const s=t.getFillRadialGradientColorStops();if(s&&r==="radial-gradient"){this._fillRadialGradient(t);return}n?this._fillColor(t):i?this._fillPattern(t):o?this._fillLinearGradient(t):s&&this._fillRadialGradient(t)}_strokeLinearGradient(t){const n=t.getStrokeLinearGradientStartPoint(),r=t.getStrokeLinearGradientEndPoint(),i=t.getStrokeLinearGradientColorStops(),o=this.createLinearGradient(n.x,n.y,r.x,r.y);if(i){for(var s=0;s<i.length;s+=2)o.addColorStop(i[s],i[s+1]);this.setAttr("strokeStyle",o)}}_stroke(t){var n=t.dash(),r=t.getStrokeScaleEnabled();if(t.hasStroke()){if(!r){this.save();var i=this.getCanvas().getPixelRatio();this.setTransform(i,0,0,i,0,0)}this._applyLineCap(t),n&&t.dashEnabled()&&(this.setLineDash(n),this.setAttr("lineDashOffset",t.dashOffset())),this.setAttr("lineWidth",t.strokeWidth()),t.getShadowForStrokeEnabled()||this.setAttr("shadowColor","rgba(0,0,0,0)");var o=t.getStrokeLinearGradientColorStops();o?this._strokeLinearGradient(t):this.setAttr("strokeStyle",t.stroke()),t._strokeFunc(this),r||this.restore()}}_applyShadow(t){var n,r,i,o=(n=t.getShadowRGBA())!==null&&n!==void 0?n:"black",s=(r=t.getShadowBlur())!==null&&r!==void 0?r:5,a=(i=t.getShadowOffset())!==null&&i!==void 0?i:{x:0,y:0},l=t.getAbsoluteScale(),u=this.canvas.getPixelRatio(),c=l.x*u,d=l.y*u;this.setAttr("shadowColor",o),this.setAttr("shadowBlur",s*Math.min(Math.abs(c),Math.abs(d))),this.setAttr("shadowOffsetX",a.x*c),this.setAttr("shadowOffsetY",a.y*d)}}rs.SceneContext=ZSe;class JSe extends _1{constructor(t){super(t),this._context=t._canvas.getContext("2d",{willReadFrequently:!0})}_fill(t){this.save(),this.setAttr("fillStyle",t.colorKey),t._fillFuncHit(this),this.restore()}strokeShape(t){t.hasHitStroke()&&this._stroke(t)}_stroke(t){if(t.hasHitStroke()){const o=t.getStrokeScaleEnabled();if(!o){this.save();var n=this.getCanvas().getPixelRatio();this.setTransform(n,0,0,n,0,0)}this._applyLineCap(t);var r=t.hitStrokeWidth(),i=r==="auto"?t.strokeWidth():r;this.setAttr("lineWidth",i),this.setAttr("strokeStyle",t.colorKey),t._strokeFuncHit(this),o||this.restore()}}}rs.HitContext=JSe;Object.defineProperty(Bi,"__esModule",{value:!0});Bi.HitCanvas=Bi.SceneCanvas=Bi.Canvas=void 0;const s0=Bt,HF=rs,qF=Le,e_e=Ie,t_e=fe;var Pg;function n_e(){if(Pg)return Pg;var e=s0.Util.createCanvasElement(),t=e.getContext("2d");return Pg=function(){var n=qF.Konva._global.devicePixelRatio||1,r=t.webkitBackingStorePixelRatio||t.mozBackingStorePixelRatio||t.msBackingStorePixelRatio||t.oBackingStorePixelRatio||t.backingStorePixelRatio||1;return n/r}(),s0.Util.releaseCanvas(e),Pg}class w1{constructor(t){this.pixelRatio=1,this.width=0,this.height=0,this.isCache=!1;var n=t||{},r=n.pixelRatio||qF.Konva.pixelRatio||n_e();this.pixelRatio=r,this._canvas=s0.Util.createCanvasElement(),this._canvas.style.padding="0",this._canvas.style.margin="0",this._canvas.style.border="0",this._canvas.style.background="transparent",this._canvas.style.position="absolute",this._canvas.style.top="0",this._canvas.style.left="0"}getContext(){return this.context}getPixelRatio(){return this.pixelRatio}setPixelRatio(t){var n=this.pixelRatio;this.pixelRatio=t,this.setSize(this.getWidth()/n,this.getHeight()/n)}setWidth(t){this.width=this._canvas.width=t*this.pixelRatio,this._canvas.style.width=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}setHeight(t){this.height=this._canvas.height=t*this.pixelRatio,this._canvas.style.height=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}getWidth(){return this.width}getHeight(){return this.height}setSize(t,n){this.setWidth(t||0),this.setHeight(n||0)}toDataURL(t,n){try{return this._canvas.toDataURL(t,n)}catch{try{return this._canvas.toDataURL()}catch(i){return s0.Util.error("Unable to get data URL. "+i.message+" For more info read https://konvajs.org/docs/posts/Tainted_Canvas.html."),""}}}}Bi.Canvas=w1;e_e.Factory.addGetterSetter(w1,"pixelRatio",void 0,(0,t_e.getNumberValidator)());class r_e extends w1{constructor(t={width:0,height:0,willReadFrequently:!1}){super(t),this.context=new HF.SceneContext(this,{willReadFrequently:t.willReadFrequently}),this.setSize(t.width,t.height)}}Bi.SceneCanvas=r_e;class i_e extends w1{constructor(t={width:0,height:0}){super(t),this.hitCanvas=!0,this.context=new HF.HitContext(this),this.setSize(t.width,t.height)}}Bi.HitCanvas=i_e;var x1={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.DD=void 0;const t=Le,n=Bt;e.DD={get isDragging(){var r=!1;return e.DD._dragElements.forEach(i=>{i.dragStatus==="dragging"&&(r=!0)}),r},justDragged:!1,get node(){var r;return e.DD._dragElements.forEach(i=>{r=i.node}),r},_dragElements:new Map,_drag(r){const i=[];e.DD._dragElements.forEach((o,s)=>{const{node:a}=o,l=a.getStage();l.setPointersPositions(r),o.pointerId===void 0&&(o.pointerId=n.Util._getFirstPointerId(r));const u=l._changedPointerPositions.find(f=>f.id===o.pointerId);if(u){if(o.dragStatus!=="dragging"){var c=a.dragDistance(),d=Math.max(Math.abs(u.x-o.startPointerPos.x),Math.abs(u.y-o.startPointerPos.y));if(d<c||(a.startDrag({evt:r}),!a.isDragging()))return}a._setDragPosition(r,o),i.push(a)}}),i.forEach(o=>{o.fire("dragmove",{type:"dragmove",target:o,evt:r},!0)})},_endDragBefore(r){const i=[];e.DD._dragElements.forEach(o=>{const{node:s}=o,a=s.getStage();if(r&&a.setPointersPositions(r),!a._changedPointerPositions.find(c=>c.id===o.pointerId))return;(o.dragStatus==="dragging"||o.dragStatus==="stopped")&&(e.DD.justDragged=!0,t.Konva._mouseListenClick=!1,t.Konva._touchListenClick=!1,t.Konva._pointerListenClick=!1,o.dragStatus="stopped");const u=o.node.getLayer()||o.node instanceof t.Konva.Stage&&o.node;u&&i.indexOf(u)===-1&&i.push(u)}),i.forEach(o=>{o.draw()})},_endDragAfter(r){e.DD._dragElements.forEach((i,o)=>{i.dragStatus==="stopped"&&i.node.fire("dragend",{type:"dragend",target:i.node,evt:r},!0),i.dragStatus!=="dragging"&&e.DD._dragElements.delete(o)})}},t.Konva.isBrowser&&(window.addEventListener("mouseup",e.DD._endDragBefore,!0),window.addEventListener("touchend",e.DD._endDragBefore,!0),window.addEventListener("mousemove",e.DD._drag),window.addEventListener("touchmove",e.DD._drag),window.addEventListener("mouseup",e.DD._endDragAfter,!1),window.addEventListener("touchend",e.DD._endDragAfter,!1))})(x1);Object.defineProperty(At,"__esModule",{value:!0});At.Node=void 0;const je=Bt,wp=Ie,kg=Bi,Ba=Le,ti=x1,Yt=fe;var _m="absoluteOpacity",Ag="allEventListeners",Uo="absoluteTransform",Mk="absoluteScale",ja="canvas",o_e="Change",s_e="children",a_e="konva",lx="listening",Nk="mouseenter",Dk="mouseleave",Lk="set",$k="Shape",wm=" ",Fk="stage",Os="transform",l_e="Stage",ux="visible",u_e=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(wm);let c_e=1,Pe=class cx{constructor(t){this._id=c_e++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(t),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(t){(t===Os||t===Uo)&&this._cache.get(t)?this._cache.get(t).dirty=!0:t?this._cache.delete(t):this._cache.clear()}_getCache(t,n){var r=this._cache.get(t),i=t===Os||t===Uo,o=r===void 0||i&&r.dirty===!0;return o&&(r=n.call(this),this._cache.set(t,r)),r}_calculate(t,n,r){if(!this._attachedDepsListeners.get(t)){const i=n.map(o=>o+"Change.konva").join(wm);this.on(i,()=>{this._clearCache(t)}),this._attachedDepsListeners.set(t,!0)}return this._getCache(t,r)}_getCanvasCache(){return this._cache.get(ja)}_clearSelfAndDescendantCache(t){this._clearCache(t),t===Uo&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(ja)){const{scene:t,filter:n,hit:r}=this._cache.get(ja);je.Util.releaseCanvas(t,n,r),this._cache.delete(ja)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(t){var n=t||{},r={};(n.x===void 0||n.y===void 0||n.width===void 0||n.height===void 0)&&(r=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var i=Math.ceil(n.width||r.width),o=Math.ceil(n.height||r.height),s=n.pixelRatio,a=n.x===void 0?Math.floor(r.x):n.x,l=n.y===void 0?Math.floor(r.y):n.y,u=n.offset||0,c=n.drawBorder||!1,d=n.hitCanvasPixelRatio||1;if(!i||!o){je.Util.error("Can not cache the node. Width or height of the node equals 0. Caching is skipped.");return}i+=u*2+1,o+=u*2+1,a-=u,l-=u;var f=new kg.SceneCanvas({pixelRatio:s,width:i,height:o}),h=new kg.SceneCanvas({pixelRatio:s,width:0,height:0,willReadFrequently:!0}),p=new kg.HitCanvas({pixelRatio:d,width:i,height:o}),m=f.getContext(),S=p.getContext();return p.isCache=!0,f.isCache=!0,this._cache.delete(ja),this._filterUpToDate=!1,n.imageSmoothingEnabled===!1&&(f.getContext()._context.imageSmoothingEnabled=!1,h.getContext()._context.imageSmoothingEnabled=!1),m.save(),S.save(),m.translate(-a,-l),S.translate(-a,-l),this._isUnderCache=!0,this._clearSelfAndDescendantCache(_m),this._clearSelfAndDescendantCache(Mk),this.drawScene(f,this),this.drawHit(p,this),this._isUnderCache=!1,m.restore(),S.restore(),c&&(m.save(),m.beginPath(),m.rect(0,0,i,o),m.closePath(),m.setAttr("strokeStyle","red"),m.setAttr("lineWidth",5),m.stroke(),m.restore()),this._cache.set(ja,{scene:f,filter:h,hit:p,x:a,y:l}),this._requestDraw(),this}isCached(){return this._cache.has(ja)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,s,a,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var c=l.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),{x:i,y:o,width:s-i,height:a-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),s,a,l,u;if(t){if(!this._filterUpToDate){var c=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(s=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/c,r.getHeight()/c),a=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l<s;l++){if(u=t[l],typeof u!="function"){je.Util.error("Filter should be type of function, but got "+typeof u+" instead. Please check correct filters");continue}u.call(this,a),o.putImageData(a,0,0)}}catch(d){je.Util.error("Unable to apply filter. "+d.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return i}return r}on(t,n){if(this._cache&&this._cache.delete(Ag),arguments.length===3)return this._delegate.apply(this,arguments);var r=t.split(wm),i=r.length,o,s,a,l,u;for(o=0;o<i;o++)s=r[o],a=s.split("."),l=a[0],u=a[1]||"",this.eventListeners[l]||(this.eventListeners[l]=[]),this.eventListeners[l].push({name:u,handler:n});return this}off(t,n){var r=(t||"").split(wm),i=r.length,o,s,a,l,u,c;if(this._cache&&this._cache.delete(Ag),!t)for(s in this.eventListeners)this._off(s);for(o=0;o<i;o++)if(a=r[o],l=a.split("."),u=l[0],c=l[1],u)this.eventListeners[u]&&this._off(u,c,n);else for(s in this.eventListeners)this._off(s,c,n);return this}dispatchEvent(t){var n={target:this,type:t.type,evt:t};return this.fire(t.type,n),this}addEventListener(t,n){return this.on(t,function(r){n.call(this,r.evt)}),this}removeEventListener(t){return this.off(t),this}_delegate(t,n,r){var i=this;this.on(t,function(o){for(var s=o.target.findAncestors(n,!0,i),a=0;a<s.length;a++)o=je.Util.cloneObject(o),o.currentTarget=s[a],r.call(s[a],o)})}remove(){return this.isDragging()&&this.stopDrag(),ti.DD._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(Uo),this._clearSelfAndDescendantCache(_m),this._clearSelfAndDescendantCache(Mk),this._clearSelfAndDescendantCache(Fk),this._clearSelfAndDescendantCache(ux),this._clearSelfAndDescendantCache(lx)}_remove(){this._clearCaches();var t=this.getParent();t&&t.children&&(t.children.splice(this.index,1),t._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this.clearCache(),this}getAttr(t){var n="get"+je.Util._capitalize(t);return je.Util._isFunction(this[n])?this[n]():this.attrs[t]}getAncestors(){for(var t=this.getParent(),n=[];t;)n.push(t),t=t.getParent();return n}getAttrs(){return this.attrs||{}}setAttrs(t){return this._batchTransformChanges(()=>{var n,r;if(!t)return this;for(n in t)n!==s_e&&(r=Lk+je.Util._capitalize(n),je.Util._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(lx,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(ux,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;ti.DD._dragElements.forEach(s=>{s.dragStatus==="dragging"&&(s.node.nodeType==="Stage"||s.node.getLayer()===r)&&(i=!0)});var o=!n&&!Ba.Konva.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,s,a;function l(u){for(i=[],o=u.length,s=0;s<o;s++)a=u[s],r++,a.nodeType!==$k&&(i=i.concat(a.getChildren().slice())),a._id===n._id&&(s=o);i.length>0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==l_e&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(Os),this._clearSelfAndDescendantCache(Uo)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new je.Transform,s=this.offset();return o.m=i.slice(),o.translate(s.x,s.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(Os);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(Os),this._clearSelfAndDescendantCache(Uo),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,s;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,s=0;s<o;s++)t(r[s])}}rotate(t){return this.rotation(this.rotation()+t),this}moveToTop(){if(!this.parent)return je.Util.warn("Node has no parent. moveToTop function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0):!1}moveUp(){if(!this.parent)return je.Util.warn("Node has no parent. moveUp function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.splice(t+1,0,this),this.parent._setChildrenIndices(),!0):!1}moveDown(){if(!this.parent)return je.Util.warn("Node has no parent. moveDown function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return je.Util.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return je.Util.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&je.Util.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(_m,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,s,a;t.attrs={};for(r in n)i=n[r],a=je.Util.isObject(i)&&!je.Util._isPlainObject(i)&&!je.Util._isArray(i),!a&&(o=typeof this[r]=="function"&&this[r],delete n[r],s=o?o.call(this):null,n[r]=i,s!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),je.Util._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i<r;i++)if(o=n[i],je.Util.isValidSelector(o)||(je.Util.warn('Selector "'+o+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),je.Util.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),je.Util.warn("Konva is awesome, right?")),o.charAt(0)==="#"){if(this.id()===o.slice(1))return!0}else if(o.charAt(0)==="."){if(this.hasName(o.slice(1)))return!0}else if(this.className===o||this.nodeType===o)return!0;return!1}getLayer(){var t=this.getParent();return t?t.getLayer():null}getStage(){return this._getCache(Fk,this._getStage)}_getStage(){var t=this.getParent();if(t)return t.getStage()}fire(t,n={},r){return n.target=n.target||this,r?this._fireAndBubble(t,n):this._fire(t,n),this}getAbsoluteTransform(t){return t?this._getAbsoluteTransform(t):this._getCache(Uo,this._getAbsoluteTransform)}_getAbsoluteTransform(t){var n;if(t)return n=new je.Transform,this._eachAncestorReverse(function(i){var o=i.transformsEnabled();o==="all"?n.multiply(i.getTransform()):o==="position"&&n.translate(i.x()-i.offsetX(),i.y()-i.offsetY())},t),n;n=this._cache.get(Uo)||new je.Transform,this.parent?this.parent.getAbsoluteTransform().copyInto(n):n.reset();var r=this.transformsEnabled();if(r==="all")n.multiply(this.getTransform());else if(r==="position"){const i=this.attrs.x||0,o=this.attrs.y||0,s=this.attrs.offsetX||0,a=this.attrs.offsetY||0;n.translate(i-s,o-a)}return n.dirty=!1,n}getAbsoluteScale(t){for(var n=this;n;)n._isUnderCache&&(t=n),n=n.getParent();const i=this.getAbsoluteTransform(t).decompose();return{x:i.scaleX,y:i.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(Os,this._getTransform)}_getTransform(){var t,n,r=this._cache.get(Os)||new je.Transform;r.reset();var i=this.x(),o=this.y(),s=Ba.Konva.getAngle(this.rotation()),a=(t=this.attrs.scaleX)!==null&&t!==void 0?t:1,l=(n=this.attrs.scaleY)!==null&&n!==void 0?n:1,u=this.attrs.skewX||0,c=this.attrs.skewY||0,d=this.attrs.offsetX||0,f=this.attrs.offsetY||0;return(i!==0||o!==0)&&r.translate(i,o),s!==0&&r.rotate(s),(u!==0||c!==0)&&r.skew(u,c),(a!==1||l!==1)&&r.scale(a,l),(d!==0||f!==0)&&r.translate(-1*d,-1*f),r.dirty=!1,r}clone(t){var n=je.Util.cloneObject(this.attrs),r,i,o,s,a;for(r in t)n[r]=t[r];var l=new this.constructor(n);for(r in this.eventListeners)for(i=this.eventListeners[r],o=i.length,s=0;s<o;s++)a=i[s],a.name.indexOf(a_e)<0&&(l.eventListeners[r]||(l.eventListeners[r]=[]),l.eventListeners[r].push(a));return l}_toKonvaCanvas(t){t=t||{};var n=this.getClientRect(),r=this.getStage(),i=t.x!==void 0?t.x:Math.floor(n.x),o=t.y!==void 0?t.y:Math.floor(n.y),s=t.pixelRatio||1,a=new kg.SceneCanvas({width:t.width||Math.ceil(n.width)||(r?r.width():0),height:t.height||Math.ceil(n.height)||(r?r.height():0),pixelRatio:s}),l=a.getContext();return t.imageSmoothingEnabled===!1&&(l._context.imageSmoothingEnabled=!1),l.save(),(i||o)&&l.translate(-1*i,-1*o),this.drawScene(a),l.restore(),a}toCanvas(t){return this._toKonvaCanvas(t)._canvas}toDataURL(t){t=t||{};var n=t.mimeType||null,r=t.quality||null,i=this._toKonvaCanvas(t).toDataURL(n,r);return t.callback&&t.callback(i),i}toImage(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,je.Util._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():Ba.Konva.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,s,a;for(o=0;o<i.length;o++)if(s=i[o].name,a=i[o].handler,(s!=="konva"||n==="konva")&&(!n||s===n)&&(!r||r===a)){if(i.splice(o,1),i.length===0){delete this.eventListeners[t];break}o--}}_fireChangeEvent(t,n,r){this._fire(t+o_e,{oldVal:n,newVal:r})}addName(t){if(!this.hasName(t)){var n=this.name(),r=n?n+" "+t:t;this.name(r)}return this}hasName(t){if(!t)return!1;const n=this.name();if(!n)return!1;var r=(n||"").split(/\s/g);return r.indexOf(t)!==-1}removeName(t){var n=(this.name()||"").split(/\s/g),r=n.indexOf(t);return r!==-1&&(n.splice(r,1),this.name(n.join(" "))),this}setAttr(t,n){var r=this[Lk+je.Util._capitalize(t)];return je.Util._isFunction(r)?r.call(this,n):this._setAttr(t,n),this}_requestDraw(){if(Ba.Konva.autoDrawEnabled){const t=this.getLayer()||this.getStage();t==null||t.batchDraw()}}_setAttr(t,n){var r=this.attrs[t];r===n&&!je.Util.isObject(n)||(n==null?delete this.attrs[t]:this.attrs[t]=n,this._shouldFireChangeEvents&&this._fireChangeEvent(t,r,n),this._requestDraw())}_setComponentAttr(t,n,r){var i;r!==void 0&&(i=this.attrs[t],i||(this.attrs[t]=this.getAttr(t)),this.attrs[t][n]=r,this._fireChangeEvent(t,i,r))}_fireAndBubble(t,n,r){n&&this.nodeType===$k&&(n.target=this);var i=(t===Nk||t===Dk)&&(r&&(this===r||this.isAncestorOf&&this.isAncestorOf(r))||this.nodeType==="Stage"&&!r);if(!i){this._fire(t,n);var o=(t===Nk||t===Dk)&&r&&r.isAncestorOf&&r.isAncestorOf(this)&&!r.isAncestorOf(this.parent);(n&&!n.cancelBubble||!n)&&this.parent&&this.parent.isListening()&&!o&&(r&&r.parent?this._fireAndBubble.call(this.parent,t,n,r):this._fireAndBubble.call(this.parent,t,n))}}_getProtoListeners(t){var n,r,i;const o=(n=this._cache.get(Ag))!==null&&n!==void 0?n:{};let s=o==null?void 0:o[t];if(s===void 0){s=[];let a=Object.getPrototypeOf(this);for(;a;){const l=(i=(r=a.eventListeners)===null||r===void 0?void 0:r[t])!==null&&i!==void 0?i:[];s.push(...l),a=Object.getPrototypeOf(a)}o[t]=s,this._cache.set(Ag,o)}return s}_fire(t,n){n=n||{},n.currentTarget=this,n.type=t;const r=this._getProtoListeners(t);if(r)for(var i=0;i<r.length;i++)r[i].handler.call(this,n);const o=this.eventListeners[t];if(o)for(var i=0;i<o.length;i++)o[i].handler.call(this,n)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(t){var n=t?t.pointerId:void 0,r=this.getStage(),i=this.getAbsolutePosition(),o=r._getPointerById(n)||r._changedPointerPositions[0]||i;ti.DD._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-i.x,y:o.y-i.y},dragStatus:"ready",pointerId:n})}startDrag(t,n=!0){ti.DD._dragElements.has(this._id)||this._createDragElement(t);const r=ti.DD._dragElements.get(this._id);r.dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:t&&t.evt},n)}_setDragPosition(t,n){const r=this.getStage()._getPointerById(n.pointerId);if(r){var i={x:r.x-n.offset.x,y:r.y-n.offset.y},o=this.dragBoundFunc();if(o!==void 0){const s=o.call(this,i,t);s?i=s:je.Util.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}(!this._lastPos||this._lastPos.x!==i.x||this._lastPos.y!==i.y)&&(this.setAbsolutePosition(i),this._requestDraw()),this._lastPos=i}}stopDrag(t){const n=ti.DD._dragElements.get(this._id);n&&(n.dragStatus="stopped"),ti.DD._endDragBefore(t),ti.DD._endDragAfter(t)}setDraggable(t){this._setAttr("draggable",t),this._dragChange()}isDragging(){const t=ti.DD._dragElements.get(this._id);return t?t.dragStatus==="dragging":!1}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",function(t){var n=t.evt.button!==void 0,r=!n||Ba.Konva.dragButtons.indexOf(t.evt.button)>=0;if(r&&!this.isDragging()){var i=!1;ti.DD._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=ti.DD._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&ti.DD._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return je.Util.haveIntersection(r,this.getClientRect())}static create(t,n){return je.Util._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=cx.prototype.getClassName.call(t),i=t.children,o,s,a;n&&(t.attrs.container=n),Ba.Konva[r]||(je.Util.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=Ba.Konva[r];if(o=new l(t.attrs),i)for(s=i.length,a=0;a<s;a++)o.add(cx._createNode(i[a]));return o}};At.Node=Pe;Pe.prototype.nodeType="Node";Pe.prototype._attrsAffectingSize=[];Pe.prototype.eventListeners={};Pe.prototype.on.call(Pe.prototype,u_e,function(){if(this._batchingTransformChange){this._needClearTransformCache=!0;return}this._clearCache(Os),this._clearSelfAndDescendantCache(Uo)});Pe.prototype.on.call(Pe.prototype,"visibleChange.konva",function(){this._clearSelfAndDescendantCache(ux)});Pe.prototype.on.call(Pe.prototype,"listeningChange.konva",function(){this._clearSelfAndDescendantCache(lx)});Pe.prototype.on.call(Pe.prototype,"opacityChange.konva",function(){this._clearSelfAndDescendantCache(_m)});const ht=wp.Factory.addGetterSetter;ht(Pe,"zIndex");ht(Pe,"absolutePosition");ht(Pe,"position");ht(Pe,"x",0,(0,Yt.getNumberValidator)());ht(Pe,"y",0,(0,Yt.getNumberValidator)());ht(Pe,"globalCompositeOperation","source-over",(0,Yt.getStringValidator)());ht(Pe,"opacity",1,(0,Yt.getNumberValidator)());ht(Pe,"name","",(0,Yt.getStringValidator)());ht(Pe,"id","",(0,Yt.getStringValidator)());ht(Pe,"rotation",0,(0,Yt.getNumberValidator)());wp.Factory.addComponentsGetterSetter(Pe,"scale",["x","y"]);ht(Pe,"scaleX",1,(0,Yt.getNumberValidator)());ht(Pe,"scaleY",1,(0,Yt.getNumberValidator)());wp.Factory.addComponentsGetterSetter(Pe,"skew",["x","y"]);ht(Pe,"skewX",0,(0,Yt.getNumberValidator)());ht(Pe,"skewY",0,(0,Yt.getNumberValidator)());wp.Factory.addComponentsGetterSetter(Pe,"offset",["x","y"]);ht(Pe,"offsetX",0,(0,Yt.getNumberValidator)());ht(Pe,"offsetY",0,(0,Yt.getNumberValidator)());ht(Pe,"dragDistance",null,(0,Yt.getNumberValidator)());ht(Pe,"width",0,(0,Yt.getNumberValidator)());ht(Pe,"height",0,(0,Yt.getNumberValidator)());ht(Pe,"listening",!0,(0,Yt.getBooleanValidator)());ht(Pe,"preventDefault",!0,(0,Yt.getBooleanValidator)());ht(Pe,"filters",null,function(e){return this._filterUpToDate=!1,e});ht(Pe,"visible",!0,(0,Yt.getBooleanValidator)());ht(Pe,"transformsEnabled","all",(0,Yt.getStringValidator)());ht(Pe,"size");ht(Pe,"dragBoundFunc");ht(Pe,"draggable",!1,(0,Yt.getBooleanValidator)());wp.Factory.backCompat(Pe,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});var $l={};Object.defineProperty($l,"__esModule",{value:!0});$l.Container=void 0;const Yc=Ie,b_=At,C1=fe;class Fl extends b_.Node{constructor(){super(...arguments),this.children=[]}getChildren(t){if(!t)return this.children||[];const n=this.children||[];var r=[];return n.forEach(function(i){t(i)&&r.push(i)}),r}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(t.length===0)return this;if(t.length>1){for(var n=0;n<t.length;n++)this.add(t[n]);return this}const r=t[0];return r.getParent()?(r.moveTo(this),this):(this._validateAdd(r),r.index=this.getChildren().length,r.parent=this,r._clearCaches(),this.getChildren().push(r),this._fire("add",{child:r}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(t){return this._generalFind(t,!1)}findOne(t){var n=this._generalFind(t,!0);return n.length>0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=b_.Node.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=b_.Node.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(a){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.hit;if(a){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),s=this.clipWidth(),a=this.clipHeight(),l=this.clipFunc(),u=s&&a||l;const c=r===this;if(u){o.save();var d=this.getAbsoluteTransform(r),f=d.getMatrix();o.transform(f[0],f[1],f[2],f[3],f[4],f[5]),o.beginPath();let S;if(l)S=l.call(this,o,this);else{var h=this.clipX(),p=this.clipY();o.rect(h,p,s,a)}o.clip.apply(o,S),f=d.copy().invert().getMatrix(),o.transform(f[0],f[1],f[2],f[3],f[4],f[5])}var m=!c&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";m&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(S){S[t](n,r)}),m&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,s,a,l,u={x:1/0,y:1/0,width:0,height:0},c=this;(n=this.children)===null||n===void 0||n.forEach(function(m){if(m.visible()){var S=m.getClientRect({relativeTo:c,skipShadow:t.skipShadow,skipStroke:t.skipStroke});S.width===0&&S.height===0||(o===void 0?(o=S.x,s=S.y,a=S.x+S.width,l=S.y+S.height):(o=Math.min(o,S.x),s=Math.min(s,S.y),a=Math.max(a,S.x+S.width),l=Math.max(l,S.y+S.height)))}});for(var d=this.find("Shape"),f=!1,h=0;h<d.length;h++){var p=d[h];if(p._isVisible(this)){f=!0;break}}return f&&o!==void 0?u={x:o,y:s,width:a-o,height:l-s}:u={x:0,y:0,width:0,height:0},r?u:this._transformedRect(u,i)}}$l.Container=Fl;Yc.Factory.addComponentsGetterSetter(Fl,"clip",["x","y","width","height"]);Yc.Factory.addGetterSetter(Fl,"clipX",void 0,(0,C1.getNumberValidator)());Yc.Factory.addGetterSetter(Fl,"clipY",void 0,(0,C1.getNumberValidator)());Yc.Factory.addGetterSetter(Fl,"clipWidth",void 0,(0,C1.getNumberValidator)());Yc.Factory.addGetterSetter(Fl,"clipHeight",void 0,(0,C1.getNumberValidator)());Yc.Factory.addGetterSetter(Fl,"clipFunc");var WF={},Br={};Object.defineProperty(Br,"__esModule",{value:!0});Br.releaseCapture=Br.setPointerCapture=Br.hasPointerCapture=Br.createEvent=Br.getCapturedShape=void 0;const d_e=Le,Ah=new Map,KF=d_e.Konva._global.PointerEvent!==void 0;function f_e(e){return Ah.get(e)}Br.getCapturedShape=f_e;function M3(e){return{evt:e,pointerId:e.pointerId}}Br.createEvent=M3;function h_e(e,t){return Ah.get(e)===t}Br.hasPointerCapture=h_e;function p_e(e,t){YF(e),t.getStage()&&(Ah.set(e,t),KF&&t._fire("gotpointercapture",M3(new PointerEvent("gotpointercapture"))))}Br.setPointerCapture=p_e;function YF(e,t){const n=Ah.get(e);if(!n)return;const r=n.getStage();r&&r.content,Ah.delete(e),KF&&n._fire("lostpointercapture",M3(new PointerEvent("lostpointercapture")))}Br.releaseCapture=YF;(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Stage=e.stages=void 0;const t=Bt,n=Ie,r=$l,i=Le,o=Bi,s=x1,a=Le,l=Br;var u="Stage",c="string",d="px",f="mouseout",h="mouseleave",p="mouseover",m="mouseenter",S="mousemove",v="mousedown",y="mouseup",g="pointermove",b="pointerdown",_="pointerup",w="pointercancel",x="lostpointercapture",C="pointerout",k="pointerleave",P="pointerover",A="pointerenter",$="contextmenu",N="touchstart",T="touchend",O="touchmove",I="touchcancel",M="wheel",R=5,D=[[m,"_pointerenter"],[v,"_pointerdown"],[S,"_pointermove"],[y,"_pointerup"],[h,"_pointerleave"],[N,"_pointerdown"],[O,"_pointermove"],[T,"_pointerup"],[I,"_pointercancel"],[p,"_pointerover"],[M,"_wheel"],[$,"_contextmenu"],[b,"_pointerdown"],[g,"_pointermove"],[_,"_pointerup"],[w,"_pointercancel"],[x,"_lostpointercapture"]];const L={mouse:{[C]:f,[k]:h,[P]:p,[A]:m,[g]:S,[b]:v,[_]:y,[w]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[C]:"touchout",[k]:"touchleave",[P]:"touchover",[A]:"touchenter",[g]:O,[b]:N,[_]:T,[w]:I,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[C]:C,[k]:k,[P]:P,[A]:A,[g]:g,[b]:b,[_]:_,[w]:w,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},V=X=>X.indexOf("pointer")>=0?"pointer":X.indexOf("touch")>=0?"touch":"mouse",z=X=>{const F=V(X);if(F==="pointer")return i.Konva.pointerEventsEnabled&&L.pointer;if(F==="touch")return L.touch;if(F==="mouse")return L.mouse};function U(X={}){return(X.clipFunc||X.clipWidth||X.clipHeight)&&t.Util.warn("Stage does not support clipping. Please use clip for Layers or Groups."),X}const K="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);";e.stages=[];class W extends r.Container{constructor(F){super(U(F)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),e.stages.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{U(this.attrs)}),this._checkVisibility()}_validateAdd(F){const q=F.getType()==="Layer",Q=F.getType()==="FastLayer";q||Q||t.Util.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const F=this.visible()?"":"none";this.content.style.display=F}setContainer(F){if(typeof F===c){if(F.charAt(0)==="."){var q=F.slice(1);F=document.getElementsByClassName(q)[0]}else{var Q;F.charAt(0)!=="#"?Q=F:Q=F.slice(1),F=document.getElementById(Q)}if(!F)throw"Can not find container in document with id "+Q}return this._setAttr("container",F),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),F.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var F=this.children,q=F.length,Q;for(Q=0;Q<q;Q++)F[Q].clear();return this}clone(F){return F||(F={}),F.container=typeof document<"u"&&document.createElement("div"),r.Container.prototype.clone.call(this,F)}destroy(){super.destroy();var F=this.content;F&&t.Util._isInDocument(F)&&this.container().removeChild(F);var q=e.stages.indexOf(this);return q>-1&&e.stages.splice(q,1),t.Util.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const F=this._pointerPositions[0]||this._changedPointerPositions[0];return F?{x:F.x,y:F.y}:(t.Util.warn(K),null)}_getPointerById(F){return this._pointerPositions.find(q=>q.id===F)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(F){F=F||{},F.x=F.x||0,F.y=F.y||0,F.width=F.width||this.width(),F.height=F.height||this.height();var q=new o.SceneCanvas({width:F.width,height:F.height,pixelRatio:F.pixelRatio||1}),Q=q.getContext()._context,J=this.children;return(F.x||F.y)&&Q.translate(-1*F.x,-1*F.y),J.forEach(function(ne){if(ne.isVisible()){var Z=ne._toKonvaCanvas(F);Q.drawImage(Z._canvas,F.x,F.y,Z.getWidth()/Z.getPixelRatio(),Z.getHeight()/Z.getPixelRatio())}}),q}getIntersection(F){if(!F)return null;var q=this.children,Q=q.length,J=Q-1,ne;for(ne=J;ne>=0;ne--){const Z=q[ne].getIntersection(F);if(Z)return Z}return null}_resizeDOM(){var F=this.width(),q=this.height();this.content&&(this.content.style.width=F+d,this.content.style.height=q+d),this.bufferCanvas.setSize(F,q),this.bufferHitCanvas.setSize(F,q),this.children.forEach(Q=>{Q.setSize({width:F,height:q}),Q.draw()})}add(F,...q){if(arguments.length>1){for(var Q=0;Q<arguments.length;Q++)this.add(arguments[Q]);return this}super.add(F);var J=this.children.length;return J>R&&t.Util.warn("The stage has "+J+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),F.setSize({width:this.width(),height:this.height()}),F.draw(),i.Konva.isBrowser&&this.content.appendChild(F.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(F){return l.hasPointerCapture(F,this)}setPointerCapture(F){l.setPointerCapture(F,this)}releaseCapture(F){l.releaseCapture(F,this)}getLayers(){return this.children}_bindContentEvents(){i.Konva.isBrowser&&D.forEach(([F,q])=>{this.content.addEventListener(F,Q=>{this[q](Q)},{passive:!1})})}_pointerenter(F){this.setPointersPositions(F);const q=z(F.type);this._fire(q.pointerenter,{evt:F,target:this,currentTarget:this})}_pointerover(F){this.setPointersPositions(F);const q=z(F.type);this._fire(q.pointerover,{evt:F,target:this,currentTarget:this})}_getTargetShape(F){let q=this[F+"targetShape"];return q&&!q.getStage()&&(q=null),q}_pointerleave(F){const q=z(F.type),Q=V(F.type);if(q){this.setPointersPositions(F);var J=this._getTargetShape(Q),ne=!s.DD.isDragging||i.Konva.hitOnDragEnabled;J&&ne?(J._fireAndBubble(q.pointerout,{evt:F}),J._fireAndBubble(q.pointerleave,{evt:F}),this._fire(q.pointerleave,{evt:F,target:this,currentTarget:this}),this[Q+"targetShape"]=null):ne&&(this._fire(q.pointerleave,{evt:F,target:this,currentTarget:this}),this._fire(q.pointerout,{evt:F,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(F){const q=z(F.type),Q=V(F.type);if(q){this.setPointersPositions(F);var J=!1;this._changedPointerPositions.forEach(ne=>{var Z=this.getIntersection(ne);if(s.DD.justDragged=!1,i.Konva["_"+Q+"ListenClick"]=!0,!(Z&&Z.isListening()))return;i.Konva.capturePointerEventsEnabled&&Z.setPointerCapture(ne.id),this[Q+"ClickStartShape"]=Z,Z._fireAndBubble(q.pointerdown,{evt:F,pointerId:ne.id}),J=!0;const ae=F.type.indexOf("touch")>=0;Z.preventDefault()&&F.cancelable&&ae&&F.preventDefault()}),J||this._fire(q.pointerdown,{evt:F,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(F){const q=z(F.type),Q=V(F.type);if(!q)return;s.DD.isDragging&&s.DD.node.preventDefault()&&F.cancelable&&F.preventDefault(),this.setPointersPositions(F);var J=!s.DD.isDragging||i.Konva.hitOnDragEnabled;if(!J)return;var ne={};let Z=!1;var ge=this._getTargetShape(Q);this._changedPointerPositions.forEach(ae=>{const oe=l.getCapturedShape(ae.id)||this.getIntersection(ae),Te=ae.id,se={evt:F,pointerId:Te};var We=ge!==oe;if(We&&ge&&(ge._fireAndBubble(q.pointerout,Object.assign({},se),oe),ge._fireAndBubble(q.pointerleave,Object.assign({},se),oe)),oe){if(ne[oe._id])return;ne[oe._id]=!0}oe&&oe.isListening()?(Z=!0,We&&(oe._fireAndBubble(q.pointerover,Object.assign({},se),ge),oe._fireAndBubble(q.pointerenter,Object.assign({},se),ge),this[Q+"targetShape"]=oe),oe._fireAndBubble(q.pointermove,Object.assign({},se))):ge&&(this._fire(q.pointerover,{evt:F,target:this,currentTarget:this,pointerId:Te}),this[Q+"targetShape"]=null)}),Z||this._fire(q.pointermove,{evt:F,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(F){const q=z(F.type),Q=V(F.type);if(!q)return;this.setPointersPositions(F);const J=this[Q+"ClickStartShape"],ne=this[Q+"ClickEndShape"];var Z={};let ge=!1;this._changedPointerPositions.forEach(ae=>{const oe=l.getCapturedShape(ae.id)||this.getIntersection(ae);if(oe){if(oe.releaseCapture(ae.id),Z[oe._id])return;Z[oe._id]=!0}const Te=ae.id,se={evt:F,pointerId:Te};let We=!1;i.Konva["_"+Q+"InDblClickWindow"]?(We=!0,clearTimeout(this[Q+"DblTimeout"])):s.DD.justDragged||(i.Konva["_"+Q+"InDblClickWindow"]=!0,clearTimeout(this[Q+"DblTimeout"])),this[Q+"DblTimeout"]=setTimeout(function(){i.Konva["_"+Q+"InDblClickWindow"]=!1},i.Konva.dblClickWindow),oe&&oe.isListening()?(ge=!0,this[Q+"ClickEndShape"]=oe,oe._fireAndBubble(q.pointerup,Object.assign({},se)),i.Konva["_"+Q+"ListenClick"]&&J&&J===oe&&(oe._fireAndBubble(q.pointerclick,Object.assign({},se)),We&&ne&&ne===oe&&oe._fireAndBubble(q.pointerdblclick,Object.assign({},se)))):(this[Q+"ClickEndShape"]=null,i.Konva["_"+Q+"ListenClick"]&&this._fire(q.pointerclick,{evt:F,target:this,currentTarget:this,pointerId:Te}),We&&this._fire(q.pointerdblclick,{evt:F,target:this,currentTarget:this,pointerId:Te}))}),ge||this._fire(q.pointerup,{evt:F,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),i.Konva["_"+Q+"ListenClick"]=!1,F.cancelable&&Q!=="touch"&&F.preventDefault()}_contextmenu(F){this.setPointersPositions(F);var q=this.getIntersection(this.getPointerPosition());q&&q.isListening()?q._fireAndBubble($,{evt:F}):this._fire($,{evt:F,target:this,currentTarget:this})}_wheel(F){this.setPointersPositions(F);var q=this.getIntersection(this.getPointerPosition());q&&q.isListening()?q._fireAndBubble(M,{evt:F}):this._fire(M,{evt:F,target:this,currentTarget:this})}_pointercancel(F){this.setPointersPositions(F);const q=l.getCapturedShape(F.pointerId)||this.getIntersection(this.getPointerPosition());q&&q._fireAndBubble(_,l.createEvent(F)),l.releaseCapture(F.pointerId)}_lostpointercapture(F){l.releaseCapture(F.pointerId)}setPointersPositions(F){var q=this._getContentPosition(),Q=null,J=null;F=F||window.event,F.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(F.touches,ne=>{this._pointerPositions.push({id:ne.identifier,x:(ne.clientX-q.left)/q.scaleX,y:(ne.clientY-q.top)/q.scaleY})}),Array.prototype.forEach.call(F.changedTouches||F.touches,ne=>{this._changedPointerPositions.push({id:ne.identifier,x:(ne.clientX-q.left)/q.scaleX,y:(ne.clientY-q.top)/q.scaleY})})):(Q=(F.clientX-q.left)/q.scaleX,J=(F.clientY-q.top)/q.scaleY,this.pointerPos={x:Q,y:J},this._pointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(F)}],this._changedPointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(F)}])}_setPointerPosition(F){t.Util.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(F)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var F=this.content.getBoundingClientRect();return{top:F.top,left:F.left,scaleX:F.width/this.content.clientWidth||1,scaleY:F.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new o.SceneCanvas({width:this.width(),height:this.height()}),this.bufferHitCanvas=new o.HitCanvas({pixelRatio:1,width:this.width(),height:this.height()}),!!i.Konva.isBrowser){var F=this.container();if(!F)throw"Stage has no container. A container is required.";F.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),F.appendChild(this.content),this._resizeDOM()}}cache(){return t.Util.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(F){F.batchDraw()}),this}}e.Stage=W,W.prototype.nodeType=u,(0,a._registerNode)(W),n.Factory.addGetterSetter(W,"container")})(WF);var xp={},pn={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Shape=e.shapes=void 0;const t=Le,n=Bt,r=Ie,i=At,o=fe,s=Le,a=Br;var l="hasShadow",u="shadowRGBA",c="patternImage",d="linearGradient",f="radialGradient";let h;function p(){return h||(h=n.Util.createCanvasElement().getContext("2d"),h)}e.shapes={};function m(k){const P=this.attrs.fillRule;P?k.fill(P):k.fill()}function S(k){k.stroke()}function v(k){k.fill()}function y(k){k.stroke()}function g(){this._clearCache(l)}function b(){this._clearCache(u)}function _(){this._clearCache(c)}function w(){this._clearCache(d)}function x(){this._clearCache(f)}class C extends i.Node{constructor(P){super(P);let A;for(;A=n.Util.getRandomColor(),!(A&&!(A in e.shapes)););this.colorKey=A,e.shapes[A]=this}getContext(){return n.Util.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return n.Util.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(l,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(c,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var P=p();const A=P.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(A&&A.setTransform){const $=new n.Transform;$.translate(this.fillPatternX(),this.fillPatternY()),$.rotate(t.Konva.getAngle(this.fillPatternRotation())),$.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),$.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const N=$.getMatrix(),T=typeof DOMMatrix>"u"?{a:N[0],b:N[1],c:N[2],d:N[3],e:N[4],f:N[5]}:new DOMMatrix(N);A.setTransform(T)}return A}}_getLinearGradient(){return this._getCache(d,this.__getLinearGradient)}__getLinearGradient(){var P=this.fillLinearGradientColorStops();if(P){for(var A=p(),$=this.fillLinearGradientStartPoint(),N=this.fillLinearGradientEndPoint(),T=A.createLinearGradient($.x,$.y,N.x,N.y),O=0;O<P.length;O+=2)T.addColorStop(P[O],P[O+1]);return T}}_getRadialGradient(){return this._getCache(f,this.__getRadialGradient)}__getRadialGradient(){var P=this.fillRadialGradientColorStops();if(P){for(var A=p(),$=this.fillRadialGradientStartPoint(),N=this.fillRadialGradientEndPoint(),T=A.createRadialGradient($.x,$.y,this.fillRadialGradientStartRadius(),N.x,N.y,this.fillRadialGradientEndRadius()),O=0;O<P.length;O+=2)T.addColorStop(P[O],P[O+1]);return T}}getShadowRGBA(){return this._getCache(u,this._getShadowRGBA)}_getShadowRGBA(){if(this.hasShadow()){var P=n.Util.colorToRGBA(this.shadowColor());if(P)return"rgba("+P.r+","+P.g+","+P.b+","+P.a*(this.shadowOpacity()||1)+")"}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const P=this.hitStrokeWidth();return P==="auto"?this.hasStroke():this.strokeEnabled()&&!!P}intersects(P){var A=this.getStage(),$=A.bufferHitCanvas,N;return $.getContext().clear(),this.drawHit($,null,!0),N=$.context.getImageData(Math.round(P.x),Math.round(P.y),1,1).data,N[3]>0}destroy(){return i.Node.prototype.destroy.call(this),delete e.shapes[this.colorKey],delete this.colorKey,this}_useBufferCanvas(P){var A;if(!this.getStage()||!((A=this.attrs.perfectDrawEnabled)!==null&&A!==void 0?A:!0))return!1;const N=P||this.hasFill(),T=this.hasStroke(),O=this.getAbsoluteOpacity()!==1;if(N&&T&&O)return!0;const I=this.hasShadow(),M=this.shadowForStrokeEnabled();return!!(N&&T&&I&&M)}setStrokeHitEnabled(P){n.Util.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),P?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var P=this.size();return{x:this._centroid?-P.width/2:0,y:this._centroid?-P.height/2:0,width:P.width,height:P.height}}getClientRect(P={}){const A=P.skipTransform,$=P.relativeTo,N=this.getSelfRect(),O=!P.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,I=N.width+O,M=N.height+O,R=!P.skipShadow&&this.hasShadow(),D=R?this.shadowOffsetX():0,L=R?this.shadowOffsetY():0,V=I+Math.abs(D),z=M+Math.abs(L),U=R&&this.shadowBlur()||0,K=V+U*2,W=z+U*2,X={width:K,height:W,x:-(O/2+U)+Math.min(D,0)+N.x,y:-(O/2+U)+Math.min(L,0)+N.y};return A?X:this._transformedRect(X,$)}drawScene(P,A){var $=this.getLayer(),N=P||$.getCanvas(),T=N.getContext(),O=this._getCanvasCache(),I=this.getSceneFunc(),M=this.hasShadow(),R,D,L,V=N.isCache,z=A===this;if(!this.isVisible()&&!z)return this;if(O){T.save();var U=this.getAbsoluteTransform(A).getMatrix();return T.transform(U[0],U[1],U[2],U[3],U[4],U[5]),this._drawCachedSceneCanvas(T),T.restore(),this}if(!I)return this;if(T.save(),this._useBufferCanvas()&&!V){R=this.getStage(),D=R.bufferCanvas,L=D.getContext(),L.clear(),L.save(),L._applyLineJoin(this);var K=this.getAbsoluteTransform(A).getMatrix();L.transform(K[0],K[1],K[2],K[3],K[4],K[5]),I.call(this,L,this),L.restore();var W=D.pixelRatio;M&&T._applyShadow(this),T._applyOpacity(this),T._applyGlobalCompositeOperation(this),T.drawImage(D._canvas,0,0,D.width/W,D.height/W)}else{if(T._applyLineJoin(this),!z){var K=this.getAbsoluteTransform(A).getMatrix();T.transform(K[0],K[1],K[2],K[3],K[4],K[5]),T._applyOpacity(this),T._applyGlobalCompositeOperation(this)}M&&T._applyShadow(this),I.call(this,T,this)}return T.restore(),this}drawHit(P,A,$=!1){if(!this.shouldDrawHit(A,$))return this;var N=this.getLayer(),T=P||N.hitCanvas,O=T&&T.getContext(),I=this.hitFunc()||this.sceneFunc(),M=this._getCanvasCache(),R=M&&M.hit;if(this.colorKey||n.Util.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),R){O.save();var D=this.getAbsoluteTransform(A).getMatrix();return O.transform(D[0],D[1],D[2],D[3],D[4],D[5]),this._drawCachedHitCanvas(O),O.restore(),this}if(!I)return this;if(O.save(),O._applyLineJoin(this),!(this===A)){var V=this.getAbsoluteTransform(A).getMatrix();O.transform(V[0],V[1],V[2],V[3],V[4],V[5])}return I.call(this,O,this),O.restore(),this}drawHitFromCache(P=0){var A=this._getCanvasCache(),$=this._getCachedSceneCanvas(),N=A.hit,T=N.getContext(),O=N.getWidth(),I=N.getHeight(),M,R,D,L,V,z;T.clear(),T.drawImage($._canvas,0,0,O,I);try{for(M=T.getImageData(0,0,O,I),R=M.data,D=R.length,L=n.Util._hexToRgb(this.colorKey),V=0;V<D;V+=4)z=R[V+3],z>P?(R[V]=L.r,R[V+1]=L.g,R[V+2]=L.b,R[V+3]=255):R[V+3]=0;T.putImageData(M,0,0)}catch(U){n.Util.error("Unable to draw hit graph from cached scene canvas. "+U.message)}return this}hasPointerCapture(P){return a.hasPointerCapture(P,this)}setPointerCapture(P){a.setPointerCapture(P,this)}releaseCapture(P){a.releaseCapture(P,this)}}e.Shape=C,C.prototype._fillFunc=m,C.prototype._strokeFunc=S,C.prototype._fillFuncHit=v,C.prototype._strokeFuncHit=y,C.prototype._centroid=!1,C.prototype.nodeType="Shape",(0,s._registerNode)(C),C.prototype.eventListeners={},C.prototype.on.call(C.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",g),C.prototype.on.call(C.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",b),C.prototype.on.call(C.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",_),C.prototype.on.call(C.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",w),C.prototype.on.call(C.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",x),r.Factory.addGetterSetter(C,"stroke",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(C,"strokeWidth",2,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillAfterStrokeEnabled",!1),r.Factory.addGetterSetter(C,"hitStrokeWidth","auto",(0,o.getNumberOrAutoValidator)()),r.Factory.addGetterSetter(C,"strokeHitEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(C,"perfectDrawEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(C,"shadowForStrokeEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(C,"lineJoin"),r.Factory.addGetterSetter(C,"lineCap"),r.Factory.addGetterSetter(C,"sceneFunc"),r.Factory.addGetterSetter(C,"hitFunc"),r.Factory.addGetterSetter(C,"dash"),r.Factory.addGetterSetter(C,"dashOffset",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"shadowColor",void 0,(0,o.getStringValidator)()),r.Factory.addGetterSetter(C,"shadowBlur",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"shadowOpacity",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(C,"shadowOffset",["x","y"]),r.Factory.addGetterSetter(C,"shadowOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"shadowOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternImage"),r.Factory.addGetterSetter(C,"fill",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(C,"fillPatternX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillLinearGradientColorStops"),r.Factory.addGetterSetter(C,"strokeLinearGradientColorStops"),r.Factory.addGetterSetter(C,"fillRadialGradientStartRadius",0),r.Factory.addGetterSetter(C,"fillRadialGradientEndRadius",0),r.Factory.addGetterSetter(C,"fillRadialGradientColorStops"),r.Factory.addGetterSetter(C,"fillPatternRepeat","repeat"),r.Factory.addGetterSetter(C,"fillEnabled",!0),r.Factory.addGetterSetter(C,"strokeEnabled",!0),r.Factory.addGetterSetter(C,"shadowEnabled",!0),r.Factory.addGetterSetter(C,"dashEnabled",!0),r.Factory.addGetterSetter(C,"strokeScaleEnabled",!0),r.Factory.addGetterSetter(C,"fillPriority","color"),r.Factory.addComponentsGetterSetter(C,"fillPatternOffset",["x","y"]),r.Factory.addGetterSetter(C,"fillPatternOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(C,"fillPatternScale",["x","y"]),r.Factory.addGetterSetter(C,"fillPatternScaleX",1,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternScaleY",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(C,"fillLinearGradientStartPoint",["x","y"]),r.Factory.addComponentsGetterSetter(C,"strokeLinearGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillLinearGradientStartPointX",0),r.Factory.addGetterSetter(C,"strokeLinearGradientStartPointX",0),r.Factory.addGetterSetter(C,"fillLinearGradientStartPointY",0),r.Factory.addGetterSetter(C,"strokeLinearGradientStartPointY",0),r.Factory.addComponentsGetterSetter(C,"fillLinearGradientEndPoint",["x","y"]),r.Factory.addComponentsGetterSetter(C,"strokeLinearGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillLinearGradientEndPointX",0),r.Factory.addGetterSetter(C,"strokeLinearGradientEndPointX",0),r.Factory.addGetterSetter(C,"fillLinearGradientEndPointY",0),r.Factory.addGetterSetter(C,"strokeLinearGradientEndPointY",0),r.Factory.addComponentsGetterSetter(C,"fillRadialGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillRadialGradientStartPointX",0),r.Factory.addGetterSetter(C,"fillRadialGradientStartPointY",0),r.Factory.addComponentsGetterSetter(C,"fillRadialGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillRadialGradientEndPointX",0),r.Factory.addGetterSetter(C,"fillRadialGradientEndPointY",0),r.Factory.addGetterSetter(C,"fillPatternRotation",0),r.Factory.addGetterSetter(C,"fillRule",void 0,(0,o.getStringValidator)()),r.Factory.backCompat(C,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"})})(pn);Object.defineProperty(xp,"__esModule",{value:!0});xp.Layer=void 0;const jo=Bt,S_=$l,au=At,N3=Ie,Bk=Bi,g_e=fe,m_e=pn,y_e=Le;var v_e="#",b_e="beforeDraw",S_e="draw",XF=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],__e=XF.length;class Xc extends S_.Container{constructor(t){super(t),this.canvas=new Bk.SceneCanvas,this.hitCanvas=new Bk.HitCanvas({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[t+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){au.Node.prototype.moveToTop.call(this);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){var t=au.Node.prototype.moveUp.call(this);if(!t)return!1;var n=this.getStage();return!n||!n.content?!1:(n.content.removeChild(this.getNativeCanvasElement()),this.index<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[this.index+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(au.Node.prototype.moveDown.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(au.Node.prototype.moveToBottom.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var t=this.getNativeCanvasElement();return au.Node.prototype.remove.call(this),t&&t.parentNode&&jo.Util._isInDocument(t)&&t.parentNode.removeChild(t),this}getStage(){return this.parent}setSize({width:t,height:n}){return this.canvas.setSize(t,n),this.hitCanvas.setSize(t,n),this._setSmoothEnabled(),this}_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&jo.Util.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(t){return t=t||{},t.width=t.width||this.getWidth(),t.height=t.height||this.getHeight(),t.x=t.x!==void 0?t.x:this.x(),t.y=t.y!==void 0?t.y:this.y(),au.Node.prototype._toKonvaCanvas.call(this,t)}_checkVisibility(){this.visible()?this.canvas._canvas.style.display="block":this.canvas._canvas.style.display="none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){jo.Util.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){jo.Util.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,jo.Util.requestAnimFrame(()=>{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i<__e;i++){const o=XF[i],s=this._getIntersection({x:t.x+o.x*n,y:t.y+o.y*n}),a=s.shape;if(a)return a;if(r=!!s.antialiased,!s.antialiased)break}if(r)n+=1;else return null}}_getIntersection(t){const n=this.hitCanvas.pixelRatio,r=this.hitCanvas.context.getImageData(Math.round(t.x*n),Math.round(t.y*n),1,1).data,i=r[3];if(i===255){const o=jo.Util._rgbToHex(r[0],r[1],r[2]),s=m_e.shapes[v_e+o];return s?{shape:s}:{antialiased:!0}}else if(i>0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(b_e,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),S_.Container.prototype.drawScene.call(this,i,n),this._fire(S_e,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),S_.Container.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){jo.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return jo.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return jo.Util.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}xp.Layer=Xc;Xc.prototype.nodeType="Layer";(0,y_e._registerNode)(Xc);N3.Factory.addGetterSetter(Xc,"imageSmoothingEnabled",!0);N3.Factory.addGetterSetter(Xc,"clearBeforeDraw",!0);N3.Factory.addGetterSetter(Xc,"hitGraphEnabled",!0,(0,g_e.getBooleanValidator)());var T1={};Object.defineProperty(T1,"__esModule",{value:!0});T1.FastLayer=void 0;const w_e=Bt,x_e=xp,C_e=Le;class D3 extends x_e.Layer{constructor(t){super(t),this.listening(!1),w_e.Util.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}T1.FastLayer=D3;D3.prototype.nodeType="FastLayer";(0,C_e._registerNode)(D3);var Qc={};Object.defineProperty(Qc,"__esModule",{value:!0});Qc.Group=void 0;const T_e=Bt,E_e=$l,P_e=Le;class L3 extends E_e.Container{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&T_e.Util.throw("You may only add groups and shapes to groups.")}}Qc.Group=L3;L3.prototype.nodeType="Group";(0,P_e._registerNode)(L3);var Zc={};Object.defineProperty(Zc,"__esModule",{value:!0});Zc.Animation=void 0;const __=Le,jk=Bt;var w_=function(){return __.glob.performance&&__.glob.performance.now?function(){return __.glob.performance.now()}:function(){return new Date().getTime()}}();class ao{constructor(t,n){this.id=ao.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:w_(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;i<r;i++)if(n[i]._id===t._id)return!1;return this.layers.push(t),!0}isRunning(){var t=ao,n=t.animations,r=n.length,i;for(i=0;i<r;i++)if(n[i].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=w_(),ao._addAnimation(this),this}stop(){return ao._removeAnimation(this),this}_updateFrameObject(t){this.frame.timeDiff=t-this.frame.lastTime,this.frame.lastTime=t,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(t){this.animations.push(t),this._handleAnimation()}static _removeAnimation(t){var n=t.id,r=this.animations,i=r.length,o;for(o=0;o<i;o++)if(r[o].id===n){this.animations.splice(o,1);break}}static _runFrames(){var t={},n=this.animations,r,i,o,s,a,l,u,c,d;for(s=0;s<n.length;s++)if(r=n[s],i=r.layers,o=r.func,r._updateFrameObject(w_()),l=i.length,o?d=o.call(r,r.frame)!==!1:d=!0,!!d)for(a=0;a<l;a++)u=i[a],u._id!==void 0&&(t[u._id]=u);for(c in t)t.hasOwnProperty(c)&&t[c].batchDraw()}static _animationLoop(){var t=ao;t.animations.length?(t._runFrames(),jk.Util.requestAnimFrame(t._animationLoop)):t.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,jk.Util.requestAnimFrame(this._animationLoop))}}Zc.Animation=ao;ao.animations=[];ao.animIdCounter=0;ao.animRunning=!1;var QF={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Easings=e.Tween=void 0;const t=Bt,n=Zc,r=At,i=Le;var o={node:1,duration:1,easing:1,onFinish:1,yoyo:1},s=1,a=2,l=3,u=0,c=["fill","stroke","shadowColor"];class d{constructor(p,m,S,v,y,g,b){this.prop=p,this.propFunc=m,this.begin=v,this._pos=v,this.duration=g,this._change=0,this.prevPos=0,this.yoyo=b,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=S,this._change=y-this.begin,this.pause()}fire(p){var m=this[p];m&&m()}setTime(p){p>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():p<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=p,this.update())}getTime(){return this._time}setPosition(p){this.prevPos=this._pos,this.propFunc(p),this._pos=p}getPosition(p){return p===void 0&&(p=this._time),this.func(p,this.begin,this._change,this.duration)}play(){this.state=a,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=l,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(p){this.pause(),this._time=p,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var p=this.getTimer()-this._startTime;this.state===a?this.setTime(p):this.state===l&&this.setTime(this.duration-p)}pause(){this.state=s,this.fire("onPause")}getTimer(){return new Date().getTime()}}class f{constructor(p){var m=this,S=p.node,v=S._id,y,g=p.easing||e.Easings.Linear,b=!!p.yoyo,_;typeof p.duration>"u"?y=.3:p.duration===0?y=.001:y=p.duration,this.node=S,this._id=u++;var w=S.getLayer()||(S instanceof i.Konva.Stage?S.getLayers():null);w||t.Util.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new n.Animation(function(){m.tween.onEnterFrame()},w),this.tween=new d(_,function(x){m._tweenFunc(x)},g,0,1,y*1e3,b),this._addListeners(),f.attrs[v]||(f.attrs[v]={}),f.attrs[v][this._id]||(f.attrs[v][this._id]={}),f.tweens[v]||(f.tweens[v]={});for(_ in p)o[_]===void 0&&this._addAttr(_,p[_]);this.reset(),this.onFinish=p.onFinish,this.onReset=p.onReset,this.onUpdate=p.onUpdate}_addAttr(p,m){var S=this.node,v=S._id,y,g,b,_,w,x,C,k;if(b=f.tweens[v][p],b&&delete f.attrs[v][b][p],y=S.getAttr(p),t.Util._isArray(m))if(g=[],w=Math.max(m.length,y.length),p==="points"&&m.length!==y.length&&(m.length>y.length?(C=y,y=t.Util._prepareArrayForTween(y,m,S.closed())):(x=m,m=t.Util._prepareArrayForTween(m,y,S.closed()))),p.indexOf("fill")===0)for(_=0;_<w;_++)if(_%2===0)g.push(m[_]-y[_]);else{var P=t.Util.colorToRGBA(y[_]);k=t.Util.colorToRGBA(m[_]),y[_]=P,g.push({r:k.r-P.r,g:k.g-P.g,b:k.b-P.b,a:k.a-P.a})}else for(_=0;_<w;_++)g.push(m[_]-y[_]);else c.indexOf(p)!==-1?(y=t.Util.colorToRGBA(y),k=t.Util.colorToRGBA(m),g={r:k.r-y.r,g:k.g-y.g,b:k.b-y.b,a:k.a-y.a}):g=m-y;f.attrs[v][this._id][p]={start:y,diff:g,end:m,trueEnd:x,trueStart:C},f.tweens[v][p]=this._id}_tweenFunc(p){var m=this.node,S=f.attrs[m._id][this._id],v,y,g,b,_,w,x,C;for(v in S){if(y=S[v],g=y.start,b=y.diff,C=y.end,t.Util._isArray(g))if(_=[],x=Math.max(g.length,C.length),v.indexOf("fill")===0)for(w=0;w<x;w++)w%2===0?_.push((g[w]||0)+b[w]*p):_.push("rgba("+Math.round(g[w].r+b[w].r*p)+","+Math.round(g[w].g+b[w].g*p)+","+Math.round(g[w].b+b[w].b*p)+","+(g[w].a+b[w].a*p)+")");else for(w=0;w<x;w++)_.push((g[w]||0)+b[w]*p);else c.indexOf(v)!==-1?_="rgba("+Math.round(g.r+b.r*p)+","+Math.round(g.g+b.g*p)+","+Math.round(g.b+b.b*p)+","+(g.a+b.a*p)+")":_=g+b*p;m.setAttr(v,_)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueEnd&&p.setAttr("points",m.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueStart&&p.points(m.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(p){return this.tween.seek(p*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var p=this.node._id,m=this._id,S=f.tweens[p],v;this.pause();for(v in S)delete f.tweens[p][v];delete f.attrs[p][m]}}e.Tween=f,f.attrs={},f.tweens={},r.Node.prototype.to=function(h){var p=h.onFinish;h.node=this,h.onFinish=function(){this.destroy(),p&&p()};var m=new f(h);m.play()},e.Easings={BackEaseIn(h,p,m,S){var v=1.70158;return m*(h/=S)*h*((v+1)*h-v)+p},BackEaseOut(h,p,m,S){var 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a=s[0],l=s[1],u=s[2],c=s[3],d=s[4],f=s[5],h=s[6],p=s[7],m=u>c?u:c,S=u>c?1:u/c,v=u>c?c/u:1;t.translate(a,l),t.rotate(h),t.scale(S,v),t.arc(0,0,m,d,d+f,1-p),t.scale(1/S,1/v),t.rotate(-h),t.translate(-a,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(u){if(u.command==="A"){var c=u.points[4],d=u.points[5],f=u.points[4]+d,h=Math.PI/180;if(Math.abs(c-f)<h&&(h=Math.abs(c-f)),d<0)for(let p=c-h;p>f;p-=h){const m=ln.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],p,0);t.push(m.x,m.y)}else for(let p=c+h;p<f;p+=h){const m=ln.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],p,0);t.push(m.x,m.y)}}else if(u.command==="C")for(let p=0;p<=1;p+=.01){const m=ln.getPointOnCubicBezier(p,u.start.x,u.start.y,u.points[0],u.points[1],u.points[2],u.points[3],u.points[4],u.points[5]);t.push(m.x,m.y)}else t=t.concat(u.points)});for(var n=t[0],r=t[0],i=t[1],o=t[1],s,a,l=0;l<t.length/2;l++)s=t[l*2],a=t[l*2+1],isNaN(s)||(n=Math.min(n,s),r=Math.max(r,s)),isNaN(a)||(i=Math.min(i,a),o=Math.max(o,a));return{x:n,y:i,width:r-n,height:o-i}}getLength(){return this.pathLength}getPointAtLength(t){return ln.getPointAtLengthOfDataArray(t,this.dataArray)}static getLineLength(t,n,r,i){return Math.sqrt((r-t)*(r-t)+(i-n)*(i-n))}static getPathLength(t){let n=0;for(var r=0;r<t.length;++r)n+=t[r].pathLength;return n}static getPointAtLengthOfDataArray(t,n){var r,i=0,o=n.length;if(!o)return null;for(;i<o&&t>n[i].pathLength;)t-=n[i].pathLength,++i;if(i===o)return r=n[i-1].points.slice(-2),{x:r[0],y:r[1]};if(t<.01)return r=n[i].points.slice(0,2),{x:r[0],y:r[1]};var s=n[i],a=s.points;switch(s.command){case"L":return ln.getPointOnLine(t,s.start.x,s.start.y,a[0],a[1]);case"C":return ln.getPointOnCubicBezier((0,lu.t2length)(t,ln.getPathLength(n),m=>(0,lu.getCubicArcLength)([s.start.x,a[0],a[2],a[4]],[s.start.y,a[1],a[3],a[5]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return ln.getPointOnQuadraticBezier((0,lu.t2length)(t,ln.getPathLength(n),m=>(0,lu.getQuadraticArcLength)([s.start.x,a[0],a[2]],[s.start.y,a[1],a[3]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3]);case"A":var l=a[0],u=a[1],c=a[2],d=a[3],f=a[4],h=a[5],p=a[6];return f+=h*t/s.pathLength,ln.getPointOnEllipticalArc(l,u,c,d,f,p)}return null}static getPointOnLine(t,n,r,i,o,s,a){s===void 0&&(s=n),a===void 0&&(a=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i<n&&(u*=-1);var c=l*u,d;if(i===n)d={x:s,y:a+c};else if((a-r)/(s-n+1e-8)===l)d={x:s+u,y:a+c};else{var f,h,p=this.getLineLength(n,r,i,o),m=(s-n)*(i-n)+(a-r)*(o-r);m=m/(p*p),f=n+m*(i-n),h=r+m*(o-r);var S=this.getLineLength(s,a,f,h),v=Math.sqrt(t*t-S*S);u=Math.sqrt(v*v/(1+l*l)),i<n&&(u*=-1),c=l*u,d={x:f+u,y:h+c}}return d}static getPointOnCubicBezier(t,n,r,i,o,s,a,l,u){function c(S){return S*S*S}function d(S){return 3*S*S*(1-S)}function f(S){return 3*S*(1-S)*(1-S)}function h(S){return(1-S)*(1-S)*(1-S)}var p=l*c(t)+s*d(t)+i*f(t)+n*h(t),m=u*c(t)+a*d(t)+o*f(t)+r*h(t);return{x:p,y:m}}static getPointOnQuadraticBezier(t,n,r,i,o,s,a){function l(h){return h*h}function u(h){return 2*h*(1-h)}function c(h){return(1-h)*(1-h)}var d=s*l(t)+i*u(t)+n*c(t),f=a*l(t)+o*u(t)+r*c(t);return{x:d,y:f}}static getPointOnEllipticalArc(t,n,r,i,o,s){var a=Math.cos(s),l=Math.sin(s),u={x:r*Math.cos(o),y:i*Math.sin(o)};return{x:t+(u.x*a-u.y*l),y:n+(u.x*l+u.y*a)}}static parsePathData(t){if(!t)return[];var n=t,r=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];n=n.replace(new RegExp(" ","g"),",");for(var i=0;i<r.length;i++)n=n.replace(new RegExp(r[i],"g"),"|"+r[i]);var o=n.split("|"),s=[],a=[],l=0,u=0,c=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi,d;for(i=1;i<o.length;i++){var f=o[i],h=f.charAt(0);for(f=f.slice(1),a.length=0;d=c.exec(f);)a.push(d[0]);for(var p=[],m=0,S=a.length;m<S;m++){if(a[m]==="00"){p.push(0,0);continue}var v=parseFloat(a[m]);isNaN(v)?p.push(0):p.push(v)}for(;p.length>0&&!isNaN(p[0]);){var y=null,g=[],b=l,_=u,w,x,C,k,P,A,$,N,T,O;switch(h){case"l":l+=p.shift(),u+=p.shift(),y="L",g.push(l,u);break;case"L":l=p.shift(),u=p.shift(),g.push(l,u);break;case"m":var I=p.shift(),M=p.shift();if(l+=I,u+=M,y="M",s.length>2&&s[s.length-1].command==="z"){for(var R=s.length-2;R>=0;R--)if(s[R].command==="M"){l=s[R].points[0]+I,u=s[R].points[1]+M;break}}g.push(l,u),h="l";break;case"M":l=p.shift(),u=p.shift(),y="M",g.push(l,u),h="L";break;case"h":l+=p.shift(),y="L",g.push(l,u);break;case"H":l=p.shift(),y="L",g.push(l,u);break;case"v":u+=p.shift(),y="L",g.push(l,u);break;case"V":u=p.shift(),y="L",g.push(l,u);break;case"C":g.push(p.shift(),p.shift(),p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"c":g.push(l+p.shift(),u+p.shift(),l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="C",g.push(l,u);break;case"S":x=l,C=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),C=u+(u-w.points[3])),g.push(x,C,p.shift(),p.shift()),l=p.shift(),u=p.shift(),y="C",g.push(l,u);break;case"s":x=l,C=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),C=u+(u-w.points[3])),g.push(x,C,l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="C",g.push(l,u);break;case"Q":g.push(p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"q":g.push(l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="Q",g.push(l,u);break;case"T":x=l,C=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),C=u+(u-w.points[1])),l=p.shift(),u=p.shift(),y="Q",g.push(x,C,l,u);break;case"t":x=l,C=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),C=u+(u-w.points[1])),l+=p.shift(),u+=p.shift(),y="Q",g.push(x,C,l,u);break;case"A":k=p.shift(),P=p.shift(),A=p.shift(),$=p.shift(),N=p.shift(),T=l,O=u,l=p.shift(),u=p.shift(),y="A",g=this.convertEndpointToCenterParameterization(T,O,l,u,$,N,k,P,A);break;case"a":k=p.shift(),P=p.shift(),A=p.shift(),$=p.shift(),N=p.shift(),T=l,O=u,l+=p.shift(),u+=p.shift(),y="A",g=this.convertEndpointToCenterParameterization(T,O,l,u,$,N,k,P,A);break}s.push({command:y||h,points:g,start:{x:b,y:_},pathLength:this.calcLength(b,_,y||h,g)})}(h==="z"||h==="Z")&&s.push({command:"z",points:[],start:void 0,pathLength:0})}return s}static calcLength(t,n,r,i){var o,s,a,l,u=ln;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":return(0,lu.getCubicArcLength)([t,i[0],i[2],i[4]],[n,i[1],i[3],i[5]],1);case"Q":return(0,lu.getQuadraticArcLength)([t,i[0],i[2]],[n,i[1],i[3]],1);case"A":o=0;var c=i[4],d=i[5],f=i[4]+d,h=Math.PI/180;if(Math.abs(c-f)<h&&(h=Math.abs(c-f)),s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],c,0),d<0)for(l=c-h;l>f;l-=h)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;else for(l=c+h;l<f;l+=h)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;return a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],f,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),o}return 0}static convertEndpointToCenterParameterization(t,n,r,i,o,s,a,l,u){var c=u*(Math.PI/180),d=Math.cos(c)*(t-r)/2+Math.sin(c)*(n-i)/2,f=-1*Math.sin(c)*(t-r)/2+Math.cos(c)*(n-i)/2,h=d*d/(a*a)+f*f/(l*l);h>1&&(a*=Math.sqrt(h),l*=Math.sqrt(h));var p=Math.sqrt((a*a*(l*l)-a*a*(f*f)-l*l*(d*d))/(a*a*(f*f)+l*l*(d*d)));o===s&&(p*=-1),isNaN(p)&&(p=0);var m=p*a*f/l,S=p*-l*d/a,v=(t+r)/2+Math.cos(c)*m-Math.sin(c)*S,y=(n+i)/2+Math.sin(c)*m+Math.cos(c)*S,g=function(P){return Math.sqrt(P[0]*P[0]+P[1]*P[1])},b=function(P,A){return(P[0]*A[0]+P[1]*A[1])/(g(P)*g(A))},_=function(P,A){return(P[0]*A[1]<P[1]*A[0]?-1:1)*Math.acos(b(P,A))},w=_([1,0],[(d-m)/a,(f-S)/l]),x=[(d-m)/a,(f-S)/l],C=[(-1*d-m)/a,(-1*f-S)/l],k=_(x,C);return b(x,C)<=-1&&(k=Math.PI),b(x,C)>=1&&(k=0),s===0&&k>0&&(k=k-2*Math.PI),s===1&&k<0&&(k=k+2*Math.PI),[v,y,a,l,w,k,c,s]}}Jc.Path=ln;ln.prototype.className="Path";ln.prototype._attrsAffectingSize=["data"];(0,N_e._registerNode)(ln);I_e.Factory.addGetterSetter(ln,"data");Object.defineProperty(A1,"__esModule",{value:!0});A1.Arrow=void 0;const R1=Ie,D_e=Cp,eB=fe,L_e=Le,Uk=Jc;class Bl extends D_e.Line{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var s=this.pointerLength(),a=r.length,l,u;if(o){const f=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[a-2],r[a-1]],h=Uk.Path.calcLength(i[i.length-4],i[i.length-3],"C",f),p=Uk.Path.getPointOnQuadraticBezier(Math.min(1,1-s/h),f[0],f[1],f[2],f[3],f[4],f[5]);l=r[a-2]-p.x,u=r[a-1]-p.y}else l=r[a-2]-r[a-4],u=r[a-1]-r[a-3];var c=(Math.atan2(u,l)+n)%n,d=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[a-2],r[a-1]),t.rotate(c),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}A1.Arrow=Bl;Bl.prototype.className="Arrow";(0,L_e._registerNode)(Bl);R1.Factory.addGetterSetter(Bl,"pointerLength",10,(0,eB.getNumberValidator)());R1.Factory.addGetterSetter(Bl,"pointerWidth",10,(0,eB.getNumberValidator)());R1.Factory.addGetterSetter(Bl,"pointerAtBeginning",!1);R1.Factory.addGetterSetter(Bl,"pointerAtEnding",!0);var I1={};Object.defineProperty(I1,"__esModule",{value:!0});I1.Circle=void 0;const $_e=Ie,F_e=pn,B_e=fe,j_e=Le;class ed extends F_e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}}I1.Circle=ed;ed.prototype._centroid=!0;ed.prototype.className="Circle";ed.prototype._attrsAffectingSize=["radius"];(0,j_e._registerNode)(ed);$_e.Factory.addGetterSetter(ed,"radius",0,(0,B_e.getNumberValidator)());var M1={};Object.defineProperty(M1,"__esModule",{value:!0});M1.Ellipse=void 0;const $3=Ie,V_e=pn,tB=fe,z_e=Le;class Oa extends V_e.Shape{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}M1.Ellipse=Oa;Oa.prototype.className="Ellipse";Oa.prototype._centroid=!0;Oa.prototype._attrsAffectingSize=["radiusX","radiusY"];(0,z_e._registerNode)(Oa);$3.Factory.addComponentsGetterSetter(Oa,"radius",["x","y"]);$3.Factory.addGetterSetter(Oa,"radiusX",0,(0,tB.getNumberValidator)());$3.Factory.addGetterSetter(Oa,"radiusY",0,(0,tB.getNumberValidator)());var N1={};Object.defineProperty(N1,"__esModule",{value:!0});N1.Image=void 0;const x_=Bt,jl=Ie,U_e=pn,G_e=Le,Tp=fe;let Ro=class nB extends U_e.Shape{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.cornerRadius(),o=this.attrs.image;let s;if(o){const a=this.attrs.cropWidth,l=this.attrs.cropHeight;a&&l?s=[o,this.cropX(),this.cropY(),a,l,0,0,n,r]:s=[o,0,0,n,r]}(this.hasFill()||this.hasStroke()||i)&&(t.beginPath(),i?x_.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),o&&(i&&t.clip(),t.drawImage.apply(t,s))}_hitFunc(t){var n=this.width(),r=this.height(),i=this.cornerRadius();t.beginPath(),i?x_.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=x_.Util.createImageElement();i.onload=function(){var o=new nB({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}};N1.Image=Ro;Ro.prototype.className="Image";(0,G_e._registerNode)(Ro);jl.Factory.addGetterSetter(Ro,"cornerRadius",0,(0,Tp.getNumberOrArrayOfNumbersValidator)(4));jl.Factory.addGetterSetter(Ro,"image");jl.Factory.addComponentsGetterSetter(Ro,"crop",["x","y","width","height"]);jl.Factory.addGetterSetter(Ro,"cropX",0,(0,Tp.getNumberValidator)());jl.Factory.addGetterSetter(Ro,"cropY",0,(0,Tp.getNumberValidator)());jl.Factory.addGetterSetter(Ro,"cropWidth",0,(0,Tp.getNumberValidator)());jl.Factory.addGetterSetter(Ro,"cropHeight",0,(0,Tp.getNumberValidator)());var Dc={};Object.defineProperty(Dc,"__esModule",{value:!0});Dc.Tag=Dc.Label=void 0;const D1=Ie,H_e=pn,q_e=Qc,F3=fe,rB=Le;var iB=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],W_e="Change.konva",K_e="none",fx="up",hx="right",px="down",gx="left",Y_e=iB.length;class B3 extends q_e.Group{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r<Y_e;r++)t.on(iB[r]+W_e,i)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var t=this.getText(),n=this.getTag(),r,i,o,s,a,l,u;if(t&&n){switch(r=t.width(),i=t.height(),o=n.pointerDirection(),s=n.pointerWidth(),u=n.pointerHeight(),a=0,l=0,o){case fx:a=r/2,l=-1*u;break;case hx:a=r+s,l=i/2;break;case px:a=r/2,l=i+u;break;case gx:a=-1*s,l=i/2;break}n.setAttrs({x:-1*a,y:-1*l,width:r,height:i}),t.setAttrs({x:-1*a,y:-1*l})}}}Dc.Label=B3;B3.prototype.className="Label";(0,rB._registerNode)(B3);class Vl extends H_e.Shape{_sceneFunc(t){var n=this.width(),r=this.height(),i=this.pointerDirection(),o=this.pointerWidth(),s=this.pointerHeight(),a=this.cornerRadius();let l=0,u=0,c=0,d=0;typeof a=="number"?l=u=c=d=Math.min(a,n/2,r/2):(l=Math.min(a[0]||0,n/2,r/2),u=Math.min(a[1]||0,n/2,r/2),d=Math.min(a[2]||0,n/2,r/2),c=Math.min(a[3]||0,n/2,r/2)),t.beginPath(),t.moveTo(l,0),i===fx&&(t.lineTo((n-o)/2,0),t.lineTo(n/2,-1*s),t.lineTo((n+o)/2,0)),t.lineTo(n-u,0),t.arc(n-u,u,u,Math.PI*3/2,0,!1),i===hx&&(t.lineTo(n,(r-s)/2),t.lineTo(n+o,r/2),t.lineTo(n,(r+s)/2)),t.lineTo(n,r-d),t.arc(n-d,r-d,d,0,Math.PI/2,!1),i===px&&(t.lineTo((n+o)/2,r),t.lineTo(n/2,r+s),t.lineTo((n-o)/2,r)),t.lineTo(c,r),t.arc(c,r-c,c,Math.PI/2,Math.PI,!1),i===gx&&(t.lineTo(0,(r+s)/2),t.lineTo(-1*o,r/2),t.lineTo(0,(r-s)/2)),t.lineTo(0,l),t.arc(l,l,l,Math.PI,Math.PI*3/2,!1),t.closePath(),t.fillStrokeShape(this)}getSelfRect(){var t=0,n=0,r=this.pointerWidth(),i=this.pointerHeight(),o=this.pointerDirection(),s=this.width(),a=this.height();return o===fx?(n-=i,a+=i):o===px?a+=i:o===gx?(t-=r*1.5,s+=r):o===hx&&(s+=r*1.5),{x:t,y:n,width:s,height:a}}}Dc.Tag=Vl;Vl.prototype.className="Tag";(0,rB._registerNode)(Vl);D1.Factory.addGetterSetter(Vl,"pointerDirection",K_e);D1.Factory.addGetterSetter(Vl,"pointerWidth",0,(0,F3.getNumberValidator)());D1.Factory.addGetterSetter(Vl,"pointerHeight",0,(0,F3.getNumberValidator)());D1.Factory.addGetterSetter(Vl,"cornerRadius",0,(0,F3.getNumberOrArrayOfNumbersValidator)(4));var Ep={};Object.defineProperty(Ep,"__esModule",{value:!0});Ep.Rect=void 0;const X_e=Ie,Q_e=pn,Z_e=Le,J_e=Bt,e2e=fe;let L1=class extends Q_e.Shape{_sceneFunc(t){var n=this.cornerRadius(),r=this.width(),i=this.height();t.beginPath(),n?J_e.Util.drawRoundedRectPath(t,r,i,n):t.rect(0,0,r,i),t.closePath(),t.fillStrokeShape(this)}};Ep.Rect=L1;L1.prototype.className="Rect";(0,Z_e._registerNode)(L1);X_e.Factory.addGetterSetter(L1,"cornerRadius",0,(0,e2e.getNumberOrArrayOfNumbersValidator)(4));var $1={};Object.defineProperty($1,"__esModule",{value:!0});$1.RegularPolygon=void 0;const oB=Ie,t2e=pn,sB=fe,n2e=Le;class zl extends t2e.Shape{_sceneFunc(t){const n=this._getPoints();t.beginPath(),t.moveTo(n[0].x,n[0].y);for(var r=1;r<n.length;r++)t.lineTo(n[r].x,n[r].y);t.closePath(),t.fillStrokeShape(this)}_getPoints(){const t=this.attrs.sides,n=this.attrs.radius||0,r=[];for(var i=0;i<t;i++)r.push({x:n*Math.sin(i*2*Math.PI/t),y:-1*n*Math.cos(i*2*Math.PI/t)});return r}getSelfRect(){const t=this._getPoints();var n=t[0].x,r=t[0].y,i=t[0].x,o=t[0].y;return t.forEach(s=>{n=Math.min(n,s.x),r=Math.max(r,s.x),i=Math.min(i,s.y),o=Math.max(o,s.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}$1.RegularPolygon=zl;zl.prototype.className="RegularPolygon";zl.prototype._centroid=!0;zl.prototype._attrsAffectingSize=["radius"];(0,n2e._registerNode)(zl);oB.Factory.addGetterSetter(zl,"radius",0,(0,sB.getNumberValidator)());oB.Factory.addGetterSetter(zl,"sides",0,(0,sB.getNumberValidator)());var F1={};Object.defineProperty(F1,"__esModule",{value:!0});F1.Ring=void 0;const aB=Ie,r2e=pn,lB=fe,i2e=Le;var Gk=Math.PI*2;class Ul extends r2e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,Gk,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),Gk,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}F1.Ring=Ul;Ul.prototype.className="Ring";Ul.prototype._centroid=!0;Ul.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,i2e._registerNode)(Ul);aB.Factory.addGetterSetter(Ul,"innerRadius",0,(0,lB.getNumberValidator)());aB.Factory.addGetterSetter(Ul,"outerRadius",0,(0,lB.getNumberValidator)());var B1={};Object.defineProperty(B1,"__esModule",{value:!0});B1.Sprite=void 0;const Gl=Ie,o2e=pn,s2e=Zc,uB=fe,a2e=Le;class Io extends o2e.Shape{constructor(t){super(t),this._updated=!0,this.anim=new s2e.Animation(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+0],l=o[i+1],u=o[i+2],c=o[i+3],d=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,c),t.closePath(),t.fillStrokeShape(this)),d)if(s){var f=s[n],h=r*2;t.drawImage(d,a,l,u,c,f[h+0],f[h+1],u,c)}else t.drawImage(d,a,l,u,c,0,0,u,c)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+2],l=o[i+3];if(t.beginPath(),s){var u=s[n],c=r*2;t.rect(u[c+0],u[c+1],a,l)}else t.rect(0,0,a,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t<o-1?this.frameIndex(t+1):this.frameIndex(0)}}B1.Sprite=Io;Io.prototype.className="Sprite";(0,a2e._registerNode)(Io);Gl.Factory.addGetterSetter(Io,"animation");Gl.Factory.addGetterSetter(Io,"animations");Gl.Factory.addGetterSetter(Io,"frameOffsets");Gl.Factory.addGetterSetter(Io,"image");Gl.Factory.addGetterSetter(Io,"frameIndex",0,(0,uB.getNumberValidator)());Gl.Factory.addGetterSetter(Io,"frameRate",17,(0,uB.getNumberValidator)());Gl.Factory.backCompat(Io,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});var j1={};Object.defineProperty(j1,"__esModule",{value:!0});j1.Star=void 0;const j3=Ie,l2e=pn,V3=fe,u2e=Le;class Ra extends l2e.Shape{_sceneFunc(t){var n=this.innerRadius(),r=this.outerRadius(),i=this.numPoints();t.beginPath(),t.moveTo(0,0-r);for(var o=1;o<i*2;o++){var s=o%2===0?r:n,a=s*Math.sin(o*Math.PI/i),l=-1*s*Math.cos(o*Math.PI/i);t.lineTo(a,l)}t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}j1.Star=Ra;Ra.prototype.className="Star";Ra.prototype._centroid=!0;Ra.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,u2e._registerNode)(Ra);j3.Factory.addGetterSetter(Ra,"numPoints",5,(0,V3.getNumberValidator)());j3.Factory.addGetterSetter(Ra,"innerRadius",0,(0,V3.getNumberValidator)());j3.Factory.addGetterSetter(Ra,"outerRadius",0,(0,V3.getNumberValidator)());var xl={};Object.defineProperty(xl,"__esModule",{value:!0});xl.Text=xl.stringToArray=void 0;const mx=Bt,yr=Ie,c2e=pn,Ia=fe,d2e=Le;function cB(e){return Array.from(e)}xl.stringToArray=cB;var uu="auto",f2e="center",Rd="justify",h2e="Change.konva",p2e="2d",Hk="-",dB="left",g2e="text",m2e="Text",y2e="top",v2e="bottom",qk="middle",fB="normal",b2e="px ",Og=" ",S2e="right",_2e="word",w2e="char",Wk="none",C_="…",hB=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],x2e=hB.length;function C2e(e){return e.split(",").map(t=>{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Rg;function T_(){return Rg||(Rg=mx.Util.createCanvasElement().getContext(p2e),Rg)}function T2e(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function E2e(e){e.setAttr("miterLimit",2),e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function P2e(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class Xt extends c2e.Shape{constructor(t){super(P2e(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n<x2e;n++)this.on(hB[n]+h2e,this._setTextData);this._setTextData()}_sceneFunc(t){var n=this.textArr,r=n.length;if(this.text()){var i=this.padding(),o=this.fontSize(),s=this.lineHeight()*o,a=this.verticalAlign(),l=0,u=this.align(),c=this.getWidth(),d=this.letterSpacing(),f=this.fill(),h=this.textDecoration(),p=h.indexOf("underline")!==-1,m=h.indexOf("line-through")!==-1,S,v=0,v=s/2,y=0,g=0;for(t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",qk),t.setAttr("textAlign",dB),a===qk?l=(this.getHeight()-r*s-i*2)/2:a===v2e&&(l=this.getHeight()-r*s-i*2),t.translate(i,l+i),S=0;S<r;S++){var y=0,g=0,b=n[S],_=b.text,w=b.width,x=b.lastInParagraph,C,k,P;if(t.save(),u===S2e?y+=c-w-i*2:u===f2e&&(y+=(c-w-i*2)/2),p){t.save(),t.beginPath(),t.moveTo(y,v+g+Math.round(o/2)),C=_.split(" ").length-1,k=C===0,P=u===Rd&&!x?c-i*2:w,t.lineTo(y+Math.round(P),v+g+Math.round(o/2)),t.lineWidth=o/15;const I=this._getLinearGradient();t.strokeStyle=I||f,t.stroke(),t.restore()}if(m){t.save(),t.beginPath(),t.moveTo(y,v+g),C=_.split(" ").length-1,k=C===0,P=u===Rd&&x&&!k?c-i*2:w,t.lineTo(y+Math.round(P),v+g),t.lineWidth=o/15;const I=this._getLinearGradient();t.strokeStyle=I||f,t.stroke(),t.restore()}if(d!==0||u===Rd){C=_.split(" ").length-1;for(var A=cB(_),$=0;$<A.length;$++){var N=A[$];N===" "&&!x&&u===Rd&&(y+=(c-i*2-w)/C),this._partialTextX=y,this._partialTextY=v+g,this._partialText=N,t.fillStrokeShape(this),y+=this.measureSize(N).width+d}}else this._partialTextX=y,this._partialTextY=v+g,this._partialText=_,t.fillStrokeShape(this);t.restore(),r>1&&(v+=s)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=mx.Util._isString(t)?t:t==null?"":t+"";return this._setAttr(g2e,n),this}getWidth(){var t=this.attrs.width===uu||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===uu||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return mx.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=T_(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+Og+this.fontVariant()+Og+(this.fontSize()+b2e)+C2e(this.fontFamily())}_addTextLine(t){this.align()===Rd&&(t=t.trim());var r=this._getTextWidth(t);return this.textArr.push({text:t,width:r,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return T_().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(`
