Remove unused check_internet() function.

README.md

@@ -12,7 +12,7 @@
 
 Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
 
-[Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
+[Installation][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
 
 <div align="center">
 

docs/contributing/frontend/WORKFLOWS.md

@@ -117,13 +117,13 @@ Stateless fields do not store their value in the node, so their field instances
 
 "Custom" fields will always be treated as stateless fields.
 
-##### Single and Collection Fields
+##### Collection and Scalar Fields
 
-Field types have a name and cardinality property which may identify it as a **SINGLE**, **COLLECTION** or **SINGLE_OR_COLLECTION** field.
+Field types have a name and two flags which may identify it as a **collection** or **collection or scalar** field.
 
-- If a field is annotated in python as a singular value or class, its field type is parsed as a **SINGLE** type (e.g. `int`, `ImageField`, `str`).
-- If a field is annotated in python as a list, its field type is parsed as a **COLLECTION** type (e.g. `list[int]`).
-- If it is annotated as a union of a type and list, the type will be parsed as a **SINGLE_OR_COLLECTION** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
+If a field is annotated in python as a list, its field type is parsed and flagged as a **collection** type (e.g. `list[int]`).
+
+If it is annotated as a union of a type and list, the type will be flagged as a **collection or scalar** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
 
 ## Implementation
 
@@ -173,7 +173,8 @@ Field types are represented as structured objects:
 ```ts
 type FieldType = {
   name: string;
-  cardinality: 'SINGLE' | 'COLLECTION' | 'SINGLE_OR_COLLECTION';
+  isCollection: boolean;
+  isCollectionOrScalar: boolean;
 };
 ```
 
@@ -185,7 +186,7 @@ There are 4 general cases for field type parsing.
 
 When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
 
-We create a field type name from this `type` string (e.g. `string` -> `StringField`). The cardinality is `"SINGLE"`.
+We create a field type name from this `type` string (e.g. `string` -> `StringField`).
 
 ##### Complex Types
 
@@ -199,13 +200,13 @@ We need to **dereference** the schema to pull these out. Dereferencing may requi
 
 When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
 
-We use the item type for field type name. The cardinality is `"COLLECTION"`.
+We use the item type for field type name, adding `isCollection: true` to the field type.
 
-##### Single or Collection Types
+##### Collection or Scalar Types
 
 When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
 
-After verifying that the union has two members (a type and list of the same type), we use the type for field type name, with cardinality `"SINGLE_OR_COLLECTION"`.
+After verifying that the union has two members (a type and list of the same type), we use the type for field type name, adding `isCollectionOrScalar: true` to the field type.
 
 ##### Optional Fields
 

docs/features/CONFIGURATION.md

@@ -51,11 +51,13 @@ The settings in this file will override the defaults. You only need
 to change this file if the default for a particular setting doesn't
 work for you.
 
-You'll find an example file next to `invokeai.yaml` that shows the default values.
-
 Some settings, like [Model Marketplace API Keys], require the YAML
 to be formatted correctly. Here is a [basic guide to YAML files].
 
+You can fix a broken `invokeai.yaml` by deleting it and running the
+configuration script again -- option [6] in the launcher, "Re-run the
+configure script".
+
 #### Custom Config File Location
 
 You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.

docs/features/CONTROLNET.md

@@ -165,7 +165,7 @@ Additionally, each  section can be expanded with the "Show Advanced" button in o
 There are several ways to install IP-Adapter models with an existing InvokeAI installation:
 
 1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
-2. Through the Model Manager UI with models from the *Tools* section of [models.invoke.ai](https://models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
+2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
 3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 
 
 #### Using IP-Adapter

docs/features/TRAINING.md

@@ -4,6 +4,278 @@ title: Training
 
 # :material-file-document: Training
 
-Invoke Training has moved to its own repository, with a dedicated UI for accessing common scripts like Textual Inversion and LoRA training.
+# Textual Inversion Training
+## **Personalizing Text-to-Image Generation**
 
-You can find more by visiting the repo at https://github.com/invoke-ai/invoke-training
+You may personalize the generated images to provide your own styles or objects
+by training a new LDM checkpoint and introducing a new vocabulary to the fixed
+model as a (.pt) embeddings file. Alternatively, you may use or train
+HuggingFace Concepts embeddings files (.bin) from
+<https://huggingface.co/sd-concepts-library> and its associated
+notebooks.
+
+## **Hardware and Software Requirements**
+
+You will need a GPU to perform training in a reasonable length of
+time, and at least 12 GB of VRAM. We recommend using the [`xformers`
+library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
+training process further. During training, about ~8 GB is temporarily
+needed in order to store intermediate models, checkpoints and logs.
+
+## **Preparing for Training**
+
+To train, prepare a folder that contains 3-5 images that illustrate
+the object or concept. It is good to provide a variety of examples or
+poses to avoid overtraining the system. Format these images as PNG
+(preferred) or JPG. You do not need to resize or crop the images in
+advance, but for more control you may wish to do so.
+
+Place the training images in a directory on the machine InvokeAI runs
+on. We recommend placing them in a subdirectory of the
+`text-inversion-training-data` folder located in the InvokeAI root
+directory, ordinarily `~/invokeai` (Linux/Mac), or
+`C:\Users\your_name\invokeai` (Windows). For example, to create an
+embedding for the "psychedelic" style, you'd place the training images
+into the directory
+`~invokeai/text-inversion-training-data/psychedelic`.
+
+## **Launching Training Using the Console Front End**
+
+InvokeAI 2.3 and higher comes with a text console-based training front
+end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
+start training tool selecting choice (3):
+
+```sh
+1 "Generate images with a browser-based interface"
+2 "Explore InvokeAI nodes using a command-line interface"
+3 "Textual inversion training"
+4 "Merge models (diffusers type only)"
+5 "Download and install models"
+6 "Change InvokeAI startup options"
+7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
+8 "Open the developer console"
+9 "Update InvokeAI"
+```
+
+Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
+you can then launch the front end with the command `invokeai-ti --gui`.
+
+This will launch a text-based front end that will look like this:
+
+<figure markdown>
+![ti-frontend](../assets/textual-inversion/ti-frontend.png)
+</figure>
+
+The interface is keyboard-based. Move from field to field using
+control-N (^N) to move to the next field and control-P (^P) to the
+previous one. <Tab> and <shift-TAB> work as well. Once a field is
+active, use the cursor keys. In a checkbox group, use the up and down
+cursor keys to move from choice to choice, and <space> to select a
+choice. In a scrollbar, use the left and right cursor keys to increase
+and decrease the value of the scroll. In textfields, type the desired
+values.
+
+The number of parameters may look intimidating, but in most cases the
+predefined defaults work fine. The red circled fields in the above
+illustration are the ones you will adjust most frequently.
+
+### Model Name
+
+This will list all the diffusers models that are currently
+installed. Select the one you wish to use as the basis for your
+embedding. Be aware that if you use a SD-1.X-based model for your
+training, you will only be able to use this embedding with other
+SD-1.X-based models. Similarly, if you train on SD-2.X, you will only
+be able to use the embeddings with models based on SD-2.X.
+
+### Trigger Term
+
+This is the prompt term you will use to trigger the embedding. Type a
+single word or phrase you wish to use as the trigger, example
+"psychedelic" (without angle brackets). Within InvokeAI, you will then
+be able to activate the trigger using the syntax `<psychedelic>`.
+
+### Initializer
+
+This is a single character that is used internally during the training
+process as a placeholder for the trigger term. It defaults to "*" and
+can usually be left alone.
+
+### Resume from last saved checkpoint
+
+As training proceeds, textual inversion will write a series of
+intermediate files that can be used to resume training from where it
+was left off in the case of an interruption. This checkbox will be
+automatically selected if you provide a previously used trigger term
+and at least one checkpoint file is found on disk.
+
+Note that as of 20 January 2023, resume does not seem to be working
+properly due to an issue with the upstream code.
+
+### Data Training Directory
+
+This is the location of the images to be used for training. When you
+select a trigger term like "my-trigger", the frontend will prepopulate
+this field with `~/invokeai/text-inversion-training-data/my-trigger`,
+but you can change the path to wherever you want.
+
+### Output Destination Directory
+
+This is the location of the logs, checkpoint files, and embedding
+files created during training. When you select a trigger term like
+"my-trigger", the frontend will prepopulate this field with
+`~/invokeai/text-inversion-output/my-trigger`, but you can change the
+path to wherever you want.
+
+### Image resolution
+
+The images in the training directory will be automatically scaled to
+the value you use here. For best results, you will want to use the
+same default resolution of the underlying model (512 pixels for
+SD-1.5, 768 for the larger version of SD-2.1).
+
+### Center crop images
+
+If this is selected, your images will be center cropped to make them
+square before resizing them to the desired resolution. Center cropping
+can indiscriminately cut off the top of subjects' heads for portrait
+aspect images, so if you have images like this, you may wish to use a
+photoeditor to manually crop them to a square aspect ratio.
+
+### Mixed precision
+
+Select the floating point precision for the embedding. "no" will
+result in a full 32-bit precision, "fp16" will provide 16-bit
+precision, and "bf16" will provide mixed precision (only available
+when XFormers is used).
+
+### Max training steps
+
+How many steps the training will take before the model converges. Most
+training sets will converge with 2000-3000 steps.
+
+### Batch size
+
+This adjusts how many training images are processed simultaneously in
+each step. Higher values will cause the training process to run more
+quickly, but use more memory. The default size will run with GPUs with
+as little as 12 GB.
+
+### Learning rate
+
+The rate at which the system adjusts its internal weights during
+training. Higher values risk overtraining (getting the same image each
+time), and lower values will take more steps to train a good
+model. The default of 0.0005 is conservative; you may wish to increase
+it to 0.005 to speed up training.
+
+### Scale learning rate by number of GPUs, steps and batch size
+
+If this is selected (the default) the system will adjust the provided
+learning rate to improve performance.
+
+### Use xformers acceleration
+
+This will activate XFormers memory-efficient attention. You need to
+have XFormers installed for this to have an effect.
+
+### Learning rate scheduler
+
+This adjusts how the learning rate changes over the course of
+training. The default "constant" means to use a constant learning rate
+for the entire training session. The other values scale the learning
+rate according to various formulas.
+
+Only "constant" is supported by the XFormers library.
+
+### Gradient accumulation steps
+
+This is a parameter that allows you to use bigger batch sizes than
+your GPU's VRAM would ordinarily accommodate, at the cost of some
+performance.
+
+### Warmup steps
+
+If "constant_with_warmup" is selected in the learning rate scheduler,
+then this provides the number of warmup steps. Warmup steps have a
+very low learning rate, and are one way of preventing early
+overtraining.
+
+## The training run
+
+Start the training run by advancing to the OK button (bottom right)
+and pressing <enter>. A series of progress messages will be displayed
+as the training process proceeds. This may take an hour or two,
+depending on settings and the speed of your system. Various log and
+checkpoint files will be written into the output directory (ordinarily
+`~/invokeai/text-inversion-output/my-model/`)
+
+At the end of successful training, the system will copy the file
+`learned_embeds.bin` into the InvokeAI root directory's `embeddings`
+directory, using a subdirectory named after the trigger token. For
+example, if the trigger token was `psychedelic`, then look for the
+embeddings file in
+`~/invokeai/embeddings/psychedelic/learned_embeds.bin`
+
+You may now launch InvokeAI and try out a prompt that uses the trigger
+term. For example `a plate of banana sushi in <psychedelic> style`.
+
+## **Training with the Command-Line Script**
+
+Training can also be done using a traditional command-line script. It
+can be launched from within the "developer's console", or from the
+command line after activating InvokeAI's virtual environment.
+
+It accepts a large number of arguments, which can be summarized by
+passing the `--help` argument:
+
+```sh
+invokeai-ti --help
+```
+
+Typical usage is shown here:
+```sh
+invokeai-ti \
+       --model=stable-diffusion-1.5 \
+       --resolution=512 \
+       --learnable_property=style \
+       --initializer_token='*' \
+       --placeholder_token='<psychedelic>' \
+       --train_data_dir=/home/lstein/invokeai/training-data/psychedelic \
+       --output_dir=/home/lstein/invokeai/text-inversion-training/psychedelic \
+       --scale_lr \
+       --train_batch_size=8 \
+       --gradient_accumulation_steps=4 \
+       --max_train_steps=3000 \
+       --learning_rate=0.0005 \
+       --resume_from_checkpoint=latest \
+       --lr_scheduler=constant \
+       --mixed_precision=fp16 \
+       --only_save_embeds
+```
+
+## Troubleshooting
+
+### `Cannot load embedding for <trigger>. It was trained on a model with token dimension 1024, but the current model has token dimension 768`
+
+Messages like this indicate you trained the embedding on a different base model than the currently selected one.
+
+For example, in the error above, the training was done on SD2.1 (768x768) but it was used on SD1.5 (512x512).
+
+## Reading
+
+For more information on textual inversion, please see the following
+resources:
+
+* The [textual inversion repository](https://github.com/rinongal/textual_inversion) and
+  associated paper for details and limitations.
+* [HuggingFace's textual inversion training
+  page](https://huggingface.co/docs/diffusers/training/text_inversion)
+* [HuggingFace example script
+  documentation](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
+  (Note that this script is similar to, but not identical, to
+  `textual_inversion`, but produces embed files that are completely compatible.
+
+---
+
+copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/installation/010_INSTALL_AUTOMATED.md

@@ -1,10 +1,8 @@
-# Automatic Install & Updates
+# Automatic Install
 
-**The same packaged installer file can be used for both new installs and updates.**
-Using the installer for updates will leave everything you've added since installation, and just update the core libraries used to run Invoke.
-Simply use the same path you installed to originally.
+The installer is used for both new installs and updates.
 
-Both release and pre-release versions can be installed using the installer. It also supports install through a wheel if needed.
+Both release and pre-release versions can be installed using it. It also supports install a wheel if needed.
 