-`),n=+this.fontSize(),r=0,i=this.lineHeight()*n,o=this.attrs.width,s=this.attrs.height,a=o!==uu&&o!==void 0,l=s!==uu&&s!==void 0,u=this.padding(),c=o-u*2,d=s-u*2,f=0,h=this.wrap(),p=h!==Wk,m=h!==w2e&&p,S=this.ellipsis();this.textArr=[],T_().font=this._getContextFont();for(var v=S?this._getTextWidth(C_):0,y=0,g=t.length;y<g;++y){var b=t[y],_=this._getTextWidth(b);if(a&&_>c)for(;b.length>0;){for(var w=0,x=b.length,C="",k=0;w<x;){var P=w+x>>>1,A=b.slice(0,P+1),$=this._getTextWidth(A)+v;$<=c?(w=P+1,C=A,k=$):x=P}if(C){if(m){var N,T=b[C.length],O=T===Og||T===Hk;O&&k<=c?N=C.length:N=Math.max(C.lastIndexOf(Og),C.lastIndexOf(Hk))+1,N>0&&(w=N,C=C.slice(0,w),k=this._getTextWidth(C))}C=C.trimRight(),this._addTextLine(C),r=Math.max(r,k),f+=i;var I=this._shouldHandleEllipsis(f);if(I){this._tryToAddEllipsisToLastLine();break}if(b=b.slice(w),b=b.trimLeft(),b.length>0&&(_=this._getTextWidth(b),_<=c)){this._addTextLine(b),f+=i,r=Math.max(r,_);break}}else break}else this._addTextLine(b),f+=i,r=Math.max(r,_),this._shouldHandleEllipsis(f)&&y<g-1&&this._tryToAddEllipsisToLastLine();if(this.textArr[this.textArr.length-1]&&(this.textArr[this.textArr.length-1].lastInParagraph=!0),l&&f+i>d)break}this.textHeight=n,this.textWidth=r}_shouldHandleEllipsis(t){var n=+this.fontSize(),r=this.lineHeight()*n,i=this.attrs.height,o=i!==uu&&i!==void 0,s=this.padding(),a=i-s*2,l=this.wrap(),u=l!==Wk;return!u||o&&t+r>a}_tryToAddEllipsisToLastLine(){var t=this.attrs.width,n=t!==uu&&t!==void 0,r=this.padding(),i=t-r*2,o=this.ellipsis(),s=this.textArr[this.textArr.length-1];if(!(!s||!o)){if(n){var a=this._getTextWidth(s.text+C_)<i;a||(s.text=s.text.slice(0,s.text.length-3))}this.textArr.splice(this.textArr.length-1,1),this._addTextLine(s.text+C_)}}getStrokeScaleEnabled(){return!0}}xl.Text=Xt;Xt.prototype._fillFunc=T2e;Xt.prototype._strokeFunc=E2e;Xt.prototype.className=m2e;Xt.prototype._attrsAffectingSize=["text","fontSize","padding","wrap","lineHeight","letterSpacing"];(0,d2e._registerNode)(Xt);yr.Factory.overWriteSetter(Xt,"width",(0,Ia.getNumberOrAutoValidator)());yr.Factory.overWriteSetter(Xt,"height",(0,Ia.getNumberOrAutoValidator)());yr.Factory.addGetterSetter(Xt,"fontFamily","Arial");yr.Factory.addGetterSetter(Xt,"fontSize",12,(0,Ia.getNumberValidator)());yr.Factory.addGetterSetter(Xt,"fontStyle",fB);yr.Factory.addGetterSetter(Xt,"fontVariant",fB);yr.Factory.addGetterSetter(Xt,"padding",0,(0,Ia.getNumberValidator)());yr.Factory.addGetterSetter(Xt,"align",dB);yr.Factory.addGetterSetter(Xt,"verticalAlign",y2e);yr.Factory.addGetterSetter(Xt,"lineHeight",1,(0,Ia.getNumberValidator)());yr.Factory.addGetterSetter(Xt,"wrap",_2e);yr.Factory.addGetterSetter(Xt,"ellipsis",!1,(0,Ia.getBooleanValidator)());yr.Factory.addGetterSetter(Xt,"letterSpacing",0,(0,Ia.getNumberValidator)());yr.Factory.addGetterSetter(Xt,"text","",(0,Ia.getStringValidator)());yr.Factory.addGetterSetter(Xt,"textDecoration","");var V1={};Object.defineProperty(V1,"__esModule",{value:!0});V1.TextPath=void 0;const E_=Bt,Wi=Ie,k2e=pn,Id=Jc,P_=xl,pB=fe,A2e=Le;var O2e="",gB="normal";function mB(e){e.fillText(this.partialText,0,0)}function yB(e){e.strokeText(this.partialText,0,0)}class gn extends k2e.Shape{constructor(t){super(t),this.dummyCanvas=E_.Util.createCanvasElement(),this.dataArray=[],this._readDataAttribute(),this.on("dataChange.konva",function(){this._readDataAttribute(),this._setTextData()}),this.on("textChange.konva alignChange.konva letterSpacingChange.konva kerningFuncChange.konva fontSizeChange.konva fontFamilyChange.konva",this._setTextData),this._setTextData()}_getTextPathLength(){return Id.Path.getPathLength(this.dataArray)}_getPointAtLength(t){if(!this.attrs.data)return null;const n=this.pathLength;return t-1>n?null:Id.Path.getPointAtLengthOfDataArray(t,this.dataArray)}_readDataAttribute(){this.dataArray=Id.Path.parsePathData(this.attrs.data),this.pathLength=this._getTextPathLength()}_sceneFunc(t){t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",this.textBaseline()),t.setAttr("textAlign","left"),t.save();var n=this.textDecoration(),r=this.fill(),i=this.fontSize(),o=this.glyphInfo;n==="underline"&&t.beginPath();for(var s=0;s<o.length;s++){t.save();var a=o[s].p0;t.translate(a.x,a.y),t.rotate(o[s].rotation),this.partialText=o[s].text,t.fillStrokeShape(this),n==="underline"&&(s===0&&t.moveTo(0,i/2+1),t.lineTo(i,i/2+1)),t.restore()}n==="underline"&&(t.strokeStyle=r,t.lineWidth=i/20,t.stroke()),t.restore()}_hitFunc(t){t.beginPath();var n=this.glyphInfo;if(n.length>=1){var r=n[0].p0;t.moveTo(r.x,r.y)}for(var i=0;i<n.length;i++){var o=n[i].p1;t.lineTo(o.x,o.y)}t.setAttr("lineWidth",this.fontSize()),t.setAttr("strokeStyle",this.colorKey),t.stroke()}getTextWidth(){return this.textWidth}getTextHeight(){return E_.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}setText(t){return P_.Text.prototype.setText.call(this,t)}_getContextFont(){return P_.Text.prototype._getContextFont.call(this)}_getTextSize(t){var n=this.dummyCanvas,r=n.getContext("2d");r.save(),r.font=this._getContextFont();var i=r.measureText(t);return r.restore(),{width:i.width,height:parseInt(`${this.fontSize()}`,10)}}_setTextData(){const{width:t,height:n}=this._getTextSize(this.attrs.text);if(this.textWidth=t,this.textHeight=n,this.glyphInfo=[],!this.attrs.data)return null;const r=this.letterSpacing(),i=this.align(),o=this.kerningFunc(),s=Math.max(this.textWidth+((this.attrs.text||"").length-1)*r,0);let a=0;i==="center"&&(a=Math.max(0,this.pathLength/2-s/2)),i==="right"&&(a=Math.max(0,this.pathLength-s));const l=(0,P_.stringToArray)(this.text());let u=a;for(var c=0;c<l.length;c++){const d=this._getPointAtLength(u);if(!d)return;let f=this._getTextSize(l[c]).width+r;if(l[c]===" "&&i==="justify"){const y=this.text().split(" ").length-1;f+=(this.pathLength-s)/y}const h=this._getPointAtLength(u+f);if(!h)return;const p=Id.Path.getLineLength(d.x,d.y,h.x,h.y);let m=0;if(o)try{m=o(l[c-1],l[c])*this.fontSize()}catch{m=0}d.x+=m,h.x+=m,this.textWidth+=m;const S=Id.Path.getPointOnLine(m+p/2,d.x,d.y,h.x,h.y),v=Math.atan2(h.y-d.y,h.x-d.x);this.glyphInfo.push({transposeX:S.x,transposeY:S.y,text:l[c],rotation:v,p0:d,p1:h}),u+=f}}getSelfRect(){if(!this.glyphInfo.length)return{x:0,y:0,width:0,height:0};var t=[];this.glyphInfo.forEach(function(c){t.push(c.p0.x),t.push(c.p0.y),t.push(c.p1.x),t.push(c.p1.y)});for(var n=t[0]||0,r=t[0]||0,i=t[1]||0,o=t[1]||0,s,a,l=0;l<t.length/2;l++)s=t[l*2],a=t[l*2+1],n=Math.min(n,s),r=Math.max(r,s),i=Math.min(i,a),o=Math.max(o,a);var u=this.fontSize();return{x:n-u/2,y:i-u/2,width:r-n+u,height:o-i+u}}destroy(){return E_.Util.releaseCanvas(this.dummyCanvas),super.destroy()}}V1.TextPath=gn;gn.prototype._fillFunc=mB;gn.prototype._strokeFunc=yB;gn.prototype._fillFuncHit=mB;gn.prototype._strokeFuncHit=yB;gn.prototype.className="TextPath";gn.prototype._attrsAffectingSize=["text","fontSize","data"];(0,A2e._registerNode)(gn);Wi.Factory.addGetterSetter(gn,"data");Wi.Factory.addGetterSetter(gn,"fontFamily","Arial");Wi.Factory.addGetterSetter(gn,"fontSize",12,(0,pB.getNumberValidator)());Wi.Factory.addGetterSetter(gn,"fontStyle",gB);Wi.Factory.addGetterSetter(gn,"align","left");Wi.Factory.addGetterSetter(gn,"letterSpacing",0,(0,pB.getNumberValidator)());Wi.Factory.addGetterSetter(gn,"textBaseline","middle");Wi.Factory.addGetterSetter(gn,"fontVariant",gB);Wi.Factory.addGetterSetter(gn,"text",O2e);Wi.Factory.addGetterSetter(gn,"textDecoration",null);Wi.Factory.addGetterSetter(gn,"kerningFunc",null);var z1={};Object.defineProperty(z1,"__esModule",{value:!0});z1.Transformer=void 0;const at=Bt,st=Ie,Kk=At,R2e=pn,I2e=Ep,Yk=Qc,Ci=Le,Ma=fe,M2e=Le;var vB="tr-konva",N2e=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange","anchorStyleFuncChange"].map(e=>e+`.${vB}`).join(" "),Xk="nodesRect",D2e=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],L2e={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const $2e="ontouchstart"in Ci.Konva._global;function F2e(e,t){if(e==="rotater")return"crosshair";t+=at.Util.degToRad(L2e[e]||0);var n=(at.Util.radToDeg(t)%360+360)%360;return at.Util._inRange(n,315+22.5,360)||at.Util._inRange(n,0,22.5)?"ns-resize":at.Util._inRange(n,45-22.5,45+22.5)?"nesw-resize":at.Util._inRange(n,90-22.5,90+22.5)?"ew-resize":at.Util._inRange(n,135-22.5,135+22.5)?"nwse-resize":at.Util._inRange(n,180-22.5,180+22.5)?"ns-resize":at.Util._inRange(n,225-22.5,225+22.5)?"nesw-resize":at.Util._inRange(n,270-22.5,270+22.5)?"ew-resize":at.Util._inRange(n,315-22.5,315+22.5)?"nwse-resize":(at.Util.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var a0=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],Qk=1e8;function B2e(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function bB(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function j2e(e,t){const n=B2e(e);return bB(e,t,n)}function V2e(e,t,n){let r=t;for(let i=0;i<e.length;i++){const o=Ci.Konva.getAngle(e[i]),s=Math.abs(o-t)%(Math.PI*2);Math.min(s,Math.PI*2-s)<n&&(r=o)}return r}class Je extends Yk.Group{constructor(t){super(t),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(N2e,this.update),this.getNode()&&this.update()}attachTo(t){return this.setNode(t),this}setNode(t){return at.Util.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([t])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return vB+this._id}setNodes(t=[]){this._nodes&&this._nodes.length&&this.detach();const n=t.filter(i=>i.isAncestorOf(this)?(at.Util.error("Konva.Transformer cannot be an a child of the node you are trying to attach"),!1):!0);this._nodes=t=n,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(i=>{const o=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},s=i._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");i.on(s,o),i.on(D2e.map(a=>a+`.${this._getEventNamespace()}`).join(" "),o),i.on(`absoluteTransformChange.${this._getEventNamespace()}`,o),this._proxyDrag(i)}),this._resetTransformCache();var r=!!this.findOne(".top-left");return r&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,s=i.y-n.y;this.nodes().forEach(a=>{if(a===t||a.isDragging())return;const l=a.getAbsolutePosition();a.setAbsolutePosition({x:l.x+o,y:l.y+s}),a.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(Xk),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(Xk,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),s=t.getAbsolutePosition(r),a=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=(Ci.Konva.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),c={x:s.x+a*Math.cos(u)+l*Math.sin(-u),y:s.y+l*Math.cos(u)+a*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return bB(c,-Ci.Konva.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-Qk,y:-Qk,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const c=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var d=[{x:c.x,y:c.y},{x:c.x+c.width,y:c.y},{x:c.x+c.width,y:c.y+c.height},{x:c.x,y:c.y+c.height}],f=u.getAbsoluteTransform();d.forEach(function(h){var p=f.point(h);n.push(p)})});const r=new at.Transform;r.rotate(-Ci.Konva.getAngle(this.rotation()));var i,o,s,a;n.forEach(function(u){var c=r.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:s-i,height:a-o,rotation:Ci.Konva.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),a0.forEach((function(t){this._createAnchor(t)}).bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new I2e.Rect({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:$2e?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=Ci.Konva.getAngle(this.rotation()),o=F2e(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new R2e.Shape({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*at.Util._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var s=t.target.getAbsolutePosition(),a=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-s.x,y:a.y-s.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),s=o.getStage();s.setPointersPositions(t);const a=s.getPointerPosition();let l={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const c=o.getAbsolutePosition();if(!(u.x===c.x&&u.y===c.y)){if(this._movingAnchorName==="rotater"){var d=this._getNodeRect();n=o.x()-d.width/2,r=-o.y()+d.height/2;let N=Math.atan2(-r,n)+Math.PI/2;d.height<0&&(N-=Math.PI);var f=Ci.Konva.getAngle(this.rotation());const T=f+N,O=Ci.Konva.getAngle(this.rotationSnapTolerance()),M=V2e(this.rotationSnaps(),T,O)-d.rotation,R=j2e(d,M);this._fitNodesInto(R,t);return}var h=this.shiftBehavior(),p;h==="inverted"?p=this.keepRatio()&&!t.shiftKey:h==="none"?p=this.keepRatio():p=this.keepRatio()||t.shiftKey;var g=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-left").x()>m.x?-1:1,v=this.findOne(".top-left").y()>m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".top-left").x(m.x-n),this.findOne(".top-left").y(m.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-m.x,2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-right").x()<m.x?-1:1,v=this.findOne(".top-right").y()>m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".top-right").x(m.x+n),this.findOne(".top-right").y(m.y-r)}var y=o.position();this.findOne(".top-left").y(y.y),this.findOne(".bottom-right").x(y.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(o.y()-m.y,2));var S=m.x<o.x()?-1:1,v=o.y()<m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,o.x(m.x-n),o.y(m.y+r)}y=o.position(),this.findOne(".top-left").x(y.x),this.findOne(".bottom-right").y(y.y)}else if(this._movingAnchorName==="bottom-center")this.findOne(".bottom-right").y(o.y());else if(this._movingAnchorName==="bottom-right"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};i=Math.sqrt(Math.pow(o.x()-m.x,2)+Math.pow(o.y()-m.y,2));var S=this.findOne(".bottom-right").x()<m.x?-1:1,v=this.findOne(".bottom-right").y()<m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".bottom-right").x(m.x+n),this.findOne(".bottom-right").y(m.y+r)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));var g=this.centeredScaling()||t.altKey;if(g){var b=this.findOne(".top-left"),_=this.findOne(".bottom-right"),w=b.x(),x=b.y(),C=this.getWidth()-_.x(),k=this.getHeight()-_.y();_.move({x:-w,y:-x}),b.move({x:C,y:k})}var P=this.findOne(".top-left").getAbsolutePosition();n=P.x,r=P.y;var A=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),$=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:n,y:r,width:A,height:$,rotation:Ci.Konva.getAngle(this.rotation())},t)}}_handleMouseUp(t){this._removeEvents(t)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(t){if(this._transforming){this._transforming=!1,typeof window<"u"&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var n=this.getNode();this._fire("transformend",{evt:t,target:n}),n&&this._nodes.forEach(r=>{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(at.Util._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(at.Util._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var s=new at.Transform;if(s.rotate(Ci.Konva.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const d=s.point({x:-this.padding()*2,y:0});if(t.x+=d.x,t.y+=d.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const d=s.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const d=s.point({x:0,y:-this.padding()*2});if(t.x+=d.x,t.y+=d.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const d=s.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const d=this.boundBoxFunc()(r,t);d?t=d:at.Util.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const a=1e7,l=new at.Transform;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/a,r.height/a);const u=new at.Transform;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/a,t.height/a);const c=u.multiply(l.invert());this._nodes.forEach(d=>{var f;const h=d.getParent().getAbsoluteTransform(),p=d.getTransform().copy();p.translate(d.offsetX(),d.offsetY());const m=new at.Transform;m.multiply(h.copy().invert()).multiply(c).multiply(h).multiply(p);const S=m.decompose();d.setAttrs(S),this._fire("transform",{evt:n,target:d}),d._fire("transform",{evt:n,target:d}),(f=d.getLayer())===null||f===void 0||f.batchDraw()}),this.rotation(at.Util._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(at.Util._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),s=this.resizeEnabled(),a=this.padding(),l=this.anchorSize();const u=this.find("._anchor");u.forEach(d=>{d.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+a,offsetY:l/2+a,visible:s&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+a,visible:s&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-a,offsetY:l/2+a,visible:s&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+a,visible:s&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-a,visible:s&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-a,visible:s&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*at.Util._sign(i)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0});const c=this.anchorStyleFunc();c&&u.forEach(d=>{c(d)}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),Yk.Group.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return Kk.Node.prototype.toObject.call(this)}clone(t){var n=Kk.Node.prototype.clone.call(this,t);return n}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}z1.Transformer=Je;function z2e(e){return e instanceof Array||at.Util.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){a0.indexOf(t)===-1&&at.Util.warn("Unknown anchor name: "+t+". Available names are: "+a0.join(", "))}),e||[]}Je.prototype.className="Transformer";(0,M2e._registerNode)(Je);st.Factory.addGetterSetter(Je,"enabledAnchors",a0,z2e);st.Factory.addGetterSetter(Je,"flipEnabled",!0,(0,Ma.getBooleanValidator)());st.Factory.addGetterSetter(Je,"resizeEnabled",!0);st.Factory.addGetterSetter(Je,"anchorSize",10,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"rotateEnabled",!0);st.Factory.addGetterSetter(Je,"rotationSnaps",[]);st.Factory.addGetterSetter(Je,"rotateAnchorOffset",50,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"rotationSnapTolerance",5,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"borderEnabled",!0);st.Factory.addGetterSetter(Je,"anchorStroke","rgb(0, 161, 255)");st.Factory.addGetterSetter(Je,"anchorStrokeWidth",1,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"anchorFill","white");st.Factory.addGetterSetter(Je,"anchorCornerRadius",0,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"borderStroke","rgb(0, 161, 255)");st.Factory.addGetterSetter(Je,"borderStrokeWidth",1,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"borderDash");st.Factory.addGetterSetter(Je,"keepRatio",!0);st.Factory.addGetterSetter(Je,"shiftBehavior","default");st.Factory.addGetterSetter(Je,"centeredScaling",!1);st.Factory.addGetterSetter(Je,"ignoreStroke",!1);st.Factory.addGetterSetter(Je,"padding",0,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"node");st.Factory.addGetterSetter(Je,"nodes");st.Factory.addGetterSetter(Je,"boundBoxFunc");st.Factory.addGetterSetter(Je,"anchorDragBoundFunc");st.Factory.addGetterSetter(Je,"anchorStyleFunc");st.Factory.addGetterSetter(Je,"shouldOverdrawWholeArea",!1);st.Factory.addGetterSetter(Je,"useSingleNodeRotation",!0);st.Factory.backCompat(Je,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});var U1={};Object.defineProperty(U1,"__esModule",{value:!0});U1.Wedge=void 0;const G1=Ie,U2e=pn,G2e=Le,SB=fe,H2e=Le;class Ss extends U2e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,G2e.Konva.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}U1.Wedge=Ss;Ss.prototype.className="Wedge";Ss.prototype._centroid=!0;Ss.prototype._attrsAffectingSize=["radius"];(0,H2e._registerNode)(Ss);G1.Factory.addGetterSetter(Ss,"radius",0,(0,SB.getNumberValidator)());G1.Factory.addGetterSetter(Ss,"angle",0,(0,SB.getNumberValidator)());G1.Factory.addGetterSetter(Ss,"clockwise",!1);G1.Factory.backCompat(Ss,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});var H1={};Object.defineProperty(H1,"__esModule",{value:!0});H1.Blur=void 0;const Zk=Ie,q2e=At,W2e=fe;function Jk(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var K2e=[512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,289,287,285,282,280,278,275,273,271,269,267,265,263,261,259],Y2e=[9,11,12,13,13,14,14,15,15,15,15,16,16,16,16,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24];function X2e(e,t){var n=e.data,r=e.width,i=e.height,o,s,a,l,u,c,d,f,h,p,m,S,v,y,g,b,_,w,x,C,k,P,A,$,N=t+t+1,T=r-1,O=i-1,I=t+1,M=I*(I+1)/2,R=new Jk,D=null,L=R,V=null,z=null,U=K2e[t],K=Y2e[t];for(a=1;a<N;a++)L=L.next=new Jk,a===I&&(D=L);for(L.next=R,d=c=0,s=0;s<i;s++){for(b=_=w=x=f=h=p=m=0,S=I*(C=n[c]),v=I*(k=n[c+1]),y=I*(P=n[c+2]),g=I*(A=n[c+3]),f+=M*C,h+=M*k,p+=M*P,m+=M*A,L=R,a=0;a<I;a++)L.r=C,L.g=k,L.b=P,L.a=A,L=L.next;for(a=1;a<I;a++)l=c+((T<a?T:a)<<2),f+=(L.r=C=n[l])*($=I-a),h+=(L.g=k=n[l+1])*$,p+=(L.b=P=n[l+2])*$,m+=(L.a=A=n[l+3])*$,b+=C,_+=k,w+=P,x+=A,L=L.next;for(V=R,z=D,o=0;o<r;o++)n[c+3]=A=m*U>>K,A!==0?(A=255/A,n[c]=(f*U>>K)*A,n[c+1]=(h*U>>K)*A,n[c+2]=(p*U>>K)*A):n[c]=n[c+1]=n[c+2]=0,f-=S,h-=v,p-=y,m-=g,S-=V.r,v-=V.g,y-=V.b,g-=V.a,l=d+((l=o+t+1)<T?l:T)<<2,b+=V.r=n[l],_+=V.g=n[l+1],w+=V.b=n[l+2],x+=V.a=n[l+3],f+=b,h+=_,p+=w,m+=x,V=V.next,S+=C=z.r,v+=k=z.g,y+=P=z.b,g+=A=z.a,b-=C,_-=k,w-=P,x-=A,z=z.next,c+=4;d+=r}for(o=0;o<r;o++){for(_=w=x=b=h=p=m=f=0,c=o<<2,S=I*(C=n[c]),v=I*(k=n[c+1]),y=I*(P=n[c+2]),g=I*(A=n[c+3]),f+=M*C,h+=M*k,p+=M*P,m+=M*A,L=R,a=0;a<I;a++)L.r=C,L.g=k,L.b=P,L.a=A,L=L.next;for(u=r,a=1;a<=t;a++)c=u+o<<2,f+=(L.r=C=n[c])*($=I-a),h+=(L.g=k=n[c+1])*$,p+=(L.b=P=n[c+2])*$,m+=(L.a=A=n[c+3])*$,b+=C,_+=k,w+=P,x+=A,L=L.next,a<O&&(u+=r);for(c=o,V=R,z=D,s=0;s<i;s++)l=c<<2,n[l+3]=A=m*U>>K,A>0?(A=255/A,n[l]=(f*U>>K)*A,n[l+1]=(h*U>>K)*A,n[l+2]=(p*U>>K)*A):n[l]=n[l+1]=n[l+2]=0,f-=S,h-=v,p-=y,m-=g,S-=V.r,v-=V.g,y-=V.b,g-=V.a,l=o+((l=s+I)<O?l:O)*r<<2,f+=b+=V.r=n[l],h+=_+=V.g=n[l+1],p+=w+=V.b=n[l+2],m+=x+=V.a=n[l+3],V=V.next,S+=C=z.r,v+=k=z.g,y+=P=z.b,g+=A=z.a,b-=C,_-=k,w-=P,x-=A,z=z.next,c+=r}}const Q2e=function(t){var n=Math.round(this.blurRadius());n>0&&X2e(t,n)};H1.Blur=Q2e;Zk.Factory.addGetterSetter(q2e.Node,"blurRadius",0,(0,W2e.getNumberValidator)(),Zk.Factory.afterSetFilter);var q1={};Object.defineProperty(q1,"__esModule",{value:!0});q1.Brighten=void 0;const eA=Ie,Z2e=At,J2e=fe,ewe=function(e){var t=this.brightness()*255,n=e.data,r=n.length,i;for(i=0;i<r;i+=4)n[i]+=t,n[i+1]+=t,n[i+2]+=t};q1.Brighten=ewe;eA.Factory.addGetterSetter(Z2e.Node,"brightness",0,(0,J2e.getNumberValidator)(),eA.Factory.afterSetFilter);var W1={};Object.defineProperty(W1,"__esModule",{value:!0});W1.Contrast=void 0;const tA=Ie,twe=At,nwe=fe,rwe=function(e){var t=Math.pow((this.contrast()+100)/100,2),n=e.data,r=n.length,i=150,o=150,s=150,a;for(a=0;a<r;a+=4)i=n[a],o=n[a+1],s=n[a+2],i/=255,i-=.5,i*=t,i+=.5,i*=255,o/=255,o-=.5,o*=t,o+=.5,o*=255,s/=255,s-=.5,s*=t,s+=.5,s*=255,i=i<0?0:i>255?255:i,o=o<0?0:o>255?255:o,s=s<0?0:s>255?255:s,n[a]=i,n[a+1]=o,n[a+2]=s};W1.Contrast=rwe;tA.Factory.addGetterSetter(twe.Node,"contrast",0,(0,nwe.getNumberValidator)(),tA.Factory.afterSetFilter);var K1={};Object.defineProperty(K1,"__esModule",{value:!0});K1.Emboss=void 0;const ba=Ie,Y1=At,iwe=Bt,_B=fe,owe=function(e){var t=this.embossStrength()*10,n=this.embossWhiteLevel()*255,r=this.embossDirection(),i=this.embossBlend(),o=0,s=0,a=e.data,l=e.width,u=e.height,c=l*4,d=u;switch(r){case"top-left":o=-1,s=-1;break;case"top":o=-1,s=0;break;case"top-right":o=-1,s=1;break;case"right":o=0,s=1;break;case"bottom-right":o=1,s=1;break;case"bottom":o=1,s=0;break;case"bottom-left":o=1,s=-1;break;case"left":o=0,s=-1;break;default:iwe.Util.error("Unknown emboss direction: "+r)}do{var f=(d-1)*c,h=o;d+h<1&&(h=0),d+h>u&&(h=0);var p=(d-1+h)*l*4,m=l;do{var S=f+(m-1)*4,v=s;m+v<1&&(v=0),m+v>l&&(v=0);var y=p+(m-1+v)*4,g=a[S]-a[y],b=a[S+1]-a[y+1],_=a[S+2]-a[y+2],w=g,x=w>0?w:-w,C=b>0?b:-b,k=_>0?_:-_;if(C>x&&(w=b),k>x&&(w=_),w*=t,i){var P=a[S]+w,A=a[S+1]+w,$=a[S+2]+w;a[S]=P>255?255:P<0?0:P,a[S+1]=A>255?255:A<0?0:A,a[S+2]=$>255?255:$<0?0:$}else{var N=n-w;N<0?N=0:N>255&&(N=255),a[S]=a[S+1]=a[S+2]=N}}while(--m)}while(--d)};K1.Emboss=owe;ba.Factory.addGetterSetter(Y1.Node,"embossStrength",.5,(0,_B.getNumberValidator)(),ba.Factory.afterSetFilter);ba.Factory.addGetterSetter(Y1.Node,"embossWhiteLevel",.5,(0,_B.getNumberValidator)(),ba.Factory.afterSetFilter);ba.Factory.addGetterSetter(Y1.Node,"embossDirection","top-left",null,ba.Factory.afterSetFilter);ba.Factory.addGetterSetter(Y1.Node,"embossBlend",!1,null,ba.Factory.afterSetFilter);var X1={};Object.defineProperty(X1,"__esModule",{value:!0});X1.Enhance=void 0;const nA=Ie,swe=At,awe=fe;function k_(e,t,n,r,i){var o=n-t,s=i-r,a;return o===0?r+s/2:s===0?r:(a=(e-t)/o,a=s*a+r,a)}const lwe=function(e){var t=e.data,n=t.length,r=t[0],i=r,o,s=t[1],a=s,l,u=t[2],c=u,d,f,h=this.enhance();if(h!==0){for(f=0;f<n;f+=4)o=t[f+0],o<r?r=o:o>i&&(i=o),l=t[f+1],l<s?s=l:l>a&&(a=l),d=t[f+2],d<u?u=d:d>c&&(c=d);i===r&&(i=255,r=0),a===s&&(a=255,s=0),c===u&&(c=255,u=0);var p,m,S,v,y,g,b,_,w;for(h>0?(m=i+h*(255-i),S=r-h*(r-0),y=a+h*(255-a),g=s-h*(s-0),_=c+h*(255-c),w=u-h*(u-0)):(p=(i+r)*.5,m=i+h*(i-p),S=r+h*(r-p),v=(a+s)*.5,y=a+h*(a-v),g=s+h*(s-v),b=(c+u)*.5,_=c+h*(c-b),w=u+h*(u-b)),f=0;f<n;f+=4)t[f+0]=k_(t[f+0],r,i,S,m),t[f+1]=k_(t[f+1],s,a,g,y),t[f+2]=k_(t[f+2],u,c,w,_)}};X1.Enhance=lwe;nA.Factory.addGetterSetter(swe.Node,"enhance",0,(0,awe.getNumberValidator)(),nA.Factory.afterSetFilter);var Q1={};Object.defineProperty(Q1,"__esModule",{value:!0});Q1.Grayscale=void 0;const uwe=function(e){var t=e.data,n=t.length,r,i;for(r=0;r<n;r+=4)i=.34*t[r]+.5*t[r+1]+.16*t[r+2],t[r]=i,t[r+1]=i,t[r+2]=i};Q1.Grayscale=uwe;var Z1={};Object.defineProperty(Z1,"__esModule",{value:!0});Z1.HSL=void 0;const Lc=Ie,z3=At,U3=fe;Lc.Factory.addGetterSetter(z3.Node,"hue",0,(0,U3.getNumberValidator)(),Lc.Factory.afterSetFilter);Lc.Factory.addGetterSetter(z3.Node,"saturation",0,(0,U3.getNumberValidator)(),Lc.Factory.afterSetFilter);Lc.Factory.addGetterSetter(z3.Node,"luminance",0,(0,U3.getNumberValidator)(),Lc.Factory.afterSetFilter);const cwe=function(e){var t=e.data,n=t.length,r=1,i=Math.pow(2,this.saturation()),o=Math.abs(this.hue()+360)%360,s=this.luminance()*127,a,l=r*i*Math.cos(o*Math.PI/180),u=r*i*Math.sin(o*Math.PI/180),c=.299*r+.701*l+.167*u,d=.587*r-.587*l+.33*u,f=.114*r-.114*l-.497*u,h=.299*r-.299*l-.328*u,p=.587*r+.413*l+.035*u,m=.114*r-.114*l+.293*u,S=.299*r-.3*l+1.25*u,v=.587*r-.586*l-1.05*u,y=.114*r+.886*l-.2*u,g,b,_,w;for(a=0;a<n;a+=4)g=t[a+0],b=t[a+1],_=t[a+2],w=t[a+3],t[a+0]=c*g+d*b+f*_+s,t[a+1]=h*g+p*b+m*_+s,t[a+2]=S*g+v*b+y*_+s,t[a+3]=w};Z1.HSL=cwe;var J1={};Object.defineProperty(J1,"__esModule",{value:!0});J1.HSV=void 0;const $c=Ie,G3=At,H3=fe,dwe=function(e){var t=e.data,n=t.length,r=Math.pow(2,this.value()),i=Math.pow(2,this.saturation()),o=Math.abs(this.hue()+360)%360,s,a=r*i*Math.cos(o*Math.PI/180),l=r*i*Math.sin(o*Math.PI/180),u=.299*r+.701*a+.167*l,c=.587*r-.587*a+.33*l,d=.114*r-.114*a-.497*l,f=.299*r-.299*a-.328*l,h=.587*r+.413*a+.035*l,p=.114*r-.114*a+.293*l,m=.299*r-.3*a+1.25*l,S=.587*r-.586*a-1.05*l,v=.114*r+.886*a-.2*l,y,g,b,_;for(s=0;s<n;s+=4)y=t[s+0],g=t[s+1],b=t[s+2],_=t[s+3],t[s+0]=u*y+c*g+d*b,t[s+1]=f*y+h*g+p*b,t[s+2]=m*y+S*g+v*b,t[s+3]=_};J1.HSV=dwe;$c.Factory.addGetterSetter(G3.Node,"hue",0,(0,H3.getNumberValidator)(),$c.Factory.afterSetFilter);$c.Factory.addGetterSetter(G3.Node,"saturation",0,(0,H3.getNumberValidator)(),$c.Factory.afterSetFilter);$c.Factory.addGetterSetter(G3.Node,"value",0,(0,H3.getNumberValidator)(),$c.Factory.afterSetFilter);var eb={};Object.defineProperty(eb,"__esModule",{value:!0});eb.Invert=void 0;const fwe=function(e){var t=e.data,n=t.length,r;for(r=0;r<n;r+=4)t[r]=255-t[r],t[r+1]=255-t[r+1],t[r+2]=255-t[r+2]};eb.Invert=fwe;var tb={};Object.defineProperty(tb,"__esModule",{value:!0});tb.Kaleidoscope=void 0;const l0=Ie,wB=At,rA=Bt,xB=fe;var hwe=function(e,t,n){var r=e.data,i=t.data,o=e.width,s=e.height,a=n.polarCenterX||o/2,l=n.polarCenterY||s/2,u,c,d,f=0,h=0,p=0,m=0,S,v=Math.sqrt(a*a+l*l);c=o-a,d=s-l,S=Math.sqrt(c*c+d*d),v=S>v?S:v;var y=s,g=o,b,_,w=360/g*Math.PI/180,x,C;for(_=0;_<g;_+=1)for(x=Math.sin(_*w),C=Math.cos(_*w),b=0;b<y;b+=1)c=Math.floor(a+v*b/y*C),d=Math.floor(l+v*b/y*x),u=(d*o+c)*4,f=r[u+0],h=r[u+1],p=r[u+2],m=r[u+3],u=(_+b*o)*4,i[u+0]=f,i[u+1]=h,i[u+2]=p,i[u+3]=m},pwe=function(e,t,n){var r=e.data,i=t.data,o=e.width,s=e.height,a=n.polarCenterX||o/2,l=n.polarCenterY||s/2,u,c,d,f,h,p=0,m=0,S=0,v=0,y,g=Math.sqrt(a*a+l*l);c=o-a,d=s-l,y=Math.sqrt(c*c+d*d),g=y>g?y:g;var b=s,_=o,w,x,C=n.polarRotation||0,k,P;for(c=0;c<o;c+=1)for(d=0;d<s;d+=1)f=c-a,h=d-l,w=Math.sqrt(f*f+h*h)*b/g,x=(Math.atan2(h,f)*180/Math.PI+360+C)%360,x=x*_/360,k=Math.floor(x),P=Math.floor(w),u=(P*o+k)*4,p=r[u+0],m=r[u+1],S=r[u+2],v=r[u+3],u=(d*o+c)*4,i[u+0]=p,i[u+1]=m,i[u+2]=S,i[u+3]=v};const gwe=function(e){var t=e.width,n=e.height,r,i,o,s,a,l,u,c,d,f,h=Math.round(this.kaleidoscopePower()),p=Math.round(this.kaleidoscopeAngle()),m=Math.floor(t*(p%360)/360);if(!(h<1)){var S=rA.Util.createCanvasElement();S.width=t,S.height=n;var v=S.getContext("2d").getImageData(0,0,t,n);rA.Util.releaseCanvas(S),hwe(e,v,{polarCenterX:t/2,polarCenterY:n/2});for(var y=t/Math.pow(2,h);y<=8;)y=y*2,h-=1;y=Math.ceil(y);var g=y,b=0,_=g,w=1;for(m+y>t&&(b=g,_=0,w=-1),i=0;i<n;i+=1)for(r=b;r!==_;r+=w)o=Math.round(r+m)%t,d=(t*i+o)*4,a=v.data[d+0],l=v.data[d+1],u=v.data[d+2],c=v.data[d+3],f=(t*i+r)*4,v.data[f+0]=a,v.data[f+1]=l,v.data[f+2]=u,v.data[f+3]=c;for(i=0;i<n;i+=1)for(g=Math.floor(y),s=0;s<h;s+=1){for(r=0;r<g+1;r+=1)d=(t*i+r)*4,a=v.data[d+0],l=v.data[d+1],u=v.data[d+2],c=v.data[d+3],f=(t*i+g*2-r-1)*4,v.data[f+0]=a,v.data[f+1]=l,v.data[f+2]=u,v.data[f+3]=c;g*=2}pwe(v,e,{polarRotation:0})}};tb.Kaleidoscope=gwe;l0.Factory.addGetterSetter(wB.Node,"kaleidoscopePower",2,(0,xB.getNumberValidator)(),l0.Factory.afterSetFilter);l0.Factory.addGetterSetter(wB.Node,"kaleidoscopeAngle",0,(0,xB.getNumberValidator)(),l0.Factory.afterSetFilter);var nb={};Object.defineProperty(nb,"__esModule",{value:!0});nb.Mask=void 0;const iA=Ie,mwe=At,ywe=fe;function Ig(e,t,n){var r=(n*e.width+t)*4,i=[];return i.push(e.data[r++],e.data[r++],e.data[r++],e.data[r++]),i}function Md(e,t){return Math.sqrt(Math.pow(e[0]-t[0],2)+Math.pow(e[1]-t[1],2)+Math.pow(e[2]-t[2],2))}function vwe(e){for(var t=[0,0,0],n=0;n<e.length;n++)t[0]+=e[n][0],t[1]+=e[n][1],t[2]+=e[n][2];return t[0]/=e.length,t[1]/=e.length,t[2]/=e.length,t}function bwe(e,t){var n=Ig(e,0,0),r=Ig(e,e.width-1,0),i=Ig(e,0,e.height-1),o=Ig(e,e.width-1,e.height-1),s=t||10;if(Md(n,r)<s&&Md(r,o)<s&&Md(o,i)<s&&Md(i,n)<s){for(var a=vwe([r,n,o,i]),l=[],u=0;u<e.width*e.height;u++){var c=Md(a,[e.data[u*4],e.data[u*4+1],e.data[u*4+2]]);l[u]=c<s?0:255}return l}}function Swe(e,t){for(var n=0;n<e.width*e.height;n++)e.data[4*n+3]=t[n]}function _we(e,t,n){for(var r=[1,1,1,1,0,1,1,1,1],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a<n;a++)for(var l=0;l<t;l++){for(var u=a*t+l,c=0,d=0;d<i;d++)for(var f=0;f<i;f++){var h=a+d-o,p=l+f-o;if(h>=0&&h<n&&p>=0&&p<t){var m=h*t+p,S=r[d*i+f];c+=e[m]*S}}s[u]=c===255*8?255:0}return s}function wwe(e,t,n){for(var r=[1,1,1,1,1,1,1,1,1],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a<n;a++)for(var l=0;l<t;l++){for(var u=a*t+l,c=0,d=0;d<i;d++)for(var f=0;f<i;f++){var h=a+d-o,p=l+f-o;if(h>=0&&h<n&&p>=0&&p<t){var m=h*t+p,S=r[d*i+f];c+=e[m]*S}}s[u]=c>=255*4?255:0}return s}function xwe(e,t,n){for(var r=[.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a<n;a++)for(var l=0;l<t;l++){for(var u=a*t+l,c=0,d=0;d<i;d++)for(var f=0;f<i;f++){var h=a+d-o,p=l+f-o;if(h>=0&&h<n&&p>=0&&p<t){var m=h*t+p,S=r[d*i+f];c+=e[m]*S}}s[u]=c}return s}const Cwe=function(e){var t=this.threshold(),n=bwe(e,t);return n&&(n=_we(n,e.width,e.height),n=wwe(n,e.width,e.height),n=xwe(n,e.width,e.height),Swe(e,n)),e};nb.Mask=Cwe;iA.Factory.addGetterSetter(mwe.Node,"threshold",0,(0,ywe.getNumberValidator)(),iA.Factory.afterSetFilter);var rb={};Object.defineProperty(rb,"__esModule",{value:!0});rb.Noise=void 0;const oA=Ie,Twe=At,Ewe=fe,Pwe=function(e){var t=this.noise()*255,n=e.data,r=n.length,i=t/2,o;for(o=0;o<r;o+=4)n[o+0]+=i-2*i*Math.random(),n[o+1]+=i-2*i*Math.random(),n[o+2]+=i-2*i*Math.random()};rb.Noise=Pwe;oA.Factory.addGetterSetter(Twe.Node,"noise",.2,(0,Ewe.getNumberValidator)(),oA.Factory.afterSetFilter);var ib={};Object.defineProperty(ib,"__esModule",{value:!0});ib.Pixelate=void 0;const sA=Ie,kwe=Bt,Awe=At,Owe=fe,Rwe=function(e){var t=Math.ceil(this.pixelSize()),n=e.width,r=e.height,i,o,s,a,l,u,c,d=Math.ceil(n/t),f=Math.ceil(r/t),h,p,m,S,v,y,g,b=e.data;if(t<=0){kwe.Util.error("pixelSize value can not be <= 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t=e.data,n=t.length,r=this.red(),i=this.green(),o=this.blue(),s,a;for(s=0;s<n;s+=4)a=(.34*t[s]+.5*t[s+1]+.16*t[s+2])/255,t[s]=a*r,t[s+1]=a*i,t[s+2]=a*o,t[s+3]=t[s+3]};sb.RGB=Lwe;u0.Factory.addGetterSetter(q3.Node,"red",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});u0.Factory.addGetterSetter(q3.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});u0.Factory.addGetterSetter(q3.Node,"blue",0,Dwe.RGBComponent,u0.Factory.afterSetFilter);var ab={};Object.defineProperty(ab,"__esModule",{value:!0});ab.RGBA=void 0;const Oh=Ie,lb=At,$we=fe,Fwe=function(e){var t=e.data,n=t.length,r=this.red(),i=this.green(),o=this.blue(),s=this.alpha(),a,l;for(a=0;a<n;a+=4)l=1-s,t[a]=r*s+t[a]*l,t[a+1]=i*s+t[a+1]*l,t[a+2]=o*s+t[a+2]*l};ab.RGBA=Fwe;Oh.Factory.addGetterSetter(lb.Node,"red",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Oh.Factory.addGetterSetter(lb.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Oh.Factory.addGetterSetter(lb.Node,"blue",0,$we.RGBComponent,Oh.Factory.afterSetFilter);Oh.Factory.addGetterSetter(lb.Node,"alpha",1,function(e){return this._filterUpToDate=!1,e>1?1:e<0?0:e});var ub={};Object.defineProperty(ub,"__esModule",{value:!0});ub.Sepia=void 0;const Bwe=function(e){var t=e.data,n=t.length,r,i,o,s;for(r=0;r<n;r+=4)i=t[r+0],o=t[r+1],s=t[r+2],t[r+0]=Math.min(255,i*.393+o*.769+s*.189),t[r+1]=Math.min(255,i*.349+o*.686+s*.168),t[r+2]=Math.min(255,i*.272+o*.534+s*.131)};ub.Sepia=Bwe;var cb={};Object.defineProperty(cb,"__esModule",{value:!0});cb.Solarize=void 0;const jwe=function(e){var t=e.data,n=e.width,r=e.height,i=n*4,o=r;do{var s=(o-1)*i,a=n;do{var l=s+(a-1)*4,u=t[l],c=t[l+1],d=t[l+2];u>127&&(u=255-u),c>127&&(c=255-c),d>127&&(d=255-d),t[l]=u,t[l+1]=c,t[l+2]=d}while(--a)}while(--o)};cb.Solarize=jwe;var db={};Object.defineProperty(db,"__esModule",{value:!0});db.Threshold=void 0;const lA=Ie,Vwe=At,zwe=fe,Uwe=function(e){var t=this.threshold()*255,n=e.data,r=n.length,i;for(i=0;i<r;i+=1)n[i]=n[i]<t?0:255};db.Threshold=Uwe;lA.Factory.addGetterSetter(Vwe.Node,"threshold",.5,(0,zwe.getNumberValidator)(),lA.Factory.afterSetFilter);Object.defineProperty(S1,"__esModule",{value:!0});S1.Konva=void 0;const 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Txe=I3.exports;Object.defineProperty(Txe,"__esModule",{value:!0});const Exe=S1;I3.exports=Exe.Konva;var Pxe=I3.exports;const Nd=Cl(Pxe),kxe=async(e,t,n)=>{const{width:r,height:i}=t,o=document.createElement("div"),s=new Nd.Stage({container:o,width:r,height:i}),a=new Nd.Layer,l=new Nd.Layer;return a.add(new Nd.Rect({...t,fill:n?"black":"white"})),e.forEach(u=>l.add(new Nd.Line({points:u.points,stroke:n?"white":"black",strokeWidth:u.strokeWidth*2,tension:0,lineCap:"round",lineJoin:"round",shadowForStrokeEnabled:!1,globalCompositeOperation:u.tool==="brush"?"source-over":"destination-out"}))),s.add(a),s.add(l),o.remove(),s},Axe=async(e,t,n)=>new Promise((r,i)=>{const o=document.createElement("canvas");o.width=t,o.height=n;const s=o.getContext("2d"),a=new Image;if(!s){o.remove(),i("Unable to get context");return}a.onload=function(){s.drawImage(a,0,0),o.remove(),r(s.getImageData(0,0,t,n))},a.src=e}),dA=async(e,t)=>{const n=e.toDataURL(t);return await 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s="",a=0;bc(r,l=>{const{model_name:u,base_model:c,weight:d}=l,f=`${$xe}_${u.replace(".","_")}`,h={type:"lora_loader",id:f,is_intermediate:!0,lora:{model_name:u,base_model:c},weight:d};o&&o.loras.push({lora:{model_name:u,base_model:c},weight:d}),t.nodes[f]=h,a===0?(t.edges.push({source:{node_id:wt,field:"unet"},destination:{node_id:f,field:"unet"}}),t.edges.push({source:{node_id:ft,field:"clip"},destination:{node_id:f,field:"clip"}})):(t.edges.push({source:{node_id:s,field:"unet"},destination:{node_id:f,field:"unet"}}),t.edges.push({source:{node_id:s,field:"clip"},destination:{node_id:f,field:"clip"}})),a===i-1&&(t.edges.push({source:{node_id:f,field:"unet"},destination:{node_id:n,field:"unet"}}),t.edges.push({source:{node_id:f,field:"clip"},destination:{node_id:Lt,field:"clip"}}),t.edges.push({source:{node_id:f,field:"clip"},destination:{node_id:Wt,field:"clip"}})),s=f,a+=1})},kp=(e,t)=>{const{vae:n}=e.generation,r=!n,i=t.nodes[fn];r||(t.nodes[Dd]={type:"vae_loader",id:Dd,is_intermediate:!0,vae_model:n}),(t.id===W3||t.id===c0)&&t.edges.push({source:{node_id:r?wt:Dd,field:"vae"},destination:{node_id:Bn,field:"vae"}}),t.id===c0&&t.edges.push({source:{node_id:r?wt:Dd,field:"vae"},destination:{node_id:dr,field:"vae"}}),t.id===kB&&t.edges.push({source:{node_id:r?wt:Dd,field:"vae"},destination:{node_id:Rs,field:"vae"}}),n&&i&&(i.vae=n)},Fxe=Se.child({namespace:"nodes"}),Bxe=(e,t)=>{const{positivePrompt:n,negativePrompt:r,model:i,cfgScale:o,scheduler:s,steps:a,img2imgStrength:l,clipSkip:u,shouldUseCpuNoise:c,shouldUseNoiseSettings:d}=e.generation,{width:f,height:h}=e.canvas.boundingBoxDimensions,{shouldAutoSave:p}=e.canvas;if(!i)throw 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e)e.hasOwnProperty(n)&&(t.length&&(t+="&"),t+=encodeURIComponent(n)+"="+encodeURIComponent(e[n]));return t}function g5e(e){let t={},n=e.split("&");for(let r=0,i=n.length;r<i;r++){let o=n[r].split("=");t[decodeURIComponent(o[0])]=decodeURIComponent(o[1])}return t}class m5e extends Error{constructor(t,n,r){super(t),this.description=n,this.context=r,this.type="TransportError"}}class X3 extends sn{constructor(t){super(),this.writable=!1,pb(this,t),this.opts=t,this.query=t.query,this.socket=t.socket}onError(t,n,r){return super.emitReserved("error",new m5e(t,n,r)),this}open(){return this.readyState="opening",this.doOpen(),this}close(){return(this.readyState==="opening"||this.readyState==="open")&&(this.doClose(),this.onClose()),this}send(t){this.readyState==="open"&&this.write(t)}onOpen(){this.readyState="open",this.writable=!0,super.emitReserved("open")}onData(t){const n=Y3(t,this.socket.binaryType);this.onPacket(n)}onPacket(t){super.emitReserved("packet",t)}onClose(t){this.readyState="closed",super.emitReserved("close",t)}pause(t){}createUri(t,n={}){return t+"://"+this._hostname()+this._port()+this.opts.path+this._query(n)}_hostname(){const t=this.opts.hostname;return t.indexOf(":")===-1?t:"["+t+"]"}_port(){return this.opts.port&&(this.opts.secure&&+(this.opts.port!==443)||!this.opts.secure&&Number(this.opts.port)!==80)?":"+this.opts.port:""}_query(t){const n=p5e(t);return n.length?"?"+n:""}}const UB="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz-_".split(""),_x=64,y5e={};let xA=0,Lg=0,CA;function TA(e){let t="";do t=UB[e%_x]+t,e=Math.floor(e/_x);while(e>0);return t}function GB(){const e=TA(+new Date);return e!==CA?(xA=0,CA=e):e+"."+TA(xA++)}for(;Lg<_x;Lg++)y5e[UB[Lg]]=Lg;let HB=!1;try{HB=typeof XMLHttpRequest<"u"&&"withCredentials"in new XMLHttpRequest}catch{}const v5e=HB;function qB(e){const t=e.xdomain;try{if(typeof 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n=r=>{if(this.readyState==="opening"&&r.type==="open"&&this.onOpen(),r.type==="close")return this.onClose({description:"transport closed by the server"}),!1;this.onPacket(r)};s5e(t,this.socket.binaryType).forEach(n),this.readyState!=="closed"&&(this.polling=!1,this.emitReserved("pollComplete"),this.readyState==="open"&&this.poll())}doClose(){const t=()=>{this.write([{type:"close"}])};this.readyState==="open"?t():this.once("open",t)}write(t){this.writable=!1,o5e(t,n=>{this.doWrite(n,()=>{this.writable=!0,this.emitReserved("drain")})})}uri(){const t=this.opts.secure?"https":"http",n=this.query||{};return this.opts.timestampRequests!==!1&&(n[this.opts.timestampParam]=GB()),!this.supportsBinary&&!n.sid&&(n.b64=1),this.createUri(t,n)}request(t={}){return Object.assign(t,{xd:this.xd,cookieJar:this.cookieJar},this.opts),new ic(this.uri(),t)}doWrite(t,n){const r=this.request({method:"POST",data:t});r.on("success",n),r.on("error",(i,o)=>{this.onError("xhr post error",i,o)})}doPoll(){const t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(n,r)=>{this.onError("xhr poll error",n,r)}),this.pollXhr=t}}let ic=class Cm extends sn{constructor(t,n){super(),pb(this,n),this.opts=n,this.method=n.method||"GET",this.uri=t,this.data=n.data!==void 0?n.data:null,this.create()}create(){var t;const n=zB(this.opts,"agent","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","autoUnref");n.xdomain=!!this.opts.xd;const r=this.xhr=new qB(n);try{r.open(this.method,this.uri,!0);try{if(this.opts.extraHeaders){r.setDisableHeaderCheck&&r.setDisableHeaderCheck(!0);for(let i in this.opts.extraHeaders)this.opts.extraHeaders.hasOwnProperty(i)&&r.setRequestHeader(i,this.opts.extraHeaders[i])}}catch{}if(this.method==="POST")try{r.setRequestHeader("Content-type","text/plain;charset=UTF-8")}catch{}try{r.setRequestHeader("Accept","*/*")}catch{}(t=this.opts.cookieJar)===null||t===void 0||t.addCookies(r),"withCredentials"in r&&(r.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(r.timeout=this.opts.requestTimeout),r.onreadystatechange=()=>{var i;r.readyState===3&&((i=this.opts.cookieJar)===null||i===void 0||i.parseCookies(r)),r.readyState===4&&(r.status===200||r.status===1223?this.onLoad():this.setTimeoutFn(()=>{this.onError(typeof r.status=="number"?r.status:0)},0))},r.send(this.data)}catch(i){this.setTimeoutFn(()=>{this.onError(i)},0);return}typeof document<"u"&&(this.index=Cm.requestsCount++,Cm.requests[this.index]=this)}onError(t){this.emitReserved("error",t,this.xhr),this.cleanup(!0)}cleanup(t){if(!(typeof this.xhr>"u"||this.xhr===null)){if(this.xhr.onreadystatechange=b5e,t)try{this.xhr.abort()}catch{}typeof document<"u"&&delete Cm.requests[this.index],this.xhr=null}}onLoad(){const t=this.xhr.responseText;t!==null&&(this.emitReserved("data",t),this.emitReserved("success"),this.cleanup())}abort(){this.cleanup()}};ic.requestsCount=0;ic.requests={};if(typeof document<"u"){if(typeof attachEvent=="function")attachEvent("onunload",EA);else if(typeof addEventListener=="function"){const e="onpagehide"in ai?"pagehide":"unload";addEventListener(e,EA,!1)}}function EA(){for(let e in ic.requests)ic.requests.hasOwnProperty(e)&&ic.requests[e].abort()}const Q3=(()=>typeof Promise=="function"&&typeof Promise.resolve=="function"?t=>Promise.resolve().then(t):(t,n)=>n(t,0))(),$g=ai.WebSocket||ai.MozWebSocket,PA=!0,w5e="arraybuffer",kA=typeof navigator<"u"&&typeof navigator.product=="string"&&navigator.product.toLowerCase()==="reactnative";class x5e extends X3{constructor(t){super(t),this.supportsBinary=!t.forceBase64}get name(){return"websocket"}doOpen(){if(!this.check())return;const t=this.uri(),n=this.opts.protocols,r=kA?{}:zB(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(r.headers=this.opts.extraHeaders);try{this.ws=PA&&!kA?n?new $g(t,n):new $g(t):new $g(t,n,r)}catch(i){return this.emitReserved("error",i)}this.ws.binaryType=this.socket.binaryType||w5e,this.addEventListeners()}addEventListeners(){this.ws.onopen=()=>{this.opts.autoUnref&&this.ws._socket.unref(),this.onOpen()},this.ws.onclose=t=>this.onClose({description:"websocket connection closed",context:t}),this.ws.onmessage=t=>this.onData(t.data),this.ws.onerror=t=>this.onError("websocket error",t)}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;K3(r,this.supportsBinary,o=>{const s={};try{PA&&this.ws.send(o)}catch{}i&&Q3(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})}}doClose(){typeof this.ws<"u"&&(this.ws.close(),this.ws=null)}uri(){const t=this.opts.secure?"wss":"ws",n=this.query||{};return this.opts.timestampRequests&&(n[this.opts.timestampParam]=GB()),this.supportsBinary||(n.b64=1),this.createUri(t,n)}check(){return!!$g}}function C5e(e,t){return e.type==="message"&&typeof e.data!="string"&&t[0]>=48&&t[0]<=54}class T5e extends X3{get name(){return"webtransport"}doOpen(){typeof WebTransport=="function"&&(this.transport=new WebTransport(this.createUri("https"),this.opts.transportOptions[this.name]),this.transport.closed.then(()=>{this.onClose()}).catch(t=>{this.onError("webtransport error",t)}),this.transport.ready.then(()=>{this.transport.createBidirectionalStream().then(t=>{const n=t.readable.getReader();this.writer=t.writable.getWriter();let r;const i=()=>{n.read().then(({done:s,value:a})=>{s||(!r&&a.byteLength===1&&a[0]===54?r=!0:(this.onPacket(a5e(a,r,"arraybuffer")),r=!1),i())}).catch(s=>{})};i();const o=this.query.sid?`0{"sid":"${this.query.sid}"}`:"0";this.writer.write(new TextEncoder().encode(o)).then(()=>this.onOpen())})}))}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;t5e(r,o=>{C5e(r,o)&&this.writer.write(Uint8Array.of(54)),this.writer.write(o).then(()=>{i&&Q3(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})})}}doClose(){var t;(t=this.transport)===null||t===void 0||t.close()}}const E5e={websocket:x5e,webtransport:T5e,polling:_5e},P5e=/^(?:(?![^:@\/?#]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@\/?#]*)(?::([^:@\/?#]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,k5e=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function wx(e){const t=e,n=e.indexOf("["),r=e.indexOf("]");n!=-1&&r!=-1&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));let i=P5e.exec(e||""),o={},s=14;for(;s--;)o[k5e[s]]=i[s]||"";return n!=-1&&r!=-1&&(o.source=t,o.host=o.host.substring(1,o.host.length-1).replace(/;/g,":"),o.authority=o.authority.replace("[","").replace("]","").replace(/;/g,":"),o.ipv6uri=!0),o.pathNames=A5e(o,o.path),o.queryKey=O5e(o,o.query),o}function A5e(e,t){const n=/\/{2,9}/g,r=t.replace(n,"/").split("/");return(t.slice(0,1)=="/"||t.length===0)&&r.splice(0,1),t.slice(-1)=="/"&&r.splice(r.length-1,1),r}function O5e(e,t){const n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,function(r,i,o){i&&(n[i]=o)}),n}let WB=class mu extends sn{constructor(t,n={}){super(),this.writeBuffer=[],t&&typeof t=="object"&&(n=t,t=null),t?(t=wx(t),n.hostname=t.host,n.secure=t.protocol==="https"||t.protocol==="wss",n.port=t.port,t.query&&(n.query=t.query)):n.host&&(n.hostname=wx(n.host).host),pb(this,n),this.secure=n.secure!=null?n.secure:typeof location<"u"&&location.protocol==="https:",n.hostname&&!n.port&&(n.port=this.secure?"443":"80"),this.hostname=n.hostname||(typeof location<"u"?location.hostname:"localhost"),this.port=n.port||(typeof location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket","webtransport"],this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!1},n),this.opts.path=this.opts.path.replace(/\/$/,"")+(this.opts.addTrailingSlash?"/":""),typeof this.opts.query=="string"&&(this.opts.query=g5e(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=VB,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port},this.opts.transportOptions[t]);return new E5e[t](r)}open(){let t;if(this.opts.rememberUpgrade&&mu.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch{this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;mu.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",d=>{if(!r)if(d.type==="pong"&&d.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;mu.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(c(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const f=new Error("probe error");f.transport=n.name,this.emitReserved("upgradeError",f)}}))};function o(){r||(r=!0,c(),n.close(),n=null)}const s=d=>{const f=new Error("probe error: "+d);f.transport=n.name,o(),this.emitReserved("upgradeError",f)};function a(){s("transport closed")}function l(){s("socket closed")}function u(d){n&&d.name!==n.name&&o()}const c=()=>{n.removeListener("open",i),n.removeListener("error",s),n.removeListener("close",a),this.off("close",l),this.off("upgrading",u)};n.once("open",i),n.once("error",s),n.once("close",a),this.once("close",l),this.once("upgrading",u),this.upgrades.indexOf("webtransport")!==-1&&t!=="webtransport"?this.setTimeoutFn(()=>{r||n.open()},200):n.open()}onOpen(){if(this.readyState="open",mu.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade){let t=0;const n=this.upgrades.length;for(;t<n;t++)this.probe(this.upgrades[t])}}onPacket(t){if(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")switch(this.emitReserved("packet",t),this.emitReserved("heartbeat"),t.type){case"open":this.onHandshake(JSON.parse(t.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const n=new Error("server error");n.code=t.data,this.onError(n);break;case"message":this.emitReserved("data",t.data),this.emitReserved("message",t.data);break}}onHandshake(t){this.emitReserved("handshake",t),this.id=t.sid,this.transport.query.sid=t.sid,this.upgrades=this.filterUpgrades(t.upgrades),this.pingInterval=t.pingInterval,this.pingTimeout=t.pingTimeout,this.maxPayload=t.maxPayload,this.onOpen(),this.readyState!=="closed"&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn(()=>{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const t=this.getWritablePackets();this.transport.send(t),this.prevBufferLen=t.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r<this.writeBuffer.length;r++){const i=this.writeBuffer[r].data;if(i&&(n+=f5e(i)),r>0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(t,n,r){return this.sendPacket("message",t,n,r),this}send(t,n,r){return this.sendPacket("message",t,n,r),this}sendPacket(t,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const o={type:t,data:n,options:r};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),i&&this.once("flush",i),this.flush()}close(){const t=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),t()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():t()}):this.upgrading?r():t()),this}onError(t){mu.priorWebsocketSuccess=!1,this.emitReserved("error",t),this.onClose("transport error",t)}onClose(t,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",t,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(t){const n=[];let r=0;const i=t.length;for(;r<i;r++)~this.transports.indexOf(t[r])&&n.push(t[r]);return n}};WB.protocol=VB;function R5e(e,t="",n){let r=e;n=n||typeof location<"u"&&location,e==null&&(e=n.protocol+"//"+n.host),typeof e=="string"&&(e.charAt(0)==="/"&&(e.charAt(1)==="/"?e=n.protocol+e:e=n.host+e),/^(https?|wss?):\/\//.test(e)||(typeof n<"u"?e=n.protocol+"//"+e:e="https://"+e),r=wx(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";const o=r.host.indexOf(":")!==-1?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}const I5e=typeof ArrayBuffer=="function",M5e=e=>typeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,KB=Object.prototype.toString,N5e=typeof Blob=="function"||typeof Blob<"u"&&KB.call(Blob)==="[object BlobConstructor]",D5e=typeof File=="function"||typeof File<"u"&&KB.call(File)==="[object FileConstructor]";function Z3(e){return I5e&&(e instanceof ArrayBuffer||M5e(e))||N5e&&e instanceof Blob||D5e&&e instanceof File}function Tm(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n<r;n++)if(Tm(e[n]))return!0;return!1}if(Z3(e))return!0;if(e.toJSON&&typeof e.toJSON=="function"&&arguments.length===1)return Tm(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&Tm(e[n]))return!0;return!1}function L5e(e){const t=[],n=e.data,r=e;return r.data=xx(n,t),r.attachments=t.length,{packet:r,buffers:t}}function xx(e,t){if(!e)return e;if(Z3(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}else if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=xx(e[r],t);return n}else if(typeof e=="object"&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=xx(e[r],t));return n}return e}function $5e(e,t){return e.data=Cx(e.data,t),delete e.attachments,e}function Cx(e,t){if(!e)return e;if(e&&e._placeholder===!0){if(typeof e.num=="number"&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}else if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=Cx(e[n],t);else if(typeof e=="object")for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=Cx(e[n],t));return e}const F5e=["connect","connect_error","disconnect","disconnecting","newListener","removeListener"],B5e=5;var He;(function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"})(He||(He={}));class j5e{constructor(t){this.replacer=t}encode(t){return(t.type===He.EVENT||t.type===He.ACK)&&Tm(t)?this.encodeAsBinary({type:t.type===He.EVENT?He.BINARY_EVENT:He.BINARY_ACK,nsp:t.nsp,data:t.data,id:t.id}):[this.encodeAsString(t)]}encodeAsString(t){let n=""+t.type;return(t.type===He.BINARY_EVENT||t.type===He.BINARY_ACK)&&(n+=t.attachments+"-"),t.nsp&&t.nsp!=="/"&&(n+=t.nsp+","),t.id!=null&&(n+=t.id),t.data!=null&&(n+=JSON.stringify(t.data,this.replacer)),n}encodeAsBinary(t){const n=L5e(t),r=this.encodeAsString(n.packet),i=n.buffers;return i.unshift(r),i}}function AA(e){return Object.prototype.toString.call(e)==="[object Object]"}class J3 extends sn{constructor(t){super(),this.reviver=t}add(t){let n;if(typeof t=="string"){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");n=this.decodeString(t);const r=n.type===He.BINARY_EVENT;r||n.type===He.BINARY_ACK?(n.type=r?He.EVENT:He.ACK,this.reconstructor=new V5e(n),n.attachments===0&&super.emitReserved("decoded",n)):super.emitReserved("decoded",n)}else if(Z3(t)||t.base64)if(this.reconstructor)n=this.reconstructor.takeBinaryData(t),n&&(this.reconstructor=null,super.emitReserved("decoded",n));else throw new Error("got binary data when not reconstructing a packet");else throw new Error("Unknown type: "+t)}decodeString(t){let n=0;const r={type:Number(t.charAt(0))};if(He[r.type]===void 0)throw new Error("unknown packet type "+r.type);if(r.type===He.BINARY_EVENT||r.type===He.BINARY_ACK){const o=n+1;for(;t.charAt(++n)!=="-"&&n!=t.length;);const s=t.substring(o,n);if(s!=Number(s)||t.charAt(n)!=="-")throw new Error("Illegal attachments");r.attachments=Number(s)}if(t.charAt(n+1)==="/"){const o=n+1;for(;++n&&!(t.charAt(n)===","||n===t.length););r.nsp=t.substring(o,n)}else r.nsp="/";const i=t.charAt(n+1);if(i!==""&&Number(i)==i){const o=n+1;for(;++n;){const s=t.charAt(n);if(s==null||Number(s)!=s){--n;break}if(n===t.length)break}r.id=Number(t.substring(o,n+1))}if(t.charAt(++n)){const o=this.tryParse(t.substr(n));if(J3.isPayloadValid(r.type,o))r.data=o;else throw new Error("invalid payload")}return r}tryParse(t){try{return JSON.parse(t,this.reviver)}catch{return!1}}static isPayloadValid(t,n){switch(t){case He.CONNECT:return AA(n);case He.DISCONNECT:return n===void 0;case He.CONNECT_ERROR:return typeof n=="string"||AA(n);case He.EVENT:case He.BINARY_EVENT:return Array.isArray(n)&&(typeof n[0]=="number"||typeof n[0]=="string"&&F5e.indexOf(n[0])===-1);case He.ACK:case He.BINARY_ACK:return Array.isArray(n)}}destroy(){this.reconstructor&&(this.reconstructor.finishedReconstruction(),this.reconstructor=null)}}class V5e{constructor(t){this.packet=t,this.buffers=[],this.reconPack=t}takeBinaryData(t){if(this.buffers.push(t),this.buffers.length===this.reconPack.attachments){const n=$5e(this.reconPack,this.buffers);return this.finishedReconstruction(),n}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const z5e=Object.freeze(Object.defineProperty({__proto__:null,Decoder:J3,Encoder:j5e,get PacketType(){return He},protocol:B5e},Symbol.toStringTag,{value:"Module"}));function Pi(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const U5e=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class YB extends sn{constructor(t,n,r){super(),this.connected=!1,this.recovered=!1,this.receiveBuffer=[],this.sendBuffer=[],this._queue=[],this._queueSeq=0,this.ids=0,this.acks={},this.flags={},this.io=t,this.nsp=n,r&&r.auth&&(this.auth=r.auth),this._opts=Object.assign({},r),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const t=this.io;this.subs=[Pi(t,"open",this.onopen.bind(this)),Pi(t,"packet",this.onpacket.bind(this)),Pi(t,"error",this.onerror.bind(this)),Pi(t,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected?this:(this.subEvents(),this.io._reconnecting||this.io.open(),this.io._readyState==="open"&&this.onopen(),this)}open(){return this.connect()}send(...t){return t.unshift("message"),this.emit.apply(this,t),this}emit(t,...n){if(U5e.hasOwnProperty(t))throw new Error('"'+t.toString()+'" is a reserved event name');if(n.unshift(t),this._opts.retries&&!this.flags.fromQueue&&!this.flags.volatile)return this._addToQueue(n),this;const r={type:He.EVENT,data:n};if(r.options={},r.options.compress=this.flags.compress!==!1,typeof n[n.length-1]=="function"){const s=this.ids++,a=n.pop();this._registerAckCallback(s,a),r.id=s}const i=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!i||!this.connected)||(this.connected?(this.notifyOutgoingListeners(r),this.packet(r)):this.sendBuffer.push(r)),this.flags={},this}_registerAckCallback(t,n){var r;const i=(r=this.flags.timeout)!==null&&r!==void 0?r:this._opts.ackTimeout;if(i===void 0){this.acks[t]=n;return}const o=this.io.setTimeoutFn(()=>{delete this.acks[t];for(let s=0;s<this.sendBuffer.length;s++)this.sendBuffer[s].id===t&&this.sendBuffer.splice(s,1);n.call(this,new Error("operation has timed out"))},i);this.acks[t]=(...s)=>{this.io.clearTimeoutFn(o),n.apply(this,[null,...s])}}emitWithAck(t,...n){const r=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((i,o)=>{n.push((s,a)=>r?s?o(s):i(a):i(s)),this.emit(t,...n)})}_addToQueue(t){let n;typeof t[t.length-1]=="function"&&(n=t.pop());const r={id:this._queueSeq++,tryCount:0,pending:!1,args:t,flags:Object.assign({fromQueue:!0},this.flags)};t.push((i,...o)=>r!==this._queue[0]?void 0:(i!==null?r.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(i)):(this._queue.shift(),n&&n(null,...o)),r.pending=!1,this._drainQueue())),this._queue.push(r),this._drainQueue()}_drainQueue(t=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!t||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this._sendConnectPacket(t)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(t){this.packet({type:He.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},t):t})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case He.CONNECT:t.data&&t.data.sid?this.onconnect(t.data.sid,t.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case He.EVENT:case He.BINARY_EVENT:this.onevent(t);break;case He.ACK:case He.BINARY_ACK:this.onack(t);break;case He.DISCONNECT:this.ondisconnect();break;case He.CONNECT_ERROR:this.destroy();const r=new Error(t.data.message);r.data=t.data.data,this.emitReserved("connect_error",r);break}}onevent(t){const n=t.data||[];t.id!=null&&n.push(this.ack(t.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(t){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,t)}super.emit.apply(this,t),this._pid&&t.length&&typeof t[t.length-1]=="string"&&(this._lastOffset=t[t.length-1])}ack(t){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:He.ACK,id:t,data:i}))}}onack(t){const n=this.acks[t.id];typeof n=="function"&&(n.apply(this,t.data),delete this.acks[t.id])}onconnect(t,n){this.id=t,this.recovered=n&&this._pid===n,this._pid=n,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}emitBuffered(){this.receiveBuffer.forEach(t=>this.emitEvent(t)),this.receiveBuffer=[],this.sendBuffer.forEach(t=>{this.notifyOutgoingListeners(t),this.packet(t)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(t=>t()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:He.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(t){return this.flags.compress=t,this}get volatile(){return this.flags.volatile=!0,this}timeout(t){return this.flags.timeout=t,this}onAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(t),this}prependAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(t),this}offAny(t){if(!this._anyListeners)return this;if(t){const n=this._anyListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(t),this}prependAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(t),this}offAnyOutgoing(t){if(!this._anyOutgoingListeners)return this;if(t){const n=this._anyOutgoingListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(t){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const n=this._anyOutgoingListeners.slice();for(const r of n)r.apply(this,t.data)}}}function td(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}td.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=Math.floor(t*10)&1?e+n:e-n}return Math.min(e,this.max)|0};td.prototype.reset=function(){this.attempts=0};td.prototype.setMin=function(e){this.ms=e};td.prototype.setMax=function(e){this.max=e};td.prototype.setJitter=function(e){this.jitter=e};class Tx extends sn{constructor(t,n){var r;super(),this.nsps={},this.subs=[],t&&typeof t=="object"&&(n=t,t=void 0),n=n||{},n.path=n.path||"/socket.io",this.opts=n,pb(this,n),this.reconnection(n.reconnection!==!1),this.reconnectionAttempts(n.reconnectionAttempts||1/0),this.reconnectionDelay(n.reconnectionDelay||1e3),this.reconnectionDelayMax(n.reconnectionDelayMax||5e3),this.randomizationFactor((r=n.randomizationFactor)!==null&&r!==void 0?r:.5),this.backoff=new td({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(n.timeout==null?2e4:n.timeout),this._readyState="closed",this.uri=t;const i=n.parser||z5e;this.encoder=new i.Encoder,this.decoder=new i.Decoder,this._autoConnect=n.autoConnect!==!1,this._autoConnect&&this.open()}reconnection(t){return arguments.length?(this._reconnection=!!t,this):this._reconnection}reconnectionAttempts(t){return t===void 0?this._reconnectionAttempts:(this._reconnectionAttempts=t,this)}reconnectionDelay(t){var n;return t===void 0?this._reconnectionDelay:(this._reconnectionDelay=t,(n=this.backoff)===null||n===void 0||n.setMin(t),this)}randomizationFactor(t){var n;return t===void 0?this._randomizationFactor:(this._randomizationFactor=t,(n=this.backoff)===null||n===void 0||n.setJitter(t),this)}reconnectionDelayMax(t){var n;return t===void 0?this._reconnectionDelayMax:(this._reconnectionDelayMax=t,(n=this.backoff)===null||n===void 0||n.setMax(t),this)}timeout(t){return arguments.length?(this._timeout=t,this):this._timeout}maybeReconnectOnOpen(){!this._reconnecting&&this._reconnection&&this.backoff.attempts===0&&this.reconnect()}open(t){if(~this._readyState.indexOf("open"))return this;this.engine=new WB(this.uri,this.opts);const n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;const i=Pi(n,"open",function(){r.onopen(),t&&t()}),o=a=>{this.cleanup(),this._readyState="closed",this.emitReserved("error",a),t?t(a):this.maybeReconnectOnOpen()},s=Pi(n,"error",o);if(this._timeout!==!1){const a=this._timeout,l=this.setTimeoutFn(()=>{i(),o(new Error("timeout")),n.close()},a);this.opts.autoUnref&&l.unref(),this.subs.push(()=>{this.clearTimeoutFn(l)})}return this.subs.push(i),this.subs.push(s),this}connect(t){return this.open(t)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const t=this.engine;this.subs.push(Pi(t,"ping",this.onping.bind(this)),Pi(t,"data",this.ondata.bind(this)),Pi(t,"error",this.onerror.bind(this)),Pi(t,"close",this.onclose.bind(this)),Pi(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(t){try{this.decoder.add(t)}catch(n){this.onclose("parse error",n)}}ondecoded(t){Q3(()=>{this.emitReserved("packet",t)},this.setTimeoutFn)}onerror(t){this.emitReserved("error",t)}socket(t,n){let r=this.nsps[t];return r?this._autoConnect&&!r.active&&r.connect():(r=new YB(this,t,n),this.nsps[t]=r),r}_destroy(t){const n=Object.keys(this.nsps);for(const r of n)if(this.nsps[r].active)return;this._close()}_packet(t){const n=this.encoder.encode(t);for(let r=0;r<n.length;r++)this.engine.write(n[r],t.options)}cleanup(){this.subs.forEach(t=>t()),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(t,n){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",t,n),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const n=this.backoff.duration();this._reconnecting=!0;const r=this.setTimeoutFn(()=>{t.skipReconnect||(this.emitReserved("reconnect_attempt",t.backoff.attempts),!t.skipReconnect&&t.open(i=>{i?(t._reconnecting=!1,t.reconnect(),this.emitReserved("reconnect_error",i)):t.onreconnect()}))},n);this.opts.autoUnref&&r.unref(),this.subs.push(()=>{this.clearTimeoutFn(r)})}}onreconnect(){const t=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",t)}}const Ld={};function Em(e,t){typeof e=="object"&&(t=e,e=void 0),t=t||{};const n=R5e(e,t.path||"/socket.io"),r=n.source,i=n.id,o=n.path,s=Ld[i]&&o in Ld[i].nsps,a=t.forceNew||t["force new connection"]||t.multiplex===!1||s;let l;return a?l=new Tx(r,t):(Ld[i]||(Ld[i]=new Tx(r,t)),l=Ld[i]),n.query&&!t.query&&(t.query=n.queryKey),l.socket(n.path,t)}Object.assign(Em,{Manager:Tx,Socket:YB,io:Em,connect:Em});var G5e=/d{1,4}|D{3,4}|m{1,4}|yy(?:yy)?|([HhMsTt])\1?|W{1,2}|[LlopSZN]|"[^"]*"|'[^']*'/g,H5e=/\b(?:[A-Z]{1,3}[A-Z][TC])(?:[-+]\d{4})?|((?:Australian 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t={x:0,y:0},n=rj(e)?{height:window.innerHeight,width:window.innerWidth}:{height:e.clientHeight,width:e.clientWidth},r={x:e.scrollWidth-n.width,y:e.scrollHeight-n.height},i=e.scrollTop<=t.y,o=e.scrollLeft<=t.x,s=e.scrollTop>=r.y,a=e.scrollLeft>=r.x;return{isTop:i,isLeft:o,isBottom:s,isRight:a,maxScroll:r,minScroll:t}}const M3e={x:.2,y:.2};function N3e(e,t,n,r,i){let{top:o,left:s,right:a,bottom:l}=n;r===void 0&&(r=10),i===void 0&&(i=M3e);const{isTop:u,isBottom:c,isLeft:d,isRight:f}=ij(e),h={x:0,y:0},p={x:0,y:0},m={height:t.height*i.y,width:t.width*i.x};return!u&&o<=t.top+m.height?(h.y=xn.Backward,p.y=r*Math.abs((t.top+m.height-o)/m.height)):!c&&l>=t.bottom-m.height&&(h.y=xn.Forward,p.y=r*Math.abs((t.bottom-m.height-l)/m.height)),!f&&a>=t.right-m.width?(h.x=xn.Forward,p.x=r*Math.abs((t.right-m.width-a)/m.width)):!d&&s<=t.left+m.width&&(h.x=xn.Backward,p.x=r*Math.abs((t.left+m.width-s)/m.width)),{direction:h,speed:p}}function D3e(e){if(e===document.scrollingElement){const{innerWidth:o,innerHeight:s}=window;return{top:0,left:0,right:o,bottom:s,width:o,height:s}}const{top:t,left:n,right:r,bottom:i}=e.getBoundingClientRect();return{top:t,left:n,right:r,bottom:i,width:e.clientWidth,height:e.clientHeight}}function oj(e){return e.reduce((t,n)=>oc(t,Ex(n)),Gi)}function L3e(e){return e.reduce((t,n)=>t+tj(n),0)}function $3e(e){return e.reduce((t,n)=>t+nj(n),0)}function sj(e,t){if(t===void 0&&(t=Rp),!e)return;const{top:n,left:r,bottom:i,right:o}=t(e);ej(e)&&(i<=0||o<=0||n>=window.innerHeight||r>=window.innerWidth)&&e.scrollIntoView({block:"center",inline:"center"})}const F3e=[["x",["left","right"],L3e],["y",["top","bottom"],$3e]];class i4{constructor(t,n){this.rect=void 0,this.width=void 0,this.height=void 0,this.top=void 0,this.bottom=void 0,this.right=void 0,this.left=void 0;const r=r4(n),i=oj(r);this.rect={...t},this.width=t.width,this.height=t.height;for(const[o,s,a]of F3e)for(const l of s)Object.defineProperty(this,l,{get:()=>{const u=a(r),c=i[o]-u;return this.rect[l]+c},enumerable:!0});Object.defineProperty(this,"rect",{enumerable:!1})}}class mf{constructor(t){this.target=void 0,this.listeners=[],this.removeAll=()=>{this.listeners.forEach(n=>{var r;return(r=this.target)==null?void 0:r.removeEventListener(...n)})},this.target=t}add(t,n,r){var i;(i=this.target)==null||i.addEventListener(t,n,r),this.listeners.push([t,n,r])}}function B3e(e){const{EventTarget:t}=vr(e);return e instanceof t?e:rd(e)}function D_(e,t){const n=Math.abs(e.x),r=Math.abs(e.y);return typeof t=="number"?Math.sqrt(n**2+r**2)>t:"x"in t&&"y"in t?n>t.x&&r>t.y:"x"in t?n>t.x:"y"in t?r>t.y:!1}var ii;(function(e){e.Click="click",e.DragStart="dragstart",e.Keydown="keydown",e.ContextMenu="contextmenu",e.Resize="resize",e.SelectionChange="selectionchange",e.VisibilityChange="visibilitychange"})(ii||(ii={}));function LA(e){e.preventDefault()}function j3e(e){e.stopPropagation()}var gt;(function(e){e.Space="Space",e.Down="ArrowDown",e.Right="ArrowRight",e.Left="ArrowLeft",e.Up="ArrowUp",e.Esc="Escape",e.Enter="Enter"})(gt||(gt={}));const aj={start:[gt.Space,gt.Enter],cancel:[gt.Esc],end:[gt.Space,gt.Enter]},V3e=(e,t)=>{let{currentCoordinates:n}=t;switch(e.code){case gt.Right:return{...n,x:n.x+25};case gt.Left:return{...n,x:n.x-25};case gt.Down:return{...n,y:n.y+25};case gt.Up:return{...n,y:n.y-25}}};class lj{constructor(t){this.props=void 0,this.autoScrollEnabled=!1,this.referenceCoordinates=void 0,this.listeners=void 0,this.windowListeners=void 0,this.props=t;const{event:{target:n}}=t;this.props=t,this.listeners=new mf(rd(n)),this.windowListeners=new mf(vr(n)),this.handleKeyDown=this.handleKeyDown.bind(this),this.handleCancel=this.handleCancel.bind(this),this.attach()}attach(){this.handleStart(),this.windowListeners.add(ii.Resize,this.handleCancel),this.windowListeners.add(ii.VisibilityChange,this.handleCancel),setTimeout(()=>this.listeners.add(ii.Keydown,this.handleKeyDown))}handleStart(){const{activeNode:t,onStart:n}=this.props,r=t.node.current;r&&sj(r),n(Gi)}handleKeyDown(t){if(n4(t)){const{active:n,context:r,options:i}=this.props,{keyboardCodes:o=aj,coordinateGetter:s=V3e,scrollBehavior:a="smooth"}=i,{code:l}=t;if(o.end.includes(l)){this.handleEnd(t);return}if(o.cancel.includes(l)){this.handleCancel(t);return}const{collisionRect:u}=r.current,c=u?{x:u.left,y:u.top}:Gi;this.referenceCoordinates||(this.referenceCoordinates=c);const d=s(t,{active:n,context:r.current,currentCoordinates:c});if(d){const f=g0(d,c),h={x:0,y:0},{scrollableAncestors:p}=r.current;for(const m of p){const S=t.code,{isTop:v,isRight:y,isLeft:g,isBottom:b,maxScroll:_,minScroll:w}=ij(m),x=D3e(m),C={x:Math.min(S===gt.Right?x.right-x.width/2:x.right,Math.max(S===gt.Right?x.left:x.left+x.width/2,d.x)),y:Math.min(S===gt.Down?x.bottom-x.height/2:x.bottom,Math.max(S===gt.Down?x.top:x.top+x.height/2,d.y))},k=S===gt.Right&&!y||S===gt.Left&&!g,P=S===gt.Down&&!b||S===gt.Up&&!v;if(k&&C.x!==d.x){const A=m.scrollLeft+f.x,$=S===gt.Right&&A<=_.x||S===gt.Left&&A>=w.x;if($&&!f.y){m.scrollTo({left:A,behavior:a});return}$?h.x=m.scrollLeft-A:h.x=S===gt.Right?m.scrollLeft-_.x:m.scrollLeft-w.x,h.x&&m.scrollBy({left:-h.x,behavior:a});break}else if(P&&C.y!==d.y){const A=m.scrollTop+f.y,$=S===gt.Down&&A<=_.y||S===gt.Up&&A>=w.y;if($&&!f.x){m.scrollTo({top:A,behavior:a});return}$?h.y=m.scrollTop-A:h.y=S===gt.Down?m.scrollTop-_.y:m.scrollTop-w.y,h.y&&m.scrollBy({top:-h.y,behavior:a});break}}this.handleMove(t,oc(g0(d,this.referenceCoordinates),h))}}}handleMove(t,n){const{onMove:r}=this.props;t.preventDefault(),r(n)}handleEnd(t){const{onEnd:n}=this.props;t.preventDefault(),this.detach(),n()}handleCancel(t){const{onCancel:n}=this.props;t.preventDefault(),this.detach(),n()}detach(){this.listeners.removeAll(),this.windowListeners.removeAll()}}lj.activators=[{eventName:"onKeyDown",handler:(e,t,n)=>{let{keyboardCodes:r=aj,onActivation:i}=t,{active:o}=n;const{code:s}=e.nativeEvent;if(r.start.includes(s)){const a=o.activatorNode.current;return a&&e.target!==a?!1:(e.preventDefault(),i==null||i({event:e.nativeEvent}),!0)}return!1}}];function $A(e){return!!(e&&"distance"in e)}function FA(e){return!!(e&&"delay"in e)}class o4{constructor(t,n,r){var i;r===void 0&&(r=B3e(t.event.target)),this.props=void 0,this.events=void 0,this.autoScrollEnabled=!0,this.document=void 0,this.activated=!1,this.initialCoordinates=void 0,this.timeoutId=null,this.listeners=void 0,this.documentListeners=void 0,this.windowListeners=void 0,this.props=t,this.events=n;const{event:o}=t,{target:s}=o;this.props=t,this.events=n,this.document=rd(s),this.documentListeners=new mf(this.document),this.listeners=new mf(r),this.windowListeners=new mf(vr(s)),this.initialCoordinates=(i=Nh(o))!=null?i:Gi,this.handleStart=this.handleStart.bind(this),this.handleMove=this.handleMove.bind(this),this.handleEnd=this.handleEnd.bind(this),this.handleCancel=this.handleCancel.bind(this),this.handleKeydown=this.handleKeydown.bind(this),this.removeTextSelection=this.removeTextSelection.bind(this),this.attach()}attach(){const{events:t,props:{options:{activationConstraint:n}}}=this;if(this.listeners.add(t.move.name,this.handleMove,{passive:!1}),this.listeners.add(t.end.name,this.handleEnd),this.windowListeners.add(ii.Resize,this.handleCancel),this.windowListeners.add(ii.DragStart,LA),this.windowListeners.add(ii.VisibilityChange,this.handleCancel),this.windowListeners.add(ii.ContextMenu,LA),this.documentListeners.add(ii.Keydown,this.handleKeydown),n){if($A(n))return;if(FA(n)){this.timeoutId=setTimeout(this.handleStart,n.delay);return}}this.handleStart()}detach(){this.listeners.removeAll(),this.windowListeners.removeAll(),setTimeout(this.documentListeners.removeAll,50),this.timeoutId!==null&&(clearTimeout(this.timeoutId),this.timeoutId=null)}handleStart(){const{initialCoordinates:t}=this,{onStart:n}=this.props;t&&(this.activated=!0,this.documentListeners.add(ii.Click,j3e,{capture:!0}),this.removeTextSelection(),this.documentListeners.add(ii.SelectionChange,this.removeTextSelection),n(t))}handleMove(t){var n;const{activated:r,initialCoordinates:i,props:o}=this,{onMove:s,options:{activationConstraint:a}}=o;if(!i)return;const l=(n=Nh(t))!=null?n:Gi,u=g0(i,l);if(!r&&a){if(FA(a))return D_(u,a.tolerance)?this.handleCancel():void 0;if($A(a))return a.tolerance!=null&&D_(u,a.tolerance)?this.handleCancel():D_(u,a.distance)?this.handleStart():void 0}t.cancelable&&t.preventDefault(),s(l)}handleEnd(){const{onEnd:t}=this.props;this.detach(),t()}handleCancel(){const{onCancel:t}=this.props;this.detach(),t()}handleKeydown(t){t.code===gt.Esc&&this.handleCancel()}removeTextSelection(){var t;(t=this.document.getSelection())==null||t.removeAllRanges()}}const z3e={move:{name:"pointermove"},end:{name:"pointerup"}};class uj extends o4{constructor(t){const{event:n}=t,r=rd(n.target);super(t,z3e,r)}}uj.activators=[{eventName:"onPointerDown",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;return!n.isPrimary||n.button!==0?!1:(r==null||r({event:n}),!0)}}];const U3e={move:{name:"mousemove"},end:{name:"mouseup"}};var Px;(function(e){e[e.RightClick=2]="RightClick"})(Px||(Px={}));class cj extends o4{constructor(t){super(t,U3e,rd(t.event.target))}}cj.activators=[{eventName:"onMouseDown",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;return n.button===Px.RightClick?!1:(r==null||r({event:n}),!0)}}];const L_={move:{name:"touchmove"},end:{name:"touchend"}};class dj extends o4{constructor(t){super(t,L_)}static setup(){return window.addEventListener(L_.move.name,t,{capture:!1,passive:!1}),function(){window.removeEventListener(L_.move.name,t)};function t(){}}}dj.activators=[{eventName:"onTouchStart",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;const{touches:i}=n;return i.length>1?!1:(r==null||r({event:n}),!0)}}];var yf;(function(e){e[e.Pointer=0]="Pointer",e[e.DraggableRect=1]="DraggableRect"})(yf||(yf={}));var y0;(function(e){e[e.TreeOrder=0]="TreeOrder",e[e.ReversedTreeOrder=1]="ReversedTreeOrder"})(y0||(y0={}));function G3e(e){let{acceleration:t,activator:n=yf.Pointer,canScroll:r,draggingRect:i,enabled:o,interval:s=5,order:a=y0.TreeOrder,pointerCoordinates:l,scrollableAncestors:u,scrollableAncestorRects:c,delta:d,threshold:f}=e;const h=q3e({delta:d,disabled:!o}),[p,m]=e3e(),S=E.useRef({x:0,y:0}),v=E.useRef({x:0,y:0}),y=E.useMemo(()=>{switch(n){case yf.Pointer:return l?{top:l.y,bottom:l.y,left:l.x,right:l.x}:null;case yf.DraggableRect:return i}},[n,i,l]),g=E.useRef(null),b=E.useCallback(()=>{const w=g.current;if(!w)return;const x=S.current.x*v.current.x,C=S.current.y*v.current.y;w.scrollBy(x,C)},[]),_=E.useMemo(()=>a===y0.TreeOrder?[...u].reverse():u,[a,u]);E.useEffect(()=>{if(!o||!u.length||!y){m();return}for(const w of _){if((r==null?void 0:r(w))===!1)continue;const x=u.indexOf(w),C=c[x];if(!C)continue;const{direction:k,speed:P}=N3e(w,C,y,t,f);for(const A of["x","y"])h[A][k[A]]||(P[A]=0,k[A]=0);if(P.x>0||P.y>0){m(),g.current=w,p(b,s),S.current=P,v.current=k;return}}S.current={x:0,y:0},v.current={x:0,y:0},m()},[t,b,r,m,o,s,JSON.stringify(y),JSON.stringify(h),p,u,_,c,JSON.stringify(f)])}const H3e={x:{[xn.Backward]:!1,[xn.Forward]:!1},y:{[xn.Backward]:!1,[xn.Forward]:!1}};function q3e(e){let{delta:t,disabled:n}=e;const r=p0(t);return Op(i=>{if(n||!r||!i)return H3e;const o={x:Math.sign(t.x-r.x),y:Math.sign(t.y-r.y)};return{x:{[xn.Backward]:i.x[xn.Backward]||o.x===-1,[xn.Forward]:i.x[xn.Forward]||o.x===1},y:{[xn.Backward]:i.y[xn.Backward]||o.y===-1,[xn.Forward]:i.y[xn.Forward]||o.y===1}}},[n,t,r])}function W3e(e,t){const n=t!==null?e.get(t):void 0,r=n?n.node.current:null;return Op(i=>{var o;return t===null?null:(o=r??i)!=null?o:null},[r,t])}function K3e(e,t){return E.useMemo(()=>e.reduce((n,r)=>{const{sensor:i}=r,o=i.activators.map(s=>({eventName:s.eventName,handler:t(s.handler,r)}));return[...n,...o]},[]),[e,t])}var Lh;(function(e){e[e.Always=0]="Always",e[e.BeforeDragging=1]="BeforeDragging",e[e.WhileDragging=2]="WhileDragging"})(Lh||(Lh={}));var kx;(function(e){e.Optimized="optimized"})(kx||(kx={}));const BA=new Map;function Y3e(e,t){let{dragging:n,dependencies:r,config:i}=t;const[o,s]=E.useState(null),{frequency:a,measure:l,strategy:u}=i,c=E.useRef(e),d=S(),f=Mh(d),h=E.useCallback(function(v){v===void 0&&(v=[]),!f.current&&s(y=>y===null?v:y.concat(v.filter(g=>!y.includes(g))))},[f]),p=E.useRef(null),m=Op(v=>{if(d&&!n)return BA;if(!v||v===BA||c.current!==e||o!=null){const y=new Map;for(let g of e){if(!g)continue;if(o&&o.length>0&&!o.includes(g.id)&&g.rect.current){y.set(g.id,g.rect.current);continue}const b=g.node.current,_=b?new i4(l(b),b):null;g.rect.current=_,_&&y.set(g.id,_)}return y}return v},[e,o,n,d,l]);return E.useEffect(()=>{c.current=e},[e]),E.useEffect(()=>{d||h()},[n,d]),E.useEffect(()=>{o&&o.length>0&&s(null)},[JSON.stringify(o)]),E.useEffect(()=>{d||typeof 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ze.createElement(C4e,null,ze.createElement(w4e,{animation:P},d&&x?ze.createElement(P4e,{key:x,id:d.id,ref:A,as:a,activatorEvent:c,adjustScale:t,className:l,transition:o,rect:k,style:{zIndex:u,...i},transform:C},n):null))}),D4e=e=>{let{activatorEvent:t,draggingNodeRect:n,transform:r}=e;if(n&&t){const i=Nh(t);if(!i)return r;const o=i.x-n.left,s=i.y-n.top;return{...r,x:r.x+o-n.width/2,y:r.y+s-n.height/2}}return r},Fg=28,HA={w:Fg,h:Fg,maxW:Fg,maxH:Fg,shadow:"dark-lg",borderRadius:"lg",opacity:.3,bg:"base.800",color:"base.50",_dark:{borderColor:"base.200",bg:"base.900",color:"base.100"}},L4e=e=>{if(e.dragData){if(e.dragData.payloadType==="IMAGE_DTO"){const{thumbnail_url:t,width:n,height:r}=e.dragData.payload.imageDTO;return j.jsx(V5,{sx:{position:"relative",width:"100%",height:"100%",display:"flex",alignItems:"center",justifyContent:"center",userSelect:"none",cursor:"none"},children:j.jsx(B5,{sx:{...HA},objectFit:"contain",src:t,width:n,height:r})})}return e.dragData.payloadType==="IMAGE_NAMES"?j.jsxs(z5,{sx:{cursor:"none",userSelect:"none",position:"relative",alignItems:"center",justifyContent:"center",flexDir:"column",...HA},children:[j.jsx(xw,{children:e.dragData.payload.image_names.length}),j.jsx(xw,{size:"sm",children:"Images"})]}):null}},$4e=E.memo(L4e);function nke(e){return _4e(e)}function rke(e){return y4e(e)}const ike=(e,t)=>{if(!e||!(t!=null&&t.data.current))return!1;const{actionType:n}=e,{payloadType:r}=t.data.current;if(e.id===t.data.current.id)return!1;switch(n){case"SET_CURRENT_IMAGE":return r==="IMAGE_DTO";case"SET_INITIAL_IMAGE":return r==="IMAGE_DTO";case"SET_CONTROLNET_IMAGE":return r==="IMAGE_DTO";case"SET_CANVAS_INITIAL_IMAGE":return r==="IMAGE_DTO";case"SET_NODES_IMAGE":return r==="IMAGE_DTO";case"SET_MULTI_NODES_IMAGE":return r==="IMAGE_DTO"||"IMAGE_NAMES";case"ADD_TO_BATCH":return r==="IMAGE_DTO"||"IMAGE_NAMES";case"MOVE_BOARD":{if(!(r==="IMAGE_DTO"||"IMAGE_NAMES"))return!1;if(r==="IMAGE_DTO"){const{imageDTO:o}=t.data.current.payload,s=o.board_id,a=e.context.boardId;return!(s===a)&&(s?!0:a)}return r!=="IMAGE_NAMES"}default:return!1}};function F4e(e){return j.jsx(p4e,{...e})}const B4e=e=>{const[t,n]=E.useState(null),r=qc(),i=E.useCallback(u=>{console.log("dragStart",u.active.data.current);const c=u.active.data.current;c&&n(c)},[]),o=E.useCallback(u=>{var d;console.log("dragEnd",u.active.data.current),u.active.data.current;const c=(d=u.over)==null?void 0:d.data.current;!t||!c||(r(DF({overData:c,activeData:t})),n(null))},[t,r]),s=NA(cj,{activationConstraint:{distance:10}}),a=NA(dj,{activationConstraint:{distance:10}}),l=p3e(s,a);return j.jsxs(F4e,{onDragStart:i,onDragEnd:o,sensors:l,collisionDetection:C3e,children:[e.children,j.jsx(N4e,{dropAnimation:null,modifiers:[D4e],children:j.jsx(L5,{children:t&&j.jsx(D5.div,{layout:!0,initial:{opacity:0,scale:.7},animate:{opacity:1,scale:1,transition:{duration:.1}},children:j.jsx($4e,{dragData:t})},"overlay-drag-image")})})]})},j4e=E.memo(B4e),V4e=E.createContext({isOpen:!1,onClose:()=>{},onClickAddToBoard:()=>{},handleAddToBoard:()=>{}}),z4e=e=>{const[t,n]=E.useState(),{isOpen:r,onOpen:i,onClose:o}=Cse(),s=qc(),a=E.useCallback(()=>{n(void 0),o()},[o]),l=E.useCallback(c=>{c&&(n(c),i())},[n,i]),u=E.useCallback(c=>{t&&(s(ye.endpoints.addImageToBoard.initiate({imageDTO:t,board_id:c})),a())},[s,a,t]);return j.jsx(V4e.Provider,{value:{isOpen:r,image:t,onClose:a,onClickAddToBoard:l,handleAddToBoard:u},children:e.children})},U4e=E.lazy(()=>sR(()=>import("./App-06ea4e5e.js"),["./App-06ea4e5e.js","./MantineProvider-b5842fc1.js","./App-6125620a.css"],import.meta.url)),G4e=E.lazy(()=>sR(()=>import("./ThemeLocaleProvider-a6269375.js"),["./ThemeLocaleProvider-a6269375.js","./MantineProvider-b5842fc1.js","./ThemeLocaleProvider-5b992bc7.css"],import.meta.url)),H4e=({apiUrl:e,token:t,config:n,headerComponent:r,middleware:i})=>(E.useEffect(()=>(t&&mh.set(t),e&&yh.set(e),fI(),i&&i.length>0?$2(IA(),...i):$2(IA()),()=>{yh.set(void 0),mh.set(void 0)}),[e,t,i]),j.jsx(ze.StrictMode,{children:j.jsx(oG,{store:LCe,children:j.jsx(ze.Suspense,{fallback:j.jsx(BCe,{}),children:j.jsx(G4e,{children:j.jsx(j4e,{children:j.jsx(z4e,{children:j.jsx(U4e,{config:n,headerComponent:r})})})})})})})),q4e=E.memo(H4e);F_.createRoot(document.getElementById("root")).render(j.jsx(q4e,{}));export{hTe as $,ise as A,Dn as B,oTe as C,L5 as D,D5 as E,Ev as F,Ao as G,yTe as H,Ml as I,TN as J,Fn as K,Zf as L,gy as M,Ou as N,aJ as O,sTe as P,am as Q,Yu as R,Wf as S,uo as T,Cl as U,_G as V,ze as W,vse as X,l6 as Y,qee as Z,Zm as _,UC as a,K6 as a$,lse as a0,vTe as a1,Ol as a2,RN as a3,Tse as a4,STe as a5,bTe as a6,Yae as a7,Wae as a8,wv as a9,tr as aA,Oi as aB,c0e as aC,h0e as aD,Dr as aE,ia as aF,Ji as aG,Y6e as aH,F$ as aI,T0e as aJ,tve as aK,Sme as aL,Nc as aM,Vn as aN,wce as aO,n7 as aP,V5 as aQ,z5 as aR,xw as aS,tke as aT,Kc as aU,cy as aV,IEe as aW,h8e as aX,sD as aY,x6e as aZ,Hv as a_,tp as aa,Tie as ab,yN as ac,$n as ad,vo as ae,Dv as af,uTe as ag,tw as ah,iTe as ai,sm as aj,s6 as ak,kle as al,mse as am,a6 as an,ale as ao,Cle as ap,HS as aq,Ql as ar,qS as as,ug as at,lle as au,_Te as av,cp as aw,_i as ax,Kt as ay,nc as az,YC as b,jTe as b$,vPe as b0,li as b1,d5 as b2,Hj as b3,X4e as b4,K4e as b5,Y4e as b6,De as b7,Q5 as b8,bN as b9,C7 as bA,XZ as bB,b4 as bC,T6 as bD,wN as bE,xN as bF,cue as bG,d6e as bH,c6e as bI,Nv as bJ,W4e as bK,IQ as bL,CTe as bM,TTe as bN,qTe as bO,WTe as bP,ATe as bQ,JTe as bR,PTe as bS,BTe as bT,ITe as bU,mbe as bV,kTe as bW,KTe as bX,ETe as bY,iEe as bZ,OTe as b_,Au as ba,o6e as bb,KL as bc,r6e as bd,i6e as be,oue as bf,t6e as bg,lue as bh,qc as bi,z6e as bj,SN as bk,Jxe as bl,nke as bm,ike as bn,_6e as bo,w6e as bp,p6e as bq,e6e as br,Fw as bs,v6e as bt,n6e as bu,B5 as bv,$Ce as bw,CEe as bx,KZ as by,u5 as bz,Uq as c,ye as c$,RTe as c0,VTe as c1,DTe as c2,UTe as c3,gbe as c4,me as c5,V4e as c6,b6e as c7,Tpe as c8,T6e as c9,REe as cA,P8e as cB,g6e as cC,E6e as cD,zL as cE,eL as cF,Vt as cG,Q4e as cH,Z4e as cI,AEe as cJ,C8e as cK,eke as cL,mCe as cM,x8e as cN,k8e as cO,Tce as cP,bEe as cQ,kEe as cR,E8e as cS,Cce as cT,MEe as cU,v8e as cV,bD as cW,gEe as cX,d8e as cY,hCe as cZ,yEe as c_,tL as ca,sPe as cb,xce as cc,Bw as cd,rke as ce,rTe as cf,h6e as cg,l6e as ch,u6e as ci,ra as cj,rbe as ck,m6e as cl,y6e as cm,s6e as cn,Cse as co,Uy as cp,D8e as cq,wEe as cr,Xo as cs,Ppe as ct,g8e as cu,G6e as cv,WL as cw,YL as cx,R8e as cy,nr as cz,PQ as d,GTe as d$,lr as d0,L8e as d1,V6e as d2,F6e as d3,B6e as d4,j6e as d5,q6e as d6,W6e as d7,QL as d8,B8e as d9,k6e as dA,j8e as dB,so as dC,LTe as dD,_p as dE,pEe as dF,oEe as dG,dEe as dH,HTe as dI,fEe as dJ,hEe as dK,uEe as dL,cEe as dM,lEe as dN,aEe as dO,nEe as dP,eEe as dQ,b8e as dR,Dl as dS,_8e as dT,_w as dU,UN as dV,HN as dW,GN as dX,KN as dY,WN as dZ,wTe as d_,bc as da,H6e as db,c7 as dc,xTe as dd,Se as de,NTe as df,F8e as dg,y8 as dh,I6e as di,f3 as dj,ype as dk,gu as dl,v8 as dm,M6e as dn,N6e as dp,IN as dq,D6e as dr,vpe as ds,L6e as dt,VL as du,A6e as dv,R6e as dw,P6e as dx,$6e as dy,Lxe as dz,KI as e,iPe as e$,zv as e0,K8e as e1,sa as e2,$8e as e3,yce as e4,V8e as e5,SD as e6,xD as e7,io as e8,zTe as e9,xR as eA,u8e as eB,n8e as eC,r8e as eD,i8e as eE,a8e as eF,l8e as eG,Ph as eH,o5 as eI,_F as eJ,c8e as eK,Z6e as eL,tCe as eM,X6e as eN,Q6e as eO,e8e as eP,J6e as eQ,t8e as eR,s8e as eS,Zve as eT,yF as eU,UF as eV,hV as eW,Le as eX,TPe as eY,GPe as eZ,UPe as e_,tEe as ea,J4e as eb,yZ as ec,rEe as ed,X8e as ee,H8e as ef,O8e as eg,G8e as eh,Y8e as ei,A8e as ej,W8e as ek,UQ as el,q8e as em,z8e as en,U8e as eo,aF as ep,vv as eq,Q8e as er,Jy as es,ve as et,Z8e as eu,ox as ev,pZ as ew,MTe as ex,eTe as ey,gR as ez,CI as f,D1e as f$,NPe as f0,qPe as f1,B1e as f2,XEe as f3,SPe as f4,Nd as f5,mPe as f6,QEe as f7,b1 as f8,bPe as f9,FEe as fA,BEe as fB,M8e as fC,N8e as fD,gPe as fE,aPe as fF,PPe as fG,MPe as fH,kPe as fI,rPe as fJ,YEe as fK,jPe as fL,BPe as fM,RPe as fN,APe as fO,OPe as fP,QPe as fQ,$Pe as fR,ZPe as fS,pPe as fT,hPe as fU,qEe as fV,HEe as fW,YPe as fX,oPe as fY,$1e as fZ,N1e as f_,WEe as fa,yPe as fb,KEe as fc,ePe as fd,$Ee as fe,LEe as ff,DEe as fg,HPe as fh,QD as fi,mh as fj,zEe as fk,UEe as fl,GEe as fm,zPe as fn,nPe as fo,tPe as fp,Zfe as fq,VPe as fr,F1e as fs,ZEe as ft,lPe as fu,uPe as fv,zd as fw,EPe as fx,CPe as fy,_Pe as fz,SQ as g,L1e as g0,jEe as g1,mEe as g2,I8e as g3,Sce as g4,sEe as g5,VEe as g6,dPe as g7,XPe as g8,YTe as g9,mTe as gA,XTe as ga,QTe as gb,ZTe as gc,fPe as gd,xEe as ge,EEe as gf,TEe as gg,PEe as gh,NEe as gi,vZ as gj,O1e as gk,f8e as gl,nTe as gm,O7 as gn,fTe as go,$ee as gp,cTe as gq,aTe as gr,dTe as gs,co as gt,lTe as gu,tTe as gv,Gee as gw,Iee as gx,dJ as gy,gTe as gz,MI as h,Jn as i,Uc as j,mv as k,En as l,Jh as m,Qh as n,zi as o,Ta as p,TI as q,ko as r,E as s,xS as t,Ze as u,j as v,Si as w,fi as x,Ae as y,Gn as z};
diff --git a/invokeai/frontend/web/dist/index.html b/invokeai/frontend/web/dist/index.html
index 802c1f222f..01867d95bc 100644
--- a/invokeai/frontend/web/dist/index.html
+++ b/invokeai/frontend/web/dist/index.html
@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-e2437518.js"></script>
+    <script type="module" crossorigin src="./assets/index-bad7ff83.js"></script>
   </head>
 