 Be sure to review the [installation requirements] and ensure your system has everything it needs to install Invoke.
 
@@ -98,7 +96,7 @@ Updating is exactly the same as installing - download the latest installer, choo
 
 If you have installation issues, please review the [FAQ]. You can also [create an issue] or ask for help on [discord].
 
-[installation requirements]: INSTALL_REQUIREMENTS.md
+[installation requirements]: INSTALLATION.md#installation-requirements
 [FAQ]: ../help/FAQ.md
 [install some models]: 050_INSTALLING_MODELS.md
 [configuration docs]: ../features/CONFIGURATION.md

docs/installation/020_INSTALL_MANUAL.md

@@ -10,7 +10,7 @@ InvokeAI is distributed as a python package on PyPI, installable with `pip`. The
 
 ### Requirements
 
-Before you start, go through the [installation requirements](./INSTALL_REQUIREMENTS.md).
+Before you start, go through the [installation requirements].
 
 ### Installation Walkthrough
 
@@ -79,7 +79,7 @@ Before you start, go through the [installation requirements](./INSTALL_REQUIREME
 
 1. Install the InvokeAI Package. The base command is `pip install InvokeAI --use-pep517`, but you may need to change this depending on your system and the desired features.
 
-    - You may need to provide an [extra index URL](https://pip.pypa.io/en/stable/cli/pip_install/#cmdoption-extra-index-url). Select your platform configuration using [this tool on the PyTorch website](https://pytorch.org/get-started/locally/). Copy the `--extra-index-url` string from this and append it to your install command.
+    - You may need to provide an [extra index URL]. Select your platform configuration using [this tool on the PyTorch website]. Copy the `--extra-index-url` string from this and append it to your install command.
 
         !!! example "Install with an extra index URL"
 
@@ -116,4 +116,4 @@ Before you start, go through the [installation requirements](./INSTALL_REQUIREME
 
     !!! warning
 
-        If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.
+        If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root_dir \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.

docs/installation/INSTALLATION.md

@@ -1,4 +1,4 @@
-# Installation and Updating Overview
+# Installation Overview
 
 Before installing, review the [installation requirements] to ensure your system is set up properly.
 
@@ -6,21 +6,14 @@ See the [FAQ] for frequently-encountered installation issues.
 
 If you need more help, join our [discord] or [create an issue].
 
-<h2>Automatic Install & Updates </h2>
+<h2>Automatic Install</h2>
 
 ✅ The automatic install is the best way to run InvokeAI. Check out the [installation guide] to get started.
 
-⬆️ The same installer is also the best way to update InvokeAI - Simply rerun it for the same folder you installed to.
-
-The installation process simply manages installation for the core libraries & application dependencies that run Invoke.
-Any models, images, or other assets in the Invoke root folder won't be affected by the installation process.
-
 <h2>Manual Install</h2>
 
 If you are familiar with python and want more control over the packages that are installed, you can [install InvokeAI manually via PyPI].
 
-Updates are managed by reinstalling the latest version through PyPi.
-
 <h2>Developer Install</h2>
 
 If you want to contribute to InvokeAI, consult the [developer install guide].

docs/installation/INSTALL_REQUIREMENTS.md

@@ -37,13 +37,13 @@ Invoke runs best with a dedicated GPU, but will fall back to running on CPU, alb
     === "Nvidia"
 
         ```
-        Any GPU with at least 8GB VRAM.
+        Any GPU with at least 8GB VRAM. Linux only.
         ```
 
     === "AMD"
 
         ```
-        Any GPU with at least 16GB VRAM. Linux only.
+        Any GPU with at least 16GB VRAM.
         ```
 
     === "Mac"

installer/templates/invoke.bat.in

@@ -10,7 +10,8 @@ set INVOKEAI_ROOT=.
 echo Desired action:
 echo 1. Generate images with the browser-based interface
 echo 2. Open the developer console
-echo 3. Command-line help
+echo 3. Run the InvokeAI image database maintenance script
+echo 4. Command-line help
 echo Q - Quit
 echo.
 echo To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.
@@ -33,6 +34,9 @@ IF /I "%choice%" == "1" (
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
 ) ELSE IF /I "%choice%" == "3" (
+   echo Running the db maintenance script...
+   python .venv\Scripts\invokeai-db-maintenance.exe
+) ELSE IF /I "%choice%" == "4" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai-web.exe --help %*
     pause

installer/templates/invoke.sh.in

@@ -47,6 +47,11 @@ do_choice() {
         bash --init-file "$file_name"
         ;;
     3)
+        clear
+        printf "Running the db maintenance script\n"
+        invokeai-db-maintenance --root ${INVOKEAI_ROOT}
+        ;;
+    4)
         clear
         printf "Command-line help\n"
         invokeai-web --help
@@ -66,7 +71,8 @@ do_line_input() {
     printf "What would you like to do?\n"
     printf "1: Generate images using the browser-based interface\n"
     printf "2: Open the developer console\n"
-    printf "3: Command-line help\n"
+    printf "3: Run the InvokeAI image database maintenance script\n"
+    printf "4: Command-line help\n"
     printf "Q: Quit\n\n"
     printf "To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.\n\n"
     read -p "Please enter 1-4, Q: [1] " yn

invokeai/app/api/dependencies.py

@@ -29,29 +29,12 @@ from ..services.model_images.model_images_default import ModelImageFileStorageDi
 from ..services.model_manager.model_manager_default import ModelManagerService
 from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
-from ..services.session_processor.session_processor_default import DefaultSessionProcessor, DefaultSessionRunner
+from ..services.session_processor.session_processor_default import DefaultSessionProcessor
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
 
-
-# TODO: is there a better way to achieve this?
-def check_internet() -> bool:
-    """
-    Return true if the internet is reachable.
-    It does this by pinging huggingface.co.
-    """
-    import urllib.request
-
-    host = "http://huggingface.co"
-    try:
-        urllib.request.urlopen(host, timeout=1)
-        return True
-    except Exception:
-        return False
-
-
 logger = InvokeAILogger.get_logger()
 
 
@@ -103,8 +86,7 @@ class ApiDependencies:
         )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
-
-        session_processor = DefaultSessionProcessor(session_runner=DefaultSessionRunner())
+        session_processor = DefaultSessionProcessor()
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
         workflow_records = SqliteWorkflowRecordsStorage(db=db)

invokeai/app/api/routers/app_info.py

@@ -13,6 +13,7 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
 from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch
+from invokeai.backend.image_util.safety_checker import SafetyChecker
 from invokeai.backend.util.logging import logging
 from invokeai.version import __version__
 
@@ -108,7 +109,9 @@ async def get_config() -> AppConfig:
         upscaling_models.append(str(Path(model).stem))
     upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
 
-    nsfw_methods = ["nsfw_checker"]
+    nsfw_methods = []
+    if SafetyChecker.safety_checker_available():
+        nsfw_methods.append("nsfw_checker")
 
     watermarking_methods = ["invisible_watermark"]
 

invokeai/app/api/routers/images.py

@@ -6,12 +6,13 @@ from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request,
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
-from pydantic import BaseModel, Field, JsonValue
+from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID, WorkflowWithoutIDValidator
 
 from ..dependencies import ApiDependencies
 
@@ -41,17 +42,13 @@ async def upload_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"),
-    metadata: Optional[JsonValue] = Body(
-        default=None, description="The metadata to associate with the image", embed=True
-    ),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type or not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
-    _metadata = None
-    _workflow = None
-    _graph = None
+    metadata = None
+    workflow = None
 
     contents = await file.read()
     try:
@@ -65,28 +62,22 @@ async def upload_image(
 
     # TODO: retain non-invokeai metadata on upload?
     # attempt to parse metadata from image
-    metadata_raw = metadata if isinstance(metadata, str) else pil_image.info.get("invokeai_metadata", None)
-    if isinstance(metadata_raw, str):
-        _metadata = metadata_raw
-    else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse metadata for uploaded image")
-        pass
+    metadata_raw = pil_image.info.get("invokeai_metadata", None)
+    if metadata_raw:
+        try:
+            metadata = MetadataFieldValidator.validate_json(metadata_raw)
+        except ValidationError:
+            ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
+            pass
 
     # attempt to parse workflow from image
     workflow_raw = pil_image.info.get("invokeai_workflow", None)
-    if isinstance(workflow_raw, str):
-        _workflow = workflow_raw
-    else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse workflow for uploaded image")
-        pass
-
-    # attempt to extract graph from image
-    graph_raw = pil_image.info.get("invokeai_graph", None)
-    if isinstance(graph_raw, str):
-        _graph = graph_raw
-    else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse graph for uploaded image")
-        pass
+    if workflow_raw is not None:
+        try:
+            workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw)
+        except ValidationError:
+            ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
+            pass
 
     try:
         image_dto = ApiDependencies.invoker.services.images.create(
@@ -95,9 +86,8 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
-            metadata=_metadata,
-            workflow=_workflow,
-            graph=_graph,
+            metadata=metadata,
+            workflow=workflow,
             is_intermediate=is_intermediate,
         )
 
@@ -195,21 +185,14 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-class WorkflowAndGraphResponse(BaseModel):
-    workflow: Optional[str] = Field(description="The workflow used to generate the image, as stringified JSON")
-    graph: Optional[str] = Field(description="The graph used to generate the image, as stringified JSON")
-
-
 @images_router.get(
-    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=WorkflowAndGraphResponse
+    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=Optional[WorkflowWithoutID]
 )
 async def get_image_workflow(
     image_name: str = Path(description="The name of image whose workflow to get"),
-) -> WorkflowAndGraphResponse:
+) -> Optional[WorkflowWithoutID]:
     try:
-        workflow = ApiDependencies.invoker.services.images.get_workflow(image_name)
-        graph = ApiDependencies.invoker.services.images.get_graph(image_name)
-        return WorkflowAndGraphResponse(workflow=workflow, graph=graph)
+        return ApiDependencies.invoker.services.images.get_workflow(image_name)
     except Exception:
         raise HTTPException(status_code=404)
 

invokeai/app/api/routers/model_manager.py

@@ -6,7 +6,7 @@ import pathlib
 import shutil
 import traceback
 from copy import deepcopy
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, Dict, List, Optional
 
 from fastapi import Body, Path, Query, Response, UploadFile
 from fastapi.responses import FileResponse
@@ -16,7 +16,6 @@ from pydantic import AnyHttpUrl, BaseModel, ConfigDict, Field
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
-from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install import ModelInstallJob
 from invokeai.app.services.model_records import (
     DuplicateModelException,
@@ -53,13 +52,6 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
-def add_cover_image_to_model_config(config: AnyModelConfig, dependencies: Type[ApiDependencies]) -> AnyModelConfig:
-    """Add a cover image URL to a model configuration."""
-    cover_image = dependencies.invoker.services.model_images.get_url(config.key)
-    config.cover_image = cover_image
-    return config
-
-
 ##############################################################################
 # These are example inputs and outputs that are used in places where Swagger
 # is unable to generate a correct example.
@@ -126,7 +118,8 @@ async def list_model_records(
             record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
         )
     for model in found_models:
-        model = add_cover_image_to_model_config(model, ApiDependencies)
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
+        model.cover_image = cover_image
     return ModelsList(models=found_models)
 
 
@@ -167,9 +160,12 @@ async def get_model_record(
     key: str = Path(description="Key of the model record to fetch."),
 ) -> AnyModelConfig:
     """Get a model record"""
+    record_store = ApiDependencies.invoker.services.model_manager.store
     try:
-        config = ApiDependencies.invoker.services.model_manager.store.get_model(key)
-        return add_cover_image_to_model_config(config, ApiDependencies)
+        config: AnyModelConfig = record_store.get_model(key)
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
+        config.cover_image = cover_image
+        return config
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
 
@@ -298,15 +294,14 @@ async def update_model_record(
     installer = ApiDependencies.invoker.services.model_manager.install
     try:
         record_store.update_model(key, changes=changes)
-        config = installer.sync_model_path(key)
-        config = add_cover_image_to_model_config(config, ApiDependencies)
+        model_response: AnyModelConfig = installer.sync_model_path(key)
         logger.info(f"Updated model: {key}")
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
-    return config
+    return model_response
 
 
 @model_manager_router.get(
@@ -653,14 +648,6 @@ async def convert_model(
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
 
-    # Update the model image if the model had one
-    try:
-        model_image = ApiDependencies.invoker.services.model_images.get(key)
-        ApiDependencies.invoker.services.model_images.save(model_image, new_key)
-        ApiDependencies.invoker.services.model_images.delete(key)
-    except ModelImageFileNotFoundException:
-        pass
-
     # delete the original safetensors file
     installer.delete(key)
 
@@ -668,8 +655,7 @@ async def convert_model(
     shutil.rmtree(cache_path)
 
     # return the config record for the new diffusers directory
-    new_config = store.get_model(new_key)
-    new_config = add_cover_image_to_model_config(new_config, ApiDependencies)
+    new_config: AnyModelConfig = store.get_model(new_key)
     return new_config
 
 

invokeai/app/api/routers/session_queue.py

@@ -203,7 +203,6 @@ async def get_batch_status(
     responses={
         200: {"model": SessionQueueItem},
     },
-    response_model_exclude_none=True,
 )
 async def get_queue_item(
     queue_id: str = Path(description="The queue id to perform this operation on"),

invokeai/app/api_app.py

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import mimetypes
 import socket
+import time
 from contextlib import asynccontextmanager
 from inspect import signature
 from pathlib import Path
@@ -20,13 +21,10 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
 from pydantic.json_schema import models_json_schema
 from torch.backends.mps import is_available as is_mps_available
 
-# for PyCharm:
-# noinspection PyUnresolvedReferences
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 import invokeai.frontend.web as web_dir
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.services.config.config_default import get_config
+from invokeai.app.services.config.config_default import InvokeAIAppConfig, get_config
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.backend.util.devices import TorchDevice
 
@@ -44,196 +42,189 @@ from .api.routers import (
     workflows,
 )
 from .api.sockets import SocketIO
-from .invocations.baseinvocation import (
-    BaseInvocation,
-    UIConfigBase,
-)
+from .invocations.baseinvocation import BaseInvocation, UIConfigBase
 from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 
-app_config = get_config()
+
+# TODO(ryand): Search for imports from api_app.py in the rest of the codebase and make sure I didn't break any of them.
+def build_app(app_config: InvokeAIAppConfig, logger: logging.Logger) -> FastAPI:
+    @asynccontextmanager
+    async def lifespan(app: FastAPI):
+        # Add startup event to load dependencies
+        ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
+        yield
+        # Shut down threads
+        ApiDependencies.shutdown()
+
+    app = FastAPI(
+        title="Invoke - Community Edition",
+        docs_url=None,
+        redoc_url=None,
+        separate_input_output_schemas=False,
+        lifespan=lifespan,
+    )
+
+    # Add event handler
+    event_handler_id: int = id(app)
+    app.add_middleware(
+        EventHandlerASGIMiddleware,
+        handlers=[local_handler],  # TODO: consider doing this in services to support different configurations
+        middleware_id=event_handler_id,
+    )
+
+    socket_io = SocketIO(app)
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=app_config.allow_origins,
+        allow_credentials=app_config.allow_credentials,
+        allow_methods=app_config.allow_methods,
+        allow_headers=app_config.allow_headers,
+    )
+
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+
+    # Include all routers
+    app.include_router(utilities.utilities_router, prefix="/api")
+    app.include_router(model_manager.model_manager_router, prefix="/api")
+    app.include_router(download_queue.download_queue_router, prefix="/api")
+    app.include_router(images.images_router, prefix="/api")
+    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(session_queue.session_queue_router, prefix="/api")
+    app.include_router(workflows.workflows_router, prefix="/api")
+
+    add_custom_openapi(app)
+
+    @app.get("/docs", include_in_schema=False)
+    def overridden_swagger() -> HTMLResponse:
+        return get_swagger_ui_html(
+            openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
+            title=f"{app.title} - Swagger UI",
+            swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
+        )
+
+    @app.get("/redoc", include_in_schema=False)
+    def overridden_redoc() -> HTMLResponse:
+        return get_redoc_html(
+            openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
+            title=f"{app.title} - Redoc",
+            redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
+        )
+
+    web_root_path = Path(list(web_dir.__path__)[0])
+
+    try:
+        app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
+    except RuntimeError:
+        logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
+
+    app.mount(
+        "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
+    )  # docs favicon is in here
+
+    return app
 
 
-if is_mps_available():
-    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+def apply_monkeypatches() -> None:
+    # TODO(ryand): Don't monkeypatch on import!
+    if is_mps_available():
+        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+    import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 
 
-logger = InvokeAILogger.get_logger(config=app_config)
-# 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")
-
-torch_device_name = TorchDevice.get_torch_device_name()
-logger.info(f"Using torch device: {torch_device_name}")
+def fix_mimetypes():
+    # 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")
 
 
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Add startup event to load dependencies
-    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
-    yield
-    # Shut down threads
-    ApiDependencies.shutdown()
+def add_custom_openapi(app: FastAPI) -> None:
+    """Add a custom .openapi() method to the FastAPI app.
 
+    This is done based on the guidance here:
+    https://fastapi.tiangolo.com/how-to/extending-openapi/#normal-fastapi
+    """
 
-# Create the app
-# TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(
-    title="Invoke - Community Edition",
-    docs_url=None,
-    redoc_url=None,
-    separate_input_output_schemas=False,
-    lifespan=lifespan,
-)
+    # Build a custom OpenAPI to include all outputs
+    # TODO: can outputs be included on metadata of invocation schemas somehow?
+    def custom_openapi() -> dict[str, Any]:
+        if app.openapi_schema:
+            return app.openapi_schema
+        openapi_schema = get_openapi(
+            title=app.title,
+            description="An API for invoking AI image operations",
+            version="1.0.0",
+            routes=app.routes,
+            separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
+        )
 
-# Add event handler
-event_handler_id: int = id(app)
-app.add_middleware(
-    EventHandlerASGIMiddleware,
-    handlers=[local_handler],  # TODO: consider doing this in services to support different configurations
-    middleware_id=event_handler_id,
-)
+        # Add all outputs
+        all_invocations = BaseInvocation.get_invocations()
+        output_types = set()
+        output_type_titles = {}
+        for invoker in all_invocations:
+            output_type = signature(invoker.invoke).return_annotation
+            output_types.add(output_type)
 
-socket_io = SocketIO(app)
+        output_schemas = models_json_schema(
+            models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
+        )
+        for schema_key, output_schema in output_schemas[1]["$defs"].items():
+            # TODO: note that we assume the schema_key here is the TYPE.__name__
+            # This could break in some cases, figure out a better way to do it
+            output_type_titles[schema_key] = output_schema["title"]
+            openapi_schema["components"]["schemas"][schema_key] = output_schema
+            openapi_schema["components"]["schemas"][schema_key]["class"] = "output"
 
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=app_config.allow_origins,
-    allow_credentials=app_config.allow_credentials,
-    allow_methods=app_config.allow_methods,
-    allow_headers=app_config.allow_headers,
-)
+        # Some models don't end up in the schemas as standalone definitions
+        additional_schemas = models_json_schema(
+            [
+                (UIConfigBase, "serialization"),
+                (InputFieldJSONSchemaExtra, "serialization"),
+                (OutputFieldJSONSchemaExtra, "serialization"),
+                (ModelIdentifierField, "serialization"),
+                (ProgressImage, "serialization"),
+            ],
+            ref_template="#/components/schemas/{model}",
+        )
+        for schema_key, schema_json in additional_schemas[1]["$defs"].items():
+            openapi_schema["components"]["schemas"][schema_key] = schema_json
 
-app.add_middleware(GZipMiddleware, minimum_size=1000)
+        # Add a reference to the output type to additionalProperties of the invoker schema
+        for invoker in all_invocations:
+            invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
+            output_type = signature(obj=invoker.invoke).return_annotation
+            output_type_title = output_type_titles[output_type.__name__]
+            invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
+            outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
+            invoker_schema["output"] = outputs_ref
+            invoker_schema["class"] = "invocation"
 
+        # This code no longer seems to be necessary?
+        # Leave it here just in case
+        #
+        # from invokeai.backend.model_manager import get_model_config_formats
+        # formats = get_model_config_formats()
+        # for model_config_name, enum_set in formats.items():
 
-# Include all routers
-app.include_router(utilities.utilities_router, prefix="/api")
-app.include_router(model_manager.model_manager_router, prefix="/api")
-app.include_router(download_queue.download_queue_router, prefix="/api")
-app.include_router(images.images_router, prefix="/api")
-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(session_queue.session_queue_router, prefix="/api")
-app.include_router(workflows.workflows_router, prefix="/api")
+        #     if model_config_name in openapi_schema["components"]["schemas"]:
+        #         # print(f"Config with name {name} already defined")
+        #         continue
 
+        #     openapi_schema["components"]["schemas"][model_config_name] = {
+        #         "title": model_config_name,
+        #         "description": "An enumeration.",
+        #         "type": "string",
+        #         "enum": [v.value for v in enum_set],
+        #     }
 
-# Build a custom OpenAPI to include all outputs
-# TODO: can outputs be included on metadata of invocation schemas somehow?
-def custom_openapi() -> dict[str, Any]:
-    if app.openapi_schema:
+        app.openapi_schema = openapi_schema
         return app.openapi_schema
-    openapi_schema = get_openapi(
-        title=app.title,
-        description="An API for invoking AI image operations",
-        version="1.0.0",
-        routes=app.routes,
-        separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
-    )
 
-    # Add all outputs
-    all_invocations = BaseInvocation.get_invocations()
-    output_types = set()
-    output_type_titles = {}
-    for invoker in all_invocations:
-        output_type = signature(invoker.invoke).return_annotation
-        output_types.add(output_type)
-
-    output_schemas = models_json_schema(
-        models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
-    )
-    for schema_key, output_schema in output_schemas[1]["$defs"].items():
-        # TODO: note that we assume the schema_key here is the TYPE.__name__
-        # This could break in some cases, figure out a better way to do it
-        output_type_titles[schema_key] = output_schema["title"]
-        openapi_schema["components"]["schemas"][schema_key] = output_schema
-        openapi_schema["components"]["schemas"][schema_key]["class"] = "output"
-
-    # Some models don't end up in the schemas as standalone definitions
-    additional_schemas = models_json_schema(
-        [
-            (UIConfigBase, "serialization"),
-            (InputFieldJSONSchemaExtra, "serialization"),
-            (OutputFieldJSONSchemaExtra, "serialization"),
-            (ModelIdentifierField, "serialization"),
-            (ProgressImage, "serialization"),
-        ],
-        ref_template="#/components/schemas/{model}",
-    )
-    for schema_key, schema_json in additional_schemas[1]["$defs"].items():
-        openapi_schema["components"]["schemas"][schema_key] = schema_json
-
-    openapi_schema["components"]["schemas"]["InvocationOutputMap"] = {
-        "type": "object",
-        "properties": {},
-        "required": [],
-    }
-
-    # Add a reference to the output type to additionalProperties of the invoker schema
-    for invoker in all_invocations:
-        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
-        output_type = signature(obj=invoker.invoke).return_annotation
-        output_type_title = output_type_titles[output_type.__name__]
-        invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
-        outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
-        invoker_schema["output"] = outputs_ref
-        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["properties"][invoker.get_type()] = outputs_ref
-        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["required"].append(invoker.get_type())
-        invoker_schema["class"] = "invocation"
-
-    # This code no longer seems to be necessary?
-    # Leave it here just in case
-    #
-    # from invokeai.backend.model_manager import get_model_config_formats
-    # formats = get_model_config_formats()
-    # for model_config_name, enum_set in formats.items():
-
-    #     if model_config_name in openapi_schema["components"]["schemas"]:
-    #         # print(f"Config with name {name} already defined")
-    #         continue
-
-    #     openapi_schema["components"]["schemas"][model_config_name] = {
-    #         "title": model_config_name,
-    #         "description": "An enumeration.",
-    #         "type": "string",
-    #         "enum": [v.value for v in enum_set],
-    #     }
-
-    app.openapi_schema = openapi_schema
-    return app.openapi_schema
-
-
-app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
-
-
-@app.get("/docs", include_in_schema=False)
-def overridden_swagger() -> HTMLResponse:
-    return get_swagger_ui_html(
-        openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=f"{app.title} - Swagger UI",
-        swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
-    )
-
-
-@app.get("/redoc", include_in_schema=False)
-def overridden_redoc() -> HTMLResponse:
-    return get_redoc_html(
-        openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=f"{app.title} - Redoc",
-        redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
-    )
-
-
-web_root_path = Path(list(web_dir.__path__)[0])
-
-try:
-    app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
-except RuntimeError:
-    logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
-app.mount(
-    "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
-)  # docs favicon is in here
+    app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
 
 
 def check_cudnn(logger: logging.Logger) -> None:
@@ -252,27 +243,39 @@ def check_cudnn(logger: logging.Logger) -> None:
             )
 
 
+def find_port(port: int) -> int:
+    """Find a port not in use starting at given port"""
+    # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
+    # https://github.com/WaylonWalker
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        if s.connect_ex(("localhost", port)) == 0:
+            return find_port(port=port + 1)
+        else:
+            return port
+
+
+def init_dev_reload(logger: logging.Logger) -> None:
+    try:
+        import jurigged
+    except ImportError as e:
+        logger.error(
+            'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
+            exc_info=e,
+        )
+    else:
+        jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+
+
 def invoke_api() -> None:
-    def find_port(port: int) -> int:
-        """Find a port not in use starting at given port"""
-        # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
-        # https://github.com/WaylonWalker
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            if s.connect_ex(("localhost", port)) == 0:
-                return find_port(port=port + 1)
-            else:
-                return port
+    start = time.time()
+    app_config = get_config()
+    logger = InvokeAILogger.get_logger(config=app_config)
+
+    apply_monkeypatches()
+    fix_mimetypes()
 
     if app_config.dev_reload:
-        try:
-            import jurigged
-        except ImportError as e:
-            logger.error(
-                'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
-                exc_info=e,
-            )
-        else:
-            jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+        init_dev_reload(logger)
 
     port = find_port(app_config.port)
     if port != app_config.port:
@@ -280,6 +283,11 @@ def invoke_api() -> None:
 
     check_cudnn(logger)
 
+    torch_device_name = TorchDevice.get_torch_device_name()
+    logger.info(f"Using torch device: {torch_device_name}")
+
+    app = build_app(app_config, logger)
+
     # Start our own event loop for eventing usage
     loop = asyncio.new_event_loop()
     config = uvicorn.Config(
@@ -299,6 +307,7 @@ def invoke_api() -> None:
         log.handlers.clear()
         for ch in logger.handlers:
             log.addHandler(ch)
+    logger.info(f"API started in {time.time() - start:.2f} seconds")
 
     loop.run_until_complete(server.serve())
 

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,6 +24,7 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
+    Input,
     InputField,
     OutputField,
     UIType,
@@ -34,16 +35,15 @@ from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
 from invokeai.backend.image_util.canny import get_canny_edges
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 from invokeai.backend.image_util.hed import HEDProcessor
 from invokeai.backend.image_util.lineart import LineartProcessor
 from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
-from invokeai.backend.image_util.util import np_to_pil, pil_to_np
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, Classification, invocation, invocation_output
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 
 
 class ControlField(BaseModel):
@@ -79,13 +79,13 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
     control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
+        description=FieldDescriptions.controlnet_model, input=Input.Direct, ui_type=UIType.ControlNetModel
     )
     control_weight: Union[float, List[float]] = InputField(
         default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
@@ -164,13 +164,13 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.3.3",
+    version="1.3.2",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     low_threshold: int = InputField(
         default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
     )
@@ -198,13 +198,13 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     # safe not supported in controlnet_aux v0.0.3
     # safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
@@ -227,13 +227,13 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     coarse: bool = InputField(default=False, description="Whether to use coarse mode")
 
     def run_processor(self, image: Image.Image) -> Image.Image:
@@ -249,13 +249,13 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image) -> Image.Image:
         processor = LineartAnimeProcessor()
@@ -272,15 +272,15 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.4",
+    version="1.2.3",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
 
     a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
     bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     # depth_and_normal not supported in controlnet_aux v0.0.3
     # depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
 