   <body dir="ltr">
diff --git a/invokeai/frontend/web/dist/locales/en.json b/invokeai/frontend/web/dist/locales/en.json
index 59cf87fbda..7c9f820729 100644
--- a/invokeai/frontend/web/dist/locales/en.json
+++ b/invokeai/frontend/web/dist/locales/en.json
@@ -23,6 +23,7 @@
         "menu": "Menu"
     },
     "common": {
+        "communityLabel": "Community",
         "hotkeysLabel": "Hotkeys",
         "darkMode": "Dark Mode",
         "lightMode": "Light Mode",
@@ -102,8 +103,7 @@
         "openInNewTab": "Open in New Tab",
         "dontAskMeAgain": "Don't ask me again",
         "areYouSure": "Are you sure?",
-        "imagePrompt": "Image Prompt",
-        "clearNodes": "Are you sure you want to clear all nodes?"
+        "imagePrompt": "Image Prompt"
     },
     "gallery": {
         "generations": "Generations",
@@ -340,6 +340,7 @@
         "allModels": "All Models",
         "checkpointModels": "Checkpoints",
         "diffusersModels": "Diffusers",
+        "loraModels": "LoRAs",
         "safetensorModels": "SafeTensors",
         "modelAdded": "Model Added",
         "modelUpdated": "Model Updated",
@@ -615,6 +616,11 @@
         "initialImageNotSetDesc": "Could not load initial image",
         "nodesSaved": "Nodes Saved",
         "nodesLoaded": "Nodes Loaded",
+        "nodesNotValidGraph": "Not a valid InvokeAI Node Graph",
+        "nodesNotValidJSON": "Not a valid JSON",
+        "nodesCorruptedGraph": "Cannot load. Graph seems to be corrupted.",
+        "nodesUnrecognizedTypes": "Cannot load. Graph has unrecognized types",
+        "nodesBrokenConnections": "Cannot load. Some connections are broken.",
         "nodesLoadedFailed": "Failed To Load Nodes",
         "nodesCleared": "Nodes Cleared"
     },
@@ -700,9 +706,10 @@
     },
     "nodes": {
         "reloadSchema": "Reload Schema",
-        "saveNodes": "Save Nodes",
-        "loadNodes": "Load Nodes",
-        "clearNodes": "Clear Nodes",
+        "saveGraph": "Save Graph",
+        "loadGraph": "Load Graph (saved from Node Editor) (Do not copy-paste metadata)",
+        "clearGraph": "Clear Graph",
+        "clearGraphDesc": "Are you sure you want to clear all nodes?",
         "zoomInNodes": "Zoom In",
         "zoomOutNodes": "Zoom Out",
         "fitViewportNodes": "Fit View",
diff --git a/invokeai/frontend/web/package.json b/invokeai/frontend/web/package.json
index c58e7bdc97..481ee8f9e7 100644
--- a/invokeai/frontend/web/package.json
+++ b/invokeai/frontend/web/package.json
@@ -53,11 +53,11 @@
     ]
   },
   "dependencies": {
-    "@chakra-ui/anatomy": "^2.1.1",
+    "@chakra-ui/anatomy": "^2.2.0",
     "@chakra-ui/icons": "^2.0.19",
-    "@chakra-ui/react": "^2.7.1",
+    "@chakra-ui/react": "^2.8.0",
     "@chakra-ui/styled-system": "^2.9.1",
-    "@chakra-ui/theme-tools": "^2.0.18",
+    "@chakra-ui/theme-tools": "^2.1.0",
     "@dagrejs/graphlib": "^2.1.13",
     "@dnd-kit/core": "^6.0.8",
     "@dnd-kit/modifiers": "^6.0.1",
diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json
index 0640ab9ef0..7c9f820729 100644
--- a/invokeai/frontend/web/public/locales/en.json
+++ b/invokeai/frontend/web/public/locales/en.json
@@ -23,6 +23,7 @@
         "menu": "Menu"
     },
     "common": {
+        "communityLabel": "Community",
         "hotkeysLabel": "Hotkeys",
         "darkMode": "Dark Mode",
         "lightMode": "Light Mode",
@@ -339,6 +340,7 @@
         "allModels": "All Models",
         "checkpointModels": "Checkpoints",
         "diffusersModels": "Diffusers",
+        "loraModels": "LoRAs",
         "safetensorModels": "SafeTensors",
         "modelAdded": "Model Added",
         "modelUpdated": "Model Updated",
diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts
index 68148a192f..2d0ece3595 100644
--- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts
+++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts
@@ -1,4 +1,8 @@
 import { setInfillMethod } from 'features/parameters/store/generationSlice';
+import {
+  shouldUseNSFWCheckerChanged,
+  shouldUseWatermarkerChanged,
+} from 'features/system/store/systemSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import { startAppListening } from '..';
 