@@ -303,13 +303,13 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
@@ -320,13 +320,13 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.3"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.2"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
     thr_d: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_d`")
 
@@ -343,13 +343,13 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.3"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.2"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
 
@@ -370,13 +370,13 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
     w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
     f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
@@ -400,7 +400,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -416,15 +416,15 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.4",
+    version="1.2.3",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
 
     max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
     min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         mediapipe_face_processor = MediapipeFaceDetector()
@@ -443,7 +443,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -451,8 +451,8 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     thr_a: float = InputField(default=0, description="Leres parameter `thr_a`")
     thr_b: float = InputField(default=0, description="Leres parameter `thr_b`")
     boost: bool = InputField(default=False, description="Whether to use boost mode")
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
@@ -472,7 +472,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -512,13 +512,13 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.4",
+    version="1.2.3",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
@@ -559,12 +559,12 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""
 
-    color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
+    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
 
     def run_processor(self, image: Image.Image):
         np_image = np.array(image, dtype=np.uint8)
@@ -591,7 +591,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
     title="Depth Anything Processor",
     tags=["controlnet", "depth", "depth anything"],
     category="controlnet",
-    version="1.1.2",
+    version="1.1.1",
 )
 class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a depth map based on the Depth Anything algorithm"""
@@ -599,7 +599,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
         default="small", description="The size of the depth model to use"
     )
-    resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
@@ -614,7 +614,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     title="DW Openpose Image Processor",
     tags=["controlnet", "dwpose", "openpose"],
     category="controlnet",
-    version="1.1.1",
+    version="1.1.0",
 )
 class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Generates an openpose pose from an image using DWPose"""
@@ -622,7 +622,7 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     draw_body: bool = InputField(default=True)
     draw_face: bool = InputField(default=False)
     draw_hands: bool = InputField(default=False)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         dw_openpose = DWOpenposeDetector()
@@ -634,27 +634,3 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
             resolution=self.image_resolution,
         )
         return processed_image
-
-
-@invocation(
-    "heuristic_resize",
-    title="Heuristic Resize",
-    tags=["image, controlnet"],
-    category="image",
-    version="1.0.1",
-    classification=Classification.Prototype,
-)
-class HeuristicResizeInvocation(BaseInvocation):
-    """Resize an image using a heuristic method. Preserves edge maps."""
-
-    image: ImageField = InputField(description="The image to resize")
-    width: int = InputField(default=512, ge=1, description="The width to resize to (px)")
-    height: int = InputField(default=512, ge=1, description="The height to resize to (px)")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, "RGB")
-        np_img = pil_to_np(image)
-        np_resized = heuristic_resize(np_img, (self.width, self.height))
-        resized = np_to_pil(np_resized)
-        image_dto = context.images.save(image=resized)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/image.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from pathlib import Path
 from typing import Literal, Optional
 
 import cv2
@@ -503,7 +504,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Blur NSFW Image",
     tags=["image", "nsfw"],
     category="image",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add blur to NSFW-flagged images"""
@@ -515,12 +516,23 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         logger = context.logger
         logger.debug("Running NSFW checker")
-        image = SafetyChecker.blur_if_nsfw(image)
+        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.images.save(image=image)
 
         return ImageOutput.build(image_dto)
 
+    def _get_caution_img(self) -> Image.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))
+
 
 @invocation(
     "img_watermark",

invokeai/app/invocations/ip_adapter.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from typing_extensions import Self
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, TensorField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, TensorField, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
@@ -58,7 +58,7 @@ class IPAdapterOutput(BaseInvocationOutput):
 CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -67,6 +67,7 @@ class IPAdapterInvocation(BaseInvocation):
     ip_adapter_model: ModelIdentifierField = InputField(
         description="The IP-Adapter model.",
         title="IP-Adapter Model",
+        input=Input.Direct,
         ui_order=-1,
         ui_type=UIType.IPAdapterModel,
     )

invokeai/app/invocations/latent.py

@@ -3,7 +3,7 @@ import inspect
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
+from typing import Any, Iterator, List, Literal, Optional, Tuple, Union
 
 import einops
 import numpy as np
@@ -11,6 +11,7 @@ import numpy.typing as npt
 import torch
 import torchvision
 import torchvision.transforms as T
+from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.adapter import T2IAdapter
@@ -20,12 +21,9 @@ from diffusers.models.attention_processor import (
     LoRAXFormersAttnProcessor,
     XFormersAttnProcessor,
 )
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
-from diffusers.schedulers.scheduling_tcd import TCDScheduler
-from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
+from diffusers.schedulers import DPMSolverSDEScheduler
+from diffusers.schedulers import SchedulerMixin as Scheduler
 from PIL import Image, ImageFilter
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
@@ -343,7 +341,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
     cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale"
+        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
     )
     denoising_start: float = InputField(
         default=0.0,
@@ -523,10 +521,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
 
         if is_sdxl:
-            return (
-                SDXLConditioningInfo(embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids),
-                regions,
-            )
+            return SDXLConditioningInfo(
+                embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids
+            ), regions
         return BasicConditioningInfo(embeds=text_embedding), regions
 
     def get_conditioning_data(
@@ -566,11 +563,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             dtype=unet.dtype,
         )
 
-        if isinstance(self.cfg_scale, list):
-            assert (
-                len(self.cfg_scale) == self.steps
-            ), "cfg_scale (list) must have the same length as the number of steps"
-
         conditioning_data = TextConditioningData(
             uncond_text=uncond_text_embedding,
             cond_text=cond_text_embedding,
@@ -586,6 +578,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
         unet: UNet2DConditionModel,
         scheduler: Scheduler,
     ) -> StableDiffusionGeneratorPipeline:
+        # TODO:
+        # configure_model_padding(
+        #    unet,
+        #    self.seamless,
+        #    self.seamless_axes,
+        # )
+
         class FakeVae:
             class FakeVaeConfig:
                 def __init__(self) -> None:
@@ -821,7 +820,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         denoising_start: float,
         denoising_end: float,
         seed: int,
-    ) -> Tuple[int, List[int], int, Dict[str, Any]]:
+    ) -> Tuple[int, List[int], int]:
         assert isinstance(scheduler, ConfigMixin)
         if scheduler.config.get("cpu_only", False):
             scheduler.set_timesteps(steps, device="cpu")
@@ -849,15 +848,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
         timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
         num_inference_steps = len(timesteps) // scheduler.order
 
-        scheduler_step_kwargs: Dict[str, Any] = {}
+        scheduler_step_kwargs = {}
         scheduler_step_signature = inspect.signature(scheduler.step)
         if "generator" in scheduler_step_signature.parameters:
             # At some point, someone decided that schedulers that accept a generator should use the original seed with
             # all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
             # reproducibility.
-            scheduler_step_kwargs.update({"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)})
-        if isinstance(scheduler, TCDScheduler):
-            scheduler_step_kwargs.update({"eta": 1.0})
+            scheduler_step_kwargs = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
 
         return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
 

invokeai/app/invocations/model.py

@@ -11,7 +11,6 @@ from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType,
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -94,46 +93,19 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
     pass
 
 
-@invocation_output("model_identifier_output")
-class ModelIdentifierOutput(BaseInvocationOutput):
-    """Model identifier output"""
-
-    model: ModelIdentifierField = OutputField(description="Model identifier", title="Model")
-
-
-@invocation(
-    "model_identifier",
-    title="Model identifier",
-    tags=["model"],
-    category="model",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class ModelIdentifierInvocation(BaseInvocation):
-    """Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as
-    input for any model, even if the model types don't match. If you connect this to a mismatched input, you'll get an
-    error."""
-
-    model: ModelIdentifierField = InputField(description="The model to select", title="Model")
-
-    def invoke(self, context: InvocationContext) -> ModelIdentifierOutput:
-        if not context.models.exists(self.model.key):
-            raise Exception(f"Unknown model {self.model.key}")
-
-        return ModelIdentifierOutput(model=self.model)
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.3",
+    version="1.0.2",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: ModelIdentifierField = InputField(description=FieldDescriptions.main_model, ui_type=UIType.MainModel)
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.main_model, input=Input.Direct, ui_type=UIType.MainModel
+    )
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
@@ -162,12 +134,12 @@ class LoRALoaderOutput(BaseInvocationOutput):
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.3")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.2")
 class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -218,75 +190,6 @@ class LoRALoaderInvocation(BaseInvocation):
         return output
 
 
-@invocation_output("lora_selector_output")
-class LoRASelectorOutput(BaseInvocationOutput):
-    """Model loader output"""
-
-    lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
-
-
-@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
-class LoRASelectorInvocation(BaseInvocation):
-    """Selects a LoRA model and weight."""
-
-    lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
-    )
-    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
-
-    def invoke(self, context: InvocationContext) -> LoRASelectorOutput:
-        return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
-
-
-@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.0.0")
-class LoRACollectionLoader(BaseInvocation):
-    """Applies a collection of LoRAs to the provided UNet and CLIP models."""
-
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
-    )
-    unet: Optional[UNetField] = InputField(
-        default=None,
-        description=FieldDescriptions.unet,
-        input=Input.Connection,
-        title="UNet",
-    )
-    clip: Optional[CLIPField] = InputField(
-        default=None,
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-        title="CLIP",
-    )
-
-    def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
-        output = LoRALoaderOutput()
-        loras = self.loras if isinstance(self.loras, list) else [self.loras]
-        added_loras: list[str] = []
-
-        for lora in loras:
-            if lora.lora.key in added_loras:
-                continue
-
-            if not context.models.exists(lora.lora.key):
-                raise Exception(f"Unknown lora: {lora.lora.key}!")
-
-            assert lora.lora.base in (BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2)
-
-            added_loras.append(lora.lora.key)
-
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
-                output.unet.loras.append(lora)
-
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
-                output.clip.loras.append(lora)
-
-        return output
-
-
 @invocation_output("sdxl_lora_loader_output")
 class SDXLLoRALoaderOutput(BaseInvocationOutput):
     """SDXL LoRA Loader Output"""
@@ -301,13 +204,13 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.3",
+    version="1.0.2",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -376,78 +279,12 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
         return output
 
 
-@invocation(
-    "sdxl_lora_collection_loader",
-    title="SDXL LoRA Collection Loader",
-    tags=["model"],
-    category="model",
-    version="1.0.0",
-)
-class SDXLLoRACollectionLoader(BaseInvocation):
-    """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
-
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
-    )
-    unet: Optional[UNetField] = InputField(
-        default=None,
-        description=FieldDescriptions.unet,
-        input=Input.Connection,
-        title="UNet",
-    )
-    clip: Optional[CLIPField] = InputField(
-        default=None,
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-        title="CLIP",
-    )
-    clip2: Optional[CLIPField] = InputField(
-        default=None,
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-        title="CLIP 2",
-    )
-
-    def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
-        output = SDXLLoRALoaderOutput()
-        loras = self.loras if isinstance(self.loras, list) else [self.loras]
-        added_loras: list[str] = []
-
-        for lora in loras:
-            if lora.lora.key in added_loras:
-                continue
-
-            if not context.models.exists(lora.lora.key):
-                raise Exception(f"Unknown lora: {lora.lora.key}!")
-
-            assert lora.lora.base is BaseModelType.StableDiffusionXL
-
-            added_loras.append(lora.lora.key)
-
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
-                output.unet.loras.append(lora)
-
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
-                output.clip.loras.append(lora)
-
-            if self.clip2 is not None:
-                if output.clip2 is None:
-                    output.clip2 = self.clip2.model_copy(deep=True)
-                output.clip2.loras.append(lora)
-
-        return output
-
-
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
     vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, title="VAE", ui_type=UIType.VAEModel
+        description=FieldDescriptions.vae_model, input=Input.Direct, title="VAE", ui_type=UIType.VAEModel
     )
 
     def invoke(self, context: InvocationContext) -> VAEOutput:

invokeai/app/invocations/sdxl.py

@@ -1,4 +1,4 @@
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import SubModelType
 
@@ -30,12 +30,12 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.2")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
     model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sdxl_main_model, ui_type=UIType.SDXLMainModel
+        description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
 
@@ -67,13 +67,13 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.3",
+    version="1.0.2",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
     model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sdxl_refiner_model, ui_type=UIType.SDXLRefinerModel
+        description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
     )
     # TODO: precision?
 

invokeai/app/invocations/t2i_adapter.py

@@ -8,7 +8,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -45,7 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.2"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -55,6 +55,7 @@ class T2IAdapterInvocation(BaseInvocation):
     t2i_adapter_model: ModelIdentifierField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
+        input=Input.Direct,
         ui_order=-1,
         ui_type=UIType.T2IAdapterModel,
     )

invokeai/app/services/download/download_default.py

@@ -318,8 +318,10 @@ class DownloadQueueService(DownloadQueueServiceBase):
         in_progress_path.rename(job.download_path)
 
     def _validate_filename(self, directory: str, filename: str) -> bool:
-        pc_name_max = get_pc_name_max(directory)
-        pc_path_max = get_pc_path_max(directory)
+        pc_name_max = os.pathconf(directory, "PC_NAME_MAX") if hasattr(os, "pathconf") else 260  # hardcoded for windows
+        pc_path_max = (
+            os.pathconf(directory, "PC_PATH_MAX") if hasattr(os, "pathconf") else 32767
+        )  # hardcoded for windows with long names enabled
         if "/" in filename:
             return False
         if filename.startswith(".."):
@@ -417,26 +419,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
             self._logger.warning(excp)
 