@@ -6,12 +10,24 @@ export const addAppConfigReceivedListener = () => {
   startAppListening({
     matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
     effect: async (action, { getState, dispatch }) => {
-      const { infill_methods } = action.payload;
+      const {
+        infill_methods = [],
+        nsfw_methods = [],
+        watermarking_methods = [],
+      } = action.payload;
       const infillMethod = getState().generation.infillMethod;
 
       if (!infill_methods.includes(infillMethod)) {
         dispatch(setInfillMethod(infill_methods[0]));
       }
+
+      if (!nsfw_methods.includes('nsfw_checker')) {
+        dispatch(shouldUseNSFWCheckerChanged(false));
+      }
+
+      if (!watermarking_methods.includes('invisible_watermark')) {
+        dispatch(shouldUseWatermarkerChanged(false));
+      }
     },
   });
 };
diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts
index ec310eac4c..32a6cce203 100644
--- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts
+++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts
@@ -1,13 +1,10 @@
 import { logger } from 'app/logging/logger';
+import { LIST_TAG } from 'services/api';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 import { modelsApi } from 'services/api/endpoints/models';
 import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { appSocketConnected, socketConnected } from 'services/events/actions';
 import { startAppListening } from '../..';
-import {
-  ALL_BASE_MODELS,
-  NON_REFINER_BASE_MODELS,
-  REFINER_BASE_MODELS,
-} from 'services/api/constants';
 
 export const addSocketConnectedEventListener = () => {
   startAppListening({
@@ -29,15 +26,18 @@ export const addSocketConnectedEventListener = () => {
       dispatch(appSocketConnected(action.payload));
 
       // update all server state
-      dispatch(modelsApi.endpoints.getMainModels.initiate(REFINER_BASE_MODELS));
       dispatch(
-        modelsApi.endpoints.getMainModels.initiate(NON_REFINER_BASE_MODELS)
+        modelsApi.util.invalidateTags([
+          { type: 'MainModel', id: LIST_TAG },
+          { type: 'SDXLRefinerModel', id: LIST_TAG },
+          { type: 'LoRAModel', id: LIST_TAG },
+          { type: 'ControlNetModel', id: LIST_TAG },
+          { type: 'VaeModel', id: LIST_TAG },
+          { type: 'TextualInversionModel', id: LIST_TAG },
+          { type: 'ScannedModels', id: LIST_TAG },
+        ])
       );
-      dispatch(modelsApi.endpoints.getMainModels.initiate(ALL_BASE_MODELS));
-      dispatch(modelsApi.endpoints.getControlNetModels.initiate());
-      dispatch(modelsApi.endpoints.getLoRAModels.initiate());
-      dispatch(modelsApi.endpoints.getTextualInversionModels.initiate());
-      dispatch(modelsApi.endpoints.getVaeModels.initiate());
+      dispatch(appInfoApi.util.invalidateTags(['AppConfig', 'AppVersion']));
     },
   });
 };
diff --git a/invokeai/frontend/web/src/common/components/IAISlider.tsx b/invokeai/frontend/web/src/common/components/IAISlider.tsx
index 53a195c7b7..fd3eed754f 100644
--- a/invokeai/frontend/web/src/common/components/IAISlider.tsx
+++ b/invokeai/frontend/web/src/common/components/IAISlider.tsx
@@ -114,6 +114,11 @@ const IAISlider = (props: IAIFullSliderProps) => {
     setLocalInputValue(value);
   }, [value]);
 
+  const numberInputMin = useMemo(
+    () => (sliderNumberInputProps?.min ? sliderNumberInputProps.min : min),
+    [min, sliderNumberInputProps?.min]
+  );
+
   const numberInputMax = useMemo(
     () => (sliderNumberInputProps?.max ? sliderNumberInputProps.max : max),
     [max, sliderNumberInputProps?.max]
@@ -129,24 +134,23 @@ const IAISlider = (props: IAIFullSliderProps) => {
   const handleInputBlur = useCallback(
     (e: FocusEvent<HTMLInputElement>) => {
       if (e.target.value === '') {
-        e.target.value = String(min);
+        e.target.value = String(numberInputMin);
       }
       const clamped = clamp(
         isInteger
           ? Math.floor(Number(e.target.value))
           : Number(localInputValue),
-        min,
+        numberInputMin,
         numberInputMax
       );
       const quantized = roundDownToMultiple(clamped, step);
       onChange(quantized);
       setLocalInputValue(quantized);
     },
-    [isInteger, localInputValue, min, numberInputMax, onChange, step]
+    [isInteger, localInputValue, numberInputMin, numberInputMax, onChange, step]
   );
 
   const handleInputChange = useCallback((v: number | string) => {
-    console.log('input');
     setLocalInputValue(v);
   }, []);
 
@@ -310,7 +314,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
 
         {withInput && (
           <NumberInput
-            min={min}
+            min={numberInputMin}
             max={numberInputMax}
             step={step}
             value={localInputValue}
diff --git a/invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts b/invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts
index 3462a26353..f43ec1851f 100644
--- a/invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts
+++ b/invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts
@@ -5,8 +5,8 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 // import { validateSeedWeights } from 'common/util/seedWeightPairs';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { forEach } from 'lodash-es';
+import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
 import { modelsApi } from '../../services/api/endpoints/models';
-import { ALL_BASE_MODELS } from 'services/api/constants';
 
 const readinessSelector = createSelector(
   [stateSelector, activeTabNameSelector],
@@ -25,7 +25,7 @@ const readinessSelector = createSelector(
     }
 
     const { isSuccess: mainModelsSuccessfullyLoaded } =
-      modelsApi.endpoints.getMainModels.select(ALL_BASE_MODELS)(state);
+      modelsApi.endpoints.getMainModels.select(NON_REFINER_BASE_MODELS)(state);
     if (!mainModelsSuccessfullyLoaded) {
       isReady = false;
       reasonsWhyNotReady.push('Models are not loaded');
diff --git a/invokeai/frontend/web/src/features/embedding/components/ParamEmbeddingPopover.tsx b/invokeai/frontend/web/src/features/embedding/components/ParamEmbeddingPopover.tsx
index 69d079034a..4eb9a67de2 100644
--- a/invokeai/frontend/web/src/features/embedding/components/ParamEmbeddingPopover.tsx
+++ b/invokeai/frontend/web/src/features/embedding/components/ParamEmbeddingPopover.tsx
@@ -57,6 +57,11 @@ const ParamEmbeddingPopover = (props: Props) => {
       });
     });
 
+    // Sort Alphabetically
+    data.sort((a, b) =>
+      a.label && b.label ? (a.label?.localeCompare(b.label) ? -1 : 1) : -1
+    );
+
     return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
   }, [embeddingQueryData, currentMainModel?.base_model]);
 
diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
index 74fea27927..237a38ca33 100644
--- a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
+++ b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
@@ -1,5 +1,6 @@
-import { Link, MenuItem } from '@chakra-ui/react';
+import { MenuItem } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
+import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { useAppToaster } from 'app/components/Toaster';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
@@ -33,10 +34,9 @@ import {
   useRemoveImageFromBoardMutation,
 } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
+import { useDebounce } from 'use-debounce';
 import { AddImageToBoardContext } from '../../../../app/contexts/AddImageToBoardContext';
 import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
-import { useDebounce } from 'use-debounce';
-import { skipToken } from '@reduxjs/toolkit/dist/query';
 
 type SingleSelectionMenuItemsProps = {
   imageDTO: ImageDTO;
@@ -154,21 +154,29 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
 
   return (
     <>
-      <Link href={imageDTO.image_url} target="_blank">
-        <MenuItem icon={<FaExternalLinkAlt />}>
-          {t('common.openInNewTab')}
-        </MenuItem>
-      </Link>
+      <MenuItem
+        as="a"
+        href={imageDTO.image_url}
+        target="_blank"
+        icon={<FaExternalLinkAlt />}
+      >
+        {t('common.openInNewTab')}
+      </MenuItem>
       {isClipboardAPIAvailable && (
         <MenuItem icon={<FaCopy />} onClickCapture={handleCopyImage}>
           {t('parameters.copyImage')}
         </MenuItem>
       )}
-      <Link download={true} href={imageDTO.image_url} target="_blank">
-        <MenuItem icon={<FaDownload />} w="100%">
-          {t('parameters.downloadImage')}
-        </MenuItem>
-      </Link>
+      <MenuItem
+        as="a"
+        download={true}
+        href={imageDTO.image_url}
+        target="_blank"
+        icon={<FaDownload />}
+        w="100%"
+      >
+        {t('parameters.downloadImage')}
+      </MenuItem>
       <MenuItem
         icon={<FaQuoteRight />}
         onClickCapture={handleRecallPrompt}
diff --git a/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx b/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx
index a1584ca13a..3432838ec0 100644
--- a/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx
+++ b/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx
@@ -48,6 +48,7 @@ const ParamLora = (props: Props) => {
         handleReset={handleReset}
         withSliderMarks
         sliderMarks={[-1, 0, 1, 2]}
+        sliderNumberInputProps={{ min: -50, max: 50 }}
       />
       <IAIIconButton
         size="sm"
diff --git a/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx b/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx
index 951037f9e3..631047aaab 100644
--- a/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx
+++ b/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx
@@ -54,7 +54,12 @@ const ParamLoRASelect = () => {
       });
     });
 
-    return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
+    // Sort Alphabetically
+    data.sort((a, b) =>
+      a.label && b.label ? (a.label?.localeCompare(b.label) ? 1 : -1) : -1
+    );
+
+    return data.sort((a, b) => (a.disabled && !b.disabled ? -1 : 1));
   }, [loras, loraModels, currentMainModel?.base_model]);
 
   const handleChange = useCallback(
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts
new file mode 100644
index 0000000000..35e7f3ac38
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts
@@ -0,0 +1,70 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import {
+  ImageNSFWBlurInvocation,
+  LatentsToImageInvocation,
+  MetadataAccumulatorInvocation,
+} from 'services/api/types';
+import {
+  LATENTS_TO_IMAGE,
+  METADATA_ACCUMULATOR,
+  NSFW_CHECKER,
+} from './constants';
+
+export const addNSFWCheckerToGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  nodeIdToAddTo = LATENTS_TO_IMAGE
+): void => {
+  const activeTabName = activeTabNameSelector(state);
+
+  const is_intermediate =
+    activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
+
+  const nodeToAddTo = graph.nodes[nodeIdToAddTo] as
+    | LatentsToImageInvocation
+    | undefined;
+
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
+
+  if (!nodeToAddTo) {
+    // something has gone terribly awry
+    return;
+  }
+
+  nodeToAddTo.is_intermediate = true;
+
+  const nsfwCheckerNode: ImageNSFWBlurInvocation = {
+    id: NSFW_CHECKER,
+    type: 'img_nsfw',
+    is_intermediate,
+  };
+
+  graph.nodes[NSFW_CHECKER] = nsfwCheckerNode;
+  graph.edges.push({
+    source: {
+      node_id: nodeIdToAddTo,
+      field: 'image',
+    },
+    destination: {
+      node_id: NSFW_CHECKER,
+      field: 'image',
+    },
+  });
+
+  if (metadataAccumulator) {
+    graph.edges.push({
+      source: {
+        node_id: METADATA_ACCUMULATOR,
+        field: 'metadata',
+      },
+      destination: {
+        node_id: NSFW_CHECKER,
+        field: 'metadata',
+      },
+    });
+  }
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts
new file mode 100644
index 0000000000..f2e8a0aeca
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts
@@ -0,0 +1,95 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import {
+  ImageNSFWBlurInvocation,
+  ImageWatermarkInvocation,
+  LatentsToImageInvocation,
+  MetadataAccumulatorInvocation,
+} from 'services/api/types';
+import {
+  LATENTS_TO_IMAGE,
+  METADATA_ACCUMULATOR,
+  NSFW_CHECKER,
+  WATERMARKER,
+} from './constants';
+
+export const addWatermarkerToGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  nodeIdToAddTo = LATENTS_TO_IMAGE
+): void => {
+  const activeTabName = activeTabNameSelector(state);
+
+  const is_intermediate =
+    activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
+
+  const nodeToAddTo = graph.nodes[nodeIdToAddTo] as
+    | LatentsToImageInvocation
+    | undefined;
+
+  const nsfwCheckerNode = graph.nodes[NSFW_CHECKER] as
+    | ImageNSFWBlurInvocation
+    | undefined;
+
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
+
+  if (!nodeToAddTo) {
+    // something has gone terribly awry
+    return;
+  }
+
+  const watermarkerNode: ImageWatermarkInvocation = {
+    id: WATERMARKER,
+    type: 'img_watermark',
+    is_intermediate,
+  };
+
+  graph.nodes[WATERMARKER] = watermarkerNode;
+
+  // no matter the situation, we want the l2i node to be intermediate
+  nodeToAddTo.is_intermediate = true;
+
+  if (nsfwCheckerNode) {
+    // if we are using NSFW checker, we need to "disable" it output by marking it intermediate,
+    // then connect it to the watermark node
+    nsfwCheckerNode.is_intermediate = true;
+    graph.edges.push({
+      source: {
+        node_id: NSFW_CHECKER,
+        field: 'image',
+      },
+      destination: {
+        node_id: WATERMARKER,
+        field: 'image',
+      },
+    });
+  } else {
+    // otherwise we just connect to the watermark node
+    graph.edges.push({
+      source: {
+        node_id: nodeIdToAddTo,
+        field: 'image',
+      },
+      destination: {
+        node_id: WATERMARKER,
+        field: 'image',
+      },
+    });
+  }
+
+  if (metadataAccumulator) {
+    graph.edges.push({
+      source: {
+        node_id: METADATA_ACCUMULATOR,
+        field: 'metadata',
+      },
+      destination: {
+        node_id: WATERMARKER,
+        field: 'metadata',
+      },
+    });
+  }
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
index 4ef3ff77d2..42f768c107 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
@@ -10,7 +10,9 @@ import {
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CLIP_SKIP,
   IMAGE_TO_IMAGE_GRAPH,
@@ -103,11 +105,6 @@ export const buildCanvasImageToImageGraph = (
         is_intermediate: true,
         skipped_layers: clipSkip,
       },
-      [LATENTS_TO_IMAGE]: {
-        is_intermediate: !shouldAutoSave,
-        type: 'l2i',
-        id: LATENTS_TO_IMAGE,
-      },
       [LATENTS_TO_LATENTS]: {
         type: 'l2l',
         id: LATENTS_TO_LATENTS,
@@ -126,6 +123,11 @@ export const buildCanvasImageToImageGraph = (
         //   image_name: initialImage.image_name,
         // },
       },
+      [LATENTS_TO_IMAGE]: {
+        type: 'l2i',
+        id: LATENTS_TO_IMAGE,
+        is_intermediate: !shouldAutoSave,
+      },
     },
     edges: [
       {
@@ -333,5 +335,16 @@ export const buildCanvasImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
index fa6e20aaa4..3cec76757f 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
@@ -8,7 +8,9 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CLIP_SKIP,
   INPAINT,
@@ -249,5 +251,16 @@ export const buildCanvasInpaintGraph = (
     (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
   }
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph, INPAINT);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph, INPAINT);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
index d0eabbc14b..13b27392ba 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
@@ -5,7 +5,9 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
@@ -238,5 +240,16 @@ export const buildCanvasTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
index 856ed89e5a..f264edc6be 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
@@ -9,7 +9,9 @@ import {
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CLIP_SKIP,
   IMAGE_TO_IMAGE_GRAPH,
@@ -297,42 +299,6 @@ export const buildLinearImageToImageGraph = (
     });
   }
 
-  // TODO: add batch functionality
-  // if (isBatchEnabled && asInitialImage && batchImageNames.length > 0) {
-  //   // we are going to connect an iterate up to the init image
-  //   delete (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image;
-
-  //   const imageCollection: ImageCollectionInvocation = {
-  //     id: IMAGE_COLLECTION,
-  //     type: 'image_collection',
-  //     images: batchImageNames.map((image_name) => ({ image_name })),
-  //   };
-
-  //   const imageCollectionIterate: IterateInvocation = {
-  //     id: IMAGE_COLLECTION_ITERATE,
-  //     type: 'iterate',
-  //   };
-
-  //   graph.nodes[IMAGE_COLLECTION] = imageCollection;
-  //   graph.nodes[IMAGE_COLLECTION_ITERATE] = imageCollectionIterate;
-
-  //   graph.edges.push({
-  //     source: { node_id: IMAGE_COLLECTION, field: 'collection' },
-  //     destination: {
-  //       node_id: IMAGE_COLLECTION_ITERATE,
-  //       field: 'collection',
-  //     },
-  //   });
-
-  //   graph.edges.push({
-  //     source: { node_id: IMAGE_COLLECTION_ITERATE, field: 'item' },
-  //     destination: {
-  //       node_id: IMAGE_TO_LATENTS,
-  //       field: 'image',
-  //     },
-  //   });
-  // }
-
   // add metadata accumulator, which is only mostly populated - some fields are added later
   graph.nodes[METADATA_ACCUMULATOR] = {
     id: METADATA_ACCUMULATOR,
@@ -379,5 +345,16 @@ export const buildLinearImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
index e80821e060..a260dbc467 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
@@ -7,7 +7,9 @@ import {
   ImageToLatentsInvocation,
 } from 'services/api/types';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   IMAGE_TO_LATENTS,
   LATENTS_TO_IMAGE,
@@ -48,6 +50,7 @@ export const buildLinearSDXLImageToImageGraph = (
   const {
     positiveStylePrompt,
     negativeStylePrompt,
+    shouldConcatSDXLStylePrompt,
     shouldUseSDXLRefiner,
     refinerStart,
     sdxlImg2ImgDenoisingStrength: strength,
@@ -89,13 +92,17 @@ export const buildLinearSDXLImageToImageGraph = (
         type: 'sdxl_compel_prompt',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
-        style: positiveStylePrompt,
+        style: shouldConcatSDXLStylePrompt
+          ? `${positivePrompt} ${positiveStylePrompt}`
+          : positiveStylePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'sdxl_compel_prompt',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
-        style: negativeStylePrompt,
+        style: shouldConcatSDXLStylePrompt
+          ? `${negativePrompt} ${negativeStylePrompt}`
+          : negativeStylePrompt,
       },
       [NOISE]: {
         type: 'noise',
@@ -365,5 +372,16 @@ export const buildLinearSDXLImageToImageGraph = (
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
index 8efcbf8876..c10e7831d3 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
@@ -3,7 +3,9 @@ import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   LATENTS_TO_IMAGE,
   METADATA_ACCUMULATOR,
@@ -37,6 +39,7 @@ export const buildLinearSDXLTextToImageGraph = (
   const {
     positiveStylePrompt,
     negativeStylePrompt,
+    shouldConcatSDXLStylePrompt,
     shouldUseSDXLRefiner,
     refinerStart,
   } = state.sdxl;
@@ -72,13 +75,17 @@ export const buildLinearSDXLTextToImageGraph = (
         type: 'sdxl_compel_prompt',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
-        style: positiveStylePrompt,
+        style: shouldConcatSDXLStylePrompt
+          ? `${positivePrompt} ${positiveStylePrompt}`
+          : positiveStylePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'sdxl_compel_prompt',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
-        style: negativeStylePrompt,
+        style: shouldConcatSDXLStylePrompt
+          ? `${negativePrompt} ${negativeStylePrompt}`
+          : negativeStylePrompt,
       },
       [NOISE]: {
         type: 'noise',
@@ -247,5 +254,16 @@ export const buildLinearSDXLTextToImageGraph = (
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
index 7b36f6b391..3766ac1ee3 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
@@ -5,7 +5,9 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
+import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
+import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
@@ -227,5 +229,16 @@ export const buildLinearTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
 
+  // NSFW & watermark - must be last thing added to graph
+  if (state.system.shouldUseNSFWChecker) {
+    // must add before watermarker!
+    addNSFWCheckerToGraph(state, graph);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    // must add after nsfw checker!
+    addWatermarkerToGraph(state, graph);
+  }
+
   return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
index f7d11d109c..8cb9c6d50d 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
@@ -3,6 +3,8 @@ export const POSITIVE_CONDITIONING = 'positive_conditioning';
 export const NEGATIVE_CONDITIONING = 'negative_conditioning';
 export const TEXT_TO_LATENTS = 'text_to_latents';
 export const LATENTS_TO_IMAGE = 'latents_to_image';
+export const NSFW_CHECKER = 'nsfw_checker';
+export const WATERMARKER = 'invisible_watermark';
 export const NOISE = 'noise';
 export const RANDOM_INT = 'rand_int';
 export const RANGE_OF_SIZE = 'range_of_size';
diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx
index 657034e362..41be41ec28 100644
--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx
@@ -7,9 +7,9 @@ import { activeTabNameSelector } from '../../../../ui/store/uiSelectors';
 
 const aspectRatios = [
   { name: 'Free', value: null },
-  { name: 'Portrait', value: 0.67 / 1 },
-  { name: 'Wide', value: 16 / 9 },
-  { name: 'Square', value: 1 / 1 },
+  { name: '2:3', value: 2 / 3 },
+  { name: '16:9', value: 16 / 9 },
+  { name: '1:1', value: 1 / 1 },
 ];
 
 export default function ParamAspectRatio() {
diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamPositiveConditioning.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamPositiveConditioning.tsx
index 5a52358670..59b5138e3e 100644
--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamPositiveConditioning.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamPositiveConditioning.tsx
@@ -148,7 +148,7 @@ const ParamPositiveConditioning = () => {
         <Box
           sx={{
             position: 'absolute',
-            top: shouldPinParametersPanel ? 6 : 0,
+            top: shouldPinParametersPanel ? 5 : 0,
             insetInlineEnd: 0,
           }}
         >
diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Prompt/ParamPromptArea.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Prompt/ParamPromptArea.tsx
new file mode 100644
index 0000000000..3066490847
--- /dev/null
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Prompt/ParamPromptArea.tsx
@@ -0,0 +1,17 @@
+import { Flex } from '@chakra-ui/react';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+
+export default function ParamPromptArea() {
+  return (
+    <Flex
+      sx={{
+        flexDirection: 'column',
+        gap: 2,
+      }}
+    >
+      <ParamPositiveConditioning />
+      <ParamNegativeConditioning />
+    </Flex>
+  );
+}
diff --git a/invokeai/frontend/web/src/features/parameters/types/constants.ts b/invokeai/frontend/web/src/features/parameters/types/constants.ts
index ca52bfb95a..dd0e738eeb 100644
--- a/invokeai/frontend/web/src/features/parameters/types/constants.ts
+++ b/invokeai/frontend/web/src/features/parameters/types/constants.ts
@@ -1,3 +1,5 @@
+import { components } from 'services/api/schema';
+
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
@@ -5,6 +7,13 @@ export const MODEL_TYPE_MAP = {
   'sdxl-refiner': 'Stable Diffusion XL Refiner',
 };
 
+export const MODEL_TYPE_SHORT_MAP = {
+  'sd-1': 'SD1',
+  'sd-2': 'SD2',
+  sdxl: 'SDXL',
+  'sdxl-refiner': 'SDXLR',
+};
+
 export const clipSkipMap = {
   'sd-1': {
     maxClip: 12,
@@ -23,3 +32,12 @@ export const clipSkipMap = {
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
   },
 };
+
+type LoRAModelFormatMap = {
+  [key in components['schemas']['LoRAModelFormat']]: string;
+};
+
+export const LORA_MODEL_FORMAT_MAP: LoRAModelFormatMap = {
+  lycoris: 'LyCORIS',
+  diffusers: 'Diffusers',
+};
diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx
new file mode 100644
index 0000000000..9393b245ef
--- /dev/null
+++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx
@@ -0,0 +1,43 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { FaLink } from 'react-icons/fa';
+import { setShouldConcatSDXLStylePrompt } from '../store/sdxlSlice';
+
+export default function ParamSDXLConcatButton() {
+  const shouldConcatSDXLStylePrompt = useAppSelector(
+    (state: RootState) => state.sdxl.shouldConcatSDXLStylePrompt
+  );
+
+  const shouldPinParametersPanel = useAppSelector(
+    (state: RootState) => state.ui.shouldPinParametersPanel
+  );
+
+  const dispatch = useAppDispatch();
+
+  const handleShouldConcatPromptChange = () => {
+    dispatch(setShouldConcatSDXLStylePrompt(!shouldConcatSDXLStylePrompt));
+  };
+
+  return (
+    <IAIIconButton
+      aria-label="Concat"
+      tooltip="Concatenates Basic Prompt with Style (Recommended)"
+      variant="outline"
+      isChecked={shouldConcatSDXLStylePrompt}
+      onClick={handleShouldConcatPromptChange}
+      icon={<FaLink />}
+      size="xs"
+      sx={{
+        position: 'absolute',
+        insetInlineEnd: 1,
+        top: shouldPinParametersPanel ? 12 : 20,
+        border: 'none',
+        color: shouldConcatSDXLStylePrompt ? 'accent.500' : 'base.500',
+        _hover: {
+          bg: 'none',
+        },
+      }}
+    ></IAIIconButton>
+  );
+}
diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx
index e2d5c5b8e4..e1533bc886 100644
--- a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx
+++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx
@@ -13,15 +13,20 @@ import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
 import AddEmbeddingButton from 'features/embedding/components/AddEmbeddingButton';
 import ParamEmbeddingPopover from 'features/embedding/components/ParamEmbeddingPopover';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { AnimatePresence } from 'framer-motion';
 import { isEqual } from 'lodash-es';
 import { flushSync } from 'react-dom';
 import { setNegativeStylePromptSDXL } from '../store/sdxlSlice';
+import SDXLConcatLink from './SDXLConcatLink';
 
 const promptInputSelector = createSelector(
   [stateSelector, activeTabNameSelector],
   ({ sdxl }, activeTabName) => {
+    const { negativeStylePrompt, shouldConcatSDXLStylePrompt } = sdxl;
+
     return {
-      prompt: sdxl.negativeStylePrompt,
+      prompt: negativeStylePrompt,
+      shouldConcatSDXLStylePrompt,
       activeTabName,
     };
   },
@@ -37,11 +42,13 @@ const promptInputSelector = createSelector(
  */
 const ParamSDXLNegativeStyleConditioning = () => {
   const dispatch = useAppDispatch();
-  const { prompt, activeTabName } = useAppSelector(promptInputSelector);
   const isReady = useIsReadyToInvoke();
   const promptRef = useRef<HTMLTextAreaElement>(null);
   const { isOpen, onClose, onOpen } = useDisclosure();
 
+  const { prompt, activeTabName, shouldConcatSDXLStylePrompt } =
+    useAppSelector(promptInputSelector);
+
   const handleChangePrompt = useCallback(
     (e: ChangeEvent<HTMLTextAreaElement>) => {
       dispatch(setNegativeStylePromptSDXL(e.target.value));
@@ -111,6 +118,20 @@ const ParamSDXLNegativeStyleConditioning = () => {
 
   return (
     <Box position="relative">
+      <AnimatePresence>
+        {shouldConcatSDXLStylePrompt && (
+          <Box
+            sx={{
+              position: 'absolute',
+              left: '3',
+              w: '94%',
+              top: '-17px',
+            }}
+          >
+            <SDXLConcatLink />
+          </Box>
+        )}
+      </AnimatePresence>
       <FormControl>
         <ParamEmbeddingPopover
           isOpen={isOpen}
diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx
index 8512aedd68..fdd29eaeb7 100644
--- a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx
+++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx
@@ -13,15 +13,20 @@ import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
 import AddEmbeddingButton from 'features/embedding/components/AddEmbeddingButton';
 import ParamEmbeddingPopover from 'features/embedding/components/ParamEmbeddingPopover';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { AnimatePresence } from 'framer-motion';
 import { isEqual } from 'lodash-es';
 import { flushSync } from 'react-dom';
 import { setPositiveStylePromptSDXL } from '../store/sdxlSlice';
+import SDXLConcatLink from './SDXLConcatLink';
 
 const promptInputSelector = createSelector(
   [stateSelector, activeTabNameSelector],
   ({ sdxl }, activeTabName) => {
+    const { positiveStylePrompt, shouldConcatSDXLStylePrompt } = sdxl;
+
     return {
-      prompt: sdxl.positiveStylePrompt,
+      prompt: positiveStylePrompt,
+      shouldConcatSDXLStylePrompt,
       activeTabName,
     };
   },
@@ -37,11 +42,13 @@ const promptInputSelector = createSelector(
  */
 const ParamSDXLPositiveStyleConditioning = () => {
   const dispatch = useAppDispatch();
-  const { prompt, activeTabName } = useAppSelector(promptInputSelector);
   const isReady = useIsReadyToInvoke();
   const promptRef = useRef<HTMLTextAreaElement>(null);
   const { isOpen, onClose, onOpen } = useDisclosure();
 
+  const { prompt, activeTabName, shouldConcatSDXLStylePrompt } =
+    useAppSelector(promptInputSelector);
+
   const handleChangePrompt = useCallback(
     (e: ChangeEvent<HTMLTextAreaElement>) => {
       dispatch(setPositiveStylePromptSDXL(e.target.value));
@@ -111,6 +118,20 @@ const ParamSDXLPositiveStyleConditioning = () => {
 
   return (
     <Box position="relative">
+      <AnimatePresence>
+        {shouldConcatSDXLStylePrompt && (
+          <Box
+            sx={{
+              position: 'absolute',
+              left: '3',
+              w: '94%',
+              top: '-17px',
+            }}
+          >
+            <SDXLConcatLink />
+          </Box>
+        )}
+      </AnimatePresence>
       <FormControl>
         <ParamEmbeddingPopover
           isOpen={isOpen}
diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPromptArea.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPromptArea.tsx
new file mode 100644
index 0000000000..ed21284df0
--- /dev/null
+++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPromptArea.tsx
@@ -0,0 +1,23 @@
+import { Flex } from '@chakra-ui/react';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+import ParamSDXLConcatButton from './ParamSDXLConcatButton';
+import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning';
+import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning';
+
+export default function ParamSDXLPromptArea() {
+  return (
+    <Flex
+      sx={{
+        flexDirection: 'column',
+        gap: 2,
+      }}
+    >
+      <ParamPositiveConditioning />
+      <ParamSDXLConcatButton />
+      <ParamSDXLPositiveStyleConditioning />
+      <ParamNegativeConditioning />
+      <ParamSDXLNegativeStyleConditioning />
+    </Flex>
+  );
+}
diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLConcatLink.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLConcatLink.tsx
new file mode 100644
index 0000000000..e982598081
--- /dev/null
+++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLConcatLink.tsx
@@ -0,0 +1,101 @@
+import { Box, Flex } from '@chakra-ui/react';
+import { CSSObject } from '@emotion/react';
+import { motion } from 'framer-motion';
+import { FaLink } from 'react-icons/fa';
+
+const sharedConcatLinkStyle: CSSObject = {
+  position: 'absolute',
+  bg: 'none',
+  w: 'full',
+  minH: 2,
+  borderRadius: 0,
+  borderLeft: 'none',
+  borderRight: 'none',
+  zIndex: 2,
+  maskImage:
+    'radial-gradient(circle at center, black, black 65%, black 30%, black 15%, transparent)',
+};
+
+export default function SDXLConcatLink() {
+  return (
+    <Flex>
+      <Box
+        as={motion.div}
+        initial={{
+          scaleX: 0,
+          borderWidth: 0,
+          display: 'none',
+        }}
+        animate={{
+          display: ['block', 'block', 'block', 'none'],
+          scaleX: [0, 0.25, 0.5, 1],
+          borderWidth: [0, 3, 3, 0],
+          transition: { duration: 0.37, times: [0, 0.25, 0.5, 1] },
+        }}
+        sx={{
+          top: '1px',
+          borderTop: 'none',
+          borderColor: 'base.400',
+          ...sharedConcatLinkStyle,
+          _dark: {
+            borderColor: 'accent.500',
+          },
+        }}
+      />
+      <Box
+        as={motion.div}
+        initial={{
+          opacity: 0,
+          scale: 0,
+        }}
+        animate={{
+          opacity: [0, 1, 1, 1],
+          scale: [0, 0.75, 1.5, 1],
+          transition: { duration: 0.42, times: [0, 0.25, 0.5, 1] },
+        }}
+        exit={{
+          opacity: 0,
+          scale: 0,
+        }}
+        sx={{
+          zIndex: 3,
+          position: 'absolute',
+          left: '48%',
+          top: '3px',
+          p: 1,
+          borderRadius: 4,
+          bg: 'accent.400',
+          color: 'base.50',
+          _dark: {
+            bg: 'accent.500',
+          },
+        }}
+      >
+        <FaLink size={12} />
+      </Box>
+      <Box
+        as={motion.div}
+        initial={{
+          scaleX: 0,
+          borderWidth: 0,
+          display: 'none',
+        }}
+        animate={{
+          display: ['block', 'block', 'block', 'none'],
+          scaleX: [0, 0.25, 0.5, 1],
+          borderWidth: [0, 3, 3, 0],
+          transition: { duration: 0.37, times: [0, 0.25, 0.5, 1] },
+        }}
+        sx={{
+          top: '17px',
+          borderBottom: 'none',
+          borderColor: 'base.400',
+          ...sharedConcatLinkStyle,
+          _dark: {
+            borderColor: 'accent.500',
+          },
+        }}
+      />
+    </Flex>
+  );
+}
diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx
index 778116eefe..c0b143a557 100644
--- a/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx
+++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx
@@ -1,21 +1,14 @@
 import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
-// import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
-import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning';
-import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning';
+import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters';
 
 const SDXLImageToImageTabParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
-      <ParamSDXLPositiveStyleConditioning />
-      <ParamNegativeConditioning />
-      <ParamSDXLNegativeStyleConditioning />
+      <ParamSDXLPromptArea />
       <ProcessButtons />
       <SDXLImageToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx
index 2175fcc9e3..35bc0b4284 100644
--- a/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx
+++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx
@@ -1,20 +1,14 @@
 import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
-import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning';
-import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning';
+import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 
 const SDXLTextToImageTabParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
-      <ParamSDXLPositiveStyleConditioning />
-      <ParamNegativeConditioning />
-      <ParamSDXLNegativeStyleConditioning />
+      <ParamSDXLPromptArea />
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
diff --git a/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts b/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts
index 16bb806029..7a8ccffa97 100644
--- a/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts
+++ b/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts
@@ -10,6 +10,7 @@ import { MainModelField } from 'services/api/types';
 type SDXLInitialState = {
   positiveStylePrompt: PositiveStylePromptSDXLParam;
   negativeStylePrompt: NegativeStylePromptSDXLParam;
+  shouldConcatSDXLStylePrompt: boolean;
   shouldUseSDXLRefiner: boolean;
   sdxlImg2ImgDenoisingStrength: number;
   refinerModel: MainModelField | null;
@@ -23,6 +24,7 @@ type SDXLInitialState = {
 const sdxlInitialState: SDXLInitialState = {
   positiveStylePrompt: '',
   negativeStylePrompt: '',
+  shouldConcatSDXLStylePrompt: true,
   shouldUseSDXLRefiner: false,
   sdxlImg2ImgDenoisingStrength: 0.7,
   refinerModel: null,
@@ -43,6 +45,9 @@ const sdxlSlice = createSlice({
     setNegativeStylePromptSDXL: (state, action: PayloadAction<string>) => {
       state.negativeStylePrompt = action.payload;
     },
+    setShouldConcatSDXLStylePrompt: (state, action: PayloadAction<boolean>) => {
+      state.shouldConcatSDXLStylePrompt = action.payload;
+    },
     setShouldUseSDXLRefiner: (state, action: PayloadAction<boolean>) => {
       state.shouldUseSDXLRefiner = action.payload;
     },
@@ -76,6 +81,7 @@ const sdxlSlice = createSlice({
 export const {
   setPositiveStylePromptSDXL,
   setNegativeStylePromptSDXL,
+  setShouldConcatSDXLStylePrompt,
   setShouldUseSDXLRefiner,
   setSDXLImg2ImgDenoisingStrength,
   refinerModelChanged,
diff --git a/invokeai/frontend/web/src/features/system/components/ColorModeButton.tsx b/invokeai/frontend/web/src/features/system/components/ColorModeButton.tsx
deleted file mode 100644
index 9bf748146c..0000000000
--- a/invokeai/frontend/web/src/features/system/components/ColorModeButton.tsx
+++ /dev/null
@@ -1,32 +0,0 @@
-import { useColorMode } from '@chakra-ui/react';
-import IAIIconButton from 'common/components/IAIIconButton';
-import { useTranslation } from 'react-i18next';
-import { FaMoon, FaSun } from 'react-icons/fa';
-
-const ColorModeButton = () => {
-  const { colorMode, toggleColorMode } = useColorMode();
-  const { t } = useTranslation();
-
-  return (
-    <IAIIconButton
-      aria-label={
-        colorMode === 'dark' ? t('common.lightMode') : t('common.darkMode')
-      }
-      tooltip={
-        colorMode === 'dark' ? t('common.lightMode') : t('common.darkMode')
-      }
-      size="sm"
-      icon={
-        colorMode === 'dark' ? (
-          <FaSun fontSize={19} />
-        ) : (
-          <FaMoon fontSize={18} />
-        )
-      }
-      onClick={toggleColorMode}
-      variant="link"
-    />
-  );
-};
-
-export default ColorModeButton;
diff --git a/invokeai/frontend/web/src/features/system/components/LanguagePicker.tsx b/invokeai/frontend/web/src/features/system/components/LanguagePicker.tsx
deleted file mode 100644
index 0a88ac9215..0000000000
--- a/invokeai/frontend/web/src/features/system/components/LanguagePicker.tsx
+++ /dev/null
@@ -1,50 +0,0 @@
-import {
-  IconButton,
-  Menu,
-  MenuButton,
-  MenuItemOption,
-  MenuList,
-  MenuOptionGroup,
-  Tooltip,
-} from '@chakra-ui/react';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { map } from 'lodash-es';
-import { useTranslation } from 'react-i18next';
-import { IoLanguage } from 'react-icons/io5';
-import { LANGUAGES } from '../store/constants';
-import { languageSelector } from '../store/systemSelectors';
-import { languageChanged } from '../store/systemSlice';
-
-export default function LanguagePicker() {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const language = useAppSelector(languageSelector);
-
-  return (
-    <Menu closeOnSelect={false}>
-      <Tooltip label={t('common.languagePickerLabel')} hasArrow>
-        <MenuButton
-          as={IconButton}
-          icon={<IoLanguage />}
-          variant="link"
-          aria-label={t('common.languagePickerLabel')}
-          fontSize={22}
-          minWidth={8}
-        />
-      </Tooltip>
-      <MenuList>
-        <MenuOptionGroup value={language}>
-          {map(LANGUAGES, (languageName, l: keyof typeof LANGUAGES) => (
-            <MenuItemOption
-              key={l}
-              value={l}
-              onClick={() => dispatch(languageChanged(l))}
-            >
-              {languageName}
-            </MenuItemOption>
-          ))}
-        </MenuOptionGroup>
-      </MenuList>
-    </Menu>
-  );
-}
diff --git a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx
index 2deccfa46d..af810f7836 100644
--- a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx
+++ b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx
@@ -10,6 +10,7 @@ import {
   ModalOverlay,
   Text,
   useDisclosure,
+  useColorMode,
 } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { VALID_LOG_LEVELS } from 'app/logging/logger';
@@ -26,6 +27,8 @@ import {
   setShouldConfirmOnDelete,
   shouldAntialiasProgressImageChanged,
   shouldLogToConsoleChanged,
+  shouldUseNSFWCheckerChanged,
+  shouldUseWatermarkerChanged,
 } from 'features/system/store/systemSlice';
 import {
   setShouldShowProgressInViewer,
@@ -42,10 +45,15 @@ import {
 } from 'react';
 import { useTranslation } from 'react-i18next';
 import { LogLevelName } from 'roarr';
+import { useGetAppConfigQuery } from 'services/api/endpoints/appInfo';
 import SettingSwitch from './SettingSwitch';
 import SettingsClearIntermediates from './SettingsClearIntermediates';
 import SettingsSchedulers from './SettingsSchedulers';
 import StyledFlex from './StyledFlex';
+import { useFeatureStatus } from '../../hooks/useFeatureStatus';
+import { LANGUAGES } from '../../store/constants';
+import { languageChanged } from '../../store/systemSlice';
+import { languageSelector } from '../../store/systemSelectors';
 
 const selector = createSelector(
   [stateSelector],
@@ -57,6 +65,8 @@ const selector = createSelector(
       shouldLogToConsole,
       shouldAntialiasProgressImage,
       isNodesEnabled,
+      shouldUseNSFWChecker,
+      shouldUseWatermarker,
     } = system;
 
     const {
@@ -78,6 +88,8 @@ const selector = createSelector(
       shouldAntialiasProgressImage,
       shouldShowAdvancedOptions,
       isNodesEnabled,
+      shouldUseNSFWChecker,
+      shouldUseWatermarker,
     };
   },
   {
@@ -92,6 +104,7 @@ type ConfigOptions = {
   shouldShowAdvancedOptionsSettings: boolean;
   shouldShowClearIntermediates: boolean;
   shouldShowNodesToggle: boolean;
+  shouldShowLocalizationToggle: boolean;
 };
 
 type SettingsModalProps = {
@@ -113,6 +126,8 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
   const shouldShowClearIntermediates =
     config?.shouldShowClearIntermediates ?? true;
   const shouldShowNodesToggle = config?.shouldShowNodesToggle ?? true;
+  const shouldShowLocalizationToggle =
+    config?.shouldShowLocalizationToggle ?? true;
 
   useEffect(() => {
     if (!shouldShowDeveloperSettings) {
@@ -120,6 +135,16 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
     }
   }, [shouldShowDeveloperSettings, dispatch]);
 
+  const { isNSFWCheckerAvailable, isWatermarkerAvailable } =
+    useGetAppConfigQuery(undefined, {
+      selectFromResult: ({ data }) => ({
+        isNSFWCheckerAvailable:
+          data?.nsfw_methods.includes('nsfw_checker') ?? false,
+        isWatermarkerAvailable:
+          data?.watermarking_methods.includes('invisible_watermark') ?? false,
+      }),
+    });
+
   const {
     isOpen: isSettingsModalOpen,
     onOpen: onSettingsModalOpen,
@@ -143,6 +168,8 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
     shouldAntialiasProgressImage,
     shouldShowAdvancedOptions,
     isNodesEnabled,
+    shouldUseNSFWChecker,
+    shouldUseWatermarker,
   } = useAppSelector(selector);
 
   const handleClickResetWebUI = useCallback(() => {
@@ -166,6 +193,13 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
     [dispatch]
   );
 
+  const handleLanguageChanged = useCallback(
+    (l: string) => {
+      dispatch(languageChanged(l as keyof typeof LANGUAGES));
+    },
+    [dispatch]
+  );
+
   const handleLogToConsoleChanged = useCallback(
     (e: ChangeEvent<HTMLInputElement>) => {
       dispatch(shouldLogToConsoleChanged(e.target.checked));
@@ -180,6 +214,12 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
     [dispatch]
   );
 
+  const { colorMode, toggleColorMode } = useColorMode();
+
+  const isLocalizationEnabled =
+    useFeatureStatus('localization').isFeatureEnabled;
+  const language = useAppSelector(languageSelector);
+
   return (
     <>
       {cloneElement(children, {
@@ -221,10 +261,31 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
               <StyledFlex>
                 <Heading size="sm">{t('settings.generation')}</Heading>
                 <SettingsSchedulers />
+                <SettingSwitch
+                  label="Enable NSFW Checker"
+                  isDisabled={!isNSFWCheckerAvailable}
+                  isChecked={shouldUseNSFWChecker}
+                  onChange={(e: ChangeEvent<HTMLInputElement>) =>
+                    dispatch(shouldUseNSFWCheckerChanged(e.target.checked))
+                  }
+                />
+                <SettingSwitch
+                  label="Enable Invisible Watermark"
+                  isDisabled={!isWatermarkerAvailable}
+                  isChecked={shouldUseWatermarker}
+                  onChange={(e: ChangeEvent<HTMLInputElement>) =>
+                    dispatch(shouldUseWatermarkerChanged(e.target.checked))
+                  }
+                />
               </StyledFlex>
 
               <StyledFlex>
                 <Heading size="sm">{t('settings.ui')}</Heading>
+                <SettingSwitch
+                  label={t('common.darkMode')}
+                  isChecked={colorMode === 'dark'}
+                  onChange={toggleColorMode}
+                />
                 <SettingSwitch
                   label={t('settings.useSlidersForAll')}
                   isChecked={shouldUseSliders}
@@ -267,6 +328,18 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
                     onChange={handleToggleNodes}
                   />
                 )}
+                {shouldShowLocalizationToggle && (
+                  <IAIMantineSelect
+                    disabled={!isLocalizationEnabled}
+                    label={t('common.languagePickerLabel')}
+                    value={language}
+                    data={Object.entries(LANGUAGES).map(([value, label]) => ({
+                      value,
+                      label,
+                    }))}
+                    onChange={handleLanguageChanged}
+                  />
+                )}
               </StyledFlex>
 
               {shouldShowDeveloperSettings && (
diff --git a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx
index 6d2e963b6a..0be58a8815 100644
--- a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx
+++ b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx
@@ -10,9 +10,9 @@ import { map } from 'lodash-es';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const data = map(SCHEDULER_LABEL_MAP, (value, label) => ({
-  value,
-  label,
+const data = map(SCHEDULER_LABEL_MAP, (label, name) => ({
+  value: name,
+  label: label,
 })).sort((a, b) => a.label.localeCompare(b.label));
 
 export default function SettingsSchedulers() {
diff --git a/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx b/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx
index 758f03f19a..70f4b12859 100644
--- a/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx
+++ b/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx
@@ -1,28 +1,40 @@
-import { Flex, Spacer } from '@chakra-ui/react';
-import { memo } from 'react';
-import StatusIndicator from './StatusIndicator';
-
-import { Link } from '@chakra-ui/react';
+import {
+  Flex,
+  Menu,
+  MenuButton,
+  MenuGroup,
+  MenuItem,
+  MenuList,
+  Spacer,
+} from '@chakra-ui/react';
 import IAIIconButton from 'common/components/IAIIconButton';
+import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { FaBug, FaDiscord, FaGithub, FaKeyboard } from 'react-icons/fa';
-import { MdSettings } from 'react-icons/md';
+import {
+  FaBars,
+  FaBug,
+  FaCog,
+  FaDiscord,
+  FaGithub,
+  FaKeyboard,
+} from 'react-icons/fa';
+import { menuListMotionProps } from 'theme/components/menu';
 import { useFeatureStatus } from '../hooks/useFeatureStatus';
-import ColorModeButton from './ColorModeButton';
 import HotkeysModal from './HotkeysModal/HotkeysModal';
 import InvokeAILogoComponent from './InvokeAILogoComponent';
-import LanguagePicker from './LanguagePicker';
 import SettingsModal from './SettingsModal/SettingsModal';
+import StatusIndicator from './StatusIndicator';
 
 const SiteHeader = () => {
   const { t } = useTranslation();
 
-  const isLocalizationEnabled =
-    useFeatureStatus('localization').isFeatureEnabled;
   const isBugLinkEnabled = useFeatureStatus('bugLink').isFeatureEnabled;
   const isDiscordLinkEnabled = useFeatureStatus('discordLink').isFeatureEnabled;
   const isGithubLinkEnabled = useFeatureStatus('githubLink').isFeatureEnabled;
 
+  const githubLink = 'http://github.com/invoke-ai/InvokeAI';
+  const discordLink = 'https://discord.gg/ZmtBAhwWhy';
+
   return (
     <Flex
       sx={{
@@ -34,87 +46,61 @@ const SiteHeader = () => {
       <Spacer />
       <StatusIndicator />
 
-      <HotkeysModal>
-        <IAIIconButton
-          aria-label={t('common.hotkeysLabel')}
-          tooltip={t('common.hotkeysLabel')}
-          size="sm"
+      <Menu>
+        <MenuButton
+          as={IAIIconButton}
           variant="link"
-          data-variant="link"
-          fontSize={20}
-          icon={<FaKeyboard />}
+          aria-label={t('accessibility.menu')}
+          icon={<FaBars />}
+          sx={{ boxSize: 8 }}
         />
-      </HotkeysModal>
-
-      {isLocalizationEnabled && <LanguagePicker />}
-
-      {isBugLinkEnabled && (
-        <Link
-          isExternal
-          href="http://github.com/invoke-ai/InvokeAI/issues"
-          marginBottom="-0.25rem"
-        >
-          <IAIIconButton
-            aria-label={t('common.reportBugLabel')}
-            tooltip={t('common.reportBugLabel')}
-            variant="link"
-            data-variant="link"
-            fontSize={20}
-            size="sm"
-            icon={<FaBug />}
-          />
-        </Link>
-      )}
-
-      {isGithubLinkEnabled && (
-        <Link
-          isExternal
-          href="http://github.com/invoke-ai/InvokeAI"
-          marginBottom="-0.25rem"
-        >
-          <IAIIconButton
-            aria-label={t('common.githubLabel')}
-            tooltip={t('common.githubLabel')}
-            variant="link"
-            data-variant="link"
-            fontSize={20}
-            size="sm"
-            icon={<FaGithub />}
-          />
-        </Link>
-      )}
-
-      {isDiscordLinkEnabled && (
-        <Link
-          isExternal
-          href="https://discord.gg/ZmtBAhwWhy"
-          marginBottom="-0.25rem"
-        >
-          <IAIIconButton
-            aria-label={t('common.discordLabel')}
-            tooltip={t('common.discordLabel')}
-            variant="link"
-            data-variant="link"
-            fontSize={20}
-            size="sm"
-            icon={<FaDiscord />}
-          />
-        </Link>
-      )}
-
-      <ColorModeButton />
-
-      <SettingsModal>
-        <IAIIconButton
-          aria-label={t('common.settingsLabel')}
-          tooltip={t('common.settingsLabel')}
-          variant="link"
-          data-variant="link"
-          fontSize={22}
-          size="sm"
-          icon={<MdSettings />}
-        />
-      </SettingsModal>
+        <MenuList motionProps={menuListMotionProps}>
+          <MenuGroup title={t('common.communityLabel')}>
+            {isGithubLinkEnabled && (
+              <MenuItem
+                as="a"
+                href={githubLink}
+                target="_blank"
+                icon={<FaGithub />}
+              >
+                {t('common.githubLabel')}
+              </MenuItem>
+            )}
+            {isBugLinkEnabled && (
+              <MenuItem
+                as="a"
+                href={`${githubLink}/issues`}
+                target="_blank"
+                icon={<FaBug />}
+              >
+                {t('common.reportBugLabel')}
+              </MenuItem>
+            )}
+            {isDiscordLinkEnabled && (
+              <MenuItem
+                as="a"
+                href={discordLink}
+                target="_blank"
+                icon={<FaDiscord />}
+              >
+                {t('common.discordLabel')}
+              </MenuItem>
+            )}
+          </MenuGroup>
+          <MenuGroup title={t('common.settingsLabel')}>
+            <HotkeysModal>
+              <MenuItem as="button" icon={<FaKeyboard />}>
+                {t('common.hotkeysLabel')}
+              </MenuItem>
+            </HotkeysModal>
+            <SettingsModal>
+              <MenuItem as="button" icon={<FaCog />}>
+                {t('common.settingsLabel')}
+              </MenuItem>
+            </SettingsModal>
+          </MenuGroup>
+        </MenuList>
+      </Menu>
     </Flex>
   );
 };
diff --git a/invokeai/frontend/web/src/features/system/components/SiteHeaderMenu.tsx b/invokeai/frontend/web/src/features/system/components/SiteHeaderMenu.tsx
deleted file mode 100644
index ba135be8d1..0000000000
--- a/invokeai/frontend/web/src/features/system/components/SiteHeaderMenu.tsx
+++ /dev/null
@@ -1,111 +0,0 @@
-import { Flex, Link } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import { FaBug, FaDiscord, FaGithub, FaKeyboard } from 'react-icons/fa';
-import { MdSettings } from 'react-icons/md';
-import HotkeysModal from './HotkeysModal/HotkeysModal';
-import LanguagePicker from './LanguagePicker';
-import SettingsModal from './SettingsModal/SettingsModal';
-
-import IAIIconButton from 'common/components/IAIIconButton';
-import { useFeatureStatus } from '../hooks/useFeatureStatus';
-
-const SiteHeaderMenu = () => {
-  const { t } = useTranslation();
-
-  const isLocalizationEnabled =
-    useFeatureStatus('localization').isFeatureEnabled;
-  const isBugLinkEnabled = useFeatureStatus('bugLink').isFeatureEnabled;
-  const isDiscordLinkEnabled = useFeatureStatus('discordLink').isFeatureEnabled;
-  const isGithubLinkEnabled = useFeatureStatus('githubLink').isFeatureEnabled;
-
-  return (
-    <Flex
-      alignItems="center"
-      flexDirection={{ base: 'column', xl: 'row' }}
-      gap={{ base: 4, xl: 1 }}
-    >
-      <HotkeysModal>
-        <IAIIconButton
-          aria-label={t('common.hotkeysLabel')}
-          tooltip={t('common.hotkeysLabel')}
-          size="sm"
-          variant="link"
-          data-variant="link"
-          fontSize={20}
-          icon={<FaKeyboard />}
-        />
-      </HotkeysModal>
-
-      {isLocalizationEnabled && <LanguagePicker />}
-
-      {isBugLinkEnabled && (
-        <Link
-          isExternal
-          href="http://github.com/invoke-ai/InvokeAI/issues"
-          marginBottom="-0.25rem"
-        >
-          <IAIIconButton
-            aria-label={t('common.reportBugLabel')}
-            tooltip={t('common.reportBugLabel')}
-            variant="link"
-            data-variant="link"
-            fontSize={20}
-            size="sm"
-            icon={<FaBug />}
-          />
-        </Link>
-      )}
-
-      {isGithubLinkEnabled && (
-        <Link
-          isExternal
-          href="http://github.com/invoke-ai/InvokeAI"
-          marginBottom="-0.25rem"
-        >
-          <IAIIconButton
-            aria-label={t('common.githubLabel')}
-            tooltip={t('common.githubLabel')}
-            variant="link"
-            data-variant="link"
-            fontSize={20}
-            size="sm"
-            icon={<FaGithub />}
-          />
-        </Link>
-      )}
-
-      {isDiscordLinkEnabled && (
-        <Link
-          isExternal
-          href="https://discord.gg/ZmtBAhwWhy"
-          marginBottom="-0.25rem"
-        >
-          <IAIIconButton
-            aria-label={t('common.discordLabel')}
-            tooltip={t('common.discordLabel')}
-            variant="link"
-            data-variant="link"
-            fontSize={20}
-            size="sm"
-            icon={<FaDiscord />}
-          />
-        </Link>
-      )}
-
-      <SettingsModal>
-        <IAIIconButton
-          aria-label={t('common.settingsLabel')}
-          tooltip={t('common.settingsLabel')}
-          variant="link"
-          data-variant="link"
-          fontSize={22}
-          size="sm"
-          icon={<MdSettings />}
-        />
-      </SettingsModal>
-    </Flex>
-  );
-};
-
-SiteHeaderMenu.displayName = 'SiteHeaderMenu';
-export default SiteHeaderMenu;
diff --git a/invokeai/frontend/web/src/features/system/store/systemSlice.ts b/invokeai/frontend/web/src/features/system/store/systemSlice.ts
index b7a5e606e2..fba980131e 100644
--- a/invokeai/frontend/web/src/features/system/store/systemSlice.ts
+++ b/invokeai/frontend/web/src/features/system/store/systemSlice.ts
@@ -87,6 +87,8 @@ export interface SystemState {
   language: keyof typeof LANGUAGES;
   isUploading: boolean;
   isNodesEnabled: boolean;
+  shouldUseNSFWChecker: boolean;
+  shouldUseWatermarker: boolean;
 }
 
 export const initialSystemState: SystemState = {
@@ -119,6 +121,8 @@ export const initialSystemState: SystemState = {
   language: 'en',
   isUploading: false,
   isNodesEnabled: false,
+  shouldUseNSFWChecker: false,
+  shouldUseWatermarker: false,
 };
 
 export const systemSlice = createSlice({
@@ -194,6 +198,12 @@ export const systemSlice = createSlice({
     setIsNodesEnabled(state, action: PayloadAction<boolean>) {
       state.isNodesEnabled = action.payload;
     },
+    shouldUseNSFWCheckerChanged(state, action: PayloadAction<boolean>) {
+      state.shouldUseNSFWChecker = action.payload;
+    },
+    shouldUseWatermarkerChanged(state, action: PayloadAction<boolean>) {
+      state.shouldUseWatermarker = action.payload;
+    },
   },
   extraReducers(builder) {
     /**
@@ -409,6 +419,8 @@ export const {
   languageChanged,
   progressImageSet,
   setIsNodesEnabled,
+  shouldUseNSFWCheckerChanged,
+  shouldUseWatermarkerChanged,
 } = systemSlice.actions;
 
 export default systemSlice.reducer;
diff --git a/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx b/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
index 26d06adfb3..87ce801ead 100644
--- a/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
@@ -227,7 +227,8 @@ const InvokeTabs = () => {
                 id="gallery"
                 order={3}
                 defaultSize={
-                  galleryMinSizePct > DEFAULT_GALLERY_PCT
+                  galleryMinSizePct > DEFAULT_GALLERY_PCT &&
+                  galleryMinSizePct < 100 // prevent this error https://github.com/bvaughn/react-resizable-panels/blob/main/packages/react-resizable-panels/src/Panel.ts#L96
                     ? galleryMinSizePct
                     : DEFAULT_GALLERY_PCT
                 }
diff --git a/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx b/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx
index 89400c09e5..393cb5bb34 100644
--- a/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx
@@ -1,7 +1,10 @@
 import { Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
+import { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import SDXLImageToImageTabParameters from 'features/sdxl/components/SDXLImageToImageTabParameters';
+import SDXLTextToImageTabParameters from 'features/sdxl/components/SDXLTextToImageTabParameters';
 import InvokeAILogoComponent from 'features/system/components/InvokeAILogoComponent';
 import {
   activeTabNameSelector,
@@ -39,13 +42,23 @@ const ParametersDrawer = () => {
     dispatch(setShouldShowParametersPanel(false));
   };
 
+  const model = useAppSelector((state: RootState) => state.generation.model);
+
   const drawerContent = useMemo(() => {
     if (activeTabName === 'txt2img') {
-      return <TextToImageTabParameters />;
+      return model && model.base_model === 'sdxl' ? (
+        <SDXLTextToImageTabParameters />
+      ) : (
+        <TextToImageTabParameters />
+      );
     }
 
     if (activeTabName === 'img2img') {
-      return <ImageToImageTabParameters />;
+      return model && model.base_model === 'sdxl' ? (
+        <SDXLImageToImageTabParameters />
+      ) : (
+        <ImageToImageTabParameters />
+      );
     }
 
     if (activeTabName === 'unifiedCanvas') {
@@ -53,7 +66,7 @@ const ParametersDrawer = () => {
     }
 
     return null;
-  }, [activeTabName]);
+  }, [activeTabName, model]);
 
   if (shouldPinParametersPanel) {
     return null;
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx
index 9de6a74ec0..b8de2f8308 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx
@@ -2,20 +2,18 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
 import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import ImageToImageTabCoreParameters from './ImageToImageTabCoreParameters';
 
 const ImageToImageTabParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
-      <ParamNegativeConditioning />
+      <ParamPromptArea />
       <ProcessButtons />
       <ImageToImageTabCoreParameters />
       <ParamControlNetCollapse />
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx
index 87eb918564..754c8822d1 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx
@@ -3,20 +3,31 @@ import { Flex, Text } from '@chakra-ui/react';
 import { useState } from 'react';
 import {
   MainModelConfigEntity,
+  DiffusersModelConfigEntity,
+  LoRAModelConfigEntity,
   useGetMainModelsQuery,
+  useGetLoRAModelsQuery,
 } from 'services/api/endpoints/models';
 import CheckpointModelEdit from './ModelManagerPanel/CheckpointModelEdit';
 import DiffusersModelEdit from './ModelManagerPanel/DiffusersModelEdit';
+import LoRAModelEdit from './ModelManagerPanel/LoRAModelEdit';
 import ModelList from './ModelManagerPanel/ModelList';
 import { ALL_BASE_MODELS } from 'services/api/constants';
 
 export default function ModelManagerPanel() {
   const [selectedModelId, setSelectedModelId] = useState<string>();
-  const { model } = useGetMainModelsQuery(ALL_BASE_MODELS, {
+  const { mainModel } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      model: selectedModelId ? data?.entities[selectedModelId] : undefined,
+      mainModel: selectedModelId ? data?.entities[selectedModelId] : undefined,
     }),
   });
+  const { loraModel } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: ({ data }) => ({
+      loraModel: selectedModelId ? data?.entities[selectedModelId] : undefined,
+    }),
+  });
+
+  const model = mainModel ? mainModel : loraModel;
 
   return (
     <Flex sx={{ gap: 8, w: 'full', h: 'full' }}>
@@ -30,7 +41,7 @@ export default function ModelManagerPanel() {
 }
 
 type ModelEditProps = {
-  model: MainModelConfigEntity | undefined;
+  model: MainModelConfigEntity | LoRAModelConfigEntity | undefined;
 };
 
 const ModelEdit = (props: ModelEditProps) => {
@@ -41,7 +52,16 @@ const ModelEdit = (props: ModelEditProps) => {
   }
 
   if (model?.model_format === 'diffusers') {
-    return <DiffusersModelEdit key={model.id} model={model} />;
+    return (
+      <DiffusersModelEdit
+        key={model.id}
+        model={model as DiffusersModelConfigEntity}
+      />
+    );
+  }
+
+  if (model?.model_type === 'lora') {
+    return <LoRAModelEdit key={model.id} model={model} />;
   }
 
   return (
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx
index cb297d3afe..6c3ebf2530 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx
@@ -1,6 +1,5 @@
 import { Badge, Divider, Flex, Text } from '@chakra-ui/react';
 import { useForm } from '@mantine/form';
-import { makeToast } from 'features/system/util/makeToast';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
 import IAIMantineTextInput from 'common/components/IAIMantineInput';
@@ -8,6 +7,7 @@ import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { useCallback, useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
@@ -115,7 +115,7 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
             {MODEL_TYPE_MAP[model.base_model]} Model
           </Text>
         </Flex>
-        {!['sdxl', 'sdxl-refiner'].includes(model.base_model) ? (
+        {![''].includes(model.base_model) ? (
           <ModelConvert model={model} />
         ) : (
           <Badge
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/LoRAModelEdit.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/LoRAModelEdit.tsx
new file mode 100644
index 0000000000..c87550c7d1
--- /dev/null
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/LoRAModelEdit.tsx
@@ -0,0 +1,137 @@
+import { Divider, Flex, Text } from '@chakra-ui/react';
+import { useForm } from '@mantine/form';
+import { makeToast } from 'features/system/util/makeToast';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIMantineTextInput from 'common/components/IAIMantineInput';
+import { selectIsBusy } from 'features/system/store/systemSelectors';
+import { addToast } from 'features/system/store/systemSlice';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import {
+  LORA_MODEL_FORMAT_MAP,
+  MODEL_TYPE_MAP,
+} from 'features/parameters/types/constants';
+import {
+  LoRAModelConfigEntity,
+  useUpdateLoRAModelsMutation,
+} from 'services/api/endpoints/models';
+import { LoRAModelConfig } from 'services/api/types';
+import BaseModelSelect from '../shared/BaseModelSelect';
+
+type LoRAModelEditProps = {
+  model: LoRAModelConfigEntity;
+};
+
+export default function LoRAModelEdit(props: LoRAModelEditProps) {
+  const isBusy = useAppSelector(selectIsBusy);
+
+  const { model } = props;
+
+  const [updateLoRAModel, { isLoading }] = useUpdateLoRAModelsMutation();
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const loraEditForm = useForm<LoRAModelConfig>({
+    initialValues: {
+      model_name: model.model_name ? model.model_name : '',
+      base_model: model.base_model,
+      model_type: 'lora',
+      path: model.path ? model.path : '',
+      description: model.description ? model.description : '',
+      model_format: model.model_format,
+    },
+    validate: {
+      path: (value) =>
+        value.trim().length === 0 ? 'Must provide a path' : null,
+    },
+  });
+
+  const editModelFormSubmitHandler = useCallback(
+    (values: LoRAModelConfig) => {
+      const responseBody = {
+        base_model: model.base_model,
+        model_name: model.model_name,
+        body: values,
+      };
+
+      updateLoRAModel(responseBody)
+        .unwrap()
+        .then((payload) => {
+          loraEditForm.setValues(payload as LoRAModelConfig);
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelUpdated'),
+                status: 'success',
+              })
+            )
+          );
+        })
+        .catch((_) => {
+          loraEditForm.reset();
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelUpdateFailed'),
+                status: 'error',
+              })
+            )
+          );
+        });
+    },
+    [
+      dispatch,
+      loraEditForm,
+      model.base_model,
+      model.model_name,
+      t,
+      updateLoRAModel,
+    ]
+  );
+
+  return (
+    <Flex flexDirection="column" rowGap={4} width="100%">
+      <Flex flexDirection="column">
+        <Text fontSize="lg" fontWeight="bold">
+          {model.model_name}
+        </Text>
+        <Text fontSize="sm" color="base.400">
+          {MODEL_TYPE_MAP[model.base_model]} Model ⋅{' '}
+          {LORA_MODEL_FORMAT_MAP[model.model_format]} format
+        </Text>
+      </Flex>
+      <Divider />
+
+      <form
+        onSubmit={loraEditForm.onSubmit((values) =>
+          editModelFormSubmitHandler(values)
+        )}
+      >
+        <Flex flexDirection="column" overflowY="scroll" gap={4}>
+          <IAIMantineTextInput
+            label={t('modelManager.name')}
+            {...loraEditForm.getInputProps('model_name')}
+          />
+          <IAIMantineTextInput
+            label={t('modelManager.description')}
+            {...loraEditForm.getInputProps('description')}
+          />
+          <BaseModelSelect {...loraEditForm.getInputProps('base_model')} />
+          <IAIMantineTextInput
+            label={t('modelManager.modelLocation')}
+            {...loraEditForm.getInputProps('path')}
+          />
+          <IAIButton
+            type="submit"
+            isDisabled={isBusy || isLoading}
+            isLoading={isLoading}
+          >
+            {t('modelManager.updateModel')}
+          </IAIButton>
+        </Flex>
+      </form>
+    </Flex>
+  );
+}
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx
index f3d0eae495..e4d8a7b15a 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx
@@ -9,6 +9,8 @@ import { useTranslation } from 'react-i18next';
 import {
   MainModelConfigEntity,
   useGetMainModelsQuery,
+  useGetLoRAModelsQuery,
+  LoRAModelConfigEntity,
 } from 'services/api/endpoints/models';
 import ModelListItem from './ModelListItem';
 import { ALL_BASE_MODELS } from 'services/api/constants';
@@ -20,22 +22,42 @@ type ModelListProps = {
 
 type ModelFormat = 'images' | 'checkpoint' | 'diffusers';
 
+type ModelType = 'main' | 'lora';
+
+type CombinedModelFormat = ModelFormat | 'lora';
+
 const ModelList = (props: ModelListProps) => {
   const { selectedModelId, setSelectedModelId } = props;
   const { t } = useTranslation();
   const [nameFilter, setNameFilter] = useState<string>('');
   const [modelFormatFilter, setModelFormatFilter] =
-    useState<ModelFormat>('images');
+    useState<CombinedModelFormat>('images');
 
   const { filteredDiffusersModels } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      filteredDiffusersModels: modelsFilter(data, 'diffusers', nameFilter),
+      filteredDiffusersModels: modelsFilter(
+        data,
+        'main',
+        'diffusers',
+        nameFilter
+      ),
     }),
   });
 
   const { filteredCheckpointModels } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      filteredCheckpointModels: modelsFilter(data, 'checkpoint', nameFilter),
+      filteredCheckpointModels: modelsFilter(
+        data,
+        'main',
+        'checkpoint',
+        nameFilter
+      ),
+    }),
+  });
+
+  const { filteredLoraModels } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: ({ data }) => ({
+      filteredLoraModels: modelsFilter(data, 'lora', undefined, nameFilter),
     }),
   });
 
@@ -68,6 +90,13 @@ const ModelList = (props: ModelListProps) => {
           >
             {t('modelManager.checkpointModels')}
           </IAIButton>
+          <IAIButton
+            size="sm"
+            onClick={() => setModelFormatFilter('lora')}
+            isChecked={modelFormatFilter === 'lora'}
+          >
+            {t('modelManager.loraModels')}
+          </IAIButton>
         </ButtonGroup>
 
         <IAIInput
@@ -118,6 +147,24 @@ const ModelList = (props: ModelListProps) => {
                 </Flex>
               </StyledModelContainer>
             )}
+          {['images', 'lora'].includes(modelFormatFilter) &&
+            filteredLoraModels.length > 0 && (
+              <StyledModelContainer>
+                <Flex sx={{ gap: 2, flexDir: 'column' }}>
+                  <Text variant="subtext" fontSize="sm">
+                    LoRAs
+                  </Text>
+                  {filteredLoraModels.map((model) => (
+                    <ModelListItem
+                      key={model.id}
+                      model={model}
+                      isSelected={selectedModelId === model.id}
+                      setSelectedModelId={setSelectedModelId}
+                    />
+                  ))}
+                </Flex>
+              </StyledModelContainer>
+            )}
         </Flex>
       </Flex>
     </Flex>
@@ -126,12 +173,13 @@ const ModelList = (props: ModelListProps) => {
 
 export default ModelList;
 
-const modelsFilter = (
-  data: EntityState<MainModelConfigEntity> | undefined,
-  model_format: ModelFormat,
+const modelsFilter = <T extends MainModelConfigEntity | LoRAModelConfigEntity>(
+  data: EntityState<T> | undefined,
+  model_type: ModelType,
+  model_format: ModelFormat | undefined,
   nameFilter: string
 ) => {
-  const filteredModels: MainModelConfigEntity[] = [];
+  const filteredModels: T[] = [];
   forEach(data?.entities, (model) => {
     if (!model) {
       return;
@@ -141,9 +189,11 @@ const modelsFilter = (
       .toLowerCase()
       .includes(nameFilter.toLowerCase());
 
-    const matchesFormat = model.model_format === model_format;
+    const matchesFormat =
+      model_format === undefined || model.model_format === model_format;
+    const matchesType = model.model_type === model_type;
 
-    if (matchesFilter && matchesFormat) {
+    if (matchesFilter && matchesFormat && matchesType) {
       filteredModels.push(model);
     }
   });
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelListItem.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelListItem.tsx
index 7f4fb0c736..9380eed688 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelListItem.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelListItem.tsx
@@ -9,29 +9,26 @@ import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
+import { MODEL_TYPE_SHORT_MAP } from 'features/parameters/types/constants';
 import {
   MainModelConfigEntity,
+  LoRAModelConfigEntity,
   useDeleteMainModelsMutation,
+  useDeleteLoRAModelsMutation,
 } from 'services/api/endpoints/models';
 
 type ModelListItemProps = {
-  model: MainModelConfigEntity;
+  model: MainModelConfigEntity | LoRAModelConfigEntity;
   isSelected: boolean;
   setSelectedModelId: (v: string | undefined) => void;
 };
 
-const modelBaseTypeMap = {
-  'sd-1': 'SD1',
-  'sd-2': 'SD2',
-  sdxl: 'SDXL',
-  'sdxl-refiner': 'SDXLR',
-};
-
 export default function ModelListItem(props: ModelListItemProps) {
   const isBusy = useAppSelector(selectIsBusy);
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const [deleteMainModel] = useDeleteMainModelsMutation();
+  const [deleteLoRAModel] = useDeleteLoRAModelsMutation();
 
   const { model, isSelected, setSelectedModelId } = props;
 
@@ -40,7 +37,10 @@ export default function ModelListItem(props: ModelListItemProps) {
   }, [model.id, setSelectedModelId]);
 
   const handleModelDelete = useCallback(() => {
-    deleteMainModel(model)
+    const method = { main: deleteMainModel, lora: deleteLoRAModel }[
+      model.model_type
+    ];
+    method(model)
       .unwrap()
       .then((_) => {
         dispatch(
@@ -60,14 +60,21 @@ export default function ModelListItem(props: ModelListItemProps) {
                 title: `${t('modelManager.modelDeleteFailed')}: ${
                   model.model_name
                 }`,
-                status: 'success',
+                status: 'error',
               })
             )
           );
         }
       });
     setSelectedModelId(undefined);
-  }, [deleteMainModel, model, setSelectedModelId, dispatch, t]);
+  }, [
+    deleteMainModel,
+    deleteLoRAModel,
+    model,
+    setSelectedModelId,
+    dispatch,
+    t,
+  ]);
 
   return (
     <Flex sx={{ gap: 2, alignItems: 'center', w: 'full' }}>
@@ -100,8 +107,8 @@ export default function ModelListItem(props: ModelListItemProps) {
         <Flex gap={4} alignItems="center">
           <Badge minWidth={14} p={0.5} fontSize="sm" variant="solid">
             {
-              modelBaseTypeMap[
-                model.base_model as keyof typeof modelBaseTypeMap
+              MODEL_TYPE_SHORT_MAP[
+                model.base_model as keyof typeof MODEL_TYPE_SHORT_MAP
               ]
             }
           </Badge>
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx
index 8c3add3d62..7e6e02ff5a 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx
@@ -1,7 +1,7 @@
 import { Flex } from '@chakra-ui/react';
 import { RootState } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
-import TextToImageSDXLTabParameters from 'features/sdxl/components/SDXLTextToImageTabParameters';
+import SDXLTextToImageTabParameters from 'features/sdxl/components/SDXLTextToImageTabParameters';
 import { memo } from 'react';
 import ParametersPinnedWrapper from '../../ParametersPinnedWrapper';
 import TextToImageTabMain from './TextToImageTabMain';
@@ -13,7 +13,7 @@ const TextToImageTab = () => {
     <Flex sx={{ gap: 4, w: 'full', h: 'full' }}>
       <ParametersPinnedWrapper>
         {model && model.base_model === 'sdxl' ? (
-          <TextToImageSDXLTabParameters />
+          <SDXLTextToImageTabParameters />
         ) : (
           <TextToImageTabParameters />
         )}
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx
index 8b6fb6f46c..75fa063e17 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx
@@ -2,20 +2,18 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
 import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
+import ParamPromptArea from '../../../../parameters/components/Parameters/Prompt/ParamPromptArea';
 import TextToImageTabCoreParameters from './TextToImageTabCoreParameters';
 
 const TextToImageTabParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
-      <ParamNegativeConditioning />
+      <ParamPromptArea />
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamControlNetCollapse />
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
index 95270c6bbc..de7ce3b084 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
@@ -4,18 +4,16 @@ import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Adv
 import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
 import ParamSeamCorrectionCollapse from 'features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
 
 const UnifiedCanvasParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
-      <ParamNegativeConditioning />
+      <ParamPromptArea />
       <ProcessButtons />
       <UnifiedCanvasCoreParameters />
       <ParamControlNetCollapse />
diff --git a/invokeai/frontend/web/src/index.ts b/invokeai/frontend/web/src/index.ts
index add4999b6d..57dbeab0e5 100644
--- a/invokeai/frontend/web/src/index.ts
+++ b/invokeai/frontend/web/src/index.ts
@@ -3,7 +3,6 @@ export type { PartialAppConfig } from './app/types/invokeai';
 export { default as IAIIconButton } from './common/components/IAIIconButton';
 export { default as IAIPopover } from './common/components/IAIPopover';
 export { default as ParamMainModelSelect } from './features/parameters/components/Parameters/MainModel/ParamMainModelSelect';
-export { default as ColorModeButton } from './features/system/components/ColorModeButton';
 export { default as InvokeAiLogoComponent } from './features/system/components/InvokeAILogoComponent';
 export { default as SettingsModal } from './features/system/components/SettingsModal/SettingsModal';
 export { default as StatusIndicator } from './features/system/components/StatusIndicator';
diff --git a/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts b/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts
index f76b56761c..2d3537998d 100644
--- a/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts
+++ b/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts
@@ -8,6 +8,7 @@ export const appInfoApi = api.injectEndpoints({
         url: `app/version`,
         method: 'GET',
       }),
+      providesTags: ['AppVersion'],
       keepUnusedDataFor: 86400000, // 1 day
     }),
     getAppConfig: build.query<AppConfig, void>({
@@ -15,6 +16,7 @@ export const appInfoApi = api.injectEndpoints({
         url: `app/config`,
         method: 'GET',
       }),
+      providesTags: ['AppConfig'],
       keepUnusedDataFor: 86400000, // 1 day
     }),
   }),
diff --git a/invokeai/frontend/web/src/services/api/endpoints/models.ts b/invokeai/frontend/web/src/services/api/endpoints/models.ts
index 3d0013a62c..9cdded4f85 100644
--- a/invokeai/frontend/web/src/services/api/endpoints/models.ts
+++ b/invokeai/frontend/web/src/services/api/endpoints/models.ts
@@ -52,9 +52,17 @@ type UpdateMainModelArg = {
   body: MainModelConfig;
 };
 
+type UpdateLoRAModelArg = {
+  base_model: BaseModelType;
+  model_name: string;
+  body: LoRAModelConfig;
+};
+
 type UpdateMainModelResponse =
   paths['/api/v1/models/{base_model}/{model_type}/{model_name}']['patch']['responses']['200']['content']['application/json'];
 
+type UpdateLoRAModelResponse = UpdateMainModelResponse;
+
 type DeleteMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
@@ -62,6 +70,10 @@ type DeleteMainModelArg = {
 
 type DeleteMainModelResponse = void;
 
+type DeleteLoRAModelArg = DeleteMainModelArg;
+
+type DeleteLoRAModelResponse = void;
+
 type ConvertMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
@@ -320,6 +332,31 @@ export const modelsApi = api.injectEndpoints({
         );
       },
     }),
+    updateLoRAModels: build.mutation<
+      UpdateLoRAModelResponse,
+      UpdateLoRAModelArg
+    >({
+      query: ({ base_model, model_name, body }) => {
+        return {
+          url: `models/${base_model}/lora/${model_name}`,
+          method: 'PATCH',
+          body: body,
+        };
+      },
+      invalidatesTags: [{ type: 'LoRAModel', id: LIST_TAG }],
+    }),
+    deleteLoRAModels: build.mutation<
+      DeleteLoRAModelResponse,
+      DeleteLoRAModelArg
+    >({
+      query: ({ base_model, model_name }) => {
+        return {
+          url: `models/${base_model}/lora/${model_name}`,
+          method: 'DELETE',
+        };
+      },
+      invalidatesTags: [{ type: 'LoRAModel', id: LIST_TAG }],
+    }),
     getControlNetModels: build.query<
       EntityState<ControlNetModelConfigEntity>,
       void
@@ -467,6 +504,8 @@ export const {
   useAddMainModelsMutation,
   useConvertMainModelsMutation,
   useMergeMainModelsMutation,
+  useDeleteLoRAModelsMutation,
+  useUpdateLoRAModelsMutation,
   useSyncModelsMutation,
   useGetModelsInFolderQuery,
   useGetCheckpointConfigsQuery,
diff --git a/invokeai/frontend/web/src/services/api/schema.d.ts b/invokeai/frontend/web/src/services/api/schema.d.ts
index 5cbf1d9c00..dcffa4572b 100644
--- a/invokeai/frontend/web/src/services/api/schema.d.ts
+++ b/invokeai/frontend/web/src/services/api/schema.d.ts
@@ -318,6 +318,21 @@ export type components = {
        * @description List of available infill methods
        */
       infill_methods: (string)[];
+      /**
+       * Upscaling Methods 
+       * @description List of upscaling methods
+       */
+      upscaling_methods: (components["schemas"]["Upscaler"])[];
+      /**
+       * Nsfw Methods 
+       * @description List of NSFW checking methods
+       */
+      nsfw_methods: (string)[];
+      /**
+       * Watermarking Methods 
+       * @description List of invisible watermark methods
+       */
+      watermarking_methods: (string)[];
     };
     /**
      * AppVersion 
@@ -886,8 +901,8 @@ export type components = {
        */
       resize_mode?: "just_resize" | "crop_resize" | "fill_resize" | "just_resize_simple";
     };
-    /** ControlNetModelConfig */
-    ControlNetModelConfig: {
+    /** ControlNetModelCheckpointConfig */
+    ControlNetModelCheckpointConfig: {
       /** Model Name */
       model_name: string;
       base_model: components["schemas"]["BaseModelType"];
@@ -900,7 +915,34 @@ export type components = {
       path: string;
       /** Description */
       description?: string;
-      model_format: components["schemas"]["ControlNetModelFormat"];
+      /**
+       * Model Format 
+       * @enum {string}
+       */
+      model_format: "checkpoint";
+      error?: components["schemas"]["ModelError"];
+      /** Config */
+      config: string;
+    };
+    /** ControlNetModelDiffusersConfig */
+    ControlNetModelDiffusersConfig: {
+      /** Model Name */
+      model_name: string;
+      base_model: components["schemas"]["BaseModelType"];
+      /**
+       * Model Type 
+       * @enum {string}
+       */
+      model_type: "controlnet";
+      /** Path */
+      path: string;
+      /** Description */
+      description?: string;
+      /**
+       * Model Format 
+       * @enum {string}
+       */
+      model_format: "diffusers";
       error?: components["schemas"]["ModelError"];
     };
     /**
@@ -916,12 +958,6 @@ export type components = {
       /** @description Base model */
       base_model: components["schemas"]["BaseModelType"];
     };
-    /**
-     * ControlNetModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    ControlNetModelFormat: "checkpoint" | "diffusers";
     /**
      * ControlOutput 
      * @description node output for ControlNet info
@@ -1345,7 +1381,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1388,7 +1424,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -1935,6 +1971,39 @@ export type components = {
        */
       image2?: components["schemas"]["ImageField"];
     };
+    /**
+     * ImageNSFWBlurInvocation 
+     * @description Add blur to NSFW-flagged images
+     */
+    ImageNSFWBlurInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default img_nsfw 
+       * @enum {string}
+       */
+      type?: "img_nsfw";
+      /**
+       * Image 
+       * @description The image to check
+       */
+      image?: components["schemas"]["ImageField"];
+      /**
+       * Metadata 
+       * @description Optional core metadata to be written to the image
+       */
+      metadata?: components["schemas"]["CoreMetadata"];
+    };
     /**
      * ImageOutput 
      * @description Base class for invocations that output an image
@@ -2215,6 +2284,45 @@ export type components = {
        */
       thumbnail_url: string;
     };
+    /**
+     * ImageWatermarkInvocation 
+     * @description Add an invisible watermark to an image
+     */
+    ImageWatermarkInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default img_watermark 
+       * @enum {string}
+       */
+      type?: "img_watermark";
+      /**
+       * Image 
+       * @description The image to check
+       */
+      image?: components["schemas"]["ImageField"];
+      /**
+       * Text 
+       * @description Watermark text 
+       * @default InvokeAI
+       */
+      text?: string;
+      /**
+       * Metadata 
+       * @description Optional core metadata to be written to the image
+       */
+      metadata?: components["schemas"]["CoreMetadata"];
+    };
     /**
      * InfillColorInvocation 
      * @description Infills transparent areas of an image with a solid color
@@ -2644,7 +2752,7 @@ export type components = {
       vae?: components["schemas"]["VaeField"];
       /**
        * Tiled 
-       * @description Decode latents by overlaping tiles(less memory consumption) 
+       * @description Decode latents by overlapping tiles(less memory consumption) 
        * @default false
        */
       tiled?: boolean;
@@ -3520,7 +3628,7 @@ export type components = {
     /** ModelsList */
     ModelsList: {
       /** Models */
-      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"])[];
+      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"])[];
     };
     /**
      * MultiplyInvocation 
@@ -5309,6 +5417,19 @@ export type components = {
        */
       loras: (components["schemas"]["LoraInfo"])[];
     };
+    /** Upscaler */
+    Upscaler: {
+      /**
+       * Upscaling Method 
+       * @description Name of upscaling method
+       */
+      upscaling_method: string;
+      /**
+       * Upscaling Models 
+       * @description List of upscaling models for this method
+       */
+      upscaling_models: (string)[];
+    };
     /**
      * VAEModelField 
      * @description Vae model field
@@ -5435,12 +5556,6 @@ export type components = {
        */
       image?: components["schemas"]["ImageField"];
     };
-    /**
-     * StableDiffusion1ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusion2ModelFormat 
      * @description An enumeration. 
@@ -5453,6 +5568,18 @@ export type components = {
      * @enum {string}
      */
     StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
+    /**
+     * ControlNetModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    ControlNetModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -5563,7 +5690,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -5600,7 +5727,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -5864,14 +5991,14 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
       };
     };
     responses: {
       /** @description The model was updated successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Bad request */
@@ -5902,7 +6029,7 @@ export type operations = {
       /** @description The model imported successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description The model could not be found */
@@ -5928,14 +6055,14 @@ export type operations = {
   add_model: {
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
       };
     };
     responses: {
       /** @description The model added successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description The model could not be found */
@@ -5975,7 +6102,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Bad request */
@@ -6064,7 +6191,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Incompatible models */
diff --git a/invokeai/frontend/web/src/services/api/types.d.ts b/invokeai/frontend/web/src/services/api/types.d.ts
index 3e945691f1..17f20ca1ac 100644
--- a/invokeai/frontend/web/src/services/api/types.d.ts
+++ b/invokeai/frontend/web/src/services/api/types.d.ts
@@ -42,8 +42,13 @@ export type ControlField = components['schemas']['ControlField'];
 // Model Configs
 export type LoRAModelConfig = components['schemas']['LoRAModelConfig'];
 export type VaeModelConfig = components['schemas']['VaeModelConfig'];
+export type ControlNetModelCheckpointConfig =
+  components['schemas']['ControlNetModelCheckpointConfig'];
+export type ControlNetModelDiffusersConfig =
+  components['schemas']['ControlNetModelDiffusersConfig'];
 export type ControlNetModelConfig =
-  components['schemas']['ControlNetModelConfig'];
+  | ControlNetModelCheckpointConfig
+  | ControlNetModelDiffusersConfig;
 export type TextualInversionModelConfig =
   components['schemas']['TextualInversionModelConfig'];
 export type DiffusersModelConfig =
@@ -134,6 +139,12 @@ export type ESRGANInvocation = TypeReq<
 export type DivideInvocation = TypeReq<
   components['schemas']['DivideInvocation']
 >;
+export type ImageNSFWBlurInvocation = TypeReq<
+  components['schemas']['ImageNSFWBlurInvocation']
+>;
+export type ImageWatermarkInvocation = TypeReq<
+  components['schemas']['ImageWatermarkInvocation']
+>;
 
 // ControlNet Nodes
 export type ControlNetInvocation = TypeReq<
diff --git a/invokeai/frontend/web/src/theme/components/menu.ts b/invokeai/frontend/web/src/theme/components/menu.ts
index 563c5ce6a9..4ab323bdb5 100644
--- a/invokeai/frontend/web/src/theme/components/menu.ts
+++ b/invokeai/frontend/web/src/theme/components/menu.ts
@@ -33,12 +33,16 @@ const invokeAI = definePartsStyle((props) => ({
     bg: mode('base.200', 'base.800')(props),
     _hover: {
       bg: mode('base.300', 'base.700')(props),
+      svg: {
+        opacity: 1,
+      },
     },
     _focus: {
       bg: mode('base.400', 'base.600')(props),
     },
     svg: {
-      opacity: 0.5,
+      opacity: 0.7,
+      fontSize: 14,
     },
   },
 }));
diff --git a/invokeai/frontend/web/src/theme/components/textarea.ts b/invokeai/frontend/web/src/theme/components/textarea.ts
index b737cf5e57..8dd59c18e0 100644
--- a/invokeai/frontend/web/src/theme/components/textarea.ts
+++ b/invokeai/frontend/web/src/theme/components/textarea.ts
@@ -13,6 +13,15 @@ const invokeAI = defineStyle((props) => ({
       var(--invokeai-colors-base-200) 70%,
       var(--invokeai-colors-base-200) 100%)`,
   },
+  _disabled: {
+    '::-webkit-resizer': {
+      backgroundImage: `linear-gradient(135deg,
+        var(--invokeai-colors-base-50) 0%,
+        var(--invokeai-colors-base-50) 70%,
+        var(--invokeai-colors-base-200) 70%,
+        var(--invokeai-colors-base-200) 100%)`,
+    },
+  },
   _dark: {
     '::-webkit-resizer': {
       backgroundImage: `linear-gradient(135deg,
@@ -21,6 +30,15 @@ const invokeAI = defineStyle((props) => ({
         var(--invokeai-colors-base-800) 70%,
         var(--invokeai-colors-base-800) 100%)`,
     },
+    _disabled: {
+      '::-webkit-resizer': {
+        backgroundImage: `linear-gradient(135deg,
+          var(--invokeai-colors-base-900) 0%,
+          var(--invokeai-colors-base-900) 70%,
+          var(--invokeai-colors-base-800) 70%,
+          var(--invokeai-colors-base-800) 100%)`,
+      },
+    },
   },
 }));
 
diff --git a/invokeai/frontend/web/yarn.lock b/invokeai/frontend/web/yarn.lock
index 35208003d0..17517ce4e0 100644
--- a/invokeai/frontend/web/yarn.lock
+++ b/invokeai/frontend/web/yarn.lock
@@ -71,56 +71,51 @@
     "@babel/helper-validator-identifier" "^7.22.5"
     to-fast-properties "^2.0.0"
 
-"@chakra-ui/accordion@2.2.0":
+"@chakra-ui/accordion@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.3.0.tgz#2c85fd2d2734b176f019f8db9f4e075007b4e1fb"
+  integrity sha512-A4TkRw3Jnt+Fam6dSSJ62rskdrvjF3JGctYcfXlojfFIpHPuIw4pDwfZgNAxlaxWkcj0e7JJKlQ88dnZW+QfFg==
+  dependencies:
+    "@chakra-ui/descendant" "3.1.0"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/shared-utils" "2.0.5"
+    "@chakra-ui/transition" "2.1.0"
+
+"@chakra-ui/alert@2.2.0":
   version "2.2.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.2.0.tgz#a38ed8e7d0a7ccc6910282f913c42cf6deea7215"
-  integrity sha512-2IK1iLzTZ22u8GKPPPn65mqJdZidn4AvkgAbv17ISdKA07VHJ8jSd4QF1T5iCXjKfZ0XaXozmhP4kDhjwF2IbQ==
+  resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.2.0.tgz#b59eadca4f083674dfcd43651b6c47b953c2c984"
+  integrity sha512-De+BT88iYOu3Con7MxQeICb1SwgAdVdgpHIYjTh3qvGlNXAQjs81rhG0fONXvwW1FIYletvr9DY2Tlg8xJe7tQ==
   dependencies:
-    "@chakra-ui/descendant" "3.0.14"
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/spinner" "2.1.0"
 
-"@chakra-ui/alert@2.1.0":
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.1.0.tgz#7a234ac6426231b39243088648455cbcf1cbdf24"
-  integrity sha512-OcfHwoXI5VrmM+tHJTHT62Bx6TfyfCxSa0PWUOueJzSyhlUOKBND5we6UtrOB7D0jwX45qKKEDJOLG5yCG21jQ==
+"@chakra-ui/anatomy@2.2.0", "@chakra-ui/anatomy@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.2.0.tgz#788229829f853dcd03314cd7ddd4f19f056ec24e"
+  integrity sha512-cD8Ms5C8+dFda0LrORMdxiFhAZwOIY1BSlCadz6/mHUIgNdQy13AHPrXiq6qWdMslqVHq10k5zH7xMPLt6kjFg==
+
+"@chakra-ui/avatar@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/avatar/-/avatar-2.3.0.tgz#f018a2714d1e3ba5970bcf66558887925fdfccf4"
+  integrity sha512-8gKSyLfygnaotbJbDMHDiJoF38OHXUYVme4gGxZ1fLnQEdPVEaIWfH+NndIjOM0z8S+YEFnT9KyGMUtvPrBk3g==
   dependencies:
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/spinner" "2.0.13"
-
-"@chakra-ui/anatomy@2.1.2":
-  version "2.1.2"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.1.2.tgz#ea66b1841e7195da08ddc862daaa3f3e56e565f5"
-  integrity sha512-pKfOS/mztc4sUXHNc8ypJ1gPWSolWT770jrgVRfolVbYlki8y5Y+As996zMF6k5lewTu6j9DQequ7Cc9a69IVQ==
-
-"@chakra-ui/anatomy@^2.1.1":
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.1.1.tgz#819a1458ff727157e5500a69fc26bfea6e944495"
-  integrity sha512-LUHAoqJAgxAqmyckG5bUpBrfEo1FleEyY+1A8hkWciy58gZ+h3GoY9oBpHcdo7XdHPpy3G+3hieK/7i9NLwxAw==
-
-"@chakra-ui/avatar@2.2.11":
-  version "2.2.11"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/avatar/-/avatar-2.2.11.tgz#1e5ded963ab3209fe1d16bba21f0aec616be56da"
-  integrity sha512-CJFkoWvlCTDJTUBrKA/aVyG5Zz6TBEIVmmsJtqC6VcQuVDTxkWod8ruXnjb0LT2DUveL7xR5qZM9a5IXcsH3zg==
-  dependencies:
-    "@chakra-ui/image" "2.0.16"
+    "@chakra-ui/image" "2.1.0"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/breadcrumb@2.1.5":
-  version "2.1.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.1.5.tgz#a43b22cc8005291a615696a8c88efc37064562f3"
-  integrity sha512-p3eQQrHQBkRB69xOmNyBJqEdfCrMt+e0eOH+Pm/DjFWfIVIbnIaFbmDCeWClqlLa21Ypc6h1hR9jEmvg8kmOog==
+"@chakra-ui/breadcrumb@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.2.0.tgz#751bc48498f3c403f97b5d9aae528ebfd405ef48"
+  integrity sha512-4cWCG24flYBxjruRi4RJREWTGF74L/KzI2CognAW/d/zWR0CjiScuJhf37Am3LFbCySP6WSoyBOtTIoTA4yLEA==
   dependencies:
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/breakpoint-utils@2.0.8":
@@ -130,39 +125,39 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/button@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/button/-/button-2.0.18.tgz#c13d2e404e22a9873ba5373fde494bedafe32fdd"
-  integrity sha512-E3c99+lOm6ou4nQVOTLkG+IdOPMjsQK+Qe7VyP8A/xeAMFONuibrWPRPpprr4ZkB4kEoLMfNuyH2+aEza3ScUA==
+"@chakra-ui/button@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/button/-/button-2.1.0.tgz#623ed32cc92fc8e52492923e9924791fc6f25447"
+  integrity sha512-95CplwlRKmmUXkdEp/21VkEWgnwcx2TOBG6NfYlsuLBDHSLlo5FKIiE2oSi4zXc4TLcopGcWPNcm/NDaSC5pvA==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/spinner" "2.0.13"
+    "@chakra-ui/spinner" "2.1.0"
 
-"@chakra-ui/card@2.1.6":
-  version "2.1.6"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/card/-/card-2.1.6.tgz#27176bdee363ecab7d563c4997c4b2fe9e835ecc"
-  integrity sha512-fFd/WAdRNVY/WOSQv4skpy0WeVhhI0f7dTY1Sm0jVl0KLmuP/GnpsWtKtqWjNcV00K963EXDyhlk6+9oxbP4gw==
+"@chakra-ui/card@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/card/-/card-2.2.0.tgz#b5e59dc51c171fced76ea76bf26088803b8bc184"
+  integrity sha512-xUB/k5MURj4CtPAhdSoXZidUbm8j3hci9vnc+eZJVDqhDOShNlD6QeniQNRPRys4lWAQLCbFcrwL29C8naDi6g==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/checkbox@2.2.15":
-  version "2.2.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.2.15.tgz#e5ff65159f698d50edecee6b661b87e341eace70"
-  integrity sha512-Ju2yQjX8azgFa5f6VLPuwdGYobZ+rdbcYqjiks848JvPc75UsPhpS05cb4XlrKT7M16I8txDA5rPJdqqFicHCA==
+"@chakra-ui/checkbox@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.3.0.tgz#6a103555100008fcde0b25a4f3f87267dd6ea073"
+  integrity sha512-fX7M5sQK27aFWoj7vqnPkf1Q3AHmML/5dIRYfm7HEIsZXYH2C1CkM6+dijeSWIk6a0mp0r3el6SNDUti2ehH8g==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/form-control" "2.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/visually-hidden" "2.0.15"
-    "@zag-js/focus-visible" "0.2.2"
+    "@chakra-ui/visually-hidden" "2.1.0"
+    "@zag-js/focus-visible" "0.10.5"
 
 "@chakra-ui/cli@^2.4.1":
   version "2.4.1"
@@ -177,73 +172,73 @@
     esbuild "^0.17.18"
     prettier "^2.8.8"
 
-"@chakra-ui/clickable@2.0.14":
-  version "2.0.14"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/clickable/-/clickable-2.0.14.tgz#88093008672a2a30bdd2a30ff815dcc2c88c01a5"
-  integrity sha512-jfsM1qaD74ZykLHmvmsKRhDyokLUxEfL8Il1VoZMNX5RBI0xW/56vKpLTFF/v/+vLPLS+Te2cZdD4+2O+G6ulA==
+"@chakra-ui/clickable@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/clickable/-/clickable-2.1.0.tgz#800fa8d10cf45a41fc50a3df32c679a3ce1921c3"
+  integrity sha512-flRA/ClPUGPYabu+/GLREZVZr9j2uyyazCAUHAdrTUEdDYCr31SVGhgh7dgKdtq23bOvAQJpIJjw/0Bs0WvbXw==
   dependencies:
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/close-button@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/close-button/-/close-button-2.0.17.tgz#d43d3a2ea1f08250f8d0da7704baf0e1fbd91b4b"
-  integrity sha512-05YPXk456t1Xa3KpqTrvm+7smx+95dmaPiwjiBN3p7LHUQVHJd8ZXSDB0V+WKi419k3cVQeJUdU/azDO2f40sw==
+"@chakra-ui/close-button@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/close-button/-/close-button-2.1.0.tgz#5af435a62919793dc713be321eaff1f61749b6d5"
+  integrity sha512-KfJcz6UAaR2dDWSIv6UrCGkZQS54Fjl+DEEVOUTJ7gf4KOP4FQZCkv8hqsAB9FeCtnwU43adq2oaw3aZH/Uzew==
   dependencies:
-    "@chakra-ui/icon" "3.0.16"
+    "@chakra-ui/icon" "3.1.0"
 
-"@chakra-ui/color-mode@2.1.12":
-  version "2.1.12"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/color-mode/-/color-mode-2.1.12.tgz#c0caeadd5f87fadbeefc6826beabac6c4a88d8f5"
-  integrity sha512-sYyfJGDoJSLYO+V2hxV9r033qhte5Nw/wAn5yRGGZnEEN1dKPEdWQ3XZvglWSDTNd0w9zkoH2w6vP4FBBYb/iw==
+"@chakra-ui/color-mode@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/color-mode/-/color-mode-2.2.0.tgz#828d47234c74ba2fb4c5dd63a63331aead20b9f6"
+  integrity sha512-niTEA8PALtMWRI9wJ4LL0CSBDo8NBfLNp4GD6/0hstcm3IlbBHTVKxN6HwSaoNYfphDQLxCjT4yG+0BJA5tFpg==
   dependencies:
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
 
-"@chakra-ui/control-box@2.0.13":
-  version "2.0.13"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/control-box/-/control-box-2.0.13.tgz#ffe9634d0c3aecb8e1eb7da19e64fb3d2b181d03"
-  integrity sha512-FEyrU4crxati80KUF/+1Z1CU3eZK6Sa0Yv7Z/ydtz9/tvGblXW9NFanoomXAOvcIFLbaLQPPATm9Gmpr7VG05A==
+"@chakra-ui/control-box@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/control-box/-/control-box-2.1.0.tgz#0f4586797b3154c02463bc5c106782e70c88f04f"
+  integrity sha512-gVrRDyXFdMd8E7rulL0SKeoljkLQiPITFnsyMO8EFHNZ+AHt5wK4LIguYVEq88APqAGZGfHFWXr79RYrNiE3Mg==
 
-"@chakra-ui/counter@2.0.14":
-  version "2.0.14"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/counter/-/counter-2.0.14.tgz#6e37a863afd2e87d7c94208245e81777640e76e2"
-  integrity sha512-KxcSRfUbb94dP77xTip2myoE7P2HQQN4V5fRJmNAGbzcyLciJ+aDylUU/UxgNcEjawUp6Q242NbWb1TSbKoqog==
+"@chakra-ui/counter@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/counter/-/counter-2.1.0.tgz#e413a2f1093a18f847bb7aa240117fde788a59e6"
+  integrity sha512-s6hZAEcWT5zzjNz2JIWUBzRubo9la/oof1W7EKZVVfPYHERnl5e16FmBC79Yfq8p09LQ+aqFKm/etYoJMMgghw==
   dependencies:
     "@chakra-ui/number-utils" "2.0.7"
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/css-reset@2.1.2":
-  version "2.1.2"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.1.2.tgz#a4cd1601e8376a74b8dd62a9089cb8aaac1ee800"
-  integrity sha512-4ySTLd+3iRpp4lX0yI9Yo2uQm2f+qwYGNOZF0cNcfN+4UJCd3IsaWxYRR/Anz+M51NVldZbYzC+TEYC/kpJc4A==
+"@chakra-ui/css-reset@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.2.0.tgz#7bd8da563941709cd68e2d1cf1e71279bec038ea"
+  integrity sha512-nn7hjquIrPwCzwI4d/Y4wzM5A5xAeswREOfT8gT0Yd+U+Qnw3pPT8NPLbNJ3DvuOfJaCV6/N5ld/6RRTgYF/sQ==
 
-"@chakra-ui/descendant@3.0.14":
-  version "3.0.14"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/descendant/-/descendant-3.0.14.tgz#fe8bac3f0e1ffe562e3e73eac393dbf222d57e13"
-  integrity sha512-+Ahvp9H4HMpfScIv9w1vaecGz7qWAaK1YFHHolz/SIsGLaLGlbdp+5UNabQC7L6TUnzzJDQDxzwif78rTD7ang==
+"@chakra-ui/descendant@3.1.0":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/descendant/-/descendant-3.1.0.tgz#f3b80ed13ffc4bf1d615b3ed5541bd0905375cca"
+  integrity sha512-VxCIAir08g5w27klLyi7PVo8BxhW4tgU/lxQyujkmi4zx7hT9ZdrcQLAted/dAa+aSIZ14S1oV0Q9lGjsAdxUQ==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
 
 "@chakra-ui/dom-utils@2.1.0":
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/@chakra-ui/dom-utils/-/dom-utils-2.1.0.tgz#d15df89e458ef19756db04c7cfd084eb552454f0"
   integrity sha512-ZmF2qRa1QZ0CMLU8M1zCfmw29DmPNtfjR9iTo74U5FPr3i1aoAh7fbJ4qAlZ197Xw9eAW28tvzQuoVWeL5C7fQ==
 
-"@chakra-ui/editable@3.0.0":
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/editable/-/editable-3.0.0.tgz#b61d4fba5a581b41856ebd85fd5d17c96a224323"
-  integrity sha512-q/7C/TM3iLaoQKlEiM8AY565i9NoaXtS6N6N4HWIEL5mZJPbMeHKxrCHUZlHxYuQJqFOGc09ZPD9fAFx1GkYwQ==
+"@chakra-ui/editable@3.1.0":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/editable/-/editable-3.1.0.tgz#065783c2e3389c3bb9ab0582cb50d38e1dc00fa1"
+  integrity sha512-j2JLrUL9wgg4YA6jLlbU88370eCRyor7DZQD9lzpY95tSOXpTljeg3uF9eOmDnCs6fxp3zDWIfkgMm/ExhcGTg==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-focus-on-pointer-down" "2.0.6"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-focus-on-pointer-down" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/event-utils@2.0.8":
@@ -251,23 +246,23 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/event-utils/-/event-utils-2.0.8.tgz#e6439ba200825a2f15d8f1973d267d1c00a6d1b4"
   integrity sha512-IGM/yGUHS+8TOQrZGpAKOJl/xGBrmRYJrmbHfUE7zrG3PpQyXvbLDP1M+RggkCFVgHlJi2wpYIf0QtQlU0XZfw==
 
-"@chakra-ui/focus-lock@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/focus-lock/-/focus-lock-2.0.17.tgz#c1896a80896e752b88e8681f9c9d626046de6dd5"
-  integrity sha512-V+m4Ml9E8QY66DUpHX/imInVvz5XJ5zx59Tl0aNancXgeVY1Rt/ZdxuZdPLCAmPC/MF3GUOgnEA+WU8i+VL6Gw==
+"@chakra-ui/focus-lock@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/focus-lock/-/focus-lock-2.1.0.tgz#580e5450fe85356987b9a246abaff8333369c667"
+  integrity sha512-EmGx4PhWGjm4dpjRqM4Aa+rCWBxP+Rq8Uc/nAVnD4YVqkEhBkrPTpui2lnjsuxqNaZ24fIAZ10cF1hlpemte/w==
   dependencies:
     "@chakra-ui/dom-utils" "2.1.0"
     react-focus-lock "^2.9.4"
 
-"@chakra-ui/form-control@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/form-control/-/form-control-2.0.18.tgz#1923f293afde70b2b07ca731d98fef3660098c56"
-  integrity sha512-I0a0jG01IAtRPccOXSNugyRdUAe8Dy40ctqedZvznMweOXzbMCF1m+sHPLdWeWC/VI13VoAispdPY0/zHOdjsQ==
+"@chakra-ui/form-control@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/form-control/-/form-control-2.1.0.tgz#a48ad454428c03daaaf71671925becd02a2c3f66"
+  integrity sha512-3QmWG9v6Rx+JOwJP3Wt89+AWZxK0F1NkVAgXP3WVfE9VDXOKFRV/faLT0GEe2V+l7WZHF5PLdEBvKG8Cgw2mkA==
   dependencies:
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/hooks@2.2.0":
@@ -287,6 +282,13 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
+"@chakra-ui/icon@3.1.0":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/icon/-/icon-3.1.0.tgz#48312c071b3a0ed20ce807c8bd24d5f3e9cfdb7f"
+  integrity sha512-t6v0lGCXRbwUJycN8A/nDTuLktMP+LRjKbYJnd2oL6Pm2vOl99XwEQ5cAEyEa4XoseYNEgXiLR+2TfvgfNFvcw==
+  dependencies:
+    "@chakra-ui/shared-utils" "2.0.5"
+
 "@chakra-ui/icons@^2.0.19":
   version "2.0.19"
   resolved "https://registry.yarnpkg.com/@chakra-ui/icons/-/icons-2.0.19.tgz#b4581a59c2e2a2b95b01ab251eabb8cf984bb00f"
@@ -294,35 +296,35 @@
   dependencies:
     "@chakra-ui/icon" "3.0.16"
 
-"@chakra-ui/image@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/image/-/image-2.0.16.tgz#0e3a48c3caa6dc1d340502ea96766d9ef31e27e8"
-  integrity sha512-iFypk1slgP3OK7VIPOtkB0UuiqVxNalgA59yoRM43xLIeZAEZpKngUVno4A2kFS61yKN0eIY4hXD3Xjm+25EJA==
+"@chakra-ui/image@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/image/-/image-2.1.0.tgz#6c205f1ca148e3bf58345b0b5d4eb3d959eb9f87"
+  integrity sha512-bskumBYKLiLMySIWDGcz0+D9Th0jPvmX6xnRMs4o92tT3Od/bW26lahmV2a2Op2ItXeCmRMY+XxJH5Gy1i46VA==
   dependencies:
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/input@2.0.22":
-  version "2.0.22"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/input/-/input-2.0.22.tgz#4c1f166f53555c698bb65950772314f78c147450"
-  integrity sha512-dCIC0/Q7mjZf17YqgoQsnXn0bus6vgriTRn8VmxOc+WcVl+KBSTBWujGrS5yu85WIFQ0aeqQvziDnDQybPqAbA==
+"@chakra-ui/input@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/input/-/input-2.1.0.tgz#45a2e2ccdd65dc9f6dee11b6e3043438864bf806"
+  integrity sha512-HItI2vq6vupCuixdzof4sIanGdLlszhDtlR5be5z8Nrda1RkXVqI+9CTJPbNsx2nIKEfwPt01pnT9mozoOSMMw==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.1.0"
     "@chakra-ui/object-utils" "2.1.0"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/layout@2.2.0":
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/layout/-/layout-2.2.0.tgz#a0832ba419743d8d7e442acfce59740626664d2f"
-  integrity sha512-WvfsWQjqzbCxv7pbpPGVKxj9eQr7MC2i37ag4Wn7ClIG7uPuwHYTUWOnjnu27O3H/zA4cRVZ4Hs3GpSPbojZFQ==
+"@chakra-ui/layout@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/layout/-/layout-2.3.0.tgz#c53219235db737202006b8b0881b82fedcf3b225"
+  integrity sha512-tp1/Bn+cHn0Q4HWKY62HtOwzhpH1GUA3i5fvs23HEhOEryTps05hyuQVeJ71fLqSs6f1QEIdm+9It+5WCj64vQ==
   dependencies:
     "@chakra-ui/breakpoint-utils" "2.0.8"
-    "@chakra-ui/icon" "3.0.16"
+    "@chakra-ui/icon" "3.1.0"
     "@chakra-ui/object-utils" "2.1.0"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/lazy-utils@2.0.5":
@@ -330,73 +332,73 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/lazy-utils/-/lazy-utils-2.0.5.tgz#363c3fa1d421362790b416ffa595acb835e1ae5b"
   integrity sha512-UULqw7FBvcckQk2n3iPO56TMJvDsNv0FKZI6PlUNJVaGsPbsYxK/8IQ60vZgaTVPtVcjY6BE+y6zg8u9HOqpyg==
 
-"@chakra-ui/live-region@2.0.13":
-  version "2.0.13"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/live-region/-/live-region-2.0.13.tgz#1d00a637b74372d1ee0b215c649ebd4a33893e58"
-  integrity sha512-Ja+Slk6ZkxSA5oJzU2VuGU7TpZpbMb/4P4OUhIf2D30ctmIeXkxTWw1Bs1nGJAVtAPcGS5sKA+zb89i8g+0cTQ==
+"@chakra-ui/live-region@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/live-region/-/live-region-2.1.0.tgz#02b4b1d997075f19a7a9a87187e08c72e82ef0dd"
+  integrity sha512-ZOxFXwtaLIsXjqnszYYrVuswBhnIHHP+XIgK1vC6DePKtyK590Wg+0J0slDwThUAd4MSSIUa/nNX84x1GMphWw==
 
-"@chakra-ui/media-query@3.2.12":
-  version "3.2.12"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/media-query/-/media-query-3.2.12.tgz#75e31f3c88818e687a4d90a2993286c2c3ca2453"
-  integrity sha512-8pSLDf3oxxhFrhd40rs7vSeIBfvOmIKHA7DJlGUC/y+9irD24ZwgmCtFnn+y3gI47hTJsopbSX+wb8nr7XPswA==
+"@chakra-ui/media-query@3.3.0":
+  version "3.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/media-query/-/media-query-3.3.0.tgz#40f9151dedb6a7af9df3be0474b59a799c92c619"
+  integrity sha512-IsTGgFLoICVoPRp9ykOgqmdMotJG0CnPsKvGQeSFOB/dZfIujdVb14TYxDU4+MURXry1MhJ7LzZhv+Ml7cr8/g==
   dependencies:
     "@chakra-ui/breakpoint-utils" "2.0.8"
-    "@chakra-ui/react-env" "3.0.0"
+    "@chakra-ui/react-env" "3.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/menu@2.1.15":
-  version "2.1.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/menu/-/menu-2.1.15.tgz#116520a2746d848e2a44fdbf03d66353cd1e1b39"
-  integrity sha512-+1fh7KBKZyhy8wi7Q6nQAzrvjM6xggyhGMnSna0rt6FJVA2jlfkjb5FozyIVPnkfJKjkKd8THVhrs9E7pHNV/w==
+"@chakra-ui/menu@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/menu/-/menu-2.2.0.tgz#ba74b538a3fa3dc46313368ed2878f92b479e6d7"
+  integrity sha512-l7HQjriW4JGeCyxDdguAzekwwB+kHGDLxACi0DJNp37sil51SRaN1S1OrneISbOHVpHuQB+KVNgU0rqhoglVew==
   dependencies:
-    "@chakra-ui/clickable" "2.0.14"
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/clickable" "2.1.0"
+    "@chakra-ui/descendant" "3.1.0"
     "@chakra-ui/lazy-utils" "2.0.5"
-    "@chakra-ui/popper" "3.0.14"
+    "@chakra-ui/popper" "3.1.0"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-animation-state" "2.0.9"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-disclosure" "2.0.8"
-    "@chakra-ui/react-use-focus-effect" "2.0.11"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
-    "@chakra-ui/react-use-outside-click" "2.1.0"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-animation-state" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-disclosure" "2.1.0"
+    "@chakra-ui/react-use-focus-effect" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/react-use-outside-click" "2.2.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/transition" "2.1.0"
 
-"@chakra-ui/modal@2.2.12":
-  version "2.2.12"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/modal/-/modal-2.2.12.tgz#8c6dc66a6db4abdaf6f5e0dae70183ee41ce361a"
-  integrity sha512-F1nNmYGvyqlmxidbwaBM3y57NhZ/Qeyc8BE9tb1FL1v9nxQhkfrPvMQ9miK0O1syPN6aZ5MMj+uD3AsRFE+/tA==
+"@chakra-ui/modal@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/modal/-/modal-2.3.0.tgz#f7c35bb457c3c4be391c9366f892a5a779af810a"
+  integrity sha512-S1sITrIeLSf21LJ0Vz8xZhj5fWEud5z5Dl2dmvOEv1ezypgOrCCBdOEnnqCkoEKZDbKvzZWZXWR5791ikLP6+g==
   dependencies:
-    "@chakra-ui/close-button" "2.0.17"
-    "@chakra-ui/focus-lock" "2.0.17"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/close-button" "2.1.0"
+    "@chakra-ui/focus-lock" "2.1.0"
+    "@chakra-ui/portal" "2.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/transition" "2.1.0"
     aria-hidden "^1.2.2"
     react-remove-scroll "^2.5.5"
 
-"@chakra-ui/number-input@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/number-input/-/number-input-2.0.19.tgz#82d4522036904c04d07e7050822fc522f9b32233"
-  integrity sha512-HDaITvtMEqOauOrCPsARDxKD9PSHmhWywpcyCSOX0lMe4xx2aaGhU0QQFhsJsykj8Er6pytMv6t0KZksdDv3YA==
+"@chakra-ui/number-input@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/number-input/-/number-input-2.1.0.tgz#7812a6602edbed26829a8f7defe8c4f9f175421a"
+  integrity sha512-/gEAzQHhrMA+1rzyCMaN8OkKtUPuER6iA+nloYEYBoT7dH/EoNlRtBkiIQhDp+E4VpgZJ0SK3OVrm9/eBbtHHg==
   dependencies:
-    "@chakra-ui/counter" "2.0.14"
-    "@chakra-ui/form-control" "2.0.18"
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/counter" "2.1.0"
+    "@chakra-ui/form-control" "2.1.0"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-    "@chakra-ui/react-use-event-listener" "2.0.7"
-    "@chakra-ui/react-use-interval" "2.0.5"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+    "@chakra-ui/react-use-event-listener" "2.1.0"
+    "@chakra-ui/react-use-interval" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/number-utils@2.0.7":
@@ -409,216 +411,216 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/object-utils/-/object-utils-2.1.0.tgz#a4ecf9cea92f1de09f5531f53ffdc41e0b19b6c3"
   integrity sha512-tgIZOgLHaoti5PYGPTwK3t/cqtcycW0owaiOXoZOcpwwX/vlVb+H1jFsQyWiiwQVPt9RkoSLtxzXamx+aHH+bQ==
 
-"@chakra-ui/pin-input@2.0.20":
-  version "2.0.20"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/pin-input/-/pin-input-2.0.20.tgz#5bf115bf4282b69fc6532a9c542cbf41f815d200"
-  integrity sha512-IHVmerrtHN8F+jRB3W1HnMir1S1TUCWhI7qDInxqPtoRffHt6mzZgLZ0izx8p1fD4HkW4c1d4/ZLEz9uH9bBRg==
+"@chakra-ui/pin-input@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/pin-input/-/pin-input-2.1.0.tgz#61e6bbf909ec510634307b2861c4f1891a9f8d81"
+  integrity sha512-x4vBqLStDxJFMt+jdAHHS8jbh294O53CPQJoL4g228P513rHylV/uPscYUHrVJXRxsHfRztQO9k45jjTYaPRMw==
   dependencies:
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/descendant" "3.1.0"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/popover@2.1.12":
-  version "2.1.12"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/popover/-/popover-2.1.12.tgz#093bb60f7c044f829e2acc3a93f0c1077ba58cfb"
-  integrity sha512-Corh8trA1f3ydcMQqomgSvYNNhAlpxiBpMY2sglwYazOJcueHA8CI05cJVD0T/wwoTob7BShabhCGFZThn61Ng==
+"@chakra-ui/popover@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/popover/-/popover-2.2.0.tgz#470c4814447010a1b7db6839fcc3e8983cbabb60"
+  integrity sha512-cTqXdgkU0vgK82AR1nWcC2MJYhEL/y6uTeprvO2+j4o2D0yPrzVMuIZZRl0abrQwiravQyVGEMgA5y0ZLYwbiQ==
   dependencies:
-    "@chakra-ui/close-button" "2.0.17"
+    "@chakra-ui/close-button" "2.1.0"
     "@chakra-ui/lazy-utils" "2.0.5"
-    "@chakra-ui/popper" "3.0.14"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/popper" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-animation-state" "2.0.9"
-    "@chakra-ui/react-use-disclosure" "2.0.8"
-    "@chakra-ui/react-use-focus-effect" "2.0.11"
-    "@chakra-ui/react-use-focus-on-pointer-down" "2.0.6"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-animation-state" "2.1.0"
+    "@chakra-ui/react-use-disclosure" "2.1.0"
+    "@chakra-ui/react-use-focus-effect" "2.1.0"
+    "@chakra-ui/react-use-focus-on-pointer-down" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/popper@3.0.14":
-  version "3.0.14"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/popper/-/popper-3.0.14.tgz#598feec8825df99270585319f7becbb6cf33558a"
-  integrity sha512-RDMmmSfjsmHJbVn2agDyoJpTbQK33fxx//njwJdeyM0zTG/3/4xjI/Cxru3acJ2Y+1jFGmPqhO81stFjnbtfIw==
+"@chakra-ui/popper@3.1.0":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/popper/-/popper-3.1.0.tgz#92a9180c6894763af3b22a6003f9a9d958fe2659"
+  integrity sha512-ciDdpdYbeFG7og6/6J8lkTFxsSvwTdMLFkpVylAF6VNC22jssiWfquj2eyD4rJnzkRFPvIWJq8hvbfhsm+AjSg==
   dependencies:
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@popperjs/core" "^2.9.3"
 
-"@chakra-ui/portal@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/portal/-/portal-2.0.16.tgz#e5ce3f9d9e559f17a95276e0c006d0e9b7703442"
-  integrity sha512-bVID0qbQ0l4xq38LdqAN4EKD4/uFkDnXzFwOlviC9sl0dNhzICDb1ltuH/Adl1d2HTMqyN60O3GO58eHy7plnQ==
+"@chakra-ui/portal@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/portal/-/portal-2.1.0.tgz#9e7f57424d7041738b6563cac80134561080bd27"
+  integrity sha512-9q9KWf6SArEcIq1gGofNcFPSWEyl+MfJjEUg/un1SMlQjaROOh3zYr+6JAwvcORiX7tyHosnmWC3d3wI2aPSQg==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
 
-"@chakra-ui/progress@2.1.6":
-  version "2.1.6"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/progress/-/progress-2.1.6.tgz#398db20440979c37adb0a34821f805ae3471873b"
-  integrity sha512-hHh5Ysv4z6bK+j2GJbi/FT9CVyto2PtNUNwBmr3oNMVsoOUMoRjczfXvvYqp0EHr9PCpxqrq7sRwgQXUzhbDSw==
+"@chakra-ui/progress@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/progress/-/progress-2.2.0.tgz#67444ea9779631d7c8395b2c9c78e5634f994999"
+  integrity sha512-qUXuKbuhN60EzDD9mHR7B67D7p/ZqNS2Aze4Pbl1qGGZfulPW0PY8Rof32qDtttDQBkzQIzFGE8d9QpAemToIQ==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
 
-"@chakra-ui/provider@2.3.0":
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/provider/-/provider-2.3.0.tgz#18b3bdc3087e90569049832b2f0f4f8afd5cedf6"
-  integrity sha512-vKgmjoLVS3NnHW8RSYwmhhda2ZTi3fQc1egkYSVwngGky4CsN15I+XDhxJitVd66H41cjah/UNJyoeq7ACseLA==
+"@chakra-ui/provider@2.4.0":
+  version "2.4.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/provider/-/provider-2.4.0.tgz#1649e607b7dddd8dd160e624946769a29f67d9a3"
+  integrity sha512-KJ/TNczpY+EStQXa2Y5PZ+senlBHrY7P+RpBgJLBZLGkQUCS3APw5KvCwgpA0COb2M4AZXCjw+rm+Ko7ontlgA==
   dependencies:
-    "@chakra-ui/css-reset" "2.1.2"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/react-env" "3.0.0"
-    "@chakra-ui/system" "2.5.8"
+    "@chakra-ui/css-reset" "2.2.0"
+    "@chakra-ui/portal" "2.1.0"
+    "@chakra-ui/react-env" "3.1.0"
+    "@chakra-ui/system" "2.6.0"
     "@chakra-ui/utils" "2.0.15"
 