 
-def get_pc_name_max(directory: str) -> int:
-    if hasattr(os, "pathconf"):
-        try:
-            return os.pathconf(directory, "PC_NAME_MAX")
-        except OSError:
-            # macOS w/ external drives raise OSError
-            pass
-    return 260  # hardcoded for windows
-
-
-def get_pc_path_max(directory: str) -> int:
-    if hasattr(os, "pathconf"):
-        try:
-            return os.pathconf(directory, "PC_PATH_MAX")
-        except OSError:
-            # some platforms may not have this value
-            pass
-    return 32767  # hardcoded for windows with long names enabled
-
-
 # Example on_progress event handler to display a TQDM status bar
 # Activate with:
 #   download_service.download(DownloadJob('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().update))

invokeai/app/services/events/events_base.py

@@ -121,10 +121,7 @@ class EventServiceBase:
         node: dict,
         source_node_id: str,
         error_type: str,
-        error_message: str,
-        error_traceback: str,
-        user_id: str | None,
-        project_id: str | None,
+        error: str,
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_queue_event(
@@ -137,10 +134,7 @@ class EventServiceBase:
                 "node": node,
                 "source_node_id": source_node_id,
                 "error_type": error_type,
-                "error_message": error_message,
-                "error_traceback": error_traceback,
-                "user_id": user_id,
-                "project_id": project_id,
+                "error": error,
             },
         )
 
@@ -259,9 +253,7 @@ class EventServiceBase:
                     "status": session_queue_item.status,
                     "batch_id": session_queue_item.batch_id,
                     "session_id": session_queue_item.session_id,
-                    "error_type": session_queue_item.error_type,
-                    "error_message": session_queue_item.error_message,
-                    "error_traceback": session_queue_item.error_traceback,
+                    "error": session_queue_item.error,
                     "created_at": str(session_queue_item.created_at) if session_queue_item.created_at else None,
                     "updated_at": str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
                     "started_at": str(session_queue_item.started_at) if session_queue_item.started_at else None,

invokeai/app/services/image_files/image_files_base.py

@@ -4,6 +4,9 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
+from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+
 
 class ImageFileStorageBase(ABC):
     """Low-level service responsible for storing and retrieving image files."""
@@ -30,9 +33,8 @@ class ImageFileStorageBase(ABC):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
         thumbnail_size: int = 256,
     ) -> None:
         """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@@ -44,11 +46,6 @@ class ImageFileStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[str]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         """Gets the workflow of an image."""
         pass
-
-    @abstractmethod
-    def get_graph(self, image_name: str) -> Optional[str]:
-        """Gets the graph of an image."""
-        pass

invokeai/app/services/image_files/image_files_disk.py

@@ -7,7 +7,9 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
+from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 from .image_files_base import ImageFileStorageBase
@@ -54,9 +56,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
@@ -67,14 +68,13 @@ class DiskImageFileStorage(ImageFileStorageBase):
             info_dict = {}
 
             if metadata is not None:
-                info_dict["invokeai_metadata"] = metadata
-                pnginfo.add_text("invokeai_metadata", metadata)
+                metadata_json = metadata.model_dump_json()
+                info_dict["invokeai_metadata"] = metadata_json
+                pnginfo.add_text("invokeai_metadata", metadata_json)
             if workflow is not None:
-                info_dict["invokeai_workflow"] = workflow
-                pnginfo.add_text("invokeai_workflow", workflow)
-            if graph is not None:
-                info_dict["invokeai_graph"] = graph
-                pnginfo.add_text("invokeai_graph", graph)
+                workflow_json = workflow.model_dump_json()
+                info_dict["invokeai_workflow"] = workflow_json
+                pnginfo.add_text("invokeai_workflow", workflow_json)
 
             # When saving the image, the image object's info field is not populated. We need to set it
             image.info = info_dict
@@ -129,18 +129,11 @@ class DiskImageFileStorage(ImageFileStorageBase):
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
 
-    def get_workflow(self, image_name: str) -> str | None:
+    def get_workflow(self, image_name: str) -> WorkflowWithoutID | None:
         image = self.get(image_name)
         workflow = image.info.get("invokeai_workflow", None)
-        if isinstance(workflow, str):
-            return workflow
-        return None
-
-    def get_graph(self, image_name: str) -> str | None:
-        image = self.get(image_name)
-        graph = image.info.get("invokeai_graph", None)
-        if isinstance(graph, str):
-            return graph
+        if workflow is not None:
+            return WorkflowWithoutID.model_validate_json(workflow)
         return None
 
     def __validate_storage_folders(self) -> None:

invokeai/app/services/image_records/image_records_base.py

@@ -80,7 +80,7 @@ class ImageRecordStorageBase(ABC):
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
         node_id: Optional[str] = None,
-        metadata: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
     ) -> datetime:
         """Saves an image record."""
         pass

invokeai/app/services/image_records/image_records_sqlite.py

@@ -328,9 +328,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
         node_id: Optional[str] = None,
-        metadata: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
     ) -> datetime:
         try:
+            metadata_json = metadata.model_dump_json() if metadata is not None else None
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
@@ -357,7 +358,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     height,
                     node_id,
                     session_id,
-                    metadata,
+                    metadata_json,
                     is_intermediate,
                     starred,
                     has_workflow,

invokeai/app/services/images/images_base.py

@@ -12,6 +12,7 @@ from invokeai.app.services.image_records.image_records_common import (
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 
 class ImageServiceABC(ABC):
@@ -50,9 +51,8 @@ class ImageServiceABC(ABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -87,12 +87,7 @@ class ImageServiceABC(ABC):
         pass
 
     @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[str]:
-        """Gets an image's workflow."""
-        pass
-
-    @abstractmethod
-    def get_graph(self, image_name: str) -> Optional[str]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         """Gets an image's workflow."""
         pass
 

invokeai/app/services/images/images_default.py

@@ -5,6 +5,7 @@ from PIL.Image import Image as PILImageType
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 from ..image_files.image_files_common import (
     ImageFileDeleteException,
@@ -41,9 +42,8 @@ class ImageService(ImageServiceABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
             raise InvalidOriginException
@@ -64,7 +64,7 @@ class ImageService(ImageServiceABC):
                 image_category=image_category,
                 width=width,
                 height=height,
-                has_workflow=workflow is not None or graph is not None,
+                has_workflow=workflow is not None,
                 # Meta fields
                 is_intermediate=is_intermediate,
                 # Nullable fields
@@ -75,7 +75,7 @@ class ImageService(ImageServiceABC):
             if board_id is not None:
                 self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
             self.__invoker.services.image_files.save(
-                image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
+                image_name=image_name, image=image, metadata=metadata, workflow=workflow
             )
             image_dto = self.get_dto(image_name)
 
@@ -157,7 +157,7 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image metadata")
             raise e
 
-    def get_workflow(self, image_name: str) -> Optional[str]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         try:
             return self.__invoker.services.image_files.get_workflow(image_name)
         except ImageFileNotFoundException:
@@ -167,16 +167,6 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image workflow")
             raise
 
-    def get_graph(self, image_name: str) -> Optional[str]:
-        try:
-            return self.__invoker.services.image_files.get_graph(image_name)
-        except ImageFileNotFoundException:
-            self.__invoker.services.logger.error("Image file not found")
-            raise
-        except Exception:
-            self.__invoker.services.logger.error("Problem getting image graph")
-            raise
-
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         try:
             return str(self.__invoker.services.image_files.get_path(image_name, thumbnail))

invokeai/app/services/session_processor/session_processor_base.py

@@ -1,49 +1,6 @@
 from abc import ABC, abstractmethod
-from threading import Event
-from typing import Optional, Protocol
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
-from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
-from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
-from invokeai.app.util.profiler import Profiler
-
-
-class SessionRunnerBase(ABC):
-    """
-    Base class for session runner.
-    """
-
-    @abstractmethod
-    def start(self, services: InvocationServices, cancel_event: Event, profiler: Optional[Profiler] = None) -> None:
-        """Starts the session runner.
-
-        Args:
-            services: The invocation services.
-            cancel_event: The cancel event.
-            profiler: The profiler to use for session profiling via cProfile. Omit to disable profiling. Basic session
-                stats will be still be recorded and logged when profiling is disabled.
-        """
-        pass
-
-    @abstractmethod
-    def run(self, queue_item: SessionQueueItem) -> None:
-        """Runs a session.
-
-        Args:
-            queue_item: The session to run.
-        """
-        pass
-
-    @abstractmethod
-    def run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem) -> None:
-        """Run a single node in the graph.
-
-        Args:
-            invocation: The invocation to run.
-            queue_item: The session queue item.
-        """
-        pass
 
 
 class SessionProcessorBase(ABC):
@@ -69,85 +26,3 @@ class SessionProcessorBase(ABC):
     def get_status(self) -> SessionProcessorStatus:
         """Gets the status of the session processor"""
         pass
-
-
-class OnBeforeRunNode(Protocol):
-    def __call__(self, invocation: BaseInvocation, queue_item: SessionQueueItem) -> None:
-        """Callback to run before executing a node.
-
-        Args:
-            invocation: The invocation that will be executed.
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnAfterRunNode(Protocol):
-    def __call__(self, invocation: BaseInvocation, queue_item: SessionQueueItem, output: BaseInvocationOutput) -> None:
-        """Callback to run before executing a node.
-
-        Args:
-            invocation: The invocation that was executed.
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnNodeError(Protocol):
-    def __call__(
-        self,
-        invocation: BaseInvocation,
-        queue_item: SessionQueueItem,
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> None:
-        """Callback to run when a node has an error.
-
-        Args:
-            invocation: The invocation that errored.
-            queue_item: The session queue item.
-            error_type: The type of error, e.g. "ValueError".
-            error_message: The error message, e.g. "Invalid value".
-            error_traceback: The stringified error traceback.
-        """
-        ...
-
-
-class OnBeforeRunSession(Protocol):
-    def __call__(self, queue_item: SessionQueueItem) -> None:
-        """Callback to run before executing a session.
-
-        Args:
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnAfterRunSession(Protocol):
-    def __call__(self, queue_item: SessionQueueItem) -> None:
-        """Callback to run after executing a session.
-
-        Args:
-            queue_item: The session queue item.
-        """
-        ...
-
-
-class OnNonFatalProcessorError(Protocol):
-    def __call__(
-        self,
-        queue_item: Optional[SessionQueueItem],
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> None:
-        """Callback to run when a non-fatal error occurs in the processor.
-
-        Args:
-            queue_item: The session queue item, if one was being executed when the error occurred.
-            error_type: The type of error, e.g. "ValueError".
-            error_message: The error message, e.g. "Invalid value".
-            error_traceback: The stringified error traceback.
-        """
-        ...

invokeai/app/services/session_processor/session_processor_default.py

@@ -7,305 +7,21 @@ from typing import Optional
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event as FastAPIEvent
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invocation_stats.invocation_stats_common import GESStatsNotFoundError
-from invokeai.app.services.session_processor.session_processor_base import (
-    OnAfterRunNode,
-    OnAfterRunSession,
-    OnBeforeRunNode,
-    OnBeforeRunSession,
-    OnNodeError,
-    OnNonFatalProcessorError,
-)
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
-from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem, SessionQueueItemNotFoundError
-from invokeai.app.services.shared.graph import NodeInputError
+from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 from invokeai.app.services.shared.invocation_context import InvocationContextData, build_invocation_context
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
-from .session_processor_base import InvocationServices, SessionProcessorBase, SessionRunnerBase
+from .session_processor_base import SessionProcessorBase
 from .session_processor_common import SessionProcessorStatus
 
 
-class DefaultSessionRunner(SessionRunnerBase):
-    """Processes a single session's invocations."""
-
-    def __init__(
-        self,
-        on_before_run_session_callbacks: Optional[list[OnBeforeRunSession]] = None,
-        on_before_run_node_callbacks: Optional[list[OnBeforeRunNode]] = None,
-        on_after_run_node_callbacks: Optional[list[OnAfterRunNode]] = None,
-        on_node_error_callbacks: Optional[list[OnNodeError]] = None,
-        on_after_run_session_callbacks: Optional[list[OnAfterRunSession]] = None,
-    ):
-        """
-        Args:
-            on_before_run_session_callbacks: Callbacks to run before the session starts.
-            on_before_run_node_callbacks: Callbacks to run before each node starts.
-            on_after_run_node_callbacks: Callbacks to run after each node completes.
-            on_node_error_callbacks: Callbacks to run when a node errors.
-            on_after_run_session_callbacks: Callbacks to run after the session completes.
-        """
-
-        self._on_before_run_session_callbacks = on_before_run_session_callbacks or []
-        self._on_before_run_node_callbacks = on_before_run_node_callbacks or []
-        self._on_after_run_node_callbacks = on_after_run_node_callbacks or []
-        self._on_node_error_callbacks = on_node_error_callbacks or []
-        self._on_after_run_session_callbacks = on_after_run_session_callbacks or []
-
-    def start(self, services: InvocationServices, cancel_event: ThreadEvent, profiler: Optional[Profiler] = None):
-        self._services = services
-        self._cancel_event = cancel_event
-        self._profiler = profiler
-
-    def run(self, queue_item: SessionQueueItem):
-        # Exceptions raised outside `run_node` are handled by the processor. There is no need to catch them here.
-
-        self._on_before_run_session(queue_item=queue_item)
-
-        # Loop over invocations until the session is complete or canceled
-        while True:
-            try:
-                invocation = queue_item.session.next()
-            # Anything other than a `NodeInputError` is handled as a processor error
-            except NodeInputError as e:
-                error_type = e.__class__.__name__
-                error_message = str(e)
-                error_traceback = traceback.format_exc()
-                self._on_node_error(
-                    invocation=e.node,
-                    queue_item=queue_item,
-                    error_type=error_type,
-                    error_message=error_message,
-                    error_traceback=error_traceback,
-                )
-                break
-
-            if invocation is None or self._cancel_event.is_set():
-                break
-
-            self.run_node(invocation, queue_item)
-
-            # The session is complete if all invocations have been run or there is an error on the session.
-            if queue_item.session.is_complete() or self._cancel_event.is_set():
-                break
-
-        self._on_after_run_session(queue_item=queue_item)
-
-    def run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
-        try:
-            # Any unhandled exception in this scope is an invocation error & will fail the graph
-            with self._services.performance_statistics.collect_stats(invocation, queue_item.session_id):
-                self._on_before_run_node(invocation, queue_item)
-
-                data = InvocationContextData(
-                    invocation=invocation,
-                    source_invocation_id=queue_item.session.prepared_source_mapping[invocation.id],
-                    queue_item=queue_item,
-                )
-                context = build_invocation_context(
-                    data=data,
-                    services=self._services,
-                    cancel_event=self._cancel_event,
-                )
-
-                # Invoke the node
-                output = invocation.invoke_internal(context=context, services=self._services)
-                # Save output and history
-                queue_item.session.complete(invocation.id, output)
-
-                self._on_after_run_node(invocation, queue_item, output)
-
-        except KeyboardInterrupt:
-            # TODO(psyche): This is expected to be caught in the main thread. Do we need to catch this here?
-            pass
-        except CanceledException:
-            # When the user cancels the graph, we first set the cancel event. The event is checked
-            # between invocations, in this loop. Some invocations are long-running, and we need to
-            # be able to cancel them mid-execution.
-            #
-            # For example, denoising is a long-running invocation with many steps. A step callback
-            # is executed after each step. This step callback checks if the canceled event is set,
-            # then raises a CanceledException to stop execution immediately.
-            #
-            # When we get a CanceledException, we don't need to do anything - just pass and let the
-            # loop go to its next iteration, and the cancel event will be handled correctly.
-            pass
-        except Exception as e:
-            error_type = e.__class__.__name__
-            error_message = str(e)
-            error_traceback = traceback.format_exc()
-            self._on_node_error(
-                invocation=invocation,
-                queue_item=queue_item,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
-
-    def _on_before_run_session(self, queue_item: SessionQueueItem) -> None:
-        """Run before a session is executed"""
-
-        self._services.logger.debug(
-            f"On before run session: queue item {queue_item.item_id}, session {queue_item.session_id}"
-        )
-
-        # If profiling is enabled, start the profiler
-        if self._profiler is not None:
-            self._profiler.start(profile_id=queue_item.session_id)
-
-        for callback in self._on_before_run_session_callbacks:
-            callback(queue_item=queue_item)
-
-    def _on_after_run_session(self, queue_item: SessionQueueItem) -> None:
-        """Run after a session is executed"""
-
-        self._services.logger.debug(
-            f"On after run session: queue item {queue_item.item_id}, session {queue_item.session_id}"
-        )
-
-        # If we are profiling, stop the profiler and dump the profile & stats
-        if self._profiler is not None:
-            profile_path = self._profiler.stop()
-            stats_path = profile_path.with_suffix(".json")
-            self._services.performance_statistics.dump_stats(
-                graph_execution_state_id=queue_item.session.id, output_path=stats_path
-            )
-
-        try:
-            # Update the queue item with the completed session. If the queue item has been removed from the queue,
-            # we'll get a SessionQueueItemNotFoundError and we can ignore it. This can happen if the queue is cleared
-            # while the session is running.
-            queue_item = self._services.session_queue.set_queue_item_session(queue_item.item_id, queue_item.session)
-
-            # TODO(psyche): This feels jumbled - we should review separation of concerns here.
-            # Send complete event. The events service will receive this and update the queue item's status.
-            self._services.events.emit_graph_execution_complete(
-                queue_batch_id=queue_item.batch_id,
-                queue_item_id=queue_item.item_id,
-                queue_id=queue_item.queue_id,
-                graph_execution_state_id=queue_item.session.id,
-            )
-
-            # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
-            # we don't care about that - suppress the error.
-            with suppress(GESStatsNotFoundError):
-                self._services.performance_statistics.log_stats(queue_item.session.id)
-                self._services.performance_statistics.reset_stats()
-
-            for callback in self._on_after_run_session_callbacks:
-                callback(queue_item=queue_item)
-        except SessionQueueItemNotFoundError:
-            pass
-
-    def _on_before_run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
-        """Run before a node is executed"""
-
-        self._services.logger.debug(
-            f"On before run node: queue item {queue_item.item_id}, session {queue_item.session_id}, node {invocation.id} ({invocation.get_type()})"
-        )
-
-        # Send starting event
-        self._services.events.emit_invocation_started(
-            queue_batch_id=queue_item.batch_id,
-            queue_item_id=queue_item.item_id,
-            queue_id=queue_item.queue_id,
-            graph_execution_state_id=queue_item.session_id,
-            node=invocation.model_dump(),
-            source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
-        )
-
-        for callback in self._on_before_run_node_callbacks:
-            callback(invocation=invocation, queue_item=queue_item)
-
-    def _on_after_run_node(
-        self, invocation: BaseInvocation, queue_item: SessionQueueItem, output: BaseInvocationOutput
-    ):
-        """Run after a node is executed"""
-
-        self._services.logger.debug(
-            f"On after run node: queue item {queue_item.item_id}, session {queue_item.session_id}, node {invocation.id} ({invocation.get_type()})"
-        )
-
-        # Send complete event on successful runs
-        self._services.events.emit_invocation_complete(
-            queue_batch_id=queue_item.batch_id,
-            queue_item_id=queue_item.item_id,
-            queue_id=queue_item.queue_id,
-            graph_execution_state_id=queue_item.session.id,
-            node=invocation.model_dump(),
-            source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
-            result=output.model_dump(),
-        )
-
-        for callback in self._on_after_run_node_callbacks:
-            callback(invocation=invocation, queue_item=queue_item, output=output)
-
-    def _on_node_error(
-        self,
-        invocation: BaseInvocation,
-        queue_item: SessionQueueItem,
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ):
-        """Run when a node errors"""
-
-        self._services.logger.debug(
-            f"On node error: queue item {queue_item.item_id}, session {queue_item.session_id}, node {invocation.id} ({invocation.get_type()})"
-        )
-
-        # Node errors do not get the full traceback. Only the queue item gets the full traceback.
-        node_error = f"{error_type}: {error_message}"
-        queue_item.session.set_node_error(invocation.id, node_error)
-        self._services.logger.error(
-            f"Error while invoking session {queue_item.session_id}, invocation {invocation.id} ({invocation.get_type()}): {error_message}"
-        )
-        self._services.logger.error(error_traceback)
-
-        # Send error event
-        self._services.events.emit_invocation_error(
-            queue_batch_id=queue_item.session_id,
-            queue_item_id=queue_item.item_id,
-            queue_id=queue_item.queue_id,
-            graph_execution_state_id=queue_item.session.id,
-            node=invocation.model_dump(),
-            source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
-            error_type=error_type,
-            error_message=error_message,
-            error_traceback=error_traceback,
-            user_id=getattr(queue_item, "user_id", None),
-            project_id=getattr(queue_item, "project_id", None),
-        )
-
-        for callback in self._on_node_error_callbacks:
-            callback(
-                invocation=invocation,
-                queue_item=queue_item,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
-
-
 class DefaultSessionProcessor(SessionProcessorBase):
-    def __init__(
-        self,
-        session_runner: Optional[SessionRunnerBase] = None,
-        on_non_fatal_processor_error_callbacks: Optional[list[OnNonFatalProcessorError]] = None,
-        thread_limit: int = 1,
-        polling_interval: int = 1,
-    ) -> None:
-        super().__init__()
-
-        self.session_runner = session_runner if session_runner else DefaultSessionRunner()
-        self._on_non_fatal_processor_error_callbacks = on_non_fatal_processor_error_callbacks or []
-        self._thread_limit = thread_limit
-        self._polling_interval = polling_interval
-
-    def start(self, invoker: Invoker) -> None:
+    def start(self, invoker: Invoker, thread_limit: int = 1, polling_interval: int = 1) -> None:
         self._invoker: Invoker = invoker
         self._queue_item: Optional[SessionQueueItem] = None
         self._invocation: Optional[BaseInvocation] = None
@@ -317,7 +33,9 @@ class DefaultSessionProcessor(SessionProcessorBase):
 
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_queue_event)
 
-        self._thread_semaphore = BoundedSemaphore(self._thread_limit)
+        self._thread_limit = thread_limit
+        self._thread_semaphore = BoundedSemaphore(thread_limit)
+        self._polling_interval = polling_interval
 
         # If profiling is enabled, create a profiler. The same profiler will be used for all sessions. Internally,
         # the profiler will create a new profile for each session.
@@ -331,7 +49,6 @@ class DefaultSessionProcessor(SessionProcessorBase):
             else None
         )
 
-        self.session_runner.start(services=invoker.services, cancel_event=self._cancel_event, profiler=self._profiler)
         self._thread = Thread(
             name="session_processor",
             target=self._process,
@@ -374,7 +91,6 @@ class DefaultSessionProcessor(SessionProcessorBase):
             "failed",
             "canceled",
         ]:
-            self._cancel_event.set()
             self._poll_now()
 
     def resume(self) -> SessionProcessorStatus:
@@ -400,8 +116,8 @@ class DefaultSessionProcessor(SessionProcessorBase):
         resume_event: ThreadEvent,
         cancel_event: ThreadEvent,
     ):
+        # Outermost processor try block; any unhandled exception is a fatal processor error
         try:
-            # Any unhandled exception in this block is a fatal processor error and will stop the processor.
             self._thread_semaphore.acquire()
             stop_event.clear()
             resume_event.set()
@@ -409,8 +125,8 @@ class DefaultSessionProcessor(SessionProcessorBase):
 
             while not stop_event.is_set():
                 poll_now_event.clear()
+                # Middle processor try block; any unhandled exception is a non-fatal processor error
                 try:
-                    # Any unhandled exception in this block is a nonfatal processor error and will be handled.
                     # If we are paused, wait for resume event
                     resume_event.wait()
 
@@ -426,62 +142,157 @@ class DefaultSessionProcessor(SessionProcessorBase):
                     self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
                     cancel_event.clear()
 
-                    # Run the graph
-                    self.session_runner.run(queue_item=self._queue_item)
+                    # If profiling is enabled, start the profiler
+                    if self._profiler is not None:
+                        self._profiler.start(profile_id=self._queue_item.session_id)
 