-"@chakra-ui/radio@2.0.22":
-  version "2.0.22"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/radio/-/radio-2.0.22.tgz#fad0ce7c9ba4051991ed517cac4cfe526d6d47d9"
-  integrity sha512-GsQ5WAnLwivWl6gPk8P1x+tCcpVakCt5R5T0HumF7DGPXKdJbjS+RaFySrbETmyTJsKY4QrfXn+g8CWVrMjPjw==
+"@chakra-ui/radio@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/radio/-/radio-2.1.0.tgz#68b2cb4193570369568052cb1254163224a1479c"
+  integrity sha512-WiRlSCqKWgy4m9106w4g77kcLYqBxqGhFRO1pTTJp99rxpM6jNadOeK+moEjqj64N9mSz3njEecMJftKKcOYdg==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/form-control" "2.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@zag-js/focus-visible" "0.2.2"
+    "@zag-js/focus-visible" "0.10.5"
 
 "@chakra-ui/react-children-utils@2.0.6":
   version "2.0.6"
   resolved "https://registry.yarnpkg.com/@chakra-ui/react-children-utils/-/react-children-utils-2.0.6.tgz#6c480c6a60678fcb75cb7d57107c7a79e5179b92"
   integrity sha512-QVR2RC7QsOsbWwEnq9YduhpqSFnZGvjjGREV8ygKi8ADhXh93C8azLECCUVgRJF2Wc+So1fgxmjLcbZfY2VmBA==
 
-"@chakra-ui/react-context@2.0.8":
-  version "2.0.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-context/-/react-context-2.0.8.tgz#5e0ed33ac3995875a21dea0e12b0ee5fc4c2e3cc"
-  integrity sha512-tRTKdn6lCTXM6WPjSokAAKCw2ioih7Eg8cNgaYRSwKBck8nkz9YqxgIIEj3dJD7MGtpl24S/SNI98iRWkRwR/A==
+"@chakra-ui/react-context@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-context/-/react-context-2.1.0.tgz#4858be1d5ff1c8ac0a0ec088d93a3b7f1cbbff99"
+  integrity sha512-iahyStvzQ4AOwKwdPReLGfDesGG+vWJfEsn0X/NoGph/SkN+HXtv2sCfYFFR9k7bb+Kvc6YfpLlSuLvKMHi2+w==
 
-"@chakra-ui/react-env@3.0.0":
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-env/-/react-env-3.0.0.tgz#2c3c9dc0e529b9b474a386a2b24988317b2a0811"
-  integrity sha512-tfMRO2v508HQWAqSADFrwZgR9oU10qC97oV6zGbjHh9ALP0/IcFR+Bi71KRTveDTm85fMeAzZYGj57P3Dsipkw==
+"@chakra-ui/react-env@3.1.0":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-env/-/react-env-3.1.0.tgz#7d3c1c05a501bb369524d9f3d38c9325eb16ab50"
+  integrity sha512-Vr96GV2LNBth3+IKzr/rq1IcnkXv+MLmwjQH6C8BRtn3sNskgDFD5vLkVXcEhagzZMCh8FR3V/bzZPojBOyNhw==
   dependencies:
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
 
 "@chakra-ui/react-types@2.0.7":
   version "2.0.7"
   resolved "https://registry.yarnpkg.com/@chakra-ui/react-types/-/react-types-2.0.7.tgz#799c166a44882b23059c8f510eac9bd5d0869ac4"
   integrity sha512-12zv2qIZ8EHwiytggtGvo4iLT0APris7T0qaAWqzpUGS0cdUtR8W+V1BJ5Ocq+7tA6dzQ/7+w5hmXih61TuhWQ==
 
-"@chakra-ui/react-use-animation-state@2.0.9":
-  version "2.0.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-animation-state/-/react-use-animation-state-2.0.9.tgz#8e6377e7583cc80c649cdc443c90ab5b48a03e78"
-  integrity sha512-WFoD5OG03PBmzJCoRwM8rVfU442AvKBPPgA0yGGlKioH29OGuX7W78Ml+cYdXxonTiB03YSRZzUwaUnP4wAy1Q==
-  dependencies:
-    "@chakra-ui/dom-utils" "2.1.0"
-    "@chakra-ui/react-use-event-listener" "2.0.7"
-
-"@chakra-ui/react-use-callback-ref@2.0.7":
-  version "2.0.7"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-callback-ref/-/react-use-callback-ref-2.0.7.tgz#9b844a81037d0ecaaa8031979fa050165635e211"
-  integrity sha512-YjT76nTpfHAK5NxplAlZsQwNju5KmQExnqsWNPFeOR6vvbC34+iPSTr+r91i1Hdy7gBSbevsOsd5Wm6RN3GuMw==
-
-"@chakra-ui/react-use-controllable-state@2.0.8":
-  version "2.0.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-controllable-state/-/react-use-controllable-state-2.0.8.tgz#6b71187e03be632c244dde9f16ed685428087ec9"
-  integrity sha512-F7rdCbLEmRjwwODqWZ3y+mKgSSHPcLQxeUygwk1BkZPXbKkJJKymOIjIynil2cbH7ku3hcSIWRvuhpCcfQWJ7Q==
-  dependencies:
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-
-"@chakra-ui/react-use-disclosure@2.0.8":
-  version "2.0.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-disclosure/-/react-use-disclosure-2.0.8.tgz#e0e0445afc6d6d96bb262b99751e675034c31497"
-  integrity sha512-2ir/mHe1YND40e+FyLHnDsnDsBQPwzKDLzfe9GZri7y31oU83JSbHdlAXAhp3bpjohslwavtRCp+S/zRxfO9aQ==
-  dependencies:
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-
-"@chakra-ui/react-use-event-listener@2.0.7":
-  version "2.0.7"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-event-listener/-/react-use-event-listener-2.0.7.tgz#ed08164164e79183d876eeb71e12c6bfaca3ad17"
-  integrity sha512-4wvpx4yudIO3B31pOrXuTHDErawmwiXnvAN7gLEOVREi16+YGNcFnRJ5X5nRrmB7j2MDUtsEDpRBFfw5Z9xQ5g==
-  dependencies:
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-
-"@chakra-ui/react-use-focus-effect@2.0.11":
-  version "2.0.11"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-focus-effect/-/react-use-focus-effect-2.0.11.tgz#9a5c76981677fc356308526c7d2b3dc48101ea06"
-  integrity sha512-/zadgjaCWD50TfuYsO1vDS2zSBs2p/l8P2DPEIA8FuaowbBubKrk9shKQDWmbfDU7KArGxPxrvo+VXvskPPjHw==
-  dependencies:
-    "@chakra-ui/dom-utils" "2.1.0"
-    "@chakra-ui/react-use-event-listener" "2.0.7"
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
-
-"@chakra-ui/react-use-focus-on-pointer-down@2.0.6":
-  version "2.0.6"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-focus-on-pointer-down/-/react-use-focus-on-pointer-down-2.0.6.tgz#13330eb518c17e591c908cb8f4a30d43a978e3f2"
-  integrity sha512-OigXiLRVySn3tyVqJ/rn57WGuukW8TQe8fJYiLwXbcNyAMuYYounvRxvCy2b53sQ7QIZamza0N0jhirbH5FNoQ==
-  dependencies:
-    "@chakra-ui/react-use-event-listener" "2.0.7"
-
-"@chakra-ui/react-use-interval@2.0.5":
-  version "2.0.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-interval/-/react-use-interval-2.0.5.tgz#c1a0043bf188b19b790a27668f4e860391335a60"
-  integrity sha512-1nbdwMi2K87V6p5f5AseOKif2CkldLaJlq1TOqaPRwb7v3aU9rltBtYdf+fIyuHSToNJUV6wd9budCFdLCl3Fg==
-  dependencies:
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-
-"@chakra-ui/react-use-latest-ref@2.0.5":
-  version "2.0.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-latest-ref/-/react-use-latest-ref-2.0.5.tgz#b61dc4dadda340f7b14df0ec1d50ab2e507b3b3e"
-  integrity sha512-3mIuFzMyIo3Ok/D8uhV9voVg7KkrYVO/pwVvNPJOHsDQqCA6DpYE4WDsrIx+fVcwad3Ta7SupexR5PoI+kq6QQ==
-
-"@chakra-ui/react-use-merge-refs@2.0.7":
-  version "2.0.7"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-merge-refs/-/react-use-merge-refs-2.0.7.tgz#1a1fe800fb5501ec3da4088fbac78c03bbad13a7"
-  integrity sha512-zds4Uhsc+AMzdH8JDDkLVet9baUBgtOjPbhC5r3A0ZXjZvGhCztFAVE3aExYiVoMPoHLKbLcqvCWE6ioFKz1lw==
-
-"@chakra-ui/react-use-outside-click@2.1.0":
+"@chakra-ui/react-use-animation-state@2.1.0":
   version "2.1.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-outside-click/-/react-use-outside-click-2.1.0.tgz#f7e27653c470e516c55d79df67ed8b0ba2c4ec8d"
-  integrity sha512-JanCo4QtWvMl9ZZUpKJKV62RlMWDFdPCE0Q64a7eWTOQgWWcpyBW7TOYRunQTqrK30FqkYFJCOlAWOtn+6Rw7A==
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-animation-state/-/react-use-animation-state-2.1.0.tgz#eab661fbafd96804fe867b0df0c27e78feefe6e2"
+  integrity sha512-CFZkQU3gmDBwhqy0vC1ryf90BVHxVN8cTLpSyCpdmExUEtSEInSCGMydj2fvn7QXsz/za8JNdO2xxgJwxpLMtg==
   dependencies:
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
+    "@chakra-ui/dom-utils" "2.1.0"
+    "@chakra-ui/react-use-event-listener" "2.1.0"
 
-"@chakra-ui/react-use-pan-event@2.0.9":
-  version "2.0.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-pan-event/-/react-use-pan-event-2.0.9.tgz#0ff33a285e75a692d1ed52dbb9f3046a593b8004"
-  integrity sha512-xu35QXkiyrgsHUOnctl+SwNcwf9Rl62uYE5y8soKOZdBm8E+FvZIt2hxUzK1EoekbJCMzEZ0Yv1ZQCssVkSLaQ==
+"@chakra-ui/react-use-callback-ref@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-callback-ref/-/react-use-callback-ref-2.1.0.tgz#a508085f4d9e7d84d4ceffdf5f41745c9ac451d7"
+  integrity sha512-efnJrBtGDa4YaxDzDE90EnKD3Vkh5a1t3w7PhnRQmsphLy3g2UieasoKTlT2Hn118TwDjIv5ZjHJW6HbzXA9wQ==
+
+"@chakra-ui/react-use-controllable-state@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-controllable-state/-/react-use-controllable-state-2.1.0.tgz#8fb6fa2f45d0c04173582ae8297e604ffdb9c7d9"
+  integrity sha512-QR/8fKNokxZUs4PfxjXuwl0fj/d71WPrmLJvEpCTkHjnzu7LnYvzoe2wB867IdooQJL0G1zBxl0Dq+6W1P3jpg==
+  dependencies:
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+
+"@chakra-ui/react-use-disclosure@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-disclosure/-/react-use-disclosure-2.1.0.tgz#90093eaf45db1bea7a6851dd0ce5cdb3eb66f90a"
+  integrity sha512-Ax4pmxA9LBGMyEZJhhUZobg9C0t3qFE4jVF1tGBsrLDcdBeLR9fwOogIPY9Hf0/wqSlAryAimICbr5hkpa5GSw==
+  dependencies:
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+
+"@chakra-ui/react-use-event-listener@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-event-listener/-/react-use-event-listener-2.1.0.tgz#afea2645bd9b38f754fc2b8eb858f9bb22385ded"
+  integrity sha512-U5greryDLS8ISP69DKDsYcsXRtAdnTQT+jjIlRYZ49K/XhUR/AqVZCK5BkR1spTDmO9H8SPhgeNKI70ODuDU/Q==
+  dependencies:
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+
+"@chakra-ui/react-use-focus-effect@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-focus-effect/-/react-use-focus-effect-2.1.0.tgz#963fb790370dfadd51d12666ff2da60706f53a2a"
+  integrity sha512-xzVboNy7J64xveLcxTIJ3jv+lUJKDwRM7Szwn9tNzUIPD94O3qwjV7DDCUzN2490nSYDF4OBMt/wuDBtaR3kUQ==
+  dependencies:
+    "@chakra-ui/dom-utils" "2.1.0"
+    "@chakra-ui/react-use-event-listener" "2.1.0"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
+
+"@chakra-ui/react-use-focus-on-pointer-down@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-focus-on-pointer-down/-/react-use-focus-on-pointer-down-2.1.0.tgz#2fbcf6bc7d06d97606747e231a908d5c387ca0cc"
+  integrity sha512-2jzrUZ+aiCG/cfanrolsnSMDykCAbv9EK/4iUyZno6BYb3vziucmvgKuoXbMPAzWNtwUwtuMhkby8rc61Ue+Lg==
+  dependencies:
+    "@chakra-ui/react-use-event-listener" "2.1.0"
+
+"@chakra-ui/react-use-interval@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-interval/-/react-use-interval-2.1.0.tgz#2602c097b3ab74b6644812e4f5efaad621218d98"
+  integrity sha512-8iWj+I/+A0J08pgEXP1J1flcvhLBHkk0ln7ZvGIyXiEyM6XagOTJpwNhiu+Bmk59t3HoV/VyvyJTa+44sEApuw==
+  dependencies:
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+
+"@chakra-ui/react-use-latest-ref@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-latest-ref/-/react-use-latest-ref-2.1.0.tgz#d1e926130102566ece1d39f8a48ed125e0c8441a"
+  integrity sha512-m0kxuIYqoYB0va9Z2aW4xP/5b7BzlDeWwyXCH6QpT2PpW3/281L3hLCm1G0eOUcdVlayqrQqOeD6Mglq+5/xoQ==
+
+"@chakra-ui/react-use-merge-refs@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-merge-refs/-/react-use-merge-refs-2.1.0.tgz#c0c233527abdbea9a1348269c192012205762314"
+  integrity sha512-lERa6AWF1cjEtWSGjxWTaSMvneccnAVH4V4ozh8SYiN9fSPZLlSG3kNxfNzdFvMEhM7dnP60vynF7WjGdTgQbQ==
+
+"@chakra-ui/react-use-outside-click@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-outside-click/-/react-use-outside-click-2.2.0.tgz#5570b772a255f6f02b69e967127397c1b5fa3d3c"
+  integrity sha512-PNX+s/JEaMneijbgAM4iFL+f3m1ga9+6QK0E5Yh4s8KZJQ/bLwZzdhMz8J/+mL+XEXQ5J0N8ivZN28B82N1kNw==
+  dependencies:
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+
+"@chakra-ui/react-use-pan-event@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-pan-event/-/react-use-pan-event-2.1.0.tgz#51c21bc3c0e9e73d1faef5ea4f7e3c3d071a2758"
+  integrity sha512-xmL2qOHiXqfcj0q7ZK5s9UjTh4Gz0/gL9jcWPA6GVf+A0Od5imEDa/Vz+533yQKWiNSm1QGrIj0eJAokc7O4fg==
   dependencies:
     "@chakra-ui/event-utils" "2.0.8"
-    "@chakra-ui/react-use-latest-ref" "2.0.5"
+    "@chakra-ui/react-use-latest-ref" "2.1.0"
     framesync "6.1.2"
 
-"@chakra-ui/react-use-previous@2.0.5":
-  version "2.0.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-previous/-/react-use-previous-2.0.5.tgz#65836cc81e3a1bf4252cd08a71094f1be827b56c"
-  integrity sha512-BIZgjycPE4Xr+MkhKe0h67uHXzQQkBX/u5rYPd65iMGdX1bCkbE0oorZNfOHLKdTmnEb4oVsNvfN6Rfr+Mnbxw==
+"@chakra-ui/react-use-previous@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-previous/-/react-use-previous-2.1.0.tgz#f6046e6f7398b1e8d7e66ff7ebb8d61c92a2d3d0"
+  integrity sha512-pjxGwue1hX8AFcmjZ2XfrQtIJgqbTF3Qs1Dy3d1krC77dEsiCUbQ9GzOBfDc8pfd60DrB5N2tg5JyHbypqh0Sg==
 
-"@chakra-ui/react-use-safe-layout-effect@2.0.5":
-  version "2.0.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-safe-layout-effect/-/react-use-safe-layout-effect-2.0.5.tgz#6cf388c37fd2a42b5295a292e149b32f860a00a7"
-  integrity sha512-MwAQBz3VxoeFLaesaSEN87reVNVbjcQBDex2WGexAg6hUB6n4gc1OWYH/iXp4tzp4kuggBNhEHkk9BMYXWfhJQ==
+"@chakra-ui/react-use-safe-layout-effect@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-safe-layout-effect/-/react-use-safe-layout-effect-2.1.0.tgz#3a95f0ba6fd5d2d0aa14919160f2c825f13e686f"
+  integrity sha512-Knbrrx/bcPwVS1TorFdzrK/zWA8yuU/eaXDkNj24IrKoRlQrSBFarcgAEzlCHtzuhufP3OULPkELTzz91b0tCw==
 
-"@chakra-ui/react-use-size@2.0.10":
-  version "2.0.10"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-size/-/react-use-size-2.0.10.tgz#6131950852490c06e5fb3760bf64097c8057391f"
-  integrity sha512-fdIkH14GDnKQrtQfxX8N3gxbXRPXEl67Y3zeD9z4bKKcQUAYIMqs0MsPZY+FMpGQw8QqafM44nXfL038aIrC5w==
+"@chakra-ui/react-use-size@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-size/-/react-use-size-2.1.0.tgz#fcf3070eaade8b4a84af8ce5341c4d5ca0a42bec"
+  integrity sha512-tbLqrQhbnqOjzTaMlYytp7wY8BW1JpL78iG7Ru1DlV4EWGiAmXFGvtnEt9HftU0NJ0aJyjgymkxfVGI55/1Z4A==
   dependencies:
-    "@zag-js/element-size" "0.3.2"
+    "@zag-js/element-size" "0.10.5"
 
-"@chakra-ui/react-use-timeout@2.0.5":
-  version "2.0.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-timeout/-/react-use-timeout-2.0.5.tgz#13c4e48e48d4b84ce1e062f0f1c9ec401ece78c9"
-  integrity sha512-QqmB+jVphh3h/CS60PieorpY7UqSPkrQCB7f7F+i9vwwIjtP8fxVHMmkb64K7VlzQiMPzv12nlID5dqkzlv0mw==
+"@chakra-ui/react-use-timeout@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-timeout/-/react-use-timeout-2.1.0.tgz#24415f54267d7241a3c1d36a5cae4d472834cef7"
+  integrity sha512-cFN0sobKMM9hXUhyCofx3/Mjlzah6ADaEl/AXl5Y+GawB5rgedgAcu2ErAgarEkwvsKdP6c68CKjQ9dmTQlJxQ==
   dependencies:
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
 
-"@chakra-ui/react-use-update-effect@2.0.7":
-  version "2.0.7"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-update-effect/-/react-use-update-effect-2.0.7.tgz#f94b7975ebb150c03d410e754b54f0e9dd263134"
-  integrity sha512-vBM2bmmM83ZdDtasWv3PXPznpTUd+FvqBC8J8rxoRmvdMEfrxTiQRBJhiGHLpS9BPLLPQlosN6KdFU97csB6zg==
+"@chakra-ui/react-use-update-effect@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-update-effect/-/react-use-update-effect-2.1.0.tgz#5c57cd1f50c2a6a8119e0f57f69510723d69884b"
+  integrity sha512-ND4Q23tETaR2Qd3zwCKYOOS1dfssojPLJMLvUtUbW5M9uW1ejYWgGUobeAiOVfSplownG8QYMmHTP86p/v0lbA==
 
 "@chakra-ui/react-utils@2.0.12":
   version "2.0.12"
@@ -627,71 +629,71 @@
   dependencies:
     "@chakra-ui/utils" "2.0.15"
 
-"@chakra-ui/react@^2.7.1":
-  version "2.7.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react/-/react-2.7.1.tgz#1419a4856b7dd74a558ced97f148a97d4953109e"
-  integrity sha512-uIYIAg+gnUoRbgdCfSEVvQnrEz0oWWXATGGSQpxmuJovNVyZKnX/Xug7NkWQfBUJPYRSG+VB69ZmsAFpyLSMtA==
+"@chakra-ui/react@^2.8.0":
+  version "2.8.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react/-/react-2.8.0.tgz#cc76a2448f9bc85f1645a1afb90d5756a5313ac3"
+  integrity sha512-tV82DaqE4fMbLIWq58BYh4Ol3gAlNEn+qYOzx8bPrZudboEDnboq8aVfSBwWOY++MLWz2Nn7CkT69YRm91e5sg==
   dependencies:
-    "@chakra-ui/accordion" "2.2.0"
-    "@chakra-ui/alert" "2.1.0"
-    "@chakra-ui/avatar" "2.2.11"
-    "@chakra-ui/breadcrumb" "2.1.5"
-    "@chakra-ui/button" "2.0.18"
-    "@chakra-ui/card" "2.1.6"
-    "@chakra-ui/checkbox" "2.2.15"
-    "@chakra-ui/close-button" "2.0.17"
-    "@chakra-ui/control-box" "2.0.13"
-    "@chakra-ui/counter" "2.0.14"
-    "@chakra-ui/css-reset" "2.1.2"
-    "@chakra-ui/editable" "3.0.0"
-    "@chakra-ui/focus-lock" "2.0.17"
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/accordion" "2.3.0"
+    "@chakra-ui/alert" "2.2.0"
+    "@chakra-ui/avatar" "2.3.0"
+    "@chakra-ui/breadcrumb" "2.2.0"
+    "@chakra-ui/button" "2.1.0"
+    "@chakra-ui/card" "2.2.0"
+    "@chakra-ui/checkbox" "2.3.0"
+    "@chakra-ui/close-button" "2.1.0"
+    "@chakra-ui/control-box" "2.1.0"
+    "@chakra-ui/counter" "2.1.0"
+    "@chakra-ui/css-reset" "2.2.0"
+    "@chakra-ui/editable" "3.1.0"
+    "@chakra-ui/focus-lock" "2.1.0"
+    "@chakra-ui/form-control" "2.1.0"
     "@chakra-ui/hooks" "2.2.0"
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/image" "2.0.16"
-    "@chakra-ui/input" "2.0.22"
-    "@chakra-ui/layout" "2.2.0"
-    "@chakra-ui/live-region" "2.0.13"
-    "@chakra-ui/media-query" "3.2.12"
-    "@chakra-ui/menu" "2.1.15"
-    "@chakra-ui/modal" "2.2.12"
-    "@chakra-ui/number-input" "2.0.19"
-    "@chakra-ui/pin-input" "2.0.20"
-    "@chakra-ui/popover" "2.1.12"
-    "@chakra-ui/popper" "3.0.14"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/progress" "2.1.6"
-    "@chakra-ui/provider" "2.3.0"
-    "@chakra-ui/radio" "2.0.22"
-    "@chakra-ui/react-env" "3.0.0"
-    "@chakra-ui/select" "2.0.19"
-    "@chakra-ui/skeleton" "2.0.24"
-    "@chakra-ui/skip-nav" "2.0.15"
-    "@chakra-ui/slider" "2.0.25"
-    "@chakra-ui/spinner" "2.0.13"
-    "@chakra-ui/stat" "2.0.18"
-    "@chakra-ui/stepper" "2.2.0"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/image" "2.1.0"
+    "@chakra-ui/input" "2.1.0"
+    "@chakra-ui/layout" "2.3.0"
+    "@chakra-ui/live-region" "2.1.0"
+    "@chakra-ui/media-query" "3.3.0"
+    "@chakra-ui/menu" "2.2.0"
+    "@chakra-ui/modal" "2.3.0"
+    "@chakra-ui/number-input" "2.1.0"
+    "@chakra-ui/pin-input" "2.1.0"
+    "@chakra-ui/popover" "2.2.0"
+    "@chakra-ui/popper" "3.1.0"
+    "@chakra-ui/portal" "2.1.0"
+    "@chakra-ui/progress" "2.2.0"
+    "@chakra-ui/provider" "2.4.0"
+    "@chakra-ui/radio" "2.1.0"
+    "@chakra-ui/react-env" "3.1.0"
+    "@chakra-ui/select" "2.1.0"
+    "@chakra-ui/skeleton" "2.1.0"
+    "@chakra-ui/skip-nav" "2.1.0"
+    "@chakra-ui/slider" "2.1.0"
+    "@chakra-ui/spinner" "2.1.0"
+    "@chakra-ui/stat" "2.1.0"
+    "@chakra-ui/stepper" "2.3.0"
     "@chakra-ui/styled-system" "2.9.1"
-    "@chakra-ui/switch" "2.0.27"
-    "@chakra-ui/system" "2.5.8"
-    "@chakra-ui/table" "2.0.17"
-    "@chakra-ui/tabs" "2.1.9"
-    "@chakra-ui/tag" "3.0.0"
-    "@chakra-ui/textarea" "2.0.19"
-    "@chakra-ui/theme" "3.1.2"
-    "@chakra-ui/theme-utils" "2.0.18"
-    "@chakra-ui/toast" "6.1.4"
-    "@chakra-ui/tooltip" "2.2.9"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/switch" "2.1.0"
+    "@chakra-ui/system" "2.6.0"
+    "@chakra-ui/table" "2.1.0"
+    "@chakra-ui/tabs" "2.2.0"
+    "@chakra-ui/tag" "3.1.0"
+    "@chakra-ui/textarea" "2.1.0"
+    "@chakra-ui/theme" "3.2.0"
+    "@chakra-ui/theme-utils" "2.0.19"
+    "@chakra-ui/toast" "7.0.0"
+    "@chakra-ui/tooltip" "2.3.0"
+    "@chakra-ui/transition" "2.1.0"
     "@chakra-ui/utils" "2.0.15"
-    "@chakra-ui/visually-hidden" "2.0.15"
+    "@chakra-ui/visually-hidden" "2.1.0"
 
-"@chakra-ui/select@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/select/-/select-2.0.19.tgz#957e95a17a890d8c0a851e2f00a8d8dd17932d66"
-  integrity sha512-eAlFh+JhwtJ17OrB6fO6gEAGOMH18ERNrXLqWbYLrs674Le7xuREgtuAYDoxUzvYXYYTTdOJtVbcHGriI3o6rA==
+"@chakra-ui/select@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/select/-/select-2.1.0.tgz#4c9a6e881281e77ed35ba7a2e343f235b7e0b2fd"
+  integrity sha512-6GEjCJNOm1pS9E7XRvodoVOuSFl82Jio3MGWgmcQrLznjJAhIZVMq85vCQqzGpjjfbHys/UctfdJY75Ctas/Jg==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/shared-utils@2.0.5":
@@ -699,59 +701,59 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/shared-utils/-/shared-utils-2.0.5.tgz#cb2b49705e113853647f1822142619570feba081"
   integrity sha512-4/Wur0FqDov7Y0nCXl7HbHzCg4aq86h+SXdoUeuCMD3dSj7dpsVnStLYhng1vxvlbUnLpdF4oz5Myt3i/a7N3Q==
 
-"@chakra-ui/skeleton@2.0.24":
-  version "2.0.24"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/skeleton/-/skeleton-2.0.24.tgz#dc9dcca6fc43005544fabfd38a444943b0a29cad"
-  integrity sha512-1jXtVKcl/jpbrJlc/TyMsFyI651GTXY5ma30kWyTXoby2E+cxbV6OR8GB/NMZdGxbQBax8/VdtYVjI0n+OBqWA==
+"@chakra-ui/skeleton@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/skeleton/-/skeleton-2.1.0.tgz#e3b25dd3afa330029d6d63be0f7cb8d44ad25531"
+  integrity sha512-JNRuMPpdZGd6zFVKjVQ0iusu3tXAdI29n4ZENYwAJEMf/fN0l12sVeirOxkJ7oEL0yOx2AgEYFSKdbcAgfUsAQ==
   dependencies:
-    "@chakra-ui/media-query" "3.2.12"
-    "@chakra-ui/react-use-previous" "2.0.5"
+    "@chakra-ui/media-query" "3.3.0"
+    "@chakra-ui/react-use-previous" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/skip-nav@2.0.15":
-  version "2.0.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/skip-nav/-/skip-nav-2.0.15.tgz#cb3b2ffb9c1bc492e66d6b0a189253aa53014257"
-  integrity sha512-5UtmlnV4BmIgEk6lQ0h81JEYhPX04wJEk5ZMoilQ2zEQYL6TkVVHkhRXyc1Zfq76hmHuZPXZV/yJeTecj6jIrA==
+"@chakra-ui/skip-nav@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/skip-nav/-/skip-nav-2.1.0.tgz#cac27eecc6eded1e83c8f0cf7445d727739cb325"
+  integrity sha512-Hk+FG+vadBSH0/7hwp9LJnLjkO0RPGnx7gBJWI4/SpoJf3e4tZlWYtwGj0toYY4aGKl93jVghuwGbDBEMoHDug==
 
-"@chakra-ui/slider@2.0.25":
-  version "2.0.25"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/slider/-/slider-2.0.25.tgz#2d69af68f4afcc648d14603d7c3163660d35e9eb"
-  integrity sha512-FnWSi0AIXP+9sHMCPboOKGqm902k8dJtsJ7tu3D0AcKkE62WtYLZ2sTqvwJxCfSl4KqVI1i571SrF9WadnnJ8w==
+"@chakra-ui/slider@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/slider/-/slider-2.1.0.tgz#1caeed18761ba2a390777418cc9389ba25e39bce"
+  integrity sha512-lUOBcLMCnFZiA/s2NONXhELJh6sY5WtbRykPtclGfynqqOo47lwWJx+VP7xaeuhDOPcWSSecWc9Y1BfPOCz9cQ==
   dependencies:
     "@chakra-ui/number-utils" "2.0.7"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-callback-ref" "2.0.7"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-latest-ref" "2.0.5"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
-    "@chakra-ui/react-use-pan-event" "2.0.9"
-    "@chakra-ui/react-use-size" "2.0.10"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
+    "@chakra-ui/react-use-callback-ref" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-latest-ref" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/react-use-pan-event" "2.1.0"
+    "@chakra-ui/react-use-size" "2.1.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
 
-"@chakra-ui/spinner@2.0.13":
-  version "2.0.13"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/spinner/-/spinner-2.0.13.tgz#64fe919c18305c653ced046e25d5883ee4c1e7d7"
-  integrity sha512-T1/aSkVpUIuiYyrjfn1+LsQEG7Onbi1UE9ccS/evgf61Dzy4GgTXQUnDuWFSgpV58owqirqOu6jn/9eCwDlzlg==
+"@chakra-ui/spinner@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/spinner/-/spinner-2.1.0.tgz#aa24a3d692c6ac90714e0f0f82c76c12c78c8e60"
+  integrity sha512-hczbnoXt+MMv/d3gE+hjQhmkzLiKuoTo42YhUG7Bs9OSv2lg1fZHW1fGNRFP3wTi6OIbD044U1P9HK+AOgFH3g==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/stat@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/stat/-/stat-2.0.18.tgz#9e5d21d162b7cf2cf92065c19291ead2d4660772"
-  integrity sha512-wKyfBqhVlIs9bkSerUc6F9KJMw0yTIEKArW7dejWwzToCLPr47u+CtYO6jlJHV6lRvkhi4K4Qc6pyvtJxZ3VpA==
+"@chakra-ui/stat@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/stat/-/stat-2.1.0.tgz#6643b507358e9cacf008387b3f12b75899497369"
+  integrity sha512-sqx0/AdFFZ80dsiM5owmhtQyYl+zON1r+IY0m70I/ABRVy+I3br06xdUhoaxh3tcP7c0O/BQgb+VCfXa9Y34CA==
   dependencies:
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/stepper@2.2.0":
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/stepper/-/stepper-2.2.0.tgz#c42562fd1b210595303f14970d9df6b32e1ad5a1"
-  integrity sha512-8ZLxV39oghSVtOUGK8dX8Z6sWVSQiKVmsK4c3OQDa8y2TvxP0VtFD0Z5U1xJlOjQMryZRWhGj9JBc3iQLukuGg==
+"@chakra-ui/stepper@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/stepper/-/stepper-2.3.0.tgz#5714df429936839145a62b5c197cff26b872b660"
+  integrity sha512-q80QX/NLrjJQIlBP1N+Q8GVJb7/HiOpMoK1PlP4denB/KxkU2K8GEjss8U2vklR1XsWJy1fwfj03+66Q78Uk/Q==
   dependencies:
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/styled-system@2.9.1", "@chakra-ui/styled-system@^2.9.1":
@@ -763,127 +765,127 @@
     csstype "^3.0.11"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/switch@2.0.27":
-  version "2.0.27"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/switch/-/switch-2.0.27.tgz#e76e5afdfc837c83fce34480de4431ff8c19fcb8"
-  integrity sha512-z76y2fxwMlvRBrC5W8xsZvo3gP+zAEbT3Nqy5P8uh/IPd5OvDsGeac90t5cgnQTyxMOpznUNNK+1eUZqtLxWnQ==
+"@chakra-ui/switch@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/switch/-/switch-2.1.0.tgz#750474ef7f0a9854062e692cbbe9f3ed0cfbc4d8"
+  integrity sha512-uWHOaIDQdGh+mszxeppj5aYVepbkSK445KZlJJkfr9Bnr6sythTwM63HSufnVDiTEE4uRqegv9jEjZK2JKA+9A==
   dependencies:
-    "@chakra-ui/checkbox" "2.2.15"
+    "@chakra-ui/checkbox" "2.3.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/system@2.5.8":
-  version "2.5.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/system/-/system-2.5.8.tgz#9026090b792320683bf1cc3a8f04af7b10c947ce"
-  integrity sha512-Vy8UUaCxikOzOGE54IP8tKouvU38rEYU1HCSquU9+oe7Jd70HaiLa4vmUKvHyMUmxkOzDHIkgZLbVQCubSnN5w==
+"@chakra-ui/system@2.6.0":
+  version "2.6.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/system/-/system-2.6.0.tgz#29f65bb0887ee0816bbb7b4b098ca5aa4918c409"
+  integrity sha512-MgAFRz9V1pW0dplwWsB99hx49LCC+LsrkMala7KXcP0OvWdrkjw+iu+voBksO3626+glzgIwlZW113Eja+7JEQ==
   dependencies:
-    "@chakra-ui/color-mode" "2.1.12"
+    "@chakra-ui/color-mode" "2.2.0"
     "@chakra-ui/object-utils" "2.1.0"
     "@chakra-ui/react-utils" "2.0.12"
     "@chakra-ui/styled-system" "2.9.1"
-    "@chakra-ui/theme-utils" "2.0.18"
+    "@chakra-ui/theme-utils" "2.0.19"
     "@chakra-ui/utils" "2.0.15"
     react-fast-compare "3.2.1"
 
-"@chakra-ui/table@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/table/-/table-2.0.17.tgz#ad394dc6dcbe5a8a9e6d899997ecca3471603977"
-  integrity sha512-OScheTEp1LOYvTki2NFwnAYvac8siAhW9BI5RKm5f5ORL2gVJo4I72RUqE0aKe1oboxgm7CYt5afT5PS5cG61A==
+"@chakra-ui/table@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/table/-/table-2.1.0.tgz#20dce14c5e4d70dc7c6c0e87cce9b05907ff8c50"
+  integrity sha512-o5OrjoHCh5uCLdiUb0Oc0vq9rIAeHSIRScc2ExTC9Qg/uVZl2ygLrjToCaKfaaKl1oQexIeAcZDKvPG8tVkHyQ==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/tabs@2.1.9":
-  version "2.1.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tabs/-/tabs-2.1.9.tgz#2e5214cb453c6cc0c240e82bd88af1042fc6fe0e"
-  integrity sha512-Yf8e0kRvaGM6jfkJum0aInQ0U3ZlCafmrYYni2lqjcTtThqu+Yosmo3iYlnullXxCw5MVznfrkb9ySvgQowuYg==
+"@chakra-ui/tabs@2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tabs/-/tabs-2.2.0.tgz#72b6bf8bff3d3da3effb115991bf24e2157e29d2"
+  integrity sha512-ulN7McHZ322qlbJXg8S+IwdN8Axh8q0HzYBOHzSdcnVphEytfv9TsfJhN0Hx5yjkpekAzG5fewn33ZdIpIpKyQ==
   dependencies:
-    "@chakra-ui/clickable" "2.0.14"
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/clickable" "2.1.0"
+    "@chakra-ui/descendant" "3.1.0"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-controllable-state" "2.0.8"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
-    "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-controllable-state" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
+    "@chakra-ui/react-use-safe-layout-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/tag@3.0.0":
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tag/-/tag-3.0.0.tgz#d86cdab59bb3ff7fc628c2dbe7a5ff1b36bd3e96"
-  integrity sha512-YWdMmw/1OWRwNkG9pX+wVtZio+B89odaPj6XeMn5nfNN8+jyhIEpouWv34+CO9G0m1lupJTxPSfgLAd7cqXZMA==
+"@chakra-ui/tag@3.1.0":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tag/-/tag-3.1.0.tgz#b2c06254e1d5aaaf77ff41e5d2e6548b404cabd7"
+  integrity sha512-Mn2u828z5HvqEBEG+tUJWe3al5tzN87bK2U0QfThx3+zqWbBCWBSCVfnWRtkNh80m+5a1TekexDAPZqu5G8zdw==
   dependencies:
-    "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/icon" "3.1.0"
+    "@chakra-ui/react-context" "2.1.0"
 
-"@chakra-ui/textarea@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/textarea/-/textarea-2.0.19.tgz#470b459f9cb3255d2abbe07d46b0a5b60a6a32c5"
-  integrity sha512-adJk+qVGsFeJDvfn56CcJKKse8k7oMGlODrmpnpTdF+xvlsiTM+1GfaJvgNSpHHuQFdz/A0z1uJtfGefk0G2ZA==
+"@chakra-ui/textarea@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/textarea/-/textarea-2.1.0.tgz#d0f157c9a09aea87c372409eead4292bd26999a0"
+  integrity sha512-4F7X/lPRsY+sPxYrWGrhh1pBtdnFvVllIOapzAwnjYwsflm+vf6c+9ZgoDWobXsNezJ9fcqN0FTPwaBnDvDQRQ==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/theme-tools@2.0.18", "@chakra-ui/theme-tools@^2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-tools/-/theme-tools-2.0.18.tgz#8160f0abe331e60b56f77426c28ff9a605c1a5c4"
-  integrity sha512-MbiRuXb2tb41FbnW41zhsYYAU0znlpfYZnu0mxCf8U2otCwPekJCfESUGYypjq4JnydQ7TDOk+Kz/Wi974l4mw==
+"@chakra-ui/theme-tools@2.1.0", "@chakra-ui/theme-tools@^2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-tools/-/theme-tools-2.1.0.tgz#ad34f2fdda61305ac96f69ad9ce38ad12f8bfdbf"
+  integrity sha512-TKv4trAY8q8+DWdZrpSabTd3SZtZrnzFDwUdzhbWBhFEDEVR3fAkRTPpnPDtf1X9w1YErWn3QAcMACVFz4+vkw==
   dependencies:
-    "@chakra-ui/anatomy" "2.1.2"
+    "@chakra-ui/anatomy" "2.2.0"
     "@chakra-ui/shared-utils" "2.0.5"
     color2k "^2.0.0"
 
-"@chakra-ui/theme-utils@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-utils/-/theme-utils-2.0.18.tgz#c240545d0f00b6cc059195a784683d1f143a44af"
-  integrity sha512-aSbkUUiFpc1NHC7lQdA6uYlr6EcZFXz6b4aJ7VRDpqTiywvqYnvfGzhmsB0z94vgtS9qXc6HoIwBp25jYGV2MA==
+"@chakra-ui/theme-utils@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-utils/-/theme-utils-2.0.19.tgz#47e6af43f8ef22403686b779ca1a869ab1b7a5ec"
+  integrity sha512-UQ+KvozTN86+0oA80rdQd1a++4rm4ulo+DEabkgwNpkK3yaWsucOxkDQpi2sMIMvw5X0oaWvNBZJuVyK7HdOXg==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/styled-system" "2.9.1"
-    "@chakra-ui/theme" "3.1.2"
+    "@chakra-ui/theme" "3.2.0"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/theme@3.1.2":
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme/-/theme-3.1.2.tgz#1e78a19083adecb38b884c1c2da6dee2c84c81f2"
-  integrity sha512-ebUXMS3LZw2OZxEQNYaFw3/XuA3jpyprhS/frjHMvZKSOaCjMW+c9z25S0jp1NnpQff08VGI8EWbyVZECXU1QA==
+"@chakra-ui/theme@3.2.0":
+  version "3.2.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme/-/theme-3.2.0.tgz#b8232d770e542895cf82535942a74ad803bb7133"
+  integrity sha512-q9mppdkhmaBnvOT8REr/lVNNBX/prwm50EzObJ+r+ErVhNQDc55gCFmtr+It3xlcCqmOteG6XUdwRCJz8qzOqg==
   dependencies:
-    "@chakra-ui/anatomy" "2.1.2"
+    "@chakra-ui/anatomy" "2.2.0"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/theme-tools" "2.0.18"
+    "@chakra-ui/theme-tools" "2.1.0"
 
-"@chakra-ui/toast@6.1.4":
-  version "6.1.4"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/toast/-/toast-6.1.4.tgz#853a844408c0e22f15c66b4f2607b8416300c649"
-  integrity sha512-wAcPHq/N/ar4jQxkUGhnsbp+lx2eKOpHxn1KaWdHXUkqCNUA1z09fvBsoMyzObSiiwbDuQPZG5RxsOhzfPZX4Q==
+"@chakra-ui/toast@7.0.0":
+  version "7.0.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/toast/-/toast-7.0.0.tgz#6c6f1b7b8dc458ed0827b2edc47eb7d4075c95dc"
+  integrity sha512-XQgSnn4DYRgfOBzBvh8GI/AZ7SfrO8wlVSmChfp92Nfmqm7tRDUT9x8ws/iNKAvMRHkhl7fmRjJ39ipeXYrMvA==
   dependencies:
-    "@chakra-ui/alert" "2.1.0"
-    "@chakra-ui/close-button" "2.0.17"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/react-context" "2.0.8"
-    "@chakra-ui/react-use-timeout" "2.0.5"
-    "@chakra-ui/react-use-update-effect" "2.0.7"
+    "@chakra-ui/alert" "2.2.0"
+    "@chakra-ui/close-button" "2.1.0"
+    "@chakra-ui/portal" "2.1.0"
+    "@chakra-ui/react-context" "2.1.0"
+    "@chakra-ui/react-use-timeout" "2.1.0"
+    "@chakra-ui/react-use-update-effect" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/styled-system" "2.9.1"
-    "@chakra-ui/theme" "3.1.2"
+    "@chakra-ui/theme" "3.2.0"
 
-"@chakra-ui/tooltip@2.2.9":
-  version "2.2.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tooltip/-/tooltip-2.2.9.tgz#a7f25f7e6d1304ea448a3420ed99f79a657537bd"
-  integrity sha512-ZoksllanqXRUyMDaiogvUVJ+RdFXwZrfrwx3RV22fejYZIQ602hZ3QHtHLB5ZnKFLbvXKMZKM23HxFTSb0Ytqg==
+"@chakra-ui/tooltip@2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tooltip/-/tooltip-2.3.0.tgz#88e61a2c5a4d99af805840377dc940d3b17c806e"
+  integrity sha512-2s23f93YIij1qEDwIK//KtEu4LLYOslhR1cUhDBk/WUzyFR3Ez0Ee+HlqlGEGfGe9x77E6/UXPnSAKKdF/cpsg==
   dependencies:
     "@chakra-ui/dom-utils" "2.1.0"
-    "@chakra-ui/popper" "3.0.14"
-    "@chakra-ui/portal" "2.0.16"
+    "@chakra-ui/popper" "3.1.0"
+    "@chakra-ui/portal" "2.1.0"
     "@chakra-ui/react-types" "2.0.7"
-    "@chakra-ui/react-use-disclosure" "2.0.8"
-    "@chakra-ui/react-use-event-listener" "2.0.7"
-    "@chakra-ui/react-use-merge-refs" "2.0.7"
+    "@chakra-ui/react-use-disclosure" "2.1.0"
+    "@chakra-ui/react-use-event-listener" "2.1.0"
+    "@chakra-ui/react-use-merge-refs" "2.1.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/transition@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/transition/-/transition-2.0.16.tgz#498c91e6835bb5d950fd1d1402f483b85f7dcd87"
-  integrity sha512-E+RkwlPc3H7P1crEXmXwDXMB2lqY2LLia2P5siQ4IEnRWIgZXlIw+8Em+NtHNgusel2N+9yuB0wT9SeZZeZ3CQ==
+"@chakra-ui/transition@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/transition/-/transition-2.1.0.tgz#c8e95564f7ab356e78119780037bae5ad150c7b3"
+  integrity sha512-orkT6T/Dt+/+kVwJNy7zwJ+U2xAZ3EU7M3XCs45RBvUnZDr/u9vdmaM/3D/rOpmQJWgQBwKPJleUXrYWUagEDQ==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
@@ -897,10 +899,10 @@
     framesync "6.1.2"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/visually-hidden@2.0.15":
-  version "2.0.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/visually-hidden/-/visually-hidden-2.0.15.tgz#60df64e0ab97d95fee4e6c61ccabd15fd5ace398"
-  integrity sha512-WWULIiucYRBIewHKFA7BssQ2ABLHLVd9lrUo3N3SZgR0u4ZRDDVEUNOy+r+9ruDze8+36dGbN9wsN1IdELtdOw==
+"@chakra-ui/visually-hidden@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/visually-hidden/-/visually-hidden-2.1.0.tgz#9ec573964e5a07f8682574a060140d78e9f91c32"
+  integrity sha512-3OHKqTz78PX7V4qto+a5Y6VvH6TbU3Pg6Z0Z2KnDkOBP3Po8fiz0kk+/OSPzIwdcSsQKiocLi0c1pnnUPdMZPg==
 
 "@dagrejs/graphlib@^2.1.13":
   version "2.1.13"
@@ -2328,15 +2330,22 @@
   resolved "https://registry.yarnpkg.com/@yarnpkg/lockfile/-/lockfile-1.1.0.tgz#e77a97fbd345b76d83245edcd17d393b1b41fb31"
   integrity sha512-GpSwvyXOcOOlV70vbnzjj4fW5xW/FdUF6nQEt1ENy7m4ZCczi1+/buVUPAqmGfqznsORNFzUMjctTIp8a9tuCQ==
 
-"@zag-js/element-size@0.3.2":
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.3.2.tgz#ebb76af2a024230482406db41344598d1a9f54f4"
-  integrity sha512-bVvvigUGvAuj7PCkE5AbzvTJDTw5f3bg9nQdv+ErhVN8SfPPppLJEmmWdxqsRzrHXgx8ypJt/+Ty0kjtISVDsQ==
+"@zag-js/dom-query@0.10.5":
+  version "0.10.5"
+  resolved "https://registry.yarnpkg.com/@zag-js/dom-query/-/dom-query-0.10.5.tgz#9fc02a51383989667694d8747925e6d20294af9e"
+  integrity sha512-zm6wA5+kqU48it6afNjaUhjVSixKZruTKB23z0V1xBqKbuiLOMMOZ5oK26cTPSXtZ5CPhDNZ2Qk4pliS5n9SVw==
 
-"@zag-js/focus-visible@0.2.2":
-  version "0.2.2"
-  resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.2.2.tgz#56233480ca1275d3218fb2e10696a33d1a6b9e64"
-  integrity sha512-0j2gZq8HiZ51z4zNnSkF1iSkqlwRDvdH+son3wHdoz+7IUdMN/5Exd4TxMJ+gq2Of1DiXReYLL9qqh2PdQ4wgA==
+"@zag-js/element-size@0.10.5":
+  version "0.10.5"
+  resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.10.5.tgz#a24bad2eeb7e2c8709e32be5336e158e1a1a174f"
+  integrity sha512-uQre5IidULANvVkNOBQ1tfgwTQcGl4hliPSe69Fct1VfYb2Fd0jdAcGzqQgPhfrXFpR62MxLPB7erxJ/ngtL8w==
+
+"@zag-js/focus-visible@0.10.5":
+  version "0.10.5"
+  resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.10.5.tgz#643e196ea768bea5ce54771102fae22f5e9ba3c7"
+  integrity sha512-EhDHKLutMtvLFCjBjyIY6h1JoJJNXG3KJz7Dj1sh4tj4LWAqo/TqLvgHyUTB29XMHwoslFHDJHKVWmLGMi+ULQ==
+  dependencies:
+    "@zag-js/dom-query" "0.10.5"
 
 acorn-jsx@^5.3.2:
   version "5.3.2"
diff --git a/invokeai/version/__init__.py b/invokeai/version/__init__.py
index 01ef84ea4d..21dfcad3ca 100644
--- a/invokeai/version/__init__.py
+++ b/invokeai/version/__init__.py
@@ -6,6 +6,7 @@ from .invokeai_version import __version__
 __app_id__ = "invoke-ai/InvokeAI"
 __app_name__ = "InvokeAI"
 
+
 def _ignore_xformers_triton_message_on_windows():
     import logging
 
diff --git a/invokeai/version/invokeai_version.py b/invokeai/version/invokeai_version.py
index 528787cfc8..0552768781 100644
--- a/invokeai/version/invokeai_version.py
+++ b/invokeai/version/invokeai_version.py
@@ -1 +1 @@
-__version__ = "3.0.0"
+__version__ = "3.0.1"
diff --git a/notebooks/notebook_helpers.py b/notebooks/notebook_helpers.py
index 663b212ac5..b87f18ba18 100644
--- a/notebooks/notebook_helpers.py
+++ b/notebooks/notebook_helpers.py
@@ -2,6 +2,7 @@ from torchvision.datasets.utils import download_url
 from ldm.util import instantiate_from_config
 import torch
 import os
+
 # todo ?
 from google.colab import files
 from IPython.display import Image as ipyimg
@@ -16,21 +17,21 @@ import time
 from omegaconf import OmegaConf
 from ldm.invoke.devices import choose_torch_device
 
-def download_models(mode):
 
+def download_models(mode):
     if mode == "superresolution":
         # this is the small bsr light model
-        url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
-        url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'
+        url_conf = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        url_ckpt = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
 
-        path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml'
-        path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt'
+        path_conf = "logs/diffusion/superresolution_bsr/configs/project.yaml"
+        path_ckpt = "logs/diffusion/superresolution_bsr/checkpoints/last.ckpt"
 
         download_url(url_conf, path_conf)
         download_url(url_ckpt, path_ckpt)
 
-        path_conf = path_conf + '/?dl=1' # fix it
-        path_ckpt = path_ckpt + '/?dl=1' # fix it
+        path_conf = path_conf + "/?dl=1"  # fix it
+        path_ckpt = path_ckpt + "/?dl=1"  # fix it
         return path_conf, path_ckpt
 
     else:
@@ -62,20 +63,20 @@ def get_custom_cond(mode):
     if mode == "superresolution":
         uploaded_img = files.upload()
         filename = next(iter(uploaded_img))
-        name, filetype = filename.split(".") # todo assumes just one dot in name !
+        name, filetype = filename.split(".")  # todo assumes just one dot in name !
         os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")
 
     elif mode == "text_conditional":
-        w = widgets.Text(value='A cake with cream!', disabled=True)
+        w = widgets.Text(value="A cake with cream!", disabled=True)
         display(w)
 
-        with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
+        with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", "w") as f:
             f.write(w.value)
 
     elif mode == "class_conditional":
         w = widgets.IntSlider(min=0, max=1000)
         display(w)
-        with open(f"{dest}/{mode}/custom.txt", 'w') as f:
+        with open(f"{dest}/{mode}/custom.txt", "w") as f:
             f.write(w.value)
 
     else:
@@ -94,11 +95,7 @@ def select_cond_path(mode):
     path = os.path.join(path, mode)
     onlyfiles = [f for f in sorted(os.listdir(path))]
 
-    selected = widgets.RadioButtons(
-        options=onlyfiles,
-        description='Select conditioning:',
-        disabled=False
-    )
+    selected = widgets.RadioButtons(options=onlyfiles, description="Select conditioning:", disabled=False)
     display(selected)
     selected_path = os.path.join(path, selected.value)
     return selected_path
@@ -113,9 +110,9 @@ def get_cond(mode, selected_path):
         c = Image.open(selected_path)
         c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
         c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)
-        c_up = rearrange(c_up, '1 c h w -> 1 h w c')
-        c = rearrange(c, '1 c h w -> 1 h w c')
-        c = 2. * c - 1.
+        c_up = rearrange(c_up, "1 c h w -> 1 h w c")
+        c = rearrange(c, "1 c h w -> 1 h w c")
+        c = 2.0 * c - 1.0
 
         device = choose_torch_device()
         c = c.to(device)
@@ -130,7 +127,6 @@ def visualize_cond_img(path):
 
 
 def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None):
-
     example = get_cond(task, selected_path)
 
     save_intermediate_vid = False
@@ -138,10 +134,10 @@ def run(model, selected_path, task, custom_steps, resize_enabled=False, classifi
     masked = False
     guider = None
     ckwargs = None
-    mode = 'ddim'
+    mode = "ddim"
     ddim_use_x0_pred = False
-    temperature = 1.
-    eta = 1.
+    temperature = 1.0
+    eta = 1.0
     make_progrow = True
     custom_shape = None
 
@@ -152,14 +148,17 @@ def run(model, selected_path, task, custom_steps, resize_enabled=False, classifi
         ks = 128
         stride = 64
         vqf = 4  #
-        model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
-                                    "vqf": vqf,
-                                    "patch_distributed_vq": True,
-                                    "tie_braker": False,
-                                    "clip_max_weight": 0.5,
-                                    "clip_min_weight": 0.01,
-                                    "clip_max_tie_weight": 0.5,
-                                    "clip_min_tie_weight": 0.01}
+        model.split_input_params = {
+            "ks": (ks, ks),
+            "stride": (stride, stride),
+            "vqf": vqf,
+            "patch_distributed_vq": True,
+            "tie_braker": False,
+            "clip_max_weight": 0.5,
+            "clip_min_weight": 0.01,
+            "clip_max_tie_weight": 0.5,
+            "clip_min_tie_weight": 0.01,
+        }
     else:
         if hasattr(model, "split_input_params"):
             delattr(model, "split_input_params")
@@ -170,53 +169,112 @@ def run(model, selected_path, task, custom_steps, resize_enabled=False, classifi
     for n in range(n_runs):
         if custom_shape is not None:
             x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
-            x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])
+            x_T = repeat(x_T, "1 c h w -> b c h w", b=custom_shape[0])
 
-        logs = make_convolutional_sample(example, model,
-                                         mode=mode, custom_steps=custom_steps,
-                                         eta=eta, swap_mode=False , masked=masked,
-                                         invert_mask=invert_mask, quantize_x0=False,
-                                         custom_schedule=None, decode_interval=10,
-                                         resize_enabled=resize_enabled, custom_shape=custom_shape,
-                                         temperature=temperature, noise_dropout=0.,
-                                         corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid,
-                                         make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred
-                                         )
+        logs = make_convolutional_sample(
+            example,
+            model,
+            mode=mode,
+            custom_steps=custom_steps,
+            eta=eta,
+            swap_mode=False,
+            masked=masked,
+            invert_mask=invert_mask,
+            quantize_x0=False,
+            custom_schedule=None,
+            decode_interval=10,
+            resize_enabled=resize_enabled,
+            custom_shape=custom_shape,
+            temperature=temperature,
+            noise_dropout=0.0,
+            corrector=guider,
+            corrector_kwargs=ckwargs,
+            x_T=x_T,
+            save_intermediate_vid=save_intermediate_vid,
+            make_progrow=make_progrow,
+            ddim_use_x0_pred=ddim_use_x0_pred,
+        )
     return logs
 
 
 @torch.no_grad()
-def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
-                    mask=None, x0=None, quantize_x0=False, img_callback=None,
-                    temperature=1., noise_dropout=0., score_corrector=None,
-                    corrector_kwargs=None, x_T=None, log_every_t=None
-                    ):
-
+def convsample_ddim(
+    model,
+    cond,
+    steps,
+    shape,
+    eta=1.0,
+    callback=None,
+    normals_sequence=None,
+    mask=None,
+    x0=None,
+    quantize_x0=False,
+    img_callback=None,
+    temperature=1.0,
+    noise_dropout=0.0,
+    score_corrector=None,
+    corrector_kwargs=None,
+    x_T=None,
+    log_every_t=None,
+):
     ddim = DDIMSampler(model)
     bs = shape[0]  # dont know where this comes from but wayne
     shape = shape[1:]  # cut batch dim
     print(f"Sampling with eta = {eta}; steps: {steps}")
-    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
-                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
-                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
-                                         score_corrector=score_corrector,
-                                         corrector_kwargs=corrector_kwargs, x_T=x_T)
+    samples, intermediates = ddim.sample(
+        steps,
+        batch_size=bs,
+        shape=shape,
+        conditioning=cond,
+        callback=callback,
+        normals_sequence=normals_sequence,
+        quantize_x0=quantize_x0,
+        eta=eta,
+        mask=mask,
+        x0=x0,
+        temperature=temperature,
+        verbose=False,
+        score_corrector=score_corrector,
+        corrector_kwargs=corrector_kwargs,
+        x_T=x_T,
+    )
 
     return samples, intermediates
 
 
 @torch.no_grad()
-def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
-                              invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
-                              resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
-                              corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):
+def make_convolutional_sample(
+    batch,
+    model,
+    mode="vanilla",
+    custom_steps=None,
+    eta=1.0,
+    swap_mode=False,
+    masked=False,
+    invert_mask=True,
+    quantize_x0=False,
+    custom_schedule=None,
+    decode_interval=1000,
+    resize_enabled=False,
+    custom_shape=None,
+    temperature=1.0,
+    noise_dropout=0.0,
+    corrector=None,
+    corrector_kwargs=None,
+    x_T=None,
+    save_intermediate_vid=False,
+    make_progrow=True,
+    ddim_use_x0_pred=False,
+):
     log = dict()
 
-    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
-                                        return_first_stage_outputs=True,
-                                        force_c_encode=not (hasattr(model, 'split_input_params')
-                                                            and model.cond_stage_key == 'coordinates_bbox'),
-                                        return_original_cond=True)
+    z, c, x, xrec, xc = model.get_input(
+        batch,
+        model.first_stage_key,
+        return_first_stage_outputs=True,
+        force_c_encode=not (hasattr(model, "split_input_params") and model.cond_stage_key == "coordinates_bbox"),
+        return_original_cond=True,
+    )
 
     log_every_t = 1 if save_intermediate_vid else None
 
@@ -231,30 +289,41 @@ def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, e
 
     if ismap(xc):
         log["original_conditioning"] = model.to_rgb(xc)
-        if hasattr(model, 'cond_stage_key'):
+        if hasattr(model, "cond_stage_key"):
             log[model.cond_stage_key] = model.to_rgb(xc)
 
     else:
         log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
         if model.cond_stage_model:
             log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
-            if model.cond_stage_key =='class_label':
+            if model.cond_stage_key == "class_label":
                 log[model.cond_stage_key] = xc[model.cond_stage_key]
 
     with model.ema_scope("Plotting"):
         t0 = time.time()
         img_cb = None
 
-        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
-                                                eta=eta,
-                                                quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
-                                                temperature=temperature, noise_dropout=noise_dropout,
-                                                score_corrector=corrector, corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T, log_every_t=log_every_t)
+        sample, intermediates = convsample_ddim(
+            model,
+            c,
+            steps=custom_steps,
+            shape=z.shape,
+            eta=eta,
+            quantize_x0=quantize_x0,
+            img_callback=img_cb,
+            mask=None,
+            x0=z0,
+            temperature=temperature,
+            noise_dropout=noise_dropout,
+            score_corrector=corrector,
+            corrector_kwargs=corrector_kwargs,
+            x_T=x_T,
+            log_every_t=log_every_t,
+        )
         t1 = time.time()
 
         if ddim_use_x0_pred:
-            sample = intermediates['pred_x0'][-1]
+            sample = intermediates["pred_x0"][-1]
 
     x_sample = model.decode_first_stage(sample)
 
diff --git a/pyproject.toml b/pyproject.toml
index 5cc6971df6..a97830c670 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "InvokeAI"
 description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
-requires-python = ">=3.9, <3.11"
+requires-python = ">=3.9, <3.12"
 readme = { content-type = "text/markdown", file = "README.md" }
 keywords = ["stable-diffusion", "AI"]
 dynamic = ["version"]
@@ -32,16 +32,16 @@ classifiers = [
   'Topic :: Scientific/Engineering :: Image Processing',
 ]
 dependencies = [
-  "accelerate~=0.16",
+  "accelerate~=0.21.0",
   "albumentations",
   "click",
-  "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
-  "compel==2.0.0",
+  "clip_anytorch",  # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
+  "compel~=2.0.0",
   "controlnet-aux>=0.0.6",
-  "timm==0.6.13",           # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
+  "timm==0.6.13",   # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
-  "diffusers[torch]~=0.18.1",
-  "dnspython==2.2.1",
+  "diffusers[torch]~=0.19.0",
+  "dnspython~=2.4.0",
   "dynamicprompts",
   "easing-functions",
   "einops",
@@ -54,37 +54,37 @@ dependencies = [
   "flask_cors==3.0.10",
   "flask_socketio==5.3.0",
   "flaskwebgui==1.0.3",
-  "gfpgan==1.3.8",
   "huggingface-hub>=0.11.1",
-  "invisible-watermark>=0.2.0", # needed to install SDXL base and refiner using their repo_ids
+  "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                 # needed for plotting of Penner easing functions
   "mediapipe",                  # needed for "mediapipeface" controlnet model
   "npyscreen",
-  "numpy<1.24",
+  "numpy==1.24.4",
   "omegaconf",
   "opencv-python",
   "picklescan",
   "pillow",
   "prompt-toolkit",
-  "pympler==1.0.1",
+  "pydantic==1.10.10",
+  "pympler~=1.0.1",
   "pypatchmatch",
   'pyperclip',
   "pyreadline3",
-  "python-multipart==0.0.6",
-  "pytorch-lightning==1.7.7",
+  "python-multipart",
+  "pytorch-lightning",
   "realesrgan",
-  "requests==2.28.2",
+  "requests~=2.28.2",
   "rich~=13.3",
   "safetensors~=0.3.0",
-  "scikit-image>=0.19",
+  "scikit-image~=0.21.0",
   "send2trash",
-  "test-tube>=0.7.5",
-  "torch~=2.0.0",
-  "torchvision>=0.14.1",
-  "torchmetrics==0.11.4",
-  "torchsde==0.2.5",
+  "test-tube~=0.7.5",
+  "torch~=2.0.1",
+  "torchvision~=0.15.2",
+  "torchmetrics~=1.0.1",
+  "torchsde~=0.2.5",
   "transformers~=4.31.0",
-  "uvicorn[standard]==0.21.1",
+  "uvicorn[standard]~=0.21.1",
   "windows-curses; sys_platform=='win32'",
 ]
 
@@ -98,7 +98,7 @@ dependencies = [
 "dev" = [
   "pudb",
 ]
-"test" = ["pytest>6.0.0", "pytest-cov"]
+"test" = ["pytest>6.0.0", "pytest-cov", "black"]
 "xformers" = [
 	   "xformers~=0.0.19; sys_platform!='darwin'",
 	   "triton; sys_platform=='linux'",
@@ -176,5 +176,8 @@ directory = "coverage/html"
 output = "coverage/index.xml"
 #=== End: PyTest and Coverage
 
-[flake8]
+[tool.flake8]
 max-line-length = 120
+
+[tool.black]
+line-length = 120
\ No newline at end of file
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 0226fa1c2a..61d32b6df5 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -4,6 +4,6 @@
 import warnings
 from invokeai.frontend.install import invokeai_configure as configure
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     warnings.warn("configure_invokeai.py is deprecated, running 'invokeai-configure'...", DeprecationWarning)
     configure()
diff --git a/scripts/controlnet_legacy_txt2img_example.py b/scripts/controlnet_legacy_txt2img_example.py
index eb299c9d47..8400cc0290 100644
--- a/scripts/controlnet_legacy_txt2img_example.py
+++ b/scripts/controlnet_legacy_txt2img_example.py
@@ -28,11 +28,12 @@ canny_image.show()
 print("loading base model stable-diffusion-1.5")
 model_config_path = os.getcwd() + "/../configs/models.yaml"
 model_manager = ModelManager(model_config_path)
-model = model_manager.get_model('stable-diffusion-1.5')
+model = model_manager.get_model("stable-diffusion-1.5")
 
 print("loading control model lllyasviel/sd-controlnet-canny")
-canny_controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny",
-                                                   torch_dtype=torch.float16).to("cuda")
+canny_controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16).to(
+    "cuda"
+)
 
 print("testing Txt2Img() constructor with control_model arg")
 txt2img_canny = Txt2Img(model, control_model=canny_controlnet)
@@ -49,6 +50,3 @@ outputs = txt2img_canny.generate(
 generate_output = next(outputs)
 out_image = generate_output.image
 out_image.show()
-
-
-
diff --git a/scripts/dream.py b/scripts/dream.py
index 66c7600c6f..12176db41e 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -3,8 +3,9 @@
 
 import warnings
 from invokeai.frontend.CLI import invokeai_command_line_interface as main
-warnings.warn("dream.py is being deprecated, please run invoke.py for the "
-              "new UI/API or legacy_api.py for the old API",
-              DeprecationWarning)
-main()
 
+warnings.warn(
+    "dream.py is being deprecated, please run invoke.py for the " "new UI/API or legacy_api.py for the old API",
+    DeprecationWarning,
+)
+main()
diff --git a/scripts/images2prompt.py b/scripts/images2prompt.py
index 625be83482..058fc0da40 100755
--- a/scripts/images2prompt.py
+++ b/scripts/images2prompt.py
@@ -1,12 +1,14 @@
 #!/usr/bin/env python
-'''This script reads the "Invoke" Stable Diffusion prompt embedded in files generated by invoke.py'''
+"""This script reads the "Invoke" Stable Diffusion prompt embedded in files generated by invoke.py"""
 
 import sys
-from PIL import Image,PngImagePlugin
+from PIL import Image, PngImagePlugin
 
 if len(sys.argv) < 2:
     print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
-    print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out the prompt used to generate them.")
+    print(
+        "This script opens up the indicated invoke.py-generated PNG file(s) and prints out the prompt used to generate them."
+    )
     exit(-1)
 
 filenames = sys.argv[1:]
@@ -14,17 +16,13 @@ for f in filenames:
     try:
         im = Image.open(f)
         try:
-            prompt = im.text['Dream']
+            prompt = im.text["Dream"]
         except KeyError:
-            prompt = ''
-        print(f'{f}: {prompt}')
+            prompt = ""
+        print(f"{f}: {prompt}")
     except FileNotFoundError:
-        sys.stderr.write(f'{f} not found\n')
+        sys.stderr.write(f"{f} not found\n")
         continue
     except PermissionError:
-        sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
+        sys.stderr.write(f"{f} could not be opened due to inadequate permissions\n")
         continue
-
-
-
-
diff --git a/scripts/invokeai-cli.py b/scripts/invokeai-cli.py
index bb67d9d540..b32a892261 100755
--- a/scripts/invokeai-cli.py
+++ b/scripts/invokeai-cli.py
@@ -3,18 +3,22 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import logging
-logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+logging.getLogger("xformers").addFilter(lambda record: "A matching Triton is not available" not in record.getMessage())
 
 import os
 import sys
 
+
 def main():
     # Change working directory to the repo root
     os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
     # TODO: Parse some top-level args here.
     from invokeai.app.cli_app import invoke_cli
+
     invoke_cli()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()
diff --git a/scripts/invokeai-model-install.py b/scripts/invokeai-model-install.py
index 97bb499812..b4b0dbc8ef 100644
--- a/scripts/invokeai-model-install.py
+++ b/scripts/invokeai-model-install.py
@@ -1,3 +1,3 @@
 from invokeai.frontend.install.model_install import main
-main()
 
+main()
diff --git a/scripts/invokeai-web.py b/scripts/invokeai-web.py
index 9ac7ee5cb9..829cc4b911 100755
--- a/scripts/invokeai-web.py
+++ b/scripts/invokeai-web.py
@@ -3,18 +3,21 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import logging
-logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+logging.getLogger("xformers").addFilter(lambda record: "A matching Triton is not available" not in record.getMessage())
 
 import os
 import sys
 
+
 def main():
     # Change working directory to the repo root
     os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
     from invokeai.app.api_app import invoke_api
+
     invoke_api()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/scripts/make_models_markdown_table.py b/scripts/make_models_markdown_table.py
index 128ced371d..8e7c528a46 100755
--- a/scripts/make_models_markdown_table.py
+++ b/scripts/make_models_markdown_table.py
@@ -1,23 +1,24 @@
 #!/usr/bin/env python
 
-'''
+"""
 This script is used at release time to generate a markdown table describing the
 starter models. This text is then manually copied into 050_INSTALL_MODELS.md.
-'''
+"""
 
 from omegaconf import OmegaConf
 from pathlib import Path
 
 
 def main():
-    initial_models_file = Path(__file__).parent / '../invokeai/configs/INITIAL_MODELS.yaml'
+    initial_models_file = Path(__file__).parent / "../invokeai/configs/INITIAL_MODELS.yaml"
     models = OmegaConf.load(initial_models_file)
-    print('|Model Name | HuggingFace Repo ID | Description | URL |')
-    print('|---------- | ---------- | ----------- | --- |')
+    print("|Model Name | HuggingFace Repo ID | Description | URL |")
+    print("|---------- | ---------- | ----------- | --- |")
     for model in models:
         repo_id = models[model].repo_id
-        url = f'https://huggingface.co/{repo_id}'
-        print(f'|{model}|{repo_id}|{models[model].description}|{url} |')
+        url = f"https://huggingface.co/{repo_id}"
+        print(f"|{model}|{repo_id}|{models[model].description}|{url} |")
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()
diff --git a/scripts/orig_scripts/img2img.py b/scripts/orig_scripts/img2img.py
index 9f74f25bf2..2601bc2562 100644
--- a/scripts/orig_scripts/img2img.py
+++ b/scripts/orig_scripts/img2img.py
@@ -18,7 +18,7 @@ from pytorch_lightning import seed_everything
 from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-from ldm.invoke.devices         import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 
 
 def chunk(it, size):
@@ -55,7 +55,7 @@ def load_img(path):
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
     image = torch.from_numpy(image)
-    return 2.*image - 1.
+    return 2.0 * image - 1.0
 
 
 def main():
@@ -66,33 +66,24 @@ def main():
         type=str,
         nargs="?",
         default="a painting of a virus monster playing guitar",
-        help="the prompt to render"
+        help="the prompt to render",
     )
 
-    parser.add_argument(
-        "--init-img",
-        type=str,
-        nargs="?",
-        help="path to the input image"
-    )
+    parser.add_argument("--init-img", type=str, nargs="?", help="path to the input image")
 
     parser.add_argument(
-        "--outdir",
-        type=str,
-        nargs="?",
-        help="dir to write results to",
-        default="outputs/img2img-samples"
+        "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/img2img-samples"
     )
 
     parser.add_argument(
         "--skip_grid",
-        action='store_true',
+        action="store_true",
         help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
     )
 
     parser.add_argument(
         "--skip_save",
-        action='store_true',
+        action="store_true",
         help="do not save indiviual samples. For speed measurements.",
     )
 
@@ -105,12 +96,12 @@ def main():
 
     parser.add_argument(
         "--plms",
-        action='store_true',
+        action="store_true",
         help="use plms sampling",
     )
     parser.add_argument(
         "--fixed_code",
-        action='store_true',
+        action="store_true",
         help="if enabled, uses the same starting code across all samples ",
     )
 
@@ -187,11 +178,7 @@ def main():
         help="the seed (for reproducible sampling)",
     )
     parser.add_argument(
-        "--precision",
-        type=str,
-        help="evaluate at this precision",
-        choices=["full", "autocast"],
-        default="autocast"
+        "--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
     )
 
     opt = parser.parse_args()
@@ -232,18 +219,18 @@ def main():
 
     assert os.path.isfile(opt.init_img)
     init_image = load_img(opt.init_img).to(device)
-    init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
+    init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
     init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space
 
     sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
 
-    assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
+    assert 0.0 <= opt.strength <= 1.0, "can only work with strength in [0.0, 1.0]"
     t_enc = int(opt.strength * opt.ddim_steps)
     print(f"target t_enc is {t_enc} steps")
 
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
-    if device.type in ['mps', 'cpu']:
-        precision_scope = nullcontext # have to use f32 on mps
+    if device.type in ["mps", "cpu"]:
+        precision_scope = nullcontext  # have to use f32 on mps
     with torch.no_grad():
         with precision_scope(device.type):
             with model.ema_scope():
@@ -259,37 +246,42 @@ def main():
                         c = model.get_learned_conditioning(prompts)
 
                         # encode (scaled latent)
-                        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
+                        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
                         # decode it
-                        samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
-                                                 unconditional_conditioning=uc,)
+                        samples = sampler.decode(
+                            z_enc,
+                            c,
+                            t_enc,
+                            unconditional_guidance_scale=opt.scale,
+                            unconditional_conditioning=uc,
+                        )
 
                         x_samples = model.decode_first_stage(samples)
                         x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
                         if not opt.skip_save:
                             for x_sample in x_samples:
-                                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                                x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
                                 Image.fromarray(x_sample.astype(np.uint8)).save(
-                                    os.path.join(sample_path, f"{base_count:05}.png"))
+                                    os.path.join(sample_path, f"{base_count:05}.png")
+                                )
                                 base_count += 1
                         all_samples.append(x_samples)
 
                 if not opt.skip_grid:
                     # additionally, save as grid
                     grid = torch.stack(all_samples, 0)
-                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                    grid = rearrange(grid, "n b c h w -> (n b) c h w")
                     grid = make_grid(grid, nrow=n_rows)
 
                     # to image
-                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+                    grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
+                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
                     grid_count += 1
 
                 toc = time.time()
 
-    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
-          f" \nEnjoy.")
+    print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
 
 
 if __name__ == "__main__":
diff --git a/scripts/orig_scripts/inpaint.py b/scripts/orig_scripts/inpaint.py
index b8245db322..8dfbbfb045 100644
--- a/scripts/orig_scripts/inpaint.py
+++ b/scripts/orig_scripts/inpaint.py
@@ -8,25 +8,26 @@ from main import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.invoke.devices import choose_torch_device
 
+
 def make_batch(image, mask, device):
     image = np.array(Image.open(image).convert("RGB"))
-    image = image.astype(np.float32)/255.0
-    image = image[None].transpose(0,3,1,2)
+    image = image.astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
     image = torch.from_numpy(image)
 
     mask = np.array(Image.open(mask).convert("L"))
-    mask = mask.astype(np.float32)/255.0
-    mask = mask[None,None]
+    mask = mask.astype(np.float32) / 255.0
+    mask = mask[None, None]
     mask[mask < 0.5] = 0
     mask[mask >= 0.5] = 1
     mask = torch.from_numpy(mask)
 
-    masked_image = (1-mask)*image
+    masked_image = (1 - mask) * image
 
     batch = {"image": image, "mask": mask, "masked_image": masked_image}
     for k in batch:
         batch[k] = batch[k].to(device=device)
-        batch[k] = batch[k]*2.0-1.0
+        batch[k] = batch[k] * 2.0 - 1.0
     return batch
 
 
@@ -58,11 +59,10 @@ if __name__ == "__main__":
 
     config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
     model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
-                          strict=False)
+    model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"], strict=False)
 
-    device  = choose_torch_device()
-    model   = model.to(device)
+    device = choose_torch_device()
+    model = model.to(device)
     sampler = DDIMSampler(model)
 
     os.makedirs(opt.outdir, exist_ok=True)
@@ -74,25 +74,19 @@ if __name__ == "__main__":
 
                 # encode masked image and concat downsampled mask
                 c = model.cond_stage_model.encode(batch["masked_image"])
-                cc = torch.nn.functional.interpolate(batch["mask"],
-                                                     size=c.shape[-2:])
+                cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:])
                 c = torch.cat((c, cc), dim=1)
 
-                shape = (c.shape[1]-1,)+c.shape[2:]
-                samples_ddim, _ = sampler.sample(S=opt.steps,
-                                                 conditioning=c,
-                                                 batch_size=c.shape[0],
-                                                 shape=shape,
-                                                 verbose=False)
+                shape = (c.shape[1] - 1,) + c.shape[2:]
+                samples_ddim, _ = sampler.sample(
+                    S=opt.steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False
+                )
                 x_samples_ddim = model.decode_first_stage(samples_ddim)
 
-                image = torch.clamp((batch["image"]+1.0)/2.0,
-                                    min=0.0, max=1.0)
-                mask = torch.clamp((batch["mask"]+1.0)/2.0,
-                                   min=0.0, max=1.0)
-                predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0,
-                                              min=0.0, max=1.0)
+                image = torch.clamp((batch["image"] + 1.0) / 2.0, min=0.0, max=1.0)
+                mask = torch.clamp((batch["mask"] + 1.0) / 2.0, min=0.0, max=1.0)
+                predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-                inpainted = (1-mask)*image+mask*predicted_image
-                inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255
+                inpainted = (1 - mask) * image + mask * predicted_image
+                inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
                 Image.fromarray(inpainted.astype(np.uint8)).save(outpath)
diff --git a/scripts/orig_scripts/knn2img.py b/scripts/orig_scripts/knn2img.py
index e6eaaecab5..845613479b 100644
--- a/scripts/orig_scripts/knn2img.py
+++ b/scripts/orig_scripts/knn2img.py
@@ -59,29 +59,24 @@ def load_model_from_config(config, ckpt, verbose=False):
 
 
 class Searcher(object):
-    def __init__(self, database, retriever_version='ViT-L/14'):
+    def __init__(self, database, retriever_version="ViT-L/14"):
         assert database in DATABASES
         # self.database = self.load_database(database)
         self.database_name = database
-        self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
-        self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
+        self.searcher_savedir = f"data/rdm/searchers/{self.database_name}"
+        self.database_path = f"data/rdm/retrieval_databases/{self.database_name}"
         self.retriever = self.load_retriever(version=retriever_version)
-        self.database = {'embedding': [],
-                         'img_id': [],
-                         'patch_coords': []}
+        self.database = {"embedding": [], "img_id": [], "patch_coords": []}
         self.load_database()
         self.load_searcher()
 
-    def train_searcher(self, k,
-                       metric='dot_product',
-                       searcher_savedir=None):
-
-        print('Start training searcher')
-        searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
-                                                  np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
-                                                  k, metric)
+    def train_searcher(self, k, metric="dot_product", searcher_savedir=None):
+        print("Start training searcher")
+        searcher = scann.scann_ops_pybind.builder(
+            self.database["embedding"] / np.linalg.norm(self.database["embedding"], axis=1)[:, np.newaxis], k, metric
+        )
         self.searcher = searcher.score_brute_force().build()
-        print('Finish training searcher')
+        print("Finish training searcher")
 
         if searcher_savedir is not None:
             print(f'Save trained searcher under "{searcher_savedir}"')
@@ -91,36 +86,40 @@ class Searcher(object):
     def load_single_file(self, saved_embeddings):
         compressed = np.load(saved_embeddings)
         self.database = {key: compressed[key] for key in compressed.files}
-        print('Finished loading of clip embeddings.')
+        print("Finished loading of clip embeddings.")
 
     def load_multi_files(self, data_archive):
         out_data = {key: [] for key in self.database}
-        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+        for d in tqdm(data_archive, desc=f"Loading datapool from {len(data_archive)} individual files."):
             for key in d.files:
                 out_data[key].append(d[key])
 
         return out_data
 
     def load_database(self):
-
         print(f'Load saved patch embedding from "{self.database_path}"')
-        file_content = glob.glob(os.path.join(self.database_path, '*.npz'))
+        file_content = glob.glob(os.path.join(self.database_path, "*.npz"))
 
         if len(file_content) == 1:
             self.load_single_file(file_content[0])
         elif len(file_content) > 1:
             data = [np.load(f) for f in file_content]
-            prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
-                                                     n_proc=min(len(data), cpu_count()), target_data_type='dict')
+            prefetched_data = parallel_data_prefetch(
+                self.load_multi_files, data, n_proc=min(len(data), cpu_count()), target_data_type="dict"
+            )
 
-            self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
-                             self.database}
+            self.database = {
+                key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in self.database
+            }
         else:
             raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
 
         print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
 
-    def load_retriever(self, version='ViT-L/14', ):
+    def load_retriever(
+        self,
+        version="ViT-L/14",
+    ):
         model = FrozenClipImageEmbedder(model=version)
         if torch.cuda.is_available():
             model.cuda()
@@ -128,14 +127,14 @@ class Searcher(object):
         return model
 
     def load_searcher(self):
-        print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
+        print(f"load searcher for database {self.database_name} from {self.searcher_savedir}")
         self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
-        print('Finished loading searcher.')
+        print("Finished loading searcher.")
 
     def search(self, x, k):
-        if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
-            self.train_searcher(k)   # quickly fit searcher on the fly for small databases
-        assert self.searcher is not None, 'Cannot search with uninitialized searcher'
+        if self.searcher is None and self.database["embedding"].shape[0] < 2e4:
+            self.train_searcher(k)  # quickly fit searcher on the fly for small databases
+        assert self.searcher is not None, "Cannot search with uninitialized searcher"
         if isinstance(x, torch.Tensor):
             x = x.detach().cpu().numpy()
         if len(x.shape) == 3:
@@ -146,17 +145,19 @@ class Searcher(object):
         nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
         end = time.time()
 
-        out_embeddings = self.database['embedding'][nns]
-        out_img_ids = self.database['img_id'][nns]
-        out_pc = self.database['patch_coords'][nns]
+        out_embeddings = self.database["embedding"][nns]
+        out_img_ids = self.database["img_id"][nns]
+        out_pc = self.database["patch_coords"][nns]
 
-        out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
-               'img_ids': out_img_ids,
-               'patch_coords': out_pc,
-               'queries': x,
-               'exec_time': end - start,
-               'nns': nns,
-               'q_embeddings': query_embeddings}
+        out = {
+            "nn_embeddings": out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
+            "img_ids": out_img_ids,
+            "patch_coords": out_pc,
+            "queries": x,
+            "exec_time": end - start,
+            "nns": nns,
+            "q_embeddings": query_embeddings,
+        }
 
         return out
 
@@ -173,20 +174,16 @@ if __name__ == "__main__":
         type=str,
         nargs="?",
         default="a painting of a virus monster playing guitar",
-        help="the prompt to render"
+        help="the prompt to render",
     )
 
     parser.add_argument(
-        "--outdir",
-        type=str,
-        nargs="?",
-        help="dir to write results to",
-        default="outputs/txt2img-samples"
+        "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples"
     )
 
     parser.add_argument(
         "--skip_grid",
-        action='store_true',
+        action="store_true",
         help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
     )
 
@@ -206,7 +203,7 @@ if __name__ == "__main__":
 
     parser.add_argument(
         "--plms",
-        action='store_true',
+        action="store_true",
         help="use plms sampling",
     )
 
@@ -287,14 +284,14 @@ if __name__ == "__main__":
     parser.add_argument(
         "--database",
         type=str,
-        default='artbench-surrealism',
+        default="artbench-surrealism",
         choices=DATABASES,
         help="The database used for the search, only applied when --use_neighbors=True",
     )
     parser.add_argument(
         "--use_neighbors",
         default=False,
-        action='store_true',
+        action="store_true",
         help="Include neighbors in addition to text prompt for conditioning",
     )
     parser.add_argument(
@@ -358,41 +355,43 @@ if __name__ == "__main__":
                     uc = None
                     if searcher is not None:
                         nn_dict = searcher(c, opt.knn)
-                        c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
+                        c = torch.cat([c, torch.from_numpy(nn_dict["nn_embeddings"]).cuda()], dim=1)
                     if opt.scale != 1.0:
                         uc = torch.zeros_like(c)
                     if isinstance(prompts, tuple):
                         prompts = list(prompts)
                     shape = [16, opt.H // 16, opt.W // 16]  # note: currently hardcoded for f16 model
-                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
-                                                     conditioning=c,
-                                                     batch_size=c.shape[0],
-                                                     shape=shape,
-                                                     verbose=False,
-                                                     unconditional_guidance_scale=opt.scale,
-                                                     unconditional_conditioning=uc,
-                                                     eta=opt.ddim_eta,
-                                                     )
+                    samples_ddim, _ = sampler.sample(
+                        S=opt.ddim_steps,
+                        conditioning=c,
+                        batch_size=c.shape[0],
+                        shape=shape,
+                        verbose=False,
+                        unconditional_guidance_scale=opt.scale,
+                        unconditional_conditioning=uc,
+                        eta=opt.ddim_eta,
+                    )
 
                     x_samples_ddim = model.decode_first_stage(samples_ddim)
                     x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
                     for x_sample in x_samples_ddim:
-                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                        x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
                         Image.fromarray(x_sample.astype(np.uint8)).save(
-                            os.path.join(sample_path, f"{base_count:05}.png"))
+                            os.path.join(sample_path, f"{base_count:05}.png")
+                        )
                         base_count += 1
                     all_samples.append(x_samples_ddim)
 
                 if not opt.skip_grid:
                     # additionally, save as grid
                     grid = torch.stack(all_samples, 0)
-                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                    grid = rearrange(grid, "n b c h w -> (n b) c h w")
                     grid = make_grid(grid, nrow=n_rows)
 
                     # to image
-                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+                    grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
+                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
                     grid_count += 1
 
     print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
diff --git a/scripts/orig_scripts/main.py b/scripts/orig_scripts/main.py
index 6a88f84380..8269809fbc 100644
--- a/scripts/orig_scripts/main.py
+++ b/scripts/orig_scripts/main.py
@@ -25,15 +25,19 @@ from pytorch_lightning.utilities import rank_zero_info
 from ldm.data.base import Txt2ImgIterableBaseDataset
 from ldm.util import instantiate_from_config
 
+
 def fix_func(orig):
-    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+
         def new_func(*args, **kw):
             device = kw.get("device", "mps")
-            kw["device"]="cpu"
+            kw["device"] = "cpu"
             return orig(*args, **kw).to(device)
+
         return new_func
     return orig
 
+
 torch.rand = fix_func(torch.rand)
 torch.rand_like = fix_func(torch.rand_like)
 torch.randn = fix_func(torch.randn)
@@ -43,18 +47,19 @@ torch.randint_like = fix_func(torch.randint_like)
 torch.bernoulli = fix_func(torch.bernoulli)
 torch.multinomial = fix_func(torch.multinomial)
 
+
 def load_model_from_config(config, ckpt, verbose=False):
-    print(f'Loading model from {ckpt}')
-    pl_sd = torch.load(ckpt, map_location='cpu')
-    sd = pl_sd['state_dict']
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cpu")
+    sd = pl_sd["state_dict"]
     config.model.params.ckpt_path = ckpt
     model = instantiate_from_config(config.model)
     m, u = model.load_state_dict(sd, strict=False)
     if len(m) > 0 and verbose:
-        print('missing keys:')
+        print("missing keys:")
         print(m)
     if len(u) > 0 and verbose:
-        print('unexpected keys:')
+        print("unexpected keys:")
         print(u)
 
     if torch.cuda.is_available():
@@ -66,132 +71,130 @@ def get_parser(**parser_kwargs):
     def str2bool(v):
         if isinstance(v, bool):
             return v
-        if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        if v.lower() in ("yes", "true", "t", "y", "1"):
             return True
-        elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        elif v.lower() in ("no", "false", "f", "n", "0"):
             return False
         else:
-            raise argparse.ArgumentTypeError('Boolean value expected.')
+            raise argparse.ArgumentTypeError("Boolean value expected.")
 
     parser = argparse.ArgumentParser(**parser_kwargs)
     parser.add_argument(
-        '-n',
-        '--name',
+        "-n",
+        "--name",
         type=str,
         const=True,
-        default='',
-        nargs='?',
-        help='postfix for logdir',
+        default="",
+        nargs="?",
+        help="postfix for logdir",
     )
     parser.add_argument(
-        '-r',
-        '--resume',
+        "-r",
+        "--resume",
         type=str,
         const=True,
-        default='',
-        nargs='?',
-        help='resume from logdir or checkpoint in logdir',
+        default="",
+        nargs="?",
+        help="resume from logdir or checkpoint in logdir",
     )
     parser.add_argument(
-        '-b',
-        '--base',
-        nargs='*',
-        metavar='base_config.yaml',
-        help='paths to base configs. Loaded from left-to-right. '
-        'Parameters can be overwritten or added with command-line options of the form `--key value`.',
+        "-b",
+        "--base",
+        nargs="*",
+        metavar="base_config.yaml",
+        help="paths to base configs. Loaded from left-to-right. "
+        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
         default=list(),
     )
     parser.add_argument(
-        '-t',
-        '--train',
+        "-t",
+        "--train",
         type=str2bool,
         const=True,
         default=False,
-        nargs='?',
-        help='train',
+        nargs="?",
+        help="train",
     )
     parser.add_argument(
-        '--no-test',
+        "--no-test",
         type=str2bool,
         const=True,
         default=False,
-        nargs='?',
-        help='disable test',
+        nargs="?",
+        help="disable test",
     )
+    parser.add_argument("-p", "--project", help="name of new or path to existing project")
     parser.add_argument(
-        '-p', '--project', help='name of new or path to existing project'
-    )
-    parser.add_argument(
-        '-d',
-        '--debug',
+        "-d",
+        "--debug",
         type=str2bool,
-        nargs='?',
+        nargs="?",
         const=True,
         default=False,
-        help='enable post-mortem debugging',
+        help="enable post-mortem debugging",
     )
     parser.add_argument(
-        '-s',
-        '--seed',
+        "-s",
+        "--seed",
         type=int,
         default=23,
-        help='seed for seed_everything',
+        help="seed for seed_everything",
     )
     parser.add_argument(
-        '-f',
-        '--postfix',
+        "-f",
+        "--postfix",
         type=str,
-        default='',
-        help='post-postfix for default name',
+        default="",
+        help="post-postfix for default name",
     )
     parser.add_argument(
-        '-l',
-        '--logdir',
+        "-l",
+        "--logdir",
         type=str,
-        default='logs',
-        help='directory for logging dat shit',
+        default="logs",
+        help="directory for logging dat shit",
     )
     parser.add_argument(
-        '--scale_lr',
+        "--scale_lr",
         type=str2bool,
-        nargs='?',
+        nargs="?",
         const=True,
         default=True,
-        help='scale base-lr by ngpu * batch_size * n_accumulate',
+        help="scale base-lr by ngpu * batch_size * n_accumulate",
     )
 
     parser.add_argument(
-        '--datadir_in_name',
+        "--datadir_in_name",
         type=str2bool,
-        nargs='?',
+        nargs="?",
         const=True,
         default=True,
-        help='Prepend the final directory in the data_root to the output directory name',
+        help="Prepend the final directory in the data_root to the output directory name",
     )
 
     parser.add_argument(
-        '--actual_resume',
+        "--actual_resume",
         type=str,
-        default='',
-        help='Path to model to actually resume from',
+        default="",
+        help="Path to model to actually resume from",
     )
     parser.add_argument(
-        '--data_root',
+        "--data_root",
         type=str,
         required=True,
-        help='Path to directory with training images',
+        help="Path to directory with training images",
     )
 
     parser.add_argument(
-        '--embedding_manager_ckpt',
+        "--embedding_manager_ckpt",
         type=str,
-        default='',
-        help='Initialize embedding manager from a checkpoint',
+        default="",
+        help="Initialize embedding manager from a checkpoint",
     )
     parser.add_argument(
-        '--init_word',
+        "--init_word",
         type=str,
-        help='Word to use as source for initial token embedding.',
+        help="Word to use as source for initial token embedding.",
     )
 
     return parser
@@ -226,9 +229,7 @@ def worker_init_fn(_):
     if isinstance(dataset, Txt2ImgIterableBaseDataset):
         split_size = dataset.num_records // worker_info.num_workers
         # reset num_records to the true number to retain reliable length information
-        dataset.sample_ids = dataset.valid_ids[
-            worker_id * split_size : (worker_id + 1) * split_size
-        ]
+        dataset.sample_ids = dataset.valid_ids[worker_id * split_size : (worker_id + 1) * split_size]
         current_id = np.random.choice(len(np.random.get_state()[1]), 1)
         return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
     else:
@@ -252,25 +253,19 @@ class DataModuleFromConfig(pl.LightningDataModule):
         super().__init__()
         self.batch_size = batch_size
         self.dataset_configs = dict()
-        self.num_workers = (
-            num_workers if num_workers is not None else batch_size * 2
-        )
+        self.num_workers = num_workers if num_workers is not None else batch_size * 2
         self.use_worker_init_fn = use_worker_init_fn
         if train is not None:
-            self.dataset_configs['train'] = train
+            self.dataset_configs["train"] = train
             self.train_dataloader = self._train_dataloader
         if validation is not None:
-            self.dataset_configs['validation'] = validation
-            self.val_dataloader = partial(
-                self._val_dataloader, shuffle=shuffle_val_dataloader
-            )
+            self.dataset_configs["validation"] = validation
+            self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
         if test is not None:
-            self.dataset_configs['test'] = test
-            self.test_dataloader = partial(
-                self._test_dataloader, shuffle=shuffle_test_loader
-            )
+            self.dataset_configs["test"] = test
+            self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
         if predict is not None:
-            self.dataset_configs['predict'] = predict
+            self.dataset_configs["predict"] = predict
             self.predict_dataloader = self._predict_dataloader
         self.wrap = wrap
 
@@ -279,24 +274,19 @@ class DataModuleFromConfig(pl.LightningDataModule):
             instantiate_from_config(data_cfg)
 
     def setup(self, stage=None):
-        self.datasets = dict(
-            (k, instantiate_from_config(self.dataset_configs[k]))
-            for k in self.dataset_configs
-        )
+        self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
         if self.wrap:
             for k in self.datasets:
                 self.datasets[k] = WrappedDataset(self.datasets[k])
 
     def _train_dataloader(self):
-        is_iterable_dataset = isinstance(
-            self.datasets['train'], Txt2ImgIterableBaseDataset
-        )
+        is_iterable_dataset = isinstance(self.datasets["train"], Txt2ImgIterableBaseDataset)
         if is_iterable_dataset or self.use_worker_init_fn:
             init_fn = worker_init_fn
         else:
             init_fn = None
         return DataLoader(
-            self.datasets['train'],
+            self.datasets["train"],
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             shuffle=False if is_iterable_dataset else True,
@@ -304,15 +294,12 @@ class DataModuleFromConfig(pl.LightningDataModule):
         )
 
     def _val_dataloader(self, shuffle=False):
-        if (
-            isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset)
-            or self.use_worker_init_fn
-        ):
+        if isinstance(self.datasets["validation"], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
             init_fn = worker_init_fn
         else:
             init_fn = None
         return DataLoader(
-            self.datasets['validation'],
+            self.datasets["validation"],
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             worker_init_fn=init_fn,
@@ -320,9 +307,7 @@ class DataModuleFromConfig(pl.LightningDataModule):
         )
 
     def _test_dataloader(self, shuffle=False):
-        is_iterable_dataset = isinstance(
-            self.datasets['train'], Txt2ImgIterableBaseDataset
-        )
+        is_iterable_dataset = isinstance(self.datasets["train"], Txt2ImgIterableBaseDataset)
         if is_iterable_dataset or self.use_worker_init_fn:
             init_fn = worker_init_fn
         else:
@@ -332,7 +317,7 @@ class DataModuleFromConfig(pl.LightningDataModule):
         shuffle = shuffle and (not is_iterable_dataset)
 
         return DataLoader(
-            self.datasets['test'],
+            self.datasets["test"],
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             worker_init_fn=init_fn,
@@ -340,15 +325,12 @@ class DataModuleFromConfig(pl.LightningDataModule):
         )
 
     def _predict_dataloader(self, shuffle=False):
-        if (
-            isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset)
-            or self.use_worker_init_fn
-        ):
+        if isinstance(self.datasets["predict"], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
             init_fn = worker_init_fn
         else:
             init_fn = None
         return DataLoader(
-            self.datasets['predict'],
+            self.datasets["predict"],
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             worker_init_fn=init_fn,
@@ -356,9 +338,7 @@ class DataModuleFromConfig(pl.LightningDataModule):
 
 
 class SetupCallback(Callback):
-    def __init__(
-        self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config
-    ):
+    def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
         super().__init__()
         self.resume = resume
         self.now = now
@@ -370,8 +350,8 @@ class SetupCallback(Callback):
 
     def on_keyboard_interrupt(self, trainer, pl_module):
         if trainer.global_rank == 0:
-            print('Summoning checkpoint.')
-            ckpt_path = os.path.join(self.ckptdir, 'last.ckpt')
+            print("Summoning checkpoint.")
+            ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
             trainer.save_checkpoint(ckpt_path)
 
     def on_pretrain_routine_start(self, trainer, pl_module):
@@ -381,36 +361,31 @@ class SetupCallback(Callback):
             os.makedirs(self.ckptdir, exist_ok=True)
             os.makedirs(self.cfgdir, exist_ok=True)
 
-            if 'callbacks' in self.lightning_config:
-                if (
-                    'metrics_over_trainsteps_checkpoint'
-                    in self.lightning_config['callbacks']
-                ):
+            if "callbacks" in self.lightning_config:
+                if "metrics_over_trainsteps_checkpoint" in self.lightning_config["callbacks"]:
                     os.makedirs(
-                        os.path.join(self.ckptdir, 'trainstep_checkpoints'),
+                        os.path.join(self.ckptdir, "trainstep_checkpoints"),
                         exist_ok=True,
                     )
-            print('Project config')
+            print("Project config")
             print(OmegaConf.to_yaml(self.config))
             OmegaConf.save(
                 self.config,
-                os.path.join(self.cfgdir, '{}-project.yaml'.format(self.now)),
+                os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)),
             )
 
-            print('Lightning config')
+            print("Lightning config")
             print(OmegaConf.to_yaml(self.lightning_config))
             OmegaConf.save(
-                OmegaConf.create({'lightning': self.lightning_config}),
-                os.path.join(
-                    self.cfgdir, '{}-lightning.yaml'.format(self.now)
-                ),
+                OmegaConf.create({"lightning": self.lightning_config}),
+                os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)),
             )
 
         else:
             # ModelCheckpoint callback created log directory --- remove it
             if not self.resume and os.path.exists(self.logdir):
                 dst, name = os.path.split(self.logdir)
-                dst = os.path.join(dst, 'child_runs', name)
+                dst = os.path.join(dst, "child_runs", name)
                 os.makedirs(os.path.split(dst)[0], exist_ok=True)
                 try:
                     os.rename(self.logdir, dst)
@@ -435,10 +410,8 @@ class ImageLogger(Callback):
         self.rescale = rescale
         self.batch_freq = batch_frequency
         self.max_images = max_images
-        self.logger_log_images = { }
-        self.log_steps = [
-            2**n for n in range(int(np.log2(self.batch_freq)) + 1)
-        ]
+        self.logger_log_images = {}
+        self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
         if not increase_log_steps:
             self.log_steps = [self.batch_freq]
         self.clamp = clamp
@@ -448,10 +421,8 @@ class ImageLogger(Callback):
         self.log_first_step = log_first_step
 
     @rank_zero_only
-    def log_local(
-        self, save_dir, split, images, global_step, current_epoch, batch_idx
-    ):
-        root = os.path.join(save_dir, 'images', split)
+    def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
+        root = os.path.join(save_dir, "images", split)
         for k in images:
             grid = torchvision.utils.make_grid(images[k], nrow=4)
             if self.rescale:
@@ -459,22 +430,16 @@ class ImageLogger(Callback):
             grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
             grid = grid.numpy()
             grid = (grid * 255).astype(np.uint8)
-            filename = '{}_gs-{:06}_e-{:06}_b-{:06}.png'.format(
-                k, global_step, current_epoch, batch_idx
-            )
+            filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
             path = os.path.join(root, filename)
             os.makedirs(os.path.split(path)[0], exist_ok=True)
             Image.fromarray(grid).save(path)
 
-    def log_img(self, pl_module, batch, batch_idx, split='train'):
-        check_idx = (
-            batch_idx if self.log_on_batch_idx else pl_module.global_step
-        )
+    def log_img(self, pl_module, batch, batch_idx, split="train"):
+        check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
         if (
             self.check_frequency(check_idx)
-            and hasattr(  # batch_idx % self.batch_freq == 0
-                pl_module, 'log_images'
-            )
+            and hasattr(pl_module, "log_images")  # batch_idx % self.batch_freq == 0
             and callable(pl_module.log_images)
             and self.max_images > 0
         ):
@@ -485,9 +450,7 @@ class ImageLogger(Callback):
                 pl_module.eval()
 
             with torch.no_grad():
-                images = pl_module.log_images(
-                    batch, split=split, **self.log_images_kwargs
-                )
+                images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
 
             for k in images:
                 N = min(images[k].shape[0], self.max_images)
@@ -506,18 +469,16 @@ class ImageLogger(Callback):
                 batch_idx,
             )
 
-            logger_log_images = self.logger_log_images.get(
-                logger, lambda *args, **kwargs: None
-            )
+            logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
             logger_log_images(pl_module, images, pl_module.global_step, split)
 
             if is_train:
                 pl_module.train()
 
     def check_frequency(self, check_idx):
-        if (
-            (check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)
-        ) and (check_idx > 0 or self.log_first_step):
+        if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
+            check_idx > 0 or self.log_first_step
+        ):
             try:
                 self.log_steps.pop(0)
             except IndexError as e:
@@ -526,23 +487,15 @@ class ImageLogger(Callback):
             return True
         return False
 
-    def on_train_batch_end(
-        self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None
-    ):
-        if not self.disabled and (
-            pl_module.global_step > 0 or self.log_first_step
-        ):
-            self.log_img(pl_module, batch, batch_idx, split='train')
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None):
+        if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
+            self.log_img(pl_module, batch, batch_idx, split="train")
 
-    def on_validation_batch_end(
-        self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None
-    ):
+    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None):
         if not self.disabled and pl_module.global_step > 0:
-            self.log_img(pl_module, batch, batch_idx, split='val')
-        if hasattr(pl_module, 'calibrate_grad_norm'):
-            if (
-                pl_module.calibrate_grad_norm and batch_idx % 25 == 0
-            ) and batch_idx > 0:
+            self.log_img(pl_module, batch, batch_idx, split="val")
+        if hasattr(pl_module, "calibrate_grad_norm"):
+            if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
                 self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
 
 
@@ -562,19 +515,17 @@ class CUDACallback(Callback):
 
         try:
             epoch_time = trainer.training_type_plugin.reduce(epoch_time)
-            rank_zero_info(f'Average Epoch time: {epoch_time:.2f} seconds')
+            rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
 
             if torch.cuda.is_available():
-                max_memory = (
-                    torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
-                )
+                max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
                 max_memory = trainer.training_type_plugin.reduce(max_memory)
-                rank_zero_info(f'Average Peak memory {max_memory:.2f}MiB')
+                rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
         except AttributeError:
             pass
 
-class ModeSwapCallback(Callback):
 
+class ModeSwapCallback(Callback):
     def __init__(self, swap_step=2000):
         super().__init__()
         self.is_frozen = False
@@ -589,7 +540,8 @@ class ModeSwapCallback(Callback):
             self.is_frozen = False
             trainer.optimizers = [pl_module.configure_opt_model()]
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     # custom parser to specify config files, train, test and debug mode,
     # postfix, resume.
     # `--key value` arguments are interpreted as arguments to the trainer.
@@ -631,7 +583,7 @@ if __name__ == '__main__':
     #           params:
     #               key: value
 
-    now = datetime.datetime.now().strftime('%Y-%m-%dT%H-%M-%S')
+    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
 
     # add cwd for convenience and to make classes in this file available when
     # running as `python main.py`
@@ -644,50 +596,47 @@ if __name__ == '__main__':
     opt, unknown = parser.parse_known_args()
     if opt.name and opt.resume:
         raise ValueError(
-            '-n/--name and -r/--resume cannot be specified both.'
-            'If you want to resume training in a new log folder, '
-            'use -n/--name in combination with --resume_from_checkpoint'
+            "-n/--name and -r/--resume cannot be specified both."
+            "If you want to resume training in a new log folder, "
+            "use -n/--name in combination with --resume_from_checkpoint"
         )
     if opt.resume:
         if not os.path.exists(opt.resume):
-            raise ValueError('Cannot find {}'.format(opt.resume))
+            raise ValueError("Cannot find {}".format(opt.resume))
         if os.path.isfile(opt.resume):
-            paths = opt.resume.split('/')
+            paths = opt.resume.split("/")
             # idx = len(paths)-paths[::-1].index("logs")+1
             # logdir = "/".join(paths[:idx])
-            logdir = '/'.join(paths[:-2])
+            logdir = "/".join(paths[:-2])
             ckpt = opt.resume
         else:
             assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip('/')
-            ckpt = os.path.join(logdir, 'checkpoints', 'last.ckpt')
+            logdir = opt.resume.rstrip("/")
+            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
 
         opt.resume_from_checkpoint = ckpt
-        base_configs = sorted(
-            glob.glob(os.path.join(logdir, 'configs/*.yaml'))
-        )
+        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
         opt.base = base_configs + opt.base
-        _tmp = logdir.split('/')
+        _tmp = logdir.split("/")
         nowname = _tmp[-1]
     else:
         if opt.name:
-            name = '_' + opt.name
+            name = "_" + opt.name
         elif opt.base:
             cfg_fname = os.path.split(opt.base[0])[-1]
             cfg_name = os.path.splitext(cfg_fname)[0]
-            name = '_' + cfg_name
+            name = "_" + cfg_name
         else:
-            name = ''
+            name = ""
 
         if opt.datadir_in_name:
             now = os.path.basename(os.path.normpath(opt.data_root)) + now
 
-
         nowname = now + name + opt.postfix
         logdir = os.path.join(opt.logdir, nowname)
 
-    ckptdir = os.path.join(logdir, 'checkpoints')
-    cfgdir = os.path.join(logdir, 'configs')
+    ckptdir = os.path.join(logdir, "checkpoints")
+    cfgdir = os.path.join(logdir, "configs")
     seed_everything(opt.seed)
 
     try:
@@ -695,19 +644,19 @@ if __name__ == '__main__':
         configs = [OmegaConf.load(cfg) for cfg in opt.base]
         cli = OmegaConf.from_dotlist(unknown)
         config = OmegaConf.merge(*configs, cli)
-        lightning_config = config.pop('lightning', OmegaConf.create())
+        lightning_config = config.pop("lightning", OmegaConf.create())
         # merge trainer cli with config
-        trainer_config = lightning_config.get('trainer', OmegaConf.create())
+        trainer_config = lightning_config.get("trainer", OmegaConf.create())
         # default to ddp
-        trainer_config['accelerator'] = 'auto'
+        trainer_config["accelerator"] = "auto"
         for k in nondefault_trainer_args(opt):
             trainer_config[k] = getattr(opt, k)
-        if not 'gpus' in trainer_config:
-            del trainer_config['accelerator']
+        if not "gpus" in trainer_config:
+            del trainer_config["accelerator"]
             cpu = True
         else:
-            gpuinfo = trainer_config['gpus']
-            print(f'Running on GPUs {gpuinfo}')
+            gpuinfo = trainer_config["gpus"]
+            print(f"Running on GPUs {gpuinfo}")
             cpu = False
         trainer_opt = argparse.Namespace(**trainer_config)
         lightning_config.trainer = trainer_config
@@ -715,9 +664,7 @@ if __name__ == '__main__':
         # model
 
         # config.model.params.personalization_config.params.init_word = opt.init_word
-        config.model.params.personalization_config.params.embedding_manager_ckpt = (
-            opt.embedding_manager_ckpt
-        )
+        config.model.params.personalization_config.params.embedding_manager_ckpt = opt.embedding_manager_ckpt
 
         if opt.init_word:
             config.model.params.personalization_config.params.initializer_words = [opt.init_word]
@@ -731,142 +678,128 @@ if __name__ == '__main__':
         trainer_kwargs = dict()
 
         # default logger configs
-        def_logger = 'csv'
-        def_logger_target = 'CSVLogger'
+        def_logger = "csv"
+        def_logger_target = "CSVLogger"
         default_logger_cfgs = {
-            'wandb': {
-                'target': 'pytorch_lightning.loggers.WandbLogger',
-                'params': {
-                    'name': nowname,
-                    'save_dir': logdir,
-                    'offline': opt.debug,
-                    'id': nowname,
+            "wandb": {
+                "target": "pytorch_lightning.loggers.WandbLogger",
+                "params": {
+                    "name": nowname,
+                    "save_dir": logdir,
+                    "offline": opt.debug,
+                    "id": nowname,
                 },
             },
             def_logger: {
-                'target': 'pytorch_lightning.loggers.' + def_logger_target,
-                'params': {
-                    'name': def_logger,
-                    'save_dir': logdir,
+                "target": "pytorch_lightning.loggers." + def_logger_target,
+                "params": {
+                    "name": def_logger,
+                    "save_dir": logdir,
                 },
             },
         }
         default_logger_cfg = default_logger_cfgs[def_logger]
-        if 'logger' in lightning_config:
+        if "logger" in lightning_config:
             logger_cfg = lightning_config.logger
         else:
             logger_cfg = OmegaConf.create()
         logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
-        trainer_kwargs['logger'] = instantiate_from_config(logger_cfg)
+        trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
 
         # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
         # specify which metric is used to determine best models
         default_modelckpt_cfg = {
-            'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
-            'params': {
-                'dirpath': ckptdir,
-                'filename': '{epoch:06}',
-                'verbose': True,
-                'save_last': True,
+            "target": "pytorch_lightning.callbacks.ModelCheckpoint",
+            "params": {
+                "dirpath": ckptdir,
+                "filename": "{epoch:06}",
+                "verbose": True,
+                "save_last": True,
             },
         }
-        if hasattr(model, 'monitor'):
-            print(f'Monitoring {model.monitor} as checkpoint metric.')
-            default_modelckpt_cfg['params']['monitor'] = model.monitor
-            default_modelckpt_cfg['params']['save_top_k'] = 1
+        if hasattr(model, "monitor"):
+            print(f"Monitoring {model.monitor} as checkpoint metric.")
+            default_modelckpt_cfg["params"]["monitor"] = model.monitor
+            default_modelckpt_cfg["params"]["save_top_k"] = 1
 
-        if 'modelcheckpoint' in lightning_config:
+        if "modelcheckpoint" in lightning_config:
             modelckpt_cfg = lightning_config.modelcheckpoint
         else:
             modelckpt_cfg = OmegaConf.create()
         modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
-        print(f'Merged modelckpt-cfg: \n{modelckpt_cfg}')
-        if version.parse(pl.__version__) < version.parse('1.4.0'):
-            trainer_kwargs['checkpoint_callback'] = instantiate_from_config(
-                modelckpt_cfg
-            )
+        print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
+        if version.parse(pl.__version__) < version.parse("1.4.0"):
+            trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
 
         # add callback which sets up log directory
         default_callbacks_cfg = {
-            'setup_callback': {
-                'target': 'main.SetupCallback',
-                'params': {
-                    'resume': opt.resume,
-                    'now': now,
-                    'logdir': logdir,
-                    'ckptdir': ckptdir,
-                    'cfgdir': cfgdir,
-                    'config': config,
-                    'lightning_config': lightning_config,
+            "setup_callback": {
+                "target": "main.SetupCallback",
+                "params": {
+                    "resume": opt.resume,
+                    "now": now,
+                    "logdir": logdir,
+                    "ckptdir": ckptdir,
+                    "cfgdir": cfgdir,
+                    "config": config,
+                    "lightning_config": lightning_config,
                 },
             },
-            'image_logger': {
-                'target': 'main.ImageLogger',
-                'params': {
-                    'batch_frequency': 750,
-                    'max_images': 4,
-                    'clamp': True,
+            "image_logger": {
+                "target": "main.ImageLogger",
+                "params": {
+                    "batch_frequency": 750,
+                    "max_images": 4,
+                    "clamp": True,
                 },
             },
-            'learning_rate_logger': {
-                'target': 'main.LearningRateMonitor',
-                'params': {
-                    'logging_interval': 'step',
+            "learning_rate_logger": {
+                "target": "main.LearningRateMonitor",
+                "params": {
+                    "logging_interval": "step",
                     # "log_momentum": True
                 },
             },
-            'cuda_callback': {'target': 'main.CUDACallback'},
+            "cuda_callback": {"target": "main.CUDACallback"},
         }
-        if version.parse(pl.__version__) >= version.parse('1.4.0'):
-            default_callbacks_cfg.update(
-                {'checkpoint_callback': modelckpt_cfg}
-            )
+        if version.parse(pl.__version__) >= version.parse("1.4.0"):
+            default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
 
-        if 'callbacks' in lightning_config:
+        if "callbacks" in lightning_config:
             callbacks_cfg = lightning_config.callbacks
         else:
             callbacks_cfg = OmegaConf.create()
 
-        if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
+        if "metrics_over_trainsteps_checkpoint" in callbacks_cfg:
             print(
-                'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.'
+                "Caution: Saving checkpoints every n train steps without deleting. This might require some free space."
             )
             default_metrics_over_trainsteps_ckpt_dict = {
-                'metrics_over_trainsteps_checkpoint': {
-                    'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
-                    'params': {
-                        'dirpath': os.path.join(
-                            ckptdir, 'trainstep_checkpoints'
-                        ),
-                        'filename': '{epoch:06}-{step:09}',
-                        'verbose': True,
-                        'save_top_k': -1,
-                        'every_n_train_steps': 10000,
-                        'save_weights_only': True,
+                "metrics_over_trainsteps_checkpoint": {
+                    "target": "pytorch_lightning.callbacks.ModelCheckpoint",
+                    "params": {
+                        "dirpath": os.path.join(ckptdir, "trainstep_checkpoints"),
+                        "filename": "{epoch:06}-{step:09}",
+                        "verbose": True,
+                        "save_top_k": -1,
+                        "every_n_train_steps": 10000,
+                        "save_weights_only": True,
                     },
                 }
             }
-            default_callbacks_cfg.update(
-                default_metrics_over_trainsteps_ckpt_dict
-            )
+            default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
 
         callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
-        if 'ignore_keys_callback' in callbacks_cfg and hasattr(
-            trainer_opt, 'resume_from_checkpoint'
-        ):
-            callbacks_cfg.ignore_keys_callback.params[
-                'ckpt_path'
-            ] = trainer_opt.resume_from_checkpoint
-        elif 'ignore_keys_callback' in callbacks_cfg:
-            del callbacks_cfg['ignore_keys_callback']
+        if "ignore_keys_callback" in callbacks_cfg and hasattr(trainer_opt, "resume_from_checkpoint"):
+            callbacks_cfg.ignore_keys_callback.params["ckpt_path"] = trainer_opt.resume_from_checkpoint
+        elif "ignore_keys_callback" in callbacks_cfg:
+            del callbacks_cfg["ignore_keys_callback"]
 
-        trainer_kwargs['callbacks'] = [
-            instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg
-        ]
-        trainer_kwargs['max_steps'] = trainer_opt.max_steps
+        trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
+        trainer_kwargs["max_steps"] = trainer_opt.max_steps
 
-        if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-            trainer_opt.accelerator = 'mps'
+        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            trainer_opt.accelerator = "mps"
             trainer_opt.detect_anomaly = False
 
         trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
@@ -882,11 +815,9 @@ if __name__ == '__main__':
         # lightning still takes care of proper multiprocessing though
         data.prepare_data()
         data.setup()
-        print('#### Data #####')
+        print("#### Data #####")
         for k in data.datasets:
-            print(
-                f'{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}'
-            )
+            print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
 
         # configure learning rate
         bs, base_lr = (
@@ -894,24 +825,20 @@ if __name__ == '__main__':
             config.model.base_learning_rate,
         )
         if not cpu:
-            gpus = str(lightning_config.trainer.gpus).strip(', ').split(',')
+            gpus = str(lightning_config.trainer.gpus).strip(", ").split(",")
             ngpu = len(gpus)
         else:
             ngpu = 1
-        if 'accumulate_grad_batches' in lightning_config.trainer:
-            accumulate_grad_batches = (
-                lightning_config.trainer.accumulate_grad_batches
-            )
+        if "accumulate_grad_batches" in lightning_config.trainer:
+            accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
         else:
             accumulate_grad_batches = 1
-        print(f'accumulate_grad_batches = {accumulate_grad_batches}')
-        lightning_config.trainer.accumulate_grad_batches = (
-            accumulate_grad_batches
-        )
+        print(f"accumulate_grad_batches = {accumulate_grad_batches}")
+        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
         if opt.scale_lr:
             model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
             print(
-                'Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)'.format(
+                "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
                     model.learning_rate,
                     accumulate_grad_batches,
                     ngpu,
@@ -921,15 +848,15 @@ if __name__ == '__main__':
             )
         else:
             model.learning_rate = base_lr
-            print('++++ NOT USING LR SCALING ++++')
-            print(f'Setting learning rate to {model.learning_rate:.2e}')
+            print("++++ NOT USING LR SCALING ++++")
+            print(f"Setting learning rate to {model.learning_rate:.2e}")
 
         # allow checkpointing via USR1
         def melk(*args, **kwargs):
             # run all checkpoint hooks
             if trainer.global_rank == 0:
-                print('Summoning checkpoint.')
-                ckpt_path = os.path.join(ckptdir, 'last.ckpt')
+                print("Summoning checkpoint.")
+                ckpt_path = os.path.join(ckptdir, "last.ckpt")
                 trainer.save_checkpoint(ckpt_path)
 
         def divein(*args, **kwargs):
@@ -964,7 +891,7 @@ if __name__ == '__main__':
         # move newly created debug project to debug_runs
         if opt.debug and not opt.resume and trainer.global_rank == 0:
             dst, name = os.path.split(logdir)
-            dst = os.path.join(dst, 'debug_runs', name)
+            dst = os.path.join(dst, "debug_runs", name)
             os.makedirs(os.path.split(dst)[0], exist_ok=True)
             os.rename(logdir, dst)
         # if trainer.global_rank == 0:
diff --git a/scripts/orig_scripts/merge_embeddings.py b/scripts/orig_scripts/merge_embeddings.py
index 97e72f9128..c2cf1acb1a 100755
--- a/scripts/orig_scripts/merge_embeddings.py
+++ b/scripts/orig_scripts/merge_embeddings.py
@@ -7,21 +7,30 @@ from functools import partial
 
 import torch
 
-def get_placeholder_loop(placeholder_string, embedder, use_bert):
 
-    new_placeholder   = None
+def get_placeholder_loop(placeholder_string, embedder, use_bert):
+    new_placeholder = None
 
     while True:
         if new_placeholder is None:
-            new_placeholder = input(f"Placeholder string {placeholder_string} was already used. Please enter a replacement string: ")
+            new_placeholder = input(
+                f"Placeholder string {placeholder_string} was already used. Please enter a replacement string: "
+            )
         else:
-            new_placeholder = input(f"Placeholder string '{new_placeholder}' maps to more than a single token. Please enter another string: ")
+            new_placeholder = input(
+                f"Placeholder string '{new_placeholder}' maps to more than a single token. Please enter another string: "
+            )
 
-        token = get_bert_token_for_string(embedder.tknz_fn, new_placeholder) if use_bert else get_clip_token_for_string(embedder.tokenizer, new_placeholder)
+        token = (
+            get_bert_token_for_string(embedder.tknz_fn, new_placeholder)
+            if use_bert
+            else get_clip_token_for_string(embedder.tokenizer, new_placeholder)
+        )
 
         if token is not None:
             return new_placeholder, token
 
+
 def get_clip_token_for_string(tokenizer, string):
     batch_encoding = tokenizer(
         string,
@@ -30,7 +39,7 @@ def get_clip_token_for_string(tokenizer, string):
         return_length=True,
         return_overflowing_tokens=False,
         padding="max_length",
-        return_tensors="pt"
+        return_tensors="pt",
     )
 
     tokens = batch_encoding["input_ids"]
@@ -40,6 +49,7 @@ def get_clip_token_for_string(tokenizer, string):
 
     return None
 
+
 def get_bert_token_for_string(tokenizer, string):
     token = tokenizer(string)
     if torch.count_nonzero(token) == 3:
@@ -49,22 +59,17 @@ def get_bert_token_for_string(tokenizer, string):
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
 
     parser.add_argument(
         "--root_dir",
         type=str,
-        default='.',
-        help="Path to the InvokeAI install directory containing 'models', 'outputs' and 'configs'."
+        default=".",
+        help="Path to the InvokeAI install directory containing 'models', 'outputs' and 'configs'.",
     )
 
     parser.add_argument(
-        "--manager_ckpts",
-        type=str,
-        nargs="+",
-        required=True,
-        help="Paths to a set of embedding managers to be merged."
+        "--manager_ckpts", type=str, nargs="+", required=True, help="Paths to a set of embedding managers to be merged."
     )
 
     parser.add_argument(
@@ -75,13 +80,14 @@ if __name__ == "__main__":
     )
 
     parser.add_argument(
-        "-sd", "--use_bert",
+        "-sd",
+        "--use_bert",
         action="store_true",
-        help="Flag to denote that we are not merging stable diffusion embeddings"
+        help="Flag to denote that we are not merging stable diffusion embeddings",
     )
 
     args = parser.parse_args()
-    Globals.root=args.root_dir
+    Globals.root = args.root_dir
 
     if args.use_bert:
         embedder = BERTEmbedder(n_embed=1280, n_layer=32).cuda()
diff --git a/scripts/orig_scripts/sample_diffusion.py b/scripts/orig_scripts/sample_diffusion.py
index 876fe3c364..9f08b6702a 100644
--- a/scripts/orig_scripts/sample_diffusion.py
+++ b/scripts/orig_scripts/sample_diffusion.py
@@ -10,12 +10,13 @@ from PIL import Image
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config
 
-rescale = lambda x: (x + 1.) / 2.
+rescale = lambda x: (x + 1.0) / 2.0
+
 
 def custom_to_pil(x):
     x = x.detach().cpu()
-    x = torch.clamp(x, -1., 1.)
-    x = (x + 1.) / 2.
+    x = torch.clamp(x, -1.0, 1.0)
+    x = (x + 1.0) / 2.0
     x = x.permute(1, 2, 0).numpy()
     x = (255 * x).astype(np.uint8)
     x = Image.fromarray(x)
@@ -51,49 +52,51 @@ def logs2pil(logs, keys=["sample"]):
 
 
 @torch.no_grad()
-def convsample(model, shape, return_intermediates=True,
-               verbose=True,
-               make_prog_row=False):
-
-
+def convsample(model, shape, return_intermediates=True, verbose=True, make_prog_row=False):
     if not make_prog_row:
-        return model.p_sample_loop(None, shape,
-                                   return_intermediates=return_intermediates, verbose=verbose)
+        return model.p_sample_loop(None, shape, return_intermediates=return_intermediates, verbose=verbose)
     else:
-        return model.progressive_denoising(
-            None, shape, verbose=True
-        )
+        return model.progressive_denoising(None, shape, verbose=True)
 
 
 @torch.no_grad()
-def convsample_ddim(model, steps, shape, eta=1.0
-                    ):
+def convsample_ddim(model, steps, shape, eta=1.0):
     ddim = DDIMSampler(model)
     bs = shape[0]
     shape = shape[1:]
-    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,)
+    samples, intermediates = ddim.sample(
+        steps,
+        batch_size=bs,
+        shape=shape,
+        eta=eta,
+        verbose=False,
+    )
     return samples, intermediates
 
 
 @torch.no_grad()
-def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,):
-
-
+def make_convolutional_sample(
+    model,
+    batch_size,
+    vanilla=False,
+    custom_steps=None,
+    eta=1.0,
+):
     log = dict()
 
-    shape = [batch_size,
-             model.model.diffusion_model.in_channels,
-             model.model.diffusion_model.image_size,
-             model.model.diffusion_model.image_size]
+    shape = [
+        batch_size,
+        model.model.diffusion_model.in_channels,
+        model.model.diffusion_model.image_size,
+        model.model.diffusion_model.image_size,
+    ]
 
     with model.ema_scope("Plotting"):
         t0 = time.time()
         if vanilla:
-            sample, progrow = convsample(model, shape,
-                                         make_prog_row=True)
+            sample, progrow = convsample(model, shape, make_prog_row=True)
         else:
-            sample, intermediates = convsample_ddim(model,  steps=custom_steps, shape=shape,
-                                                    eta=eta)
+            sample, intermediates = convsample_ddim(model, steps=custom_steps, shape=shape, eta=eta)
 
         t1 = time.time()
 
@@ -101,32 +104,32 @@ def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=Non
 
     log["sample"] = x_sample
     log["time"] = t1 - t0
-    log['throughput'] = sample.shape[0] / (t1 - t0)
+    log["throughput"] = sample.shape[0] / (t1 - t0)
     print(f'Throughput for this batch: {log["throughput"]}')
     return log
 
+
 def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):
     if vanilla:
-        print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.')
+        print(f"Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.")
     else:
-        print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}')
-
+        print(f"Using DDIM sampling with {custom_steps} sampling steps and eta={eta}")
 
     tstart = time.time()
-    n_saved = len(glob.glob(os.path.join(logdir,'*.png')))-1
+    n_saved = len(glob.glob(os.path.join(logdir, "*.png"))) - 1
     # path = logdir
     if model.cond_stage_model is None:
         all_images = []
 
         print(f"Running unconditional sampling for {n_samples} samples")
         for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):
-            logs = make_convolutional_sample(model, batch_size=batch_size,
-                                             vanilla=vanilla, custom_steps=custom_steps,
-                                             eta=eta)
+            logs = make_convolutional_sample(
+                model, batch_size=batch_size, vanilla=vanilla, custom_steps=custom_steps, eta=eta
+            )
             n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")
             all_images.extend([custom_to_np(logs["sample"])])
             if n_saved >= n_samples:
-                print(f'Finish after generating {n_saved} samples')
+                print(f"Finish after generating {n_saved} samples")
                 break
         all_img = np.concatenate(all_images, axis=0)
         all_img = all_img[:n_samples]
@@ -135,7 +138,7 @@ def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None
         np.savez(nppath, all_img)
 
     else:
-       raise NotImplementedError('Currently only sampling for unconditional models supported.')
+        raise NotImplementedError("Currently only sampling for unconditional models supported.")
 
     print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")
 
@@ -168,58 +171,33 @@ def get_parser():
         nargs="?",
         help="load from logdir or checkpoint in logdir",
     )
-    parser.add_argument(
-        "-n",
-        "--n_samples",
-        type=int,
-        nargs="?",
-        help="number of samples to draw",
-        default=50000
-    )
+    parser.add_argument("-n", "--n_samples", type=int, nargs="?", help="number of samples to draw", default=50000)
     parser.add_argument(
         "-e",
         "--eta",
         type=float,
         nargs="?",
         help="eta for ddim sampling (0.0 yields deterministic sampling)",
-        default=1.0
+        default=1.0,
     )
     parser.add_argument(
         "-v",
         "--vanilla_sample",
         default=False,
-        action='store_true',
+        action="store_true",
         help="vanilla sampling (default option is DDIM sampling)?",
     )
+    parser.add_argument("-l", "--logdir", type=str, nargs="?", help="extra logdir", default="none")
     parser.add_argument(
-        "-l",
-        "--logdir",
-        type=str,
-        nargs="?",
-        help="extra logdir",
-        default="none"
-    )
-    parser.add_argument(
-        "-c",
-        "--custom_steps",
-        type=int,
-        nargs="?",
-        help="number of steps for ddim and fastdpm sampling",
-        default=50
-    )
-    parser.add_argument(
-        "--batch_size",
-        type=int,
-        nargs="?",
-        help="the bs",
-        default=10
+        "-c", "--custom_steps", type=int, nargs="?", help="number of steps for ddim and fastdpm sampling", default=50
     )
+    parser.add_argument("--batch_size", type=int, nargs="?", help="the bs", default=10)
     return parser
 
 
 def load_model_from_config(config, sd):
     model = instantiate_from_config(config)
-    model.load_state_dict(sd,strict=False)
+    model.load_state_dict(sd, strict=False)
     model.cuda()
     model.eval()
     return model
@@ -233,8 +211,7 @@ def load_model(config, ckpt, gpu, eval_mode):
     else:
         pl_sd = {"state_dict": None}
         global_step = None
-    model = load_model_from_config(config.model,
-                                   pl_sd["state_dict"])
+    model = load_model_from_config(config.model, pl_sd["state_dict"])
 
     return model, global_step
 
@@ -253,9 +230,9 @@ if __name__ == "__main__":
     if os.path.isfile(opt.resume):
         # paths = opt.resume.split("/")
         try:
-            logdir = '/'.join(opt.resume.split('/')[:-1])
+            logdir = "/".join(opt.resume.split("/")[:-1])
             # idx = len(paths)-paths[::-1].index("logs")+1
-            print(f'Logdir is {logdir}')
+            print(f"Logdir is {logdir}")
         except ValueError:
             paths = opt.resume.split("/")
             idx = -2  # take a guess: path/to/logdir/checkpoints/model.ckpt
@@ -278,7 +255,8 @@ if __name__ == "__main__":
 
     if opt.logdir != "none":
         locallog = logdir.split(os.sep)[-1]
-        if locallog == "": locallog = logdir.split(os.sep)[-2]
+        if locallog == "":
+            locallog = logdir.split(os.sep)[-2]
         print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")
         logdir = os.path.join(opt.logdir, locallog)
 
@@ -301,13 +279,19 @@ if __name__ == "__main__":
     sampling_file = os.path.join(logdir, "sampling_config.yaml")
     sampling_conf = vars(opt)
 
-    with open(sampling_file, 'w') as f:
+    with open(sampling_file, "w") as f:
         yaml.dump(sampling_conf, f, default_flow_style=False)
     print(sampling_conf)
 