-                except Exception as e:
-                    error_type = e.__class__.__name__
-                    error_message = str(e)
-                    error_traceback = traceback.format_exc()
-                    self._on_non_fatal_processor_error(
-                        queue_item=self._queue_item,
-                        error_type=error_type,
-                        error_message=error_message,
-                        error_traceback=error_traceback,
+                    # Prepare invocations and take the first
+                    self._invocation = self._queue_item.session.next()
+
+                    # Loop over invocations until the session is complete or canceled
+                    while self._invocation is not None and not cancel_event.is_set():
+                        # get the source node id to provide to clients (the prepared node id is not as useful)
+                        source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
+
+                        # Send starting event
+                        self._invoker.services.events.emit_invocation_started(
+                            queue_batch_id=self._queue_item.batch_id,
+                            queue_item_id=self._queue_item.item_id,
+                            queue_id=self._queue_item.queue_id,
+                            graph_execution_state_id=self._queue_item.session_id,
+                            node=self._invocation.model_dump(),
+                            source_node_id=source_invocation_id,
+                        )
+
+                        # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
+                        try:
+                            with self._invoker.services.performance_statistics.collect_stats(
+                                self._invocation, self._queue_item.session.id
+                            ):
+                                # Build invocation context (the node-facing API)
+                                data = InvocationContextData(
+                                    invocation=self._invocation,
+                                    source_invocation_id=source_invocation_id,
+                                    queue_item=self._queue_item,
+                                )
+                                context = build_invocation_context(
+                                    data=data,
+                                    services=self._invoker.services,
+                                    cancel_event=self._cancel_event,
+                                )
+
+                                # Invoke the node
+                                outputs = self._invocation.invoke_internal(
+                                    context=context, services=self._invoker.services
+                                )
+
+                                # Save outputs and history
+                                self._queue_item.session.complete(self._invocation.id, outputs)
+
+                                # Send complete event
+                                self._invoker.services.events.emit_invocation_complete(
+                                    queue_batch_id=self._queue_item.batch_id,
+                                    queue_item_id=self._queue_item.item_id,
+                                    queue_id=self._queue_item.queue_id,
+                                    graph_execution_state_id=self._queue_item.session.id,
+                                    node=self._invocation.model_dump(),
+                                    source_node_id=source_invocation_id,
+                                    result=outputs.model_dump(),
+                                )
+
+                        except KeyboardInterrupt:
+                            # TODO(MM2): Create an event for this
+                            pass
+
+                        except CanceledException:
+                            # When the user cancels the graph, we first set the cancel event. The event is checked
+                            # between invocations, in this loop. Some invocations are long-running, and we need to
+                            # be able to cancel them mid-execution.
+                            #
+                            # For example, denoising is a long-running invocation with many steps. A step callback
+                            # is executed after each step. This step callback checks if the canceled event is set,
+                            # then raises a CanceledException to stop execution immediately.
+                            #
+                            # When we get a CanceledException, we don't need to do anything - just pass and let the
+                            # loop go to its next iteration, and the cancel event will be handled correctly.
+                            pass
+
+                        except Exception as e:
+                            error = traceback.format_exc()
+
+                            # Save error
+                            self._queue_item.session.set_node_error(self._invocation.id, error)
+                            self._invoker.services.logger.error(
+                                f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
+                            )
+                            self._invoker.services.logger.error(error)
+
+                            # Send error event
+                            self._invoker.services.events.emit_invocation_error(
+                                queue_batch_id=self._queue_item.session_id,
+                                queue_item_id=self._queue_item.item_id,
+                                queue_id=self._queue_item.queue_id,
+                                graph_execution_state_id=self._queue_item.session.id,
+                                node=self._invocation.model_dump(),
+                                source_node_id=source_invocation_id,
+                                error_type=e.__class__.__name__,
+                                error=error,
+                            )
+                            pass
+
+                        # The session is complete if the all invocations are complete or there was an error
+                        if self._queue_item.session.is_complete() or cancel_event.is_set():
+                            # Send complete event
+                            self._invoker.services.events.emit_graph_execution_complete(
+                                queue_batch_id=self._queue_item.batch_id,
+                                queue_item_id=self._queue_item.item_id,
+                                queue_id=self._queue_item.queue_id,
+                                graph_execution_state_id=self._queue_item.session.id,
+                            )
+                            # If we are profiling, stop the profiler and dump the profile & stats
+                            if self._profiler:
+                                profile_path = self._profiler.stop()
+                                stats_path = profile_path.with_suffix(".json")
+                                self._invoker.services.performance_statistics.dump_stats(
+                                    graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
+                                )
+                            # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
+                            # we don't care about that - suppress the error.
+                            with suppress(GESStatsNotFoundError):
+                                self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
+                                self._invoker.services.performance_statistics.reset_stats()
+
+                            # Set the invocation to None to prepare for the next session
+                            self._invocation = None
+                        else:
+                            # Prepare the next invocation
+                            self._invocation = self._queue_item.session.next()
+                    else:
+                        # The queue was empty, wait for next polling interval or event to try again
+                        self._invoker.services.logger.debug("Waiting for next polling interval or event")
+                        poll_now_event.wait(self._polling_interval)
+                        continue
+                except Exception:
+                    # Non-fatal error in processor
+                    self._invoker.services.logger.error(
+                        f"Non-fatal error in session processor:\n{traceback.format_exc()}"
                     )
-                    # Wait for next polling interval or event to try again
+                    # Cancel the queue item
+                    if self._queue_item is not None:
+                        self._invoker.services.session_queue.cancel_queue_item(
+                            self._queue_item.item_id, error=traceback.format_exc()
+                        )
+                    # Reset the invocation to None to prepare for the next session
+                    self._invocation = None
+                    # Immediately poll for next queue item
                     poll_now_event.wait(self._polling_interval)
                     continue
-        except Exception as e:
+        except Exception:
             # Fatal error in processor, log and pass - we're done here
-            error_type = e.__class__.__name__
-            error_message = str(e)
-            error_traceback = traceback.format_exc()
-            self._invoker.services.logger.error(f"Fatal Error in session processor {error_type}: {error_message}")
-            self._invoker.services.logger.error(error_traceback)
+            self._invoker.services.logger.error(f"Fatal Error in session processor:\n{traceback.format_exc()}")
             pass
         finally:
             stop_event.clear()
             poll_now_event.clear()
             self._queue_item = None
             self._thread_semaphore.release()
-
-    def _on_non_fatal_processor_error(
-        self,
-        queue_item: Optional[SessionQueueItem],
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> None:
-        # Non-fatal error in processor
-        self._invoker.services.logger.error(f"Non-fatal error in session processor {error_type}: {error_message}")
-        self._invoker.services.logger.error(error_traceback)
-
-        if queue_item is not None:
-            # Update the queue item with the completed session
-            self._invoker.services.session_queue.set_queue_item_session(queue_item.item_id, queue_item.session)
-            # Fail the queue item
-            self._invoker.services.session_queue.fail_queue_item(
-                item_id=queue_item.item_id,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
-
-        for callback in self._on_non_fatal_processor_error_callbacks:
-            callback(
-                queue_item=queue_item,
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )

invokeai/app/services/session_queue/session_queue_base.py

@@ -16,7 +16,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemDTO,
     SessionQueueStatus,
 )
-from invokeai.app.services.shared.graph import GraphExecutionState
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
 
@@ -74,17 +73,10 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         """Cancels a session queue item"""
         pass
 
-    @abstractmethod
-    def fail_queue_item(
-        self, item_id: int, error_type: str, error_message: str, error_traceback: str
-    ) -> SessionQueueItem:
-        """Fails a session queue item"""
-        pass
-
     @abstractmethod
     def cancel_by_batch_ids(self, queue_id: str, batch_ids: list[str]) -> CancelByBatchIDsResult:
         """Cancels all queue items with matching batch IDs"""
@@ -111,8 +103,3 @@ class SessionQueueBase(ABC):
     def get_queue_item(self, item_id: int) -> SessionQueueItem:
         """Gets a session queue item by ID"""
         pass
-
-    @abstractmethod
-    def set_queue_item_session(self, item_id: int, session: GraphExecutionState) -> SessionQueueItem:
-        """Sets the session for a session queue item. Use this to update the session state."""
-        pass

invokeai/app/services/session_queue/session_queue_common.py

@@ -3,16 +3,7 @@ import json
 from itertools import chain, product
 from typing import Generator, Iterable, Literal, NamedTuple, Optional, TypeAlias, Union, cast
 
-from pydantic import (
-    AliasChoices,
-    BaseModel,
-    ConfigDict,
-    Field,
-    StrictStr,
-    TypeAdapter,
-    field_validator,
-    model_validator,
-)
+from pydantic import BaseModel, ConfigDict, Field, StrictStr, TypeAdapter, field_validator, model_validator
 from pydantic_core import to_jsonable_python
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
@@ -198,13 +189,7 @@ class SessionQueueItemWithoutGraph(BaseModel):
     session_id: str = Field(
         description="The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed."
     )
-    error_type: Optional[str] = Field(default=None, description="The error type if this queue item errored")
-    error_message: Optional[str] = Field(default=None, description="The error message if this queue item errored")
-    error_traceback: Optional[str] = Field(
-        default=None,
-        description="The error traceback if this queue item errored",
-        validation_alias=AliasChoices("error_traceback", "error"),
-    )
+    error: Optional[str] = Field(default=None, description="The error message if this queue item errored")
     created_at: Union[datetime.datetime, str] = Field(description="When this queue item was created")
     updated_at: Union[datetime.datetime, str] = Field(description="When this queue item was updated")
     started_at: Optional[Union[datetime.datetime, str]] = Field(description="When this queue item was started")

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -27,7 +27,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     calc_session_count,
     prepare_values_to_insert,
 )
-from invokeai.app.services.shared.graph import GraphExecutionState
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
@@ -82,18 +81,10 @@ class SqliteSessionQueue(SessionQueueBase):
     async def _handle_error_event(self, event: FastAPIEvent) -> None:
         try:
             item_id = event[1]["data"]["queue_item_id"]
-            error_type = event[1]["data"]["error_type"]
-            error_message = event[1]["data"]["error_message"]
-            error_traceback = event[1]["data"]["error_traceback"]
+            error = event[1]["data"]["error"]
             queue_item = self.get_queue_item(item_id)
             # always set to failed if have an error, even if previously the item was marked completed or canceled
-            queue_item = self._set_queue_item_status(
-                item_id=queue_item.item_id,
-                status="failed",
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
+            queue_item = self._set_queue_item_status(item_id=queue_item.item_id, status="failed", error=error)
         except SessionQueueItemNotFoundError:
             return
 
@@ -280,22 +271,17 @@ class SqliteSessionQueue(SessionQueueBase):
         return SessionQueueItem.queue_item_from_dict(dict(result))
 
     def _set_queue_item_status(
-        self,
-        item_id: int,
-        status: QUEUE_ITEM_STATUS,
-        error_type: Optional[str] = None,
-        error_message: Optional[str] = None,
-        error_traceback: Optional[str] = None,
+        self, item_id: int, status: QUEUE_ITEM_STATUS, error: Optional[str] = None
     ) -> SessionQueueItem:
         try:
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
                 UPDATE session_queue
-                SET status = ?, error_type = ?, error_message = ?, error_traceback = ?
+                SET status = ?, error = ?
                 WHERE item_id = ?
                 """,
-                (status, error_type, error_message, error_traceback, item_id),
+                (status, error, item_id),
             )
             self.__conn.commit()
         except Exception:
@@ -352,6 +338,26 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return IsFullResult(is_full=is_full)
 
+    def delete_queue_item(self, item_id: int) -> SessionQueueItem:
+        queue_item = self.get_queue_item(item_id=item_id)
+        try:
+            self.__lock.acquire()
+            self.__cursor.execute(
+                """--sql
+                DELETE FROM session_queue
+                WHERE
+                  item_id = ?
+                """,
+                (item_id,),
+            )
+            self.__conn.commit()
+        except Exception:
+            self.__conn.rollback()
+            raise
+        finally:
+            self.__lock.release()
+        return queue_item
+
     def clear(self, queue_id: str) -> ClearResult:
         try:
             self.__lock.acquire()
@@ -418,34 +424,11 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return PruneResult(deleted=count)
 
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         queue_item = self.get_queue_item(item_id)
         if queue_item.status not in ["canceled", "failed", "completed"]:
-            queue_item = self._set_queue_item_status(item_id=item_id, status="canceled")
-            self.__invoker.services.events.emit_session_canceled(
-                queue_item_id=queue_item.item_id,
-                queue_id=queue_item.queue_id,
-                queue_batch_id=queue_item.batch_id,
-                graph_execution_state_id=queue_item.session_id,
-            )
-        return queue_item
-
-    def fail_queue_item(
-        self,
-        item_id: int,
-        error_type: str,
-        error_message: str,
-        error_traceback: str,
-    ) -> SessionQueueItem:
-        queue_item = self.get_queue_item(item_id)
-        if queue_item.status not in ["canceled", "failed", "completed"]:
-            queue_item = self._set_queue_item_status(
-                item_id=item_id,
-                status="failed",
-                error_type=error_type,
-                error_message=error_message,
-                error_traceback=error_traceback,
-            )
+            status = "failed" if error is not None else "canceled"
+            queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error)  # type: ignore [arg-type] # mypy seems to not narrow the Literals here
             self.__invoker.services.events.emit_session_canceled(
                 queue_item_id=queue_item.item_id,
                 queue_id=queue_item.queue_id,
@@ -579,29 +562,6 @@ class SqliteSessionQueue(SessionQueueBase):
             raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}")
         return SessionQueueItem.queue_item_from_dict(dict(result))
 
-    def set_queue_item_session(self, item_id: int, session: GraphExecutionState) -> SessionQueueItem:
-        try:
-            # Use exclude_none so we don't end up with a bunch of nulls in the graph - this can cause validation errors
-            # when the graph is loaded. Graph execution occurs purely in memory - the session saved here is not referenced
-            # during execution.
-            session_json = session.model_dump_json(warnings=False, exclude_none=True)
-            self.__lock.acquire()
-            self.__cursor.execute(
-                """--sql
-                UPDATE session_queue
-                SET session = ?
-                WHERE item_id = ?
-                """,
-                (session_json, item_id),
-            )
-            self.__conn.commit()
-        except Exception:
-            self.__conn.rollback()
-            raise
-        finally:
-            self.__lock.release()
-        return self.get_queue_item(item_id)
-
     def list_queue_items(
         self,
         queue_id: str,
@@ -618,9 +578,7 @@ class SqliteSessionQueue(SessionQueueBase):
                     status,
                     priority,
                     field_values,
-                    error_type,
-                    error_message,
-                    error_traceback,
+                    error,
                     created_at,
                     updated_at,
                     completed_at,

invokeai/app/services/shared/graph.py

@@ -8,7 +8,6 @@ import networkx as nx
 from pydantic import (
     BaseModel,
     GetJsonSchemaHandler,
-    ValidationError,
     field_validator,
 )
 from pydantic.fields import Field
@@ -191,39 +190,6 @@ class UnknownGraphValidationError(ValueError):
     pass
 
 
-class NodeInputError(ValueError):
-    """Raised when a node fails preparation. This occurs when a node's inputs are being set from its incomers, but an
-    input fails validation.
-
-    Attributes:
-        node: The node that failed preparation. Note: only successfully set fields will be accurate. Review the error to
-            determine which field caused the failure.
-    """
-
-    def __init__(self, node: BaseInvocation, e: ValidationError):
-        self.original_error = e
-        self.node = node
-        # When preparing a node, we set each input one-at-a-time. We may thus safely assume that the first error
-        # represents the first input that failed.
-        self.failed_input = loc_to_dot_sep(e.errors()[0]["loc"])
-        super().__init__(f"Node {node.id} has invalid incoming input for {self.failed_input}")
-
-
-def loc_to_dot_sep(loc: tuple[Union[str, int], ...]) -> str:
-    """Helper to pretty-print pydantic error locations as dot-separated strings.
-    Taken from https://docs.pydantic.dev/latest/errors/errors/#customize-error-messages
-    """
-    path = ""
-    for i, x in enumerate(loc):
-        if isinstance(x, str):
-            if i > 0:
-                path += "."
-            path += x
-        else:
-            path += f"[{x}]"
-    return path
-
-
 @invocation_output("iterate_output")
 class IterateInvocationOutput(BaseInvocationOutput):
     """Used to connect iteration outputs. Will be expanded to a specific output."""
@@ -855,10 +821,7 @@ class GraphExecutionState(BaseModel):
 
         # Get values from edges
         if next_node is not None:
-            try:
-                self._prepare_inputs(next_node)
-            except ValidationError as e:
-                raise NodeInputError(next_node, e)
+            self._prepare_inputs(next_node)
 
         # If next is still none, there's no next node, return None
         return next_node

invokeai/app/services/shared/invocation_context.py

@@ -180,9 +180,9 @@ class ImagesInterface(InvocationContextInterface):
         # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
         metadata_ = None
         if metadata:
-            metadata_ = metadata.model_dump_json()
-        elif isinstance(self._data.invocation, WithMetadata) and self._data.invocation.metadata:
-            metadata_ = self._data.invocation.metadata.model_dump_json()
+            metadata_ = metadata
+        elif isinstance(self._data.invocation, WithMetadata):
+            metadata_ = self._data.invocation.metadata
 