-
-    run(model, imglogdir, eta=opt.eta,
-        vanilla=opt.vanilla_sample,  n_samples=opt.n_samples, custom_steps=opt.custom_steps,
-        batch_size=opt.batch_size, nplog=numpylogdir)
+    run(
+        model,
+        imglogdir,
+        eta=opt.eta,
+        vanilla=opt.vanilla_sample,
+        n_samples=opt.n_samples,
+        custom_steps=opt.custom_steps,
+        batch_size=opt.batch_size,
+        nplog=numpylogdir,
+    )
 
     print("done.")
diff --git a/scripts/orig_scripts/train_searcher.py b/scripts/orig_scripts/train_searcher.py
index 1e7904889c..7ba57f1165 100644
--- a/scripts/orig_scripts/train_searcher.py
+++ b/scripts/orig_scripts/train_searcher.py
@@ -13,21 +13,26 @@ def search_bruteforce(searcher):
     return searcher.score_brute_force().build()
 
 
-def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,
-                        partioning_trainsize, num_leaves, num_leaves_to_search):
-    return searcher.tree(num_leaves=num_leaves,
-                         num_leaves_to_search=num_leaves_to_search,
-                         training_sample_size=partioning_trainsize). \
-        score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
+def search_partioned_ah(
+    searcher, dims_per_block, aiq_threshold, reorder_k, partioning_trainsize, num_leaves, num_leaves_to_search
+):
+    return (
+        searcher.tree(
+            num_leaves=num_leaves, num_leaves_to_search=num_leaves_to_search, training_sample_size=partioning_trainsize
+        )
+        .score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold)
+        .reorder(reorder_k)
+        .build()
+    )
 
 
 def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
-    return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(
-        reorder_k).build()
+    return (
+        searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
+    )
+
 
 def load_datapool(dpath):
-
-
     def load_single_file(saved_embeddings):
         compressed = np.load(saved_embeddings)
         database = {key: compressed[key] for key in compressed.files}
@@ -35,23 +40,26 @@ def load_datapool(dpath):
 
     def load_multi_files(data_archive):
         database = {key: [] for key in data_archive[0].files}
-        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+        for d in tqdm(data_archive, desc=f"Loading datapool from {len(data_archive)} individual files."):
             for key in d.files:
                 database[key].append(d[key])
 
         return database
 
     print(f'Load saved patch embedding from "{dpath}"')
-    file_content = glob.glob(os.path.join(dpath, '*.npz'))
+    file_content = glob.glob(os.path.join(dpath, "*.npz"))
 
     if len(file_content) == 1:
         data_pool = load_single_file(file_content[0])
     elif len(file_content) > 1:
         data = [np.load(f) for f in file_content]
-        prefetched_data = parallel_data_prefetch(load_multi_files, data,
-                                                 n_proc=min(len(data), cpu_count()), target_data_type='dict')
+        prefetched_data = parallel_data_prefetch(
+            load_multi_files, data, n_proc=min(len(data), cpu_count()), target_data_type="dict"
+        )
 
-        data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}
+        data_pool = {
+            key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()
+        }
     else:
         raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
 
@@ -59,16 +67,17 @@ def load_datapool(dpath):
     return data_pool
 
 
-def train_searcher(opt,
-                   metric='dot_product',
-                   partioning_trainsize=None,
-                   reorder_k=None,
-                   # todo tune
-                   aiq_thld=0.2,
-                   dims_per_block=2,
-                   num_leaves=None,
-                   num_leaves_to_search=None,):
-
+def train_searcher(
+    opt,
+    metric="dot_product",
+    partioning_trainsize=None,
+    reorder_k=None,
+    # todo tune
+    aiq_thld=0.2,
+    dims_per_block=2,
+    num_leaves=None,
+    num_leaves_to_search=None,
+):
     data_pool = load_datapool(opt.database)
     k = opt.knn
 
@@ -77,71 +86,83 @@ def train_searcher(opt,
 
     # normalize
     # embeddings =
-    searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)
-    pool_size = data_pool['embedding'].shape[0]
+    searcher = scann.scann_ops_pybind.builder(
+        data_pool["embedding"] / np.linalg.norm(data_pool["embedding"], axis=1)[:, np.newaxis], k, metric
+    )
+    pool_size = data_pool["embedding"].shape[0]
 
-    print(*(['#'] * 100))
-    print('Initializing scaNN searcher with the following values:')
-    print(f'k: {k}')
-    print(f'metric: {metric}')
-    print(f'reorder_k: {reorder_k}')
-    print(f'anisotropic_quantization_threshold: {aiq_thld}')
-    print(f'dims_per_block: {dims_per_block}')
-    print(*(['#'] * 100))
-    print('Start training searcher....')
-    print(f'N samples in pool is {pool_size}')
+    print(*(["#"] * 100))
+    print("Initializing scaNN searcher with the following values:")
+    print(f"k: {k}")
+    print(f"metric: {metric}")
+    print(f"reorder_k: {reorder_k}")
+    print(f"anisotropic_quantization_threshold: {aiq_thld}")
+    print(f"dims_per_block: {dims_per_block}")
+    print(*(["#"] * 100))
+    print("Start training searcher....")
+    print(f"N samples in pool is {pool_size}")
 
     # this reflects the recommended design choices proposed at
     # https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
     if pool_size < 2e4:
-        print('Using brute force search.')
+        print("Using brute force search.")
         searcher = search_bruteforce(searcher)
     elif 2e4 <= pool_size and pool_size < 1e5:
-        print('Using asymmetric hashing search and reordering.')
+        print("Using asymmetric hashing search and reordering.")
         searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
     else:
-        print('Using using partioning, asymmetric hashing search and reordering.')
+        print("Using using partioning, asymmetric hashing search and reordering.")
 
         if not partioning_trainsize:
-            partioning_trainsize = data_pool['embedding'].shape[0] // 10
+            partioning_trainsize = data_pool["embedding"].shape[0] // 10
         if not num_leaves:
             num_leaves = int(np.sqrt(pool_size))
 
         if not num_leaves_to_search:
             num_leaves_to_search = max(num_leaves // 20, 1)
 
-        print('Partitioning params:')
-        print(f'num_leaves: {num_leaves}')
-        print(f'num_leaves_to_search: {num_leaves_to_search}')
+        print("Partitioning params:")
+        print(f"num_leaves: {num_leaves}")
+        print(f"num_leaves_to_search: {num_leaves_to_search}")
         # self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
-        searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,
-                                                 partioning_trainsize, num_leaves, num_leaves_to_search)
+        searcher = search_partioned_ah(
+            searcher, dims_per_block, aiq_thld, reorder_k, partioning_trainsize, num_leaves, num_leaves_to_search
+        )
 
-    print('Finish training searcher')
+    print("Finish training searcher")
     searcher_savedir = opt.target_path
     os.makedirs(searcher_savedir, exist_ok=True)
     searcher.serialize(searcher_savedir)
     print(f'Saved trained searcher under "{searcher_savedir}"')
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     sys.path.append(os.getcwd())
     parser = argparse.ArgumentParser()
-    parser.add_argument('--database',
-                        '-d',
-                        default='data/rdm/retrieval_databases/openimages',
-                        type=str,
-                        help='path to folder containing the clip feature of the database')
-    parser.add_argument('--target_path',
-                        '-t',
-                        default='data/rdm/searchers/openimages',
-                        type=str,
-                        help='path to the target folder where the searcher shall be stored.')
-    parser.add_argument('--knn',
-                        '-k',
-                        default=20,
-                        type=int,
-                        help='number of nearest neighbors, for which the searcher shall be optimized')
+    parser.add_argument(
+        "--database",
+        "-d",
+        default="data/rdm/retrieval_databases/openimages",
+        type=str,
+        help="path to folder containing the clip feature of the database",
+    )
+    parser.add_argument(
+        "--target_path",
+        "-t",
+        default="data/rdm/searchers/openimages",
+        type=str,
+        help="path to the target folder where the searcher shall be stored.",
+    )
+    parser.add_argument(
+        "--knn",
+        "-k",
+        default=20,
+        type=int,
+        help="number of nearest neighbors, for which the searcher shall be optimized",
+    )
 
-    opt, _  = parser.parse_known_args()
+    opt, _ = parser.parse_known_args()
 
-    train_searcher(opt,)
\ No newline at end of file
+    train_searcher(
+        opt,
+    )
diff --git a/scripts/orig_scripts/txt2img.py b/scripts/orig_scripts/txt2img.py
index 0d350d2c73..58767c122d 100644
--- a/scripts/orig_scripts/txt2img.py
+++ b/scripts/orig_scripts/txt2img.py
@@ -15,10 +15,11 @@ from contextlib import contextmanager, nullcontext
 import k_diffusion as K
 import torch.nn as nn
 
-from ldm.util                  import instantiate_from_config
+from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-from ldm.invoke.devices         import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
+
 
 def chunk(it, size):
     it = iter(it)
@@ -53,23 +54,19 @@ def main():
         type=str,
         nargs="?",
         default="a painting of a virus monster playing guitar",
-        help="the prompt to render"
+        help="the prompt to render",
     )
     parser.add_argument(
-        "--outdir",
-        type=str,
-        nargs="?",
-        help="dir to write results to",
-        default="outputs/txt2img-samples"
+        "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples"
     )
     parser.add_argument(
         "--skip_grid",
-        action='store_true',
+        action="store_true",
         help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
     )
     parser.add_argument(
         "--skip_save",
-        action='store_true',
+        action="store_true",
         help="do not save individual samples. For speed measurements.",
     )
     parser.add_argument(
@@ -80,22 +77,22 @@ def main():
     )
     parser.add_argument(
         "--plms",
-        action='store_true',
+        action="store_true",
         help="use plms sampling",
     )
     parser.add_argument(
         "--klms",
-        action='store_true',
+        action="store_true",
         help="use klms sampling",
     )
     parser.add_argument(
         "--laion400m",
-        action='store_true',
+        action="store_true",
         help="uses the LAION400M model",
     )
     parser.add_argument(
         "--fixed_code",
-        action='store_true',
+        action="store_true",
         help="if enabled, uses the same starting code across samples ",
     )
     parser.add_argument(
@@ -176,11 +173,7 @@ def main():
         help="the seed (for reproducible sampling)",
     )
     parser.add_argument(
-        "--precision",
-        type=str,
-        help="evaluate at this precision",
-        choices=["full", "autocast"],
-        default="autocast"
+        "--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
     )
     opt = parser.parse_args()
 
@@ -190,17 +183,17 @@ def main():
         opt.ckpt = "models/ldm/text2img-large/model.ckpt"
         opt.outdir = "outputs/txt2img-samples-laion400m"
 
-
     config = OmegaConf.load(f"{opt.config}")
     model = load_model_from_config(config, f"{opt.ckpt}")
 
     seed_everything(opt.seed)
 
     device = torch.device(choose_torch_device())
-    model  = model.to(device)
+    model = model.to(device)
 
-    #for klms
+    # for klms
     model_wrap = K.external.CompVisDenoiser(model)
+
     class CFGDenoiser(nn.Module):
         def __init__(self, model):
             super().__init__()
@@ -232,10 +225,10 @@ def main():
         print(f"reading prompts from {opt.from_file}")
         with open(opt.from_file, "r") as f:
             data = f.read().splitlines()
-            if (len(data) >= batch_size):
+            if len(data) >= batch_size:
                 data = list(chunk(data, batch_size))
             else:
-                while (len(data) < batch_size):
+                while len(data) < batch_size:
                     data.append(data[-1])
                 data = [data]
 
@@ -247,14 +240,14 @@ def main():
     start_code = None
     if opt.fixed_code:
         shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]
-        if device.type == 'mps':
-            start_code = torch.randn(shape, device='cpu').to(device)
+        if device.type == "mps":
+            start_code = torch.randn(shape, device="cpu").to(device)
         else:
             torch.randn(shape, device=device)
 
-    precision_scope = autocast if opt.precision=="autocast" else nullcontext
-    if device.type in ['mps', 'cpu']:
-        precision_scope = nullcontext # have to use f32 on mps
+    precision_scope = autocast if opt.precision == "autocast" else nullcontext
+    if device.type in ["mps", "cpu"]:
+        precision_scope = nullcontext  # have to use f32 on mps
     with torch.no_grad():
         with precision_scope(device.type):
             with model.ema_scope():
@@ -271,23 +264,25 @@ def main():
                         shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
 
                         if not opt.klms:
-                            samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
-                                                            conditioning=c,
-                                                            batch_size=opt.n_samples,
-                                                            shape=shape,
-                                                            verbose=False,
-                                                            unconditional_guidance_scale=opt.scale,
-                                                            unconditional_conditioning=uc,
-                                                            eta=opt.ddim_eta,
-                                                            x_T=start_code)
+                            samples_ddim, _ = sampler.sample(
+                                S=opt.ddim_steps,
+                                conditioning=c,
+                                batch_size=opt.n_samples,
+                                shape=shape,
+                                verbose=False,
+                                unconditional_guidance_scale=opt.scale,
+                                unconditional_conditioning=uc,
+                                eta=opt.ddim_eta,
+                                x_T=start_code,
+                            )
                         else:
                             sigmas = model_wrap.get_sigmas(opt.ddim_steps)
                             if start_code:
                                 x = start_code
                             else:
-                                x = torch.randn([opt.n_samples, *shape], device=device) * sigmas[0] # for GPU draw
+                                x = torch.randn([opt.n_samples, *shape], device=device) * sigmas[0]  # for GPU draw
                             model_wrap_cfg = CFGDenoiser(model_wrap)
-                            extra_args = {'cond': c, 'uncond': uc, 'cond_scale': opt.scale}
+                            extra_args = {"cond": c, "uncond": uc, "cond_scale": opt.scale}
                             samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args)
 
                         x_samples_ddim = model.decode_first_stage(samples_ddim)
@@ -295,9 +290,10 @@ def main():
 
                         if not opt.skip_save:
                             for x_sample in x_samples_ddim:
-                                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                                x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
                                 Image.fromarray(x_sample.astype(np.uint8)).save(
-                                    os.path.join(sample_path, f"{base_count:05}.png"))
+                                    os.path.join(sample_path, f"{base_count:05}.png")
+                                )
                                 base_count += 1
 
                         if not opt.skip_grid:
@@ -306,18 +302,17 @@ def main():
                 if not opt.skip_grid:
                     # additionally, save as grid
                     grid = torch.stack(all_samples, 0)
-                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                    grid = rearrange(grid, "n b c h w -> (n b) c h w")
                     grid = make_grid(grid, nrow=n_rows)
 
                     # to image
-                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+                    grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
+                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
                     grid_count += 1
 
                 toc = time.time()
 
-    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
-          f" \nEnjoy.")
+    print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
 
 
 if __name__ == "__main__":
diff --git a/scripts/probe-model.py b/scripts/probe-model.py
new file mode 100755
index 0000000000..7281dafc3f
--- /dev/null
+++ b/scripts/probe-model.py
@@ -0,0 +1,16 @@
+#!/bin/env python
+
+import argparse
+import sys
+from pathlib import Path
+from invokeai.backend.model_management.model_probe import ModelProbe
+
+parser = argparse.ArgumentParser(description="Probe model type")
+parser.add_argument(
+    "model_path",
+    type=Path,
+)
+args = parser.parse_args()
+
+info = ModelProbe().probe(args.model_path)
+print(info)
diff --git a/scripts/pypi_helper.py b/scripts/pypi_helper.py
index 4dccd6383d..6c1f9b9033 100755
--- a/scripts/pypi_helper.py
+++ b/scripts/pypi_helper.py
@@ -5,7 +5,8 @@ import requests
 from invokeai.version import __version__
 
 local_version = str(__version__).replace("-", "")
-package_name = 'InvokeAI'
+package_name = "InvokeAI"
+
 
 def get_pypi_versions(package_name=package_name) -> list[str]:
     """Get the versions of the package from PyPI"""
diff --git a/scripts/scan_models_directory.py b/scripts/scan_models_directory.py
index 778a6c5ed5..de7278f5b7 100755
--- a/scripts/scan_models_directory.py
+++ b/scripts/scan_models_directory.py
@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 
-'''
+"""
 Scan the models directory and print out a new models.yaml
-'''
+"""
 
 import os
 import sys
@@ -11,49 +11,51 @@ import argparse
 from pathlib import Path
 from omegaconf import OmegaConf
 
+
 def main():
     parser = argparse.ArgumentParser(description="Model directory scanner")
-    parser.add_argument('models_directory')
-    parser.add_argument('--all-models',
-                        default=False,
-                        action='store_true',
-                        help='If true, then generates stanzas for all models; otherwise just diffusers'
-                        )
-                        
+    parser.add_argument("models_directory")
+    parser.add_argument(
+        "--all-models",
+        default=False,
+        action="store_true",
+        help="If true, then generates stanzas for all models; otherwise just diffusers",
+    )
+
     args = parser.parse_args()
     directory = args.models_directory
 
     conf = OmegaConf.create()
-    conf['_version'] = '3.0.0'
-    
+    conf["_version"] = "3.0.0"
+
     for root, dirs, files in os.walk(directory):
-        parents = root.split('/')
-        subpaths = parents[parents.index('models')+1:]
+        parents = root.split("/")
+        subpaths = parents[parents.index("models") + 1 :]
         if len(subpaths) < 2:
             continue
         base, model_type, *_ = subpaths
-        
-        if args.all_models or model_type=='diffusers':
+
+        if args.all_models or model_type == "diffusers":
             for d in dirs:
-                conf[f'{base}/{model_type}/{d}'] = dict(
-                    path = os.path.join(root,d),
-                    description = f'{model_type} model {d}',
-                    format = 'folder',
-                    base = base,
+                conf[f"{base}/{model_type}/{d}"] = dict(
+                    path=os.path.join(root, d),
+                    description=f"{model_type} model {d}",
+                    format="folder",
+                    base=base,
                 )
 
             for f in files:
                 basename = Path(f).stem
                 format = Path(f).suffix[1:]
-                conf[f'{base}/{model_type}/{basename}'] = dict(
-                    path = os.path.join(root,f),
-                    description = f'{model_type} model {basename}',
-                    format = format,
-                    base = base,
+                conf[f"{base}/{model_type}/{basename}"] = dict(
+                    path=os.path.join(root, f),
+                    description=f"{model_type} model {basename}",
+                    format=format,
+                    base=base,
                 )
-                
-    OmegaConf.save(config=dict(sorted(conf.items())), f=sys.stdout)
-    
 
-if __name__ == '__main__':
+    OmegaConf.save(config=dict(sorted(conf.items())), f=sys.stdout)
+
+
+if __name__ == "__main__":
     main()
diff --git a/scripts/sd-metadata.py b/scripts/sd-metadata.py
index 6f9c73757d..1a27e73d95 100755
--- a/scripts/sd-metadata.py
+++ b/scripts/sd-metadata.py
@@ -13,10 +13,10 @@ filenames = sys.argv[1:]
 for f in filenames:
     try:
         metadata = retrieve_metadata(f)
-        print(f'{f}:\n',json.dumps(metadata['sd-metadata'], indent=4))
+        print(f"{f}:\n", json.dumps(metadata["sd-metadata"], indent=4))
     except FileNotFoundError:
-        sys.stderr.write(f'{f} not found\n')
+        sys.stderr.write(f"{f} not found\n")
         continue
     except PermissionError:
-        sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
+        sys.stderr.write(f"{f} could not be opened due to inadequate permissions\n")
         continue
diff --git a/tests/nodes/test_graph_execution_state.py b/tests/nodes/test_graph_execution_state.py
index bc4a3f4176..e0ee120b54 100644
--- a/tests/nodes/test_graph_execution_state.py
+++ b/tests/nodes/test_graph_execution_state.py
@@ -42,26 +42,24 @@ def simple_graph():
 def mock_services() -> InvocationServices:
     # NOTE: none of these are actually called by the test invocations
     return InvocationServices(
-        model_manager = None, # type: ignore
-        events = TestEventService(),
-        logger = None, # type: ignore
-        images = None, # type: ignore
-        latents = None, # type: ignore
-        boards = None, # type: ignore
-        board_images = None, # type: ignore
-        queue = MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=sqlite_memory, table_name="graphs"
+        model_manager=None,  # type: ignore
+        events=TestEventService(),
+        logger=None,  # type: ignore
+        images=None,  # type: ignore
+        latents=None,  # type: ignore
+        boards=None,  # type: ignore
+        board_images=None,  # type: ignore
+        queue=MemoryInvocationQueue(),
+        graph_library=SqliteItemStorage[LibraryGraph](filename=sqlite_memory, table_name="graphs"),
+        graph_execution_manager=SqliteItemStorage[GraphExecutionState](
+            filename=sqlite_memory, table_name="graph_executions"
         ),
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
-        processor = DefaultInvocationProcessor(),
-        configuration = None, # type: ignore
+        processor=DefaultInvocationProcessor(),
+        configuration=None,  # type: ignore
     )
 
 
-def invoke_next(
-    g: GraphExecutionState, services: InvocationServices
-) -> tuple[BaseInvocation, BaseInvocationOutput]:
+def invoke_next(g: GraphExecutionState, services: InvocationServices) -> tuple[BaseInvocation, BaseInvocationOutput]:
     n = g.next()
     if n is None:
         return (None, None)
@@ -130,9 +128,7 @@ def test_graph_state_expands_iterator(mock_services):
 def test_graph_state_collects(mock_services):
     graph = Graph()
     test_prompts = ["Banana sushi", "Cat sushi"]
-    graph.add_node(
-        PromptCollectionTestInvocation(id="1", collection=list(test_prompts))
-    )
+    graph.add_node(PromptCollectionTestInvocation(id="1", collection=list(test_prompts)))
     graph.add_node(IterateInvocation(id="2"))
     graph.add_node(PromptTestInvocation(id="3"))
     graph.add_node(CollectInvocation(id="4"))
@@ -158,16 +154,10 @@ def test_graph_state_prepares_eagerly(mock_services):
     graph = Graph()
 
     test_prompts = ["Banana sushi", "Cat sushi"]
-    graph.add_node(
-        PromptCollectionTestInvocation(
-            id="prompt_collection", collection=list(test_prompts)
-        )
-    )
+    graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
     graph.add_node(IterateInvocation(id="iterate"))
     graph.add_node(PromptTestInvocation(id="prompt_iterated"))
-    graph.add_edge(
-        create_edge("prompt_collection", "collection", "iterate", "collection")
-    )
+    graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
     graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
 
     # separated, fully-preparable chain of nodes
@@ -193,21 +183,13 @@ def test_graph_executes_depth_first(mock_services):
     graph = Graph()
 
     test_prompts = ["Banana sushi", "Cat sushi"]
-    graph.add_node(
-        PromptCollectionTestInvocation(
-            id="prompt_collection", collection=list(test_prompts)
-        )
-    )
+    graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
     graph.add_node(IterateInvocation(id="iterate"))
     graph.add_node(PromptTestInvocation(id="prompt_iterated"))
     graph.add_node(PromptTestInvocation(id="prompt_successor"))
-    graph.add_edge(
-        create_edge("prompt_collection", "collection", "iterate", "collection")
-    )
+    graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
     graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
-    graph.add_edge(
-        create_edge("prompt_iterated", "prompt", "prompt_successor", "prompt")
-    )
+    graph.add_edge(create_edge("prompt_iterated", "prompt", "prompt_successor", "prompt"))
 
     g = GraphExecutionState(graph=graph)
     n1 = invoke_next(g, mock_services)
diff --git a/tests/nodes/test_invoker.py b/tests/nodes/test_invoker.py
index 4741e7f58b..8eba6d468f 100644
--- a/tests/nodes/test_invoker.py
+++ b/tests/nodes/test_invoker.py
@@ -35,20 +35,20 @@ def simple_graph():
 def mock_services() -> InvocationServices:
     # NOTE: none of these are actually called by the test invocations
     return InvocationServices(
-        model_manager = None, # type: ignore
-        events = TestEventService(),
-        logger = None, # type: ignore
-        images = None, # type: ignore
-        latents = None, # type: ignore
-        boards = None, # type: ignore
-        board_images = None, # type: ignore
-        queue = MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=sqlite_memory, table_name="graphs"
+        model_manager=None,  # type: ignore
+        events=TestEventService(),
+        logger=None,  # type: ignore
+        images=None,  # type: ignore
+        latents=None,  # type: ignore
+        boards=None,  # type: ignore
+        board_images=None,  # type: ignore
+        queue=MemoryInvocationQueue(),
+        graph_library=SqliteItemStorage[LibraryGraph](filename=sqlite_memory, table_name="graphs"),
+        graph_execution_manager=SqliteItemStorage[GraphExecutionState](
+            filename=sqlite_memory, table_name="graph_executions"
         ),
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
-        processor = DefaultInvocationProcessor(),
-        configuration = None, # type: ignore
+        processor=DefaultInvocationProcessor(),
+        configuration=None,  # type: ignore
     )
 
 
diff --git a/tests/nodes/test_node_graph.py b/tests/nodes/test_node_graph.py
index cd995141ab..0f893cb14c 100644
--- a/tests/nodes/test_node_graph.py
+++ b/tests/nodes/test_node_graph.py
@@ -1,5 +1,21 @@
-from .test_nodes import ImageToImageTestInvocation, TextToImageTestInvocation, ListPassThroughInvocation, PromptTestInvocation
-from invokeai.app.services.graph import Edge, Graph, GraphInvocation, InvalidEdgeError, NodeAlreadyInGraphError, NodeNotFoundError, are_connections_compatible, EdgeConnection, CollectInvocation, IterateInvocation
+from .test_nodes import (
+    ImageToImageTestInvocation,
+    TextToImageTestInvocation,
+    ListPassThroughInvocation,
+    PromptTestInvocation,
+)
+from invokeai.app.services.graph import (
+    Edge,
+    Graph,
+    GraphInvocation,
+    InvalidEdgeError,
+    NodeAlreadyInGraphError,
+    NodeNotFoundError,
+    are_connections_compatible,
+    EdgeConnection,
+    CollectInvocation,
+    IterateInvocation,
+)
 from invokeai.app.invocations.upscale import ESRGANInvocation
 from invokeai.app.invocations.image import *
 from invokeai.app.invocations.math import AddInvocation, SubtractInvocation
@@ -11,35 +27,38 @@ import pytest
 # Helpers
 def create_edge(from_id: str, from_field: str, to_id: str, to_field: str) -> Edge:
     return Edge(
-        source=EdgeConnection(node_id = from_id, field = from_field),
-        destination=EdgeConnection(node_id = to_id, field = to_field)
+        source=EdgeConnection(node_id=from_id, field=from_field),
+        destination=EdgeConnection(node_id=to_id, field=to_field),
     )
 
+
 # Tests
 def test_connections_are_compatible():
-    from_node = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    from_node = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     from_field = "image"
-    to_node = ESRGANInvocation(id = "2")
+    to_node = ESRGANInvocation(id="2")
     to_field = "image"
 
     result = are_connections_compatible(from_node, from_field, to_node, to_field)
 
     assert result == True
 
+
 def test_connections_are_incompatible():
-    from_node = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    from_node = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     from_field = "image"
-    to_node = ESRGANInvocation(id = "2")
+    to_node = ESRGANInvocation(id="2")
     to_field = "strength"
 
     result = are_connections_compatible(from_node, from_field, to_node, to_field)
 
     assert result == False
 
+
 def test_connections_incompatible_with_invalid_fields():
-    from_node = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    from_node = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     from_field = "invalid_field"
-    to_node = ESRGANInvocation(id = "2")
+    to_node = ESRGANInvocation(id="2")
     to_field = "image"
 
     # From field is invalid
@@ -53,138 +72,152 @@ def test_connections_incompatible_with_invalid_fields():
     result = are_connections_compatible(from_node, from_field, to_node, to_field)
     assert result == False
 
+
 def test_graph_can_add_node():
     g = Graph()
-    n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.add_node(n)
 
     assert n.id in g.nodes
 
+
 def test_graph_fails_to_add_node_with_duplicate_id():
     g = Graph()
-    n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.add_node(n)
-    n2 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi the second")
+    n2 = TextToImageTestInvocation(id="1", prompt="Banana sushi the second")
 
     with pytest.raises(NodeAlreadyInGraphError):
         g.add_node(n2)
 
+
 def test_graph_updates_node():
     g = Graph()
-    n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.add_node(n)
-    n2 = TextToImageTestInvocation(id = "2", prompt = "Banana sushi the second")
+    n2 = TextToImageTestInvocation(id="2", prompt="Banana sushi the second")
     g.add_node(n2)
 
-    nu = TextToImageTestInvocation(id = "1", prompt = "Banana sushi updated")
+    nu = TextToImageTestInvocation(id="1", prompt="Banana sushi updated")
 
     g.update_node("1", nu)
 
     assert g.nodes["1"].prompt == "Banana sushi updated"
 
+
 def test_graph_fails_to_update_node_if_type_changes():
     g = Graph()
-    n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.add_node(n)
-    n2 = ESRGANInvocation(id = "2")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n2)
 
-    nu = ESRGANInvocation(id = "1")
+    nu = ESRGANInvocation(id="1")
 
     with pytest.raises(TypeError):
         g.update_node("1", nu)
 
+
 def test_graph_allows_non_conflicting_id_change():
     g = Graph()
-    n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.add_node(n)
-    n2 = ESRGANInvocation(id = "2")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n2)
-    e1 = create_edge(n.id,"image",n2.id,"image")
+    e1 = create_edge(n.id, "image", n2.id, "image")
     g.add_edge(e1)
-    
-    nu = TextToImageTestInvocation(id = "3", prompt = "Banana sushi")
+
+    nu = TextToImageTestInvocation(id="3", prompt="Banana sushi")
     g.update_node("1", nu)
 
     with pytest.raises(NodeNotFoundError):
         g.get_node("1")
-    
+
     assert g.get_node("3").prompt == "Banana sushi"
 
     assert len(g.edges) == 1
-    assert Edge(source=EdgeConnection(node_id = "3", field = "image"), destination=EdgeConnection(node_id = "2", field = "image")) in g.edges
+    assert (
+        Edge(source=EdgeConnection(node_id="3", field="image"), destination=EdgeConnection(node_id="2", field="image"))
+        in g.edges
+    )
+
 
 def test_graph_fails_to_update_node_id_if_conflict():
     g = Graph()
-    n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.add_node(n)
-    n2 = TextToImageTestInvocation(id = "2", prompt = "Banana sushi the second")
+    n2 = TextToImageTestInvocation(id="2", prompt="Banana sushi the second")
     g.add_node(n2)
-    
-    nu = TextToImageTestInvocation(id = "2", prompt = "Banana sushi")
+
+    nu = TextToImageTestInvocation(id="2", prompt="Banana sushi")
     with pytest.raises(NodeAlreadyInGraphError):
         g.update_node("1", nu)
 
+
 def test_graph_adds_edge():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e = create_edge(n1.id,"image",n2.id,"image")
+    e = create_edge(n1.id, "image", n2.id, "image")
 
     g.add_edge(e)
 
     assert e in g.edges
 
+
 def test_graph_fails_to_add_edge_with_cycle():
     g = Graph()
-    n1 = ESRGANInvocation(id = "1")
+    n1 = ESRGANInvocation(id="1")
     g.add_node(n1)
-    e = create_edge(n1.id,"image",n1.id,"image")
+    e = create_edge(n1.id, "image", n1.id, "image")
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e)
 
+
 def test_graph_fails_to_add_edge_with_long_cycle():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
-    n3 = ESRGANInvocation(id = "3")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
+    n3 = ESRGANInvocation(id="3")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
-    e1 = create_edge(n1.id,"image",n2.id,"image")
-    e2 = create_edge(n2.id,"image",n3.id,"image")
-    e3 = create_edge(n3.id,"image",n2.id,"image")
+    e1 = create_edge(n1.id, "image", n2.id, "image")
+    e2 = create_edge(n2.id, "image", n3.id, "image")
+    e3 = create_edge(n3.id, "image", n2.id, "image")
     g.add_edge(e1)
     g.add_edge(e2)
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e3)
 
+
 def test_graph_fails_to_add_edge_with_missing_node_id():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e1 = create_edge("1","image","3","image")
-    e2 = create_edge("3","image","1","image")
+    e1 = create_edge("1", "image", "3", "image")
+    e2 = create_edge("3", "image", "1", "image")
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e1)
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e2)
 
+
 def test_graph_fails_to_add_edge_when_destination_exists():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
-    n3 = ESRGANInvocation(id = "3")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
+    n3 = ESRGANInvocation(id="3")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
-    e1 = create_edge(n1.id,"image",n2.id,"image")
-    e2 = create_edge(n1.id,"image",n3.id,"image")
-    e3 = create_edge(n2.id,"image",n3.id,"image")
+    e1 = create_edge(n1.id, "image", n2.id, "image")
+    e2 = create_edge(n1.id, "image", n3.id, "image")
+    e3 = create_edge(n2.id, "image", n3.id, "image")
     g.add_edge(e1)
     g.add_edge(e2)
     with pytest.raises(InvalidEdgeError):
@@ -193,208 +226,223 @@ def test_graph_fails_to_add_edge_when_destination_exists():
 
 def test_graph_fails_to_add_edge_with_mismatched_types():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e1 = create_edge("1","image","2","strength")
+    e1 = create_edge("1", "image", "2", "strength")
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e1)
 
+
 def test_graph_connects_collector():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = TextToImageTestInvocation(id = "2", prompt = "Banana sushi 2")
-    n3 = CollectInvocation(id = "3")
-    n4 = ListPassThroughInvocation(id = "4")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = TextToImageTestInvocation(id="2", prompt="Banana sushi 2")
+    n3 = CollectInvocation(id="3")
+    n4 = ListPassThroughInvocation(id="4")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
     g.add_node(n4)
 
-    e1 = create_edge("1","image","3","item")
-    e2 = create_edge("2","image","3","item")
-    e3 = create_edge("3","collection","4","collection")
+    e1 = create_edge("1", "image", "3", "item")
+    e2 = create_edge("2", "image", "3", "item")
+    e3 = create_edge("3", "collection", "4", "collection")
     g.add_edge(e1)
     g.add_edge(e2)
     g.add_edge(e3)
 
+
 # TODO: test that derived types mixed with base types are compatible
 
+
 def test_graph_collector_invalid_with_varying_input_types():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = PromptTestInvocation(id = "2", prompt = "banana sushi 2")
-    n3 = CollectInvocation(id = "3")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = PromptTestInvocation(id="2", prompt="banana sushi 2")
+    n3 = CollectInvocation(id="3")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
 
-    e1 = create_edge("1","image","3","item")
-    e2 = create_edge("2","prompt","3","item")
+    e1 = create_edge("1", "image", "3", "item")
+    e2 = create_edge("2", "prompt", "3", "item")
     g.add_edge(e1)
-    
+
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e2)
 
+
 def test_graph_collector_invalid_with_varying_input_output():
     g = Graph()
-    n1 = PromptTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = PromptTestInvocation(id = "2", prompt = "Banana sushi 2")
-    n3 = CollectInvocation(id = "3")
-    n4 = ListPassThroughInvocation(id = "4")
+    n1 = PromptTestInvocation(id="1", prompt="Banana sushi")
+    n2 = PromptTestInvocation(id="2", prompt="Banana sushi 2")
+    n3 = CollectInvocation(id="3")
+    n4 = ListPassThroughInvocation(id="4")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
     g.add_node(n4)
 
-    e1 = create_edge("1","prompt","3","item")
-    e2 = create_edge("2","prompt","3","item")
-    e3 = create_edge("3","collection","4","collection")
+    e1 = create_edge("1", "prompt", "3", "item")
+    e2 = create_edge("2", "prompt", "3", "item")
+    e3 = create_edge("3", "collection", "4", "collection")
     g.add_edge(e1)
     g.add_edge(e2)
 
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e3)
 
+
 def test_graph_collector_invalid_with_non_list_output():
     g = Graph()
-    n1 = PromptTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = PromptTestInvocation(id = "2", prompt = "Banana sushi 2")
-    n3 = CollectInvocation(id = "3")
-    n4 = PromptTestInvocation(id = "4")
+    n1 = PromptTestInvocation(id="1", prompt="Banana sushi")
+    n2 = PromptTestInvocation(id="2", prompt="Banana sushi 2")
+    n3 = CollectInvocation(id="3")
+    n4 = PromptTestInvocation(id="4")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
     g.add_node(n4)
 
-    e1 = create_edge("1","prompt","3","item")
-    e2 = create_edge("2","prompt","3","item")
-    e3 = create_edge("3","collection","4","prompt")
+    e1 = create_edge("1", "prompt", "3", "item")
+    e2 = create_edge("2", "prompt", "3", "item")
+    e3 = create_edge("3", "collection", "4", "prompt")
     g.add_edge(e1)
     g.add_edge(e2)
 
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e3)
 
+
 def test_graph_connects_iterator():
     g = Graph()
-    n1 = ListPassThroughInvocation(id = "1")
-    n2 = IterateInvocation(id = "2")
-    n3 = ImageToImageTestInvocation(id = "3", prompt = "Banana sushi")
+    n1 = ListPassThroughInvocation(id="1")
+    n2 = IterateInvocation(id="2")
+    n3 = ImageToImageTestInvocation(id="3", prompt="Banana sushi")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
 
-    e1 = create_edge("1","collection","2","collection")
-    e2 = create_edge("2","item","3","image")
+    e1 = create_edge("1", "collection", "2", "collection")
+    e2 = create_edge("2", "item", "3", "image")
     g.add_edge(e1)
     g.add_edge(e2)
 
+
 # TODO: TEST INVALID ITERATOR SCENARIOS
 
+
 def test_graph_iterator_invalid_if_multiple_inputs():
     g = Graph()
-    n1 = ListPassThroughInvocation(id = "1")
-    n2 = IterateInvocation(id = "2")
-    n3 = ImageToImageTestInvocation(id = "3", prompt = "Banana sushi")
-    n4 = ListPassThroughInvocation(id = "4")
+    n1 = ListPassThroughInvocation(id="1")
+    n2 = IterateInvocation(id="2")
+    n3 = ImageToImageTestInvocation(id="3", prompt="Banana sushi")
+    n4 = ListPassThroughInvocation(id="4")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
     g.add_node(n4)
 
-    e1 = create_edge("1","collection","2","collection")
-    e2 = create_edge("2","item","3","image")
-    e3 = create_edge("4","collection","2","collection")
+    e1 = create_edge("1", "collection", "2", "collection")
+    e2 = create_edge("2", "item", "3", "image")
+    e3 = create_edge("4", "collection", "2", "collection")
     g.add_edge(e1)
     g.add_edge(e2)
 
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e3)
 
+
 def test_graph_iterator_invalid_if_input_not_list():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = IterateInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = IterateInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
 
-    e1 = create_edge("1","collection","2","collection")
+    e1 = create_edge("1", "collection", "2", "collection")
 
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e1)
 
+
 def test_graph_iterator_invalid_if_output_and_input_types_different():
     g = Graph()
-    n1 = ListPassThroughInvocation(id = "1")
-    n2 = IterateInvocation(id = "2")
-    n3 = PromptTestInvocation(id = "3", prompt = "Banana sushi")
+    n1 = ListPassThroughInvocation(id="1")
+    n2 = IterateInvocation(id="2")
+    n3 = PromptTestInvocation(id="3", prompt="Banana sushi")
     g.add_node(n1)
     g.add_node(n2)
     g.add_node(n3)
 
-    e1 = create_edge("1","collection","2","collection")
-    e2 = create_edge("2","item","3","prompt")
+    e1 = create_edge("1", "collection", "2", "collection")
+    e2 = create_edge("2", "item", "3", "prompt")
     g.add_edge(e1)
 
     with pytest.raises(InvalidEdgeError):
         g.add_edge(e2)
 
+
 def test_graph_validates():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e1 = create_edge("1","image","2","image")
+    e1 = create_edge("1", "image", "2", "image")
     g.add_edge(e1)
 
     assert g.is_valid() == True
 
+
 def test_graph_invalid_if_edges_reference_missing_nodes():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     g.nodes[n1.id] = n1
-    e1 = create_edge("1","image","2","image")
+    e1 = create_edge("1", "image", "2", "image")
     g.edges.append(e1)
 
     assert g.is_valid() == False
 
+
 def test_graph_invalid_if_subgraph_invalid():
     g = Graph()
-    n1 = GraphInvocation(id = "1")
+    n1 = GraphInvocation(id="1")
     n1.graph = Graph()
 
-    n1_1 = TextToImageTestInvocation(id = "2", prompt = "Banana sushi")
+    n1_1 = TextToImageTestInvocation(id="2", prompt="Banana sushi")
     n1.graph.nodes[n1_1.id] = n1_1
-    e1 = create_edge("1","image","2","image")
+    e1 = create_edge("1", "image", "2", "image")
     n1.graph.edges.append(e1)
 
     g.nodes[n1.id] = n1
 
     assert g.is_valid() == False
 
+
 def test_graph_invalid_if_has_cycle():
     g = Graph()
-    n1 = ESRGANInvocation(id = "1")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = ESRGANInvocation(id="1")
+    n2 = ESRGANInvocation(id="2")
     g.nodes[n1.id] = n1
     g.nodes[n2.id] = n2
-    e1 = create_edge("1","image","2","image")
-    e2 = create_edge("2","image","1","image")
+    e1 = create_edge("1", "image", "2", "image")
+    e2 = create_edge("2", "image", "1", "image")
     g.edges.append(e1)
     g.edges.append(e2)
 
     assert g.is_valid() == False
 
+
 def test_graph_invalid_with_invalid_connection():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.nodes[n1.id] = n1
     g.nodes[n2.id] = n2
-    e1 = create_edge("1","image","2","strength")
+    e1 = create_edge("1", "image", "2", "strength")
     g.edges.append(e1)
 
     assert g.is_valid() == False
@@ -403,47 +451,47 @@ def test_graph_invalid_with_invalid_connection():
 # TODO: Subgraph operations
 def test_graph_gets_subgraph_node():
     g = Graph()
-    n1 = GraphInvocation(id = "1")
+    n1 = GraphInvocation(id="1")
     n1.graph = Graph()
     n1.graph.add_node
 
-    n1_1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n1_1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     n1.graph.add_node(n1_1)
 
     g.add_node(n1)
 
-    result = g.get_node('1.1')
+    result = g.get_node("1.1")
 
     assert result is not None
-    assert result.id == '1'
+    assert result.id == "1"
     assert result == n1_1
 
 
 def test_graph_expands_subgraph():
     g = Graph()
-    n1 = GraphInvocation(id = "1")
+    n1 = GraphInvocation(id="1")
     n1.graph = Graph()
 
-    n1_1 = AddInvocation(id = "1", a = 1, b = 2)
-    n1_2 = SubtractInvocation(id = "2", b = 3)
+    n1_1 = AddInvocation(id="1", a=1, b=2)
+    n1_2 = SubtractInvocation(id="2", b=3)
     n1.graph.add_node(n1_1)
     n1.graph.add_node(n1_2)
-    n1.graph.add_edge(create_edge("1","a","2","a"))
+    n1.graph.add_edge(create_edge("1", "a", "2", "a"))
 
     g.add_node(n1)
 
-    n2 = AddInvocation(id = "2", b = 5)
+    n2 = AddInvocation(id="2", b=5)
     g.add_node(n2)
-    g.add_edge(create_edge("1.2","a","2","a"))
+    g.add_edge(create_edge("1.2", "a", "2", "a"))
 
     dg = g.nx_graph_flat()
-    assert set(dg.nodes) == set(['1.1', '1.2', '2'])
-    assert set(dg.edges) == set([('1.1', '1.2'), ('1.2', '2')])
+    assert set(dg.nodes) == set(["1.1", "1.2", "2"])
+    assert set(dg.edges) == set([("1.1", "1.2"), ("1.2", "2")])
 
 
 def test_graph_subgraph_t2i():
     g = Graph()
-    n1 = GraphInvocation(id = "1")
+    n1 = GraphInvocation(id="1")
 
     # Get text to image default graph
     lg = create_text_to_image()
@@ -451,28 +499,26 @@ def test_graph_subgraph_t2i():
 
     g.add_node(n1)
 
-    n2 = ParamIntInvocation(id = "2", a = 512)
-    n3 = ParamIntInvocation(id = "3", a = 256)
+    n2 = ParamIntInvocation(id="2", a=512)
+    n3 = ParamIntInvocation(id="3", a=256)
 
     g.add_node(n2)
     g.add_node(n3)
 
-    g.add_edge(create_edge("2","a","1.width","a"))
-    g.add_edge(create_edge("3","a","1.height","a"))
-    
-    n4 = ShowImageInvocation(id = "4")
+    g.add_edge(create_edge("2", "a", "1.width", "a"))
+    g.add_edge(create_edge("3", "a", "1.height", "a"))
+
+    n4 = ShowImageInvocation(id="4")
     g.add_node(n4)
-    g.add_edge(create_edge("1.7","image","4","image"))
+    g.add_edge(create_edge("1.8", "image", "4", "image"))
 
     # Validate
     dg = g.nx_graph_flat()
-    assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '2', '3', '4'])
-    expected_edges = [(f'1.{e.source.node_id}',f'1.{e.destination.node_id}') for e in lg.graph.edges]
-    expected_edges.extend([
-        ('2','1.width'),
-        ('3','1.height'),
-        ('1.7','4')
-    ])
+    assert set(dg.nodes) == set(
+        ["1.width", "1.height", "1.seed", "1.3", "1.4", "1.5", "1.6", "1.7", "1.8", "2", "3", "4"]
+    )
+    expected_edges = [(f"1.{e.source.node_id}", f"1.{e.destination.node_id}") for e in lg.graph.edges]
+    expected_edges.extend([("2", "1.width"), ("3", "1.height"), ("1.8", "4")])
     print(expected_edges)
     print(list(dg.edges))
     assert set(dg.edges) == set(expected_edges)
@@ -480,86 +526,90 @@ def test_graph_subgraph_t2i():
 
 def test_graph_fails_to_get_missing_subgraph_node():
     g = Graph()
-    n1 = GraphInvocation(id = "1")
+    n1 = GraphInvocation(id="1")
     n1.graph = Graph()
     n1.graph.add_node
 
-    n1_1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n1_1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     n1.graph.add_node(n1_1)
 
     g.add_node(n1)
 
     with pytest.raises(NodeNotFoundError):
-        result = g.get_node('1.2')
+        result = g.get_node("1.2")
+
 
 def test_graph_fails_to_enumerate_non_subgraph_node():
     g = Graph()
-    n1 = GraphInvocation(id = "1")
+    n1 = GraphInvocation(id="1")
     n1.graph = Graph()
     n1.graph.add_node
 
-    n1_1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
+    n1_1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
     n1.graph.add_node(n1_1)
 
     g.add_node(n1)
-    
-    n2 = ESRGANInvocation(id = "2")
+
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n2)
 
     with pytest.raises(NodeNotFoundError):
-        result = g.get_node('2.1')
+        result = g.get_node("2.1")
+
 
 def test_graph_gets_networkx_graph():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e = create_edge(n1.id,"image",n2.id,"image")
+    e = create_edge(n1.id, "image", n2.id, "image")
     g.add_edge(e)
 
     nxg = g.nx_graph()
 
-    assert '1' in nxg.nodes
-    assert '2' in nxg.nodes
-    assert ('1','2') in nxg.edges
+    assert "1" in nxg.nodes
+    assert "2" in nxg.nodes
+    assert ("1", "2") in nxg.edges
 
 
 # TODO: Graph serializes and deserializes
 def test_graph_can_serialize():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e = create_edge(n1.id,"image",n2.id,"image")
+    e = create_edge(n1.id, "image", n2.id, "image")
     g.add_edge(e)
 
     # Not throwing on this line is sufficient
     json = g.json()
 
+
 def test_graph_can_deserialize():
     g = Graph()
-    n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
-    n2 = ESRGANInvocation(id = "2")
+    n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi")
+    n2 = ESRGANInvocation(id="2")
     g.add_node(n1)
     g.add_node(n2)
-    e = create_edge(n1.id,"image",n2.id,"image")
+    e = create_edge(n1.id, "image", n2.id, "image")
     g.add_edge(e)
 
     json = g.json()
     g2 = Graph.parse_raw(json)
 
     assert g2 is not None
-    assert g2.nodes['1'] is not None
-    assert g2.nodes['2'] is not None
+    assert g2.nodes["1"] is not None
+    assert g2.nodes["2"] is not None
     assert len(g2.edges) == 1
-    assert g2.edges[0].source.node_id == '1'
-    assert g2.edges[0].source.field == 'image'
-    assert g2.edges[0].destination.node_id == '2'
-    assert g2.edges[0].destination.field == 'image'
+    assert g2.edges[0].source.node_id == "1"
+    assert g2.edges[0].source.field == "image"
+    assert g2.edges[0].destination.node_id == "2"
+    assert g2.edges[0].destination.field == "image"
+
 
 def test_graph_can_generate_schema():
     # Not throwing on this line is sufficient
     # NOTE: if this test fails, it's PROBABLY because a new invocation type is breaking schema generation
-    schema = Graph.schema_json(indent = 2)
+    schema = Graph.schema_json(indent=2)
diff --git a/tests/nodes/test_nodes.py b/tests/nodes/test_nodes.py
index af011954c5..13338e9261 100644
--- a/tests/nodes/test_nodes.py
+++ b/tests/nodes/test_nodes.py
@@ -5,79 +5,92 @@ from invokeai.app.services.invocation_services import InvocationServices
 from pydantic import Field
 import pytest
 
+
 # Define test invocations before importing anything that uses invocations
 class ListPassThroughInvocationOutput(BaseInvocationOutput):
-    type: Literal['test_list_output'] = 'test_list_output'
+    type: Literal["test_list_output"] = "test_list_output"
 
     collection: list[ImageField] = Field(default_factory=list)
 
+
 class ListPassThroughInvocation(BaseInvocation):
-    type: Literal['test_list'] = 'test_list'
+    type: Literal["test_list"] = "test_list"
 
     collection: list[ImageField] = Field(default_factory=list)
 
     def invoke(self, context: InvocationContext) -> ListPassThroughInvocationOutput:
-        return ListPassThroughInvocationOutput(collection = self.collection)
+        return ListPassThroughInvocationOutput(collection=self.collection)
+
 
 class PromptTestInvocationOutput(BaseInvocationOutput):
-    type: Literal['test_prompt_output'] = 'test_prompt_output'
+    type: Literal["test_prompt_output"] = "test_prompt_output"
+
+    prompt: str = Field(default="")
 
-    prompt: str = Field(default = "")
 
 class PromptTestInvocation(BaseInvocation):
-    type: Literal['test_prompt'] = 'test_prompt'
+    type: Literal["test_prompt"] = "test_prompt"
 
-    prompt: str = Field(default = "")
+    prompt: str = Field(default="")
 
     def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
-        return PromptTestInvocationOutput(prompt = self.prompt)
+        return PromptTestInvocationOutput(prompt=self.prompt)
+
 
 class ErrorInvocation(BaseInvocation):
-    type: Literal['test_error'] = 'test_error'
+    type: Literal["test_error"] = "test_error"
 
     def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
         raise Exception("This invocation is supposed to fail")
 
+
 class ImageTestInvocationOutput(BaseInvocationOutput):
-    type: Literal['test_image_output'] = 'test_image_output'
+    type: Literal["test_image_output"] = "test_image_output"
 
     image: ImageField = Field()
 
-class TextToImageTestInvocation(BaseInvocation):
-    type: Literal['test_text_to_image'] = 'test_text_to_image'
 
-    prompt: str = Field(default = "")
+class TextToImageTestInvocation(BaseInvocation):
+    type: Literal["test_text_to_image"] = "test_text_to_image"
+
+    prompt: str = Field(default="")
 
     def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
         return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
 
-class ImageToImageTestInvocation(BaseInvocation):
-    type: Literal['test_image_to_image'] = 'test_image_to_image'
 
-    prompt: str = Field(default = "")
+class ImageToImageTestInvocation(BaseInvocation):
+    type: Literal["test_image_to_image"] = "test_image_to_image"
+
+    prompt: str = Field(default="")
     image: Union[ImageField, None] = Field(default=None)
 
     def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
         return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
 
+
 class PromptCollectionTestInvocationOutput(BaseInvocationOutput):
-    type: Literal['test_prompt_collection_output'] = 'test_prompt_collection_output'
+    type: Literal["test_prompt_collection_output"] = "test_prompt_collection_output"
     collection: list[str] = Field(default_factory=list)
 
+
 class PromptCollectionTestInvocation(BaseInvocation):
-    type: Literal['test_prompt_collection'] = 'test_prompt_collection'
+    type: Literal["test_prompt_collection"] = "test_prompt_collection"
     collection: list[str] = Field()
 
     def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput:
         return PromptCollectionTestInvocationOutput(collection=self.collection.copy())
 
+
 from invokeai.app.services.events import EventServiceBase
 from invokeai.app.services.graph import Edge, EdgeConnection
 
+
 def create_edge(from_id: str, from_field: str, to_id: str, to_field: str) -> Edge:
     return Edge(
-        source=EdgeConnection(node_id = from_id, field = from_field),
-        destination=EdgeConnection(node_id = to_id, field = to_field))
+        source=EdgeConnection(node_id=from_id, field=from_field),
+        destination=EdgeConnection(node_id=to_id, field=to_field),
+    )
 
 
 class TestEvent:
@@ -88,6 +101,7 @@ class TestEvent:
         self.event_name = event_name
         self.payload = payload
 
+
 class TestEventService(EventServiceBase):
     events: list
 
@@ -98,8 +112,10 @@ class TestEventService(EventServiceBase):
     def dispatch(self, event_name: str, payload: Any) -> None:
         pass
 
+
 def wait_until(condition: Callable[[], bool], timeout: int = 10, interval: float = 0.1) -> None:
     import time
+
     start_time = time.time()
     while time.time() - start_time < timeout:
         if condition():
diff --git a/tests/nodes/test_sqlite.py b/tests/nodes/test_sqlite.py
index a803af5635..a9eb542e44 100644
--- a/tests/nodes/test_sqlite.py
+++ b/tests/nodes/test_sqlite.py
@@ -3,110 +3,131 @@ from pydantic import BaseModel, Field
 
 
 class TestModel(BaseModel):
-    id: str = Field(description = "ID")
-    name: str = Field(description = "Name")
+    id: str = Field(description="ID")
+    name: str = Field(description="Name")
 
 
 def test_sqlite_service_can_create_and_get():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    assert db.get('1') == TestModel(id = '1', name = 'Test')
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    assert db.get("1") == TestModel(id="1", name="Test")
+
 
 def test_sqlite_service_can_list():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.set(TestModel(id = '2', name = 'Test'))
-    db.set(TestModel(id = '3', name = 'Test'))
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.set(TestModel(id="2", name="Test"))
+    db.set(TestModel(id="3", name="Test"))
     results = db.list()
     assert results.page == 0
     assert results.pages == 1
     assert results.per_page == 10
     assert results.total == 3
-    assert results.items == [TestModel(id = '1', name = 'Test'), TestModel(id = '2', name = 'Test'), TestModel(id = '3', name = 'Test')]
+    assert results.items == [
+        TestModel(id="1", name="Test"),
+        TestModel(id="2", name="Test"),
+        TestModel(id="3", name="Test"),
+    ]
+
 
 def test_sqlite_service_can_delete():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.delete('1')
-    assert db.get('1') is None
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.delete("1")
+    assert db.get("1") is None
+
 
 def test_sqlite_service_calls_set_callback():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
     called = False
+
     def on_changed(item: TestModel):
         nonlocal called
         called = True
+
     db.on_changed(on_changed)
-    db.set(TestModel(id = '1', name = 'Test'))
+    db.set(TestModel(id="1", name="Test"))
     assert called
 
+
 def test_sqlite_service_calls_delete_callback():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
     called = False
+
     def on_deleted(item_id: str):
         nonlocal called
         called = True
+
     db.on_deleted(on_deleted)
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.delete('1')
+    db.set(TestModel(id="1", name="Test"))
+    db.delete("1")
     assert called
 
+
 def test_sqlite_service_can_list_with_pagination():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.set(TestModel(id = '2', name = 'Test'))
-    db.set(TestModel(id = '3', name = 'Test'))
-    results = db.list(page = 0, per_page = 2)
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.set(TestModel(id="2", name="Test"))
+    db.set(TestModel(id="3", name="Test"))
+    results = db.list(page=0, per_page=2)
     assert results.page == 0
     assert results.pages == 2
     assert results.per_page == 2
     assert results.total == 3
-    assert results.items == [TestModel(id = '1', name = 'Test'), TestModel(id = '2', name = 'Test')]
+    assert results.items == [TestModel(id="1", name="Test"), TestModel(id="2", name="Test")]
+
 
 def test_sqlite_service_can_list_with_pagination_and_offset():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.set(TestModel(id = '2', name = 'Test'))
-    db.set(TestModel(id = '3', name = 'Test'))
-    results = db.list(page = 1, per_page = 2)
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.set(TestModel(id="2", name="Test"))
+    db.set(TestModel(id="3", name="Test"))
+    results = db.list(page=1, per_page=2)
     assert results.page == 1
     assert results.pages == 2
     assert results.per_page == 2
     assert results.total == 3
-    assert results.items == [TestModel(id = '3', name = 'Test')]
+    assert results.items == [TestModel(id="3", name="Test")]
+
 
 def test_sqlite_service_can_search():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.set(TestModel(id = '2', name = 'Test'))
-    db.set(TestModel(id = '3', name = 'Test'))
-    results = db.search(query = 'Test')
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.set(TestModel(id="2", name="Test"))
+    db.set(TestModel(id="3", name="Test"))
+    results = db.search(query="Test")
     assert results.page == 0
     assert results.pages == 1
     assert results.per_page == 10
     assert results.total == 3
-    assert results.items == [TestModel(id = '1', name = 'Test'), TestModel(id = '2', name = 'Test'), TestModel(id = '3', name = 'Test')]
+    assert results.items == [
+        TestModel(id="1", name="Test"),
+        TestModel(id="2", name="Test"),
+        TestModel(id="3", name="Test"),
+    ]
+
 
 def test_sqlite_service_can_search_with_pagination():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.set(TestModel(id = '2', name = 'Test'))
-    db.set(TestModel(id = '3', name = 'Test'))
-    results = db.search(query = 'Test', page = 0, per_page = 2)
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.set(TestModel(id="2", name="Test"))
+    db.set(TestModel(id="3", name="Test"))
+    results = db.search(query="Test", page=0, per_page=2)
     assert results.page == 0
     assert results.pages == 2
     assert results.per_page == 2
     assert results.total == 3
-    assert results.items == [TestModel(id = '1', name = 'Test'), TestModel(id = '2', name = 'Test')]
+    assert results.items == [TestModel(id="1", name="Test"), TestModel(id="2", name="Test")]
+
 
 def test_sqlite_service_can_search_with_pagination_and_offset():
-    db = SqliteItemStorage[TestModel](sqlite_memory, 'test', 'id')
-    db.set(TestModel(id = '1', name = 'Test'))
-    db.set(TestModel(id = '2', name = 'Test'))
-    db.set(TestModel(id = '3', name = 'Test'))
-    results = db.search(query = 'Test', page = 1, per_page = 2)
+    db = SqliteItemStorage[TestModel](sqlite_memory, "test", "id")
+    db.set(TestModel(id="1", name="Test"))
+    db.set(TestModel(id="2", name="Test"))
+    db.set(TestModel(id="3", name="Test"))
+    results = db.search(query="Test", page=1, per_page=2)
     assert results.page == 1
     assert results.pages == 2
     assert results.per_page == 2
     assert results.total == 3
-    assert results.items == [TestModel(id = '3', name = 'Test')]
+    assert results.items == [TestModel(id="3", name="Test")]
diff --git a/tests/test_config.py b/tests/test_config.py
index d6c9d95d7e..5d3dc46aa4 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -5,84 +5,91 @@ import sys
 from omegaconf import OmegaConf
 from pathlib import Path
 
-os.environ['INVOKEAI_ROOT']='/tmp'
+os.environ["INVOKEAI_ROOT"] = "/tmp"
 
 from invokeai.app.services.config import InvokeAIAppConfig
 
 init1 = OmegaConf.create(
-'''
+    """
 InvokeAI:
   Features:
-    nsfw_checker: False
+    always_use_cpu: false
   Memory/Performance:
     max_cache_size: 5
-'''
+    tiled_decode: false
+"""
 )
 
 init2 = OmegaConf.create(
-'''
+    """
 InvokeAI:
   Features:
-    nsfw_checker: true
+    always_use_cpu: true
   Memory/Performance:
     max_cache_size: 2
-'''
+    tiled_decode: true
+"""
 )
 
+
 def test_use_init():
     # note that we explicitly set omegaconf dict and argv here
     # so that the values aren't read from ~invokeai/invokeai.yaml and
     # sys.argv respectively.
     conf1 = InvokeAIAppConfig.get_config()
     assert conf1
-    conf1.parse_args(conf=init1,argv=[])
-    assert conf1.max_cache_size==5
-    assert not conf1.nsfw_checker
+    conf1.parse_args(conf=init1, argv=[])
+    assert not conf1.tiled_decode
+    assert conf1.max_cache_size == 5
+    assert not conf1.always_use_cpu
 
     conf2 = InvokeAIAppConfig.get_config()
     assert conf2
-    conf2.parse_args(conf=init2,argv=[])
-    assert conf2.nsfw_checker
-    assert conf2.max_cache_size==2
-    assert not hasattr(conf2,'invalid_attribute')
-    
+    conf2.parse_args(conf=init2, argv=[])
+    assert conf2.tiled_decode
+    assert conf2.max_cache_size == 2
+    assert not hasattr(conf2, "invalid_attribute")
+
+
 def test_argv_override():
     conf = InvokeAIAppConfig.get_config()
-    conf.parse_args(conf=init1,argv=['--nsfw_checker','--max_cache=10'])
-    assert conf.nsfw_checker
-    assert conf.max_cache_size==10
-    assert conf.outdir==Path('outputs')  # this is the default
-    
+    conf.parse_args(conf=init1, argv=["--always_use_cpu", "--max_cache=10"])
+    assert conf.always_use_cpu
+    assert conf.max_cache_size == 10
+    assert conf.outdir == Path("outputs")  # this is the default
+
+
 def test_env_override():
-    # argv overrides 
+    # argv overrides
     conf = InvokeAIAppConfig()
-    conf.parse_args(conf=init1,argv=['--max_cache=10'])
-    assert conf.nsfw_checker==False
-    os.environ['INVOKEAI_nsfw_checker'] = 'True'
-    conf.parse_args(conf=init1,argv=['--max_cache=10'])
-    assert conf.nsfw_checker==True
+    conf.parse_args(conf=init1, argv=["--max_cache=10"])
+    assert conf.always_use_cpu == False
+    os.environ["INVOKEAI_always_use_cpu"] = "True"
+    conf.parse_args(conf=init1, argv=["--max_cache=10"])
+    assert conf.always_use_cpu == True
 
     # environment variables should be case insensitive
-    os.environ['InvokeAI_Max_Cache_Size'] = '15'
+    os.environ["InvokeAI_Max_Cache_Size"] = "15"
     conf = InvokeAIAppConfig()
-    conf.parse_args(conf=init1,argv=[])
+    conf.parse_args(conf=init1, argv=[])
     assert conf.max_cache_size == 15
 
     conf = InvokeAIAppConfig()
-    conf.parse_args(conf=init1,argv=['--no-nsfw_checker','--max_cache=10'])
-    assert conf.nsfw_checker==False
-    assert conf.max_cache_size==10
+    conf.parse_args(conf=init1, argv=["--no-always_use_cpu", "--max_cache=10"])
+    assert conf.always_use_cpu == False
+    assert conf.max_cache_size == 10
 
     conf = InvokeAIAppConfig.get_config(max_cache_size=20)
-    conf.parse_args(conf=init1,argv=[])
-    assert conf.max_cache_size==20
+    conf.parse_args(conf=init1, argv=[])
+    assert conf.max_cache_size == 20
+
 
 def test_type_coercion():
     conf = InvokeAIAppConfig().get_config()
-    conf.parse_args(argv=['--root=/tmp/foobar'])
-    assert conf.root==Path('/tmp/foobar')
-    assert isinstance(conf.root,Path)
-    conf = InvokeAIAppConfig.get_config(root='/tmp/different')
-    conf.parse_args(argv=['--root=/tmp/foobar'])
-    assert conf.root==Path('/tmp/different')
-    assert isinstance(conf.root,Path)
+    conf.parse_args(argv=["--root=/tmp/foobar"])
+    assert conf.root == Path("/tmp/foobar")
+    assert isinstance(conf.root, Path)
+    conf = InvokeAIAppConfig.get_config(root="/tmp/different")
+    conf.parse_args(argv=["--root=/tmp/foobar"])
+    assert conf.root == Path("/tmp/different")
+    assert isinstance(conf.root, Path)
diff --git a/tests/test_path.py b/tests/test_path.py
index 6076c6554f..0da5f2f8dc 100644
--- a/tests/test_path.py
+++ b/tests/test_path.py
@@ -9,7 +9,8 @@ from PIL import Image
 
 import invokeai.frontend.web.dist as frontend
 import invokeai.configs as configs
-import invokeai.assets.web as assets_web
+import invokeai.app.assets.images as image_assets
+
 
 class ConfigsTestCase(unittest.TestCase):
     """Test the configuration related imports and objects"""
@@ -35,7 +36,7 @@ class ConfigsTestCase(unittest.TestCase):
 
     def test_caution_img(self):
         """Verify the caution image"""
-        caution_img = Image.open(osp.join(assets_web.__path__[0], "caution.png"))
+        caution_img = Image.open(osp.join(image_assets.__path__[0], "caution.png"))
         assert caution_img.width == int(500)
         assert caution_img.height == int(441)
         assert caution_img.format == str("PNG")