         # If `board_id` is provided directly, use that. Else, use the board provided by `WithBoard`, falling back to None.
         board_id_ = None
@@ -191,14 +191,6 @@ class ImagesInterface(InvocationContextInterface):
         elif isinstance(self._data.invocation, WithBoard) and self._data.invocation.board:
             board_id_ = self._data.invocation.board.board_id
 
-        workflow_ = None
-        if self._data.queue_item.workflow:
-            workflow_ = self._data.queue_item.workflow.model_dump_json()
-
-        graph_ = None
-        if self._data.queue_item.session.graph:
-            graph_ = self._data.queue_item.session.graph.model_dump_json()
-
         return self._services.images.create(
             image=image,
             is_intermediate=self._data.invocation.is_intermediate,
@@ -206,8 +198,7 @@ class ImagesInterface(InvocationContextInterface):
             board_id=board_id_,
             metadata=metadata_,
             image_origin=ResourceOrigin.INTERNAL,
-            workflow=workflow_,
-            graph=graph_,
+            workflow=self._data.queue_item.workflow,
             session_id=self._data.queue_item.session_id,
             node_id=self._data.invocation.id,
         )

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -12,7 +12,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_6 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_7 import build_migration_7
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import build_migration_8
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_9 import build_migration_9
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_10 import build_migration_10
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -42,7 +41,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_7())
     migrator.register_migration(build_migration_8(app_config=config))
     migrator.register_migration(build_migration_9())
-    migrator.register_migration(build_migration_10())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_10.py

@@ -1,35 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration10Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._update_error_cols(cursor)
-
-    def _update_error_cols(self, cursor: sqlite3.Cursor) -> None:
-        """
-        - Adds `error_type` and `error_message` columns to the session queue table.
-        - Renames the `error` column to `error_traceback`.
-        """
-
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN error_type TEXT;")
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN error_message TEXT;")
-        cursor.execute("ALTER TABLE session_queue RENAME COLUMN error TO error_traceback;")
-
-
-def build_migration_10() -> Migration:
-    """
-    Build the migration from database version 9 to 10.
-
-    This migration does the following:
-    - Adds `error_type` and `error_message` columns to the session queue table.
-    - Renames the `error` column to `error_traceback`.
-    """
-    migration_10 = Migration(
-        from_version=9,
-        to_version=10,
-        callback=Migration10Callback(),
-    )
-
-    return migration_10

invokeai/backend/image_util/__init__.py

@@ -4,4 +4,5 @@ Initialization file for invokeai.backend.image_util methods.
 
 from .infill_methods.patchmatch import PatchMatch  # noqa: F401
 from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata  # noqa: F401
+from .seamless import configure_model_padding  # noqa: F401
 from .util import InitImageResizer, make_grid  # noqa: F401

invokeai/backend/image_util/safety_checker.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 import numpy as np
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from PIL import Image, ImageFilter
+from PIL import Image
 from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
@@ -16,7 +16,6 @@ from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
-repo_id = "CompVis/stable-diffusion-safety-checker"
 CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
 
 
@@ -25,30 +24,30 @@ class SafetyChecker:
     Wrapper around SafetyChecker model.
     """
 
-    feature_extractor = None
     safety_checker = None
+    feature_extractor = None
+    tried_load: bool = False
 
     @classmethod
     def _load_safety_checker(cls):
-        if cls.safety_checker is not None and cls.feature_extractor is not None:
+        if cls.tried_load:
             return
 
         try:
-            model_path = get_config().models_path / CHECKER_PATH
-            if model_path.exists():
-                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(model_path)
-                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_path)
-            else:
-                model_path.mkdir(parents=True, exist_ok=True)
-                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
-                cls.feature_extractor.save_pretrained(model_path, safe_serialization=True)
-                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(repo_id)
-                cls.safety_checker.save_pretrained(model_path, safe_serialization=True)
+            cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(get_config().models_path / CHECKER_PATH)
+            cls.feature_extractor = AutoFeatureExtractor.from_pretrained(get_config().models_path / CHECKER_PATH)
         except Exception as e:
             logger.warning(f"Could not load NSFW checker: {str(e)}")
+        cls.tried_load = True
+
+    @classmethod
+    def safety_checker_available(cls) -> bool:
+        return Path(get_config().models_path, CHECKER_PATH).exists()
 
     @classmethod
     def has_nsfw_concept(cls, image: Image.Image) -> bool:
+        if not cls.safety_checker_available() and cls.tried_load:
+            return False
         cls._load_safety_checker()
         if cls.safety_checker is None or cls.feature_extractor is None:
             return False
@@ -61,24 +60,3 @@ class SafetyChecker:
         with SilenceWarnings():
             checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
         return has_nsfw_concept[0]
-
-    @classmethod
-    def blur_if_nsfw(cls, image: Image.Image) -> Image.Image:
-        if cls.has_nsfw_concept(image):
-            logger.warning("A potentially NSFW image has been detected. Image will be blurred.")
-            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
-            caution = cls._get_caution_img()
-            # Center the caution image on the blurred image
-            x = (blurry_image.width - caution.width) // 2
-            y = (blurry_image.height - caution.height) // 2
-            blurry_image.paste(caution, (x, y), caution)
-            image = blurry_image
-
-        return image
-
-    @classmethod
-    def _get_caution_img(cls) -> Image.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))

invokeai/backend/image_util/seamless.py

@@ -0,0 +1,52 @@
+import torch.nn as nn
+
+
+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"])
+    return nn.functional.conv2d(
+        working,
+        weight,
+        bias,
+        self.stride,
+        nn.modules.utils._pair(0),
+        self.dilation,
+        self.groups,
+    )
+
+
+def configure_model_padding(model, seamless, seamless_axes):
+    """
+    Modifies the 2D convolution layers to use a circular padding mode based on
+    the `seamless` and `seamless_axes` options.
+    """
+    # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+            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["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["y"] = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            else:
+                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
+                if hasattr(m, "asymmetric_padding_mode"):
+                    del m.asymmetric_padding_mode
+                if hasattr(m, "asymmetric_padding"):
+                    del m.asymmetric_padding

invokeai/backend/image_util/util.py

@@ -144,8 +144,10 @@ def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.n
     h = float(input_image.shape[0])
     w = float(input_image.shape[1])
     scaling_factor = float(resolution) / min(h, w)
-    h = int(h * scaling_factor)
-    w = int(w * scaling_factor)
+    h *= scaling_factor
+    w *= scaling_factor
+    h = int(np.round(h / 64.0)) * 64
+    w = int(np.round(w / 64.0)) * 64
     if scaling_factor > 1:
         return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_LANCZOS4)
     else:

invokeai/backend/model_manager/probe.py

@@ -51,7 +51,6 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched
     },
     BaseModelType.StableDiffusionXL: {
         ModelVariantType.Normal: "sd_xl_base.yaml",
-        ModelVariantType.Inpaint: "sd_xl_inpaint.yaml",
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelVariantType.Normal: "sd_xl_refiner.yaml",

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -13,7 +13,6 @@ from diffusers import (
     LCMScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
-    TCDScheduler,
     UniPCMultistepScheduler,
 )
 
@@ -41,5 +40,4 @@ SCHEDULER_MAP = {
     "dpmpp_sde_k": (DPMSolverSDEScheduler, {"use_karras_sigmas": True, "noise_sampler_seed": 0}),
     "unipc": (UniPCMultistepScheduler, {"cpu_only": True}),
     "lcm": (LCMScheduler, {}),
-    "tcd": (TCDScheduler, {}),
 }

invokeai/backend/stable_diffusion/seamless.py

@@ -1,51 +1,89 @@
-from contextlib import contextmanager
-from typing import Callable, List, Optional, Tuple, Union
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import Callable, List, Union
 
-import torch
 import torch.nn as nn
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
-from diffusers.models.lora import LoRACompatibleConv
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 
 
+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"])
+    return nn.functional.conv2d(
+        working,
+        weight,
+        bias,
+        self.stride,
+        nn.modules.utils._pair(0),
+        self.dilation,
+        self.groups,
+    )
+
+
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
     if not seamless_axes:
         yield
         return
 
-    # override conv_forward
-    # https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
-    def _conv_forward_asymmetric(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
-        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
-        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
-        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
-        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
-        return torch.nn.functional.conv2d(
-            working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
-        )
-
-    original_layers: List[Tuple[nn.Conv2d, Callable]] = []
-
+    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
+    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:
-        x_mode = "circular" if "x" in seamless_axes else "constant"
-        y_mode = "circular" if "y" in seamless_axes else "constant"
+        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
+        skipped_layers = 1
+        for m_name, m in model.named_modules():
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                continue
 
-        conv_layers: List[torch.nn.Conv2d] = []
+            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
+                # down_blocks.1.resnets.1.conv1
+                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
+                block_num = int(block_num)
+                resnet_num = int(resnet_num)
 
-        for module in model.modules():
-            if isinstance(module, torch.nn.Conv2d):
-                conv_layers.append(module)
+                if block_num >= len(model.down_blocks) - skipped_layers:
+                    continue
 
-        for layer in conv_layers:
-            if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
-                layer.lora_layer = lambda *x: 0
-            original_layers.append((layer, layer._conv_forward))
-            layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)
+                # Skip the second resnet (could be configurable)
+                if resnet_num > 0:
+                    continue
+
+                # Skip Conv2d layers (could be configurable)
+                if submodule_name == "conv2":
+                    continue
+
+            m.asymmetric_padding_mode = {}
+            m.asymmetric_padding = {}
+            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["y"] = (
+                0,
+                0,
+                m._reversed_padding_repeated_twice[2],
+                m._reversed_padding_repeated_twice[3],
+            )
+
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 
     finally:
-        for layer, orig_conv_forward in original_layers:
-            layer._conv_forward = orig_conv_forward
+        for module, orig_conv_forward in to_restore:
+            module._conv_forward = orig_conv_forward
+            if hasattr(module, "asymmetric_padding_mode"):
+                del module.asymmetric_padding_mode
+            if hasattr(module, "asymmetric_padding"):
+                del module.asymmetric_padding

invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml

@@ -1,98 +0,0 @@
-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: 9
-        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

invokeai/frontend/web/.eslintrc.js

@@ -10,8 +10,6 @@ module.exports = {
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
     // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
     'i18next/no-literal-string': 'error',
-    // https://eslint.org/docs/latest/rules/no-console
-    'no-console': 'error',
   },
   overrides: [
     /**

invokeai/frontend/web/.gitignore

@@ -43,5 +43,4 @@ stats.html
 yalc.lock
 
 # vitest
-tsconfig.vitest-temp.json
-coverage/
+tsconfig.vitest-temp.json

invokeai/frontend/web/package.json

@@ -25,7 +25,7 @@
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
     "lint:knip": "knip",
-    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
+    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:0 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
@@ -35,7 +35,6 @@
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "test": "vitest",
-    "test:ui": "vitest --coverage --ui",
     "test:no-watch": "vitest --no-watch"
   },
   "madge": {
@@ -53,48 +52,47 @@
   },
   "dependencies": {
     "@chakra-ui/react-use-size": "^2.1.0",
-    "@dagrejs/dagre": "^1.1.2",
-    "@dagrejs/graphlib": "^2.2.2",
+    "@dagrejs/dagre": "^1.1.1",
+    "@dagrejs/graphlib": "^2.2.1",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
-    "@fontsource-variable/inter": "^5.0.18",
-    "@invoke-ai/ui-library": "^0.0.25",
+    "@fontsource-variable/inter": "^5.0.17",
+    "@invoke-ai/ui-library": "^0.0.21",
     "@nanostores/react": "^0.7.2",
-    "@reduxjs/toolkit": "2.2.3",
+    "@reduxjs/toolkit": "2.2.2",
     "@roarr/browser-log-writer": "^1.3.0",
     "chakra-react-select": "^4.7.6",
     "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
-    "fracturedjsonjs": "^4.0.1",
-    "framer-motion": "^11.1.8",
-    "i18next": "^23.11.3",
-    "i18next-http-backend": "^2.5.1",
+    "framer-motion": "^11.0.22",
+    "i18next": "^23.10.1",
+    "i18next-http-backend": "^2.5.0",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.6",
     "lodash-es": "^4.17.21",
-    "nanostores": "^0.10.3",
+    "nanostores": "^0.10.0",
     "new-github-issue-url": "^1.0.0",
-    "overlayscrollbars": "^2.7.3",
-    "overlayscrollbars-react": "^0.5.6",
+    "overlayscrollbars": "^2.6.1",
+    "overlayscrollbars-react": "^0.5.5",
     "query-string": "^9.0.0",
-    "react": "^18.3.1",
+    "react": "^18.2.0",
     "react-colorful": "^5.6.1",
-    "react-dom": "^18.3.1",
+    "react-dom": "^18.2.0",
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.51.4",
+    "react-hook-form": "^7.51.2",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^14.1.1",
-    "react-icons": "^5.2.0",
+    "react-i18next": "^14.1.0",
+    "react-icons": "^5.0.1",
     "react-konva": "^18.2.10",
-    "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.0.19",
+    "react-redux": "9.1.0",
+    "react-resizable-panels": "^2.0.16",
     "react-select": "5.8.0",
     "react-use": "^17.5.0",
-    "react-virtuoso": "^4.7.10",
-    "reactflow": "^11.11.3",
+    "react-virtuoso": "^4.7.5",
+    "reactflow": "^11.10.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
     "redux-undo": "^1.1.0",
@@ -103,11 +101,10 @@
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
     "use-debounce": "^10.0.0",
-    "use-device-pixel-ratio": "^1.1.2",
     "use-image": "^1.1.1",
     "uuid": "^9.0.1",
-    "zod": "^3.23.6",
-    "zod-validation-error": "^3.2.0"
+    "zod": "^3.22.4",
+    "zod-validation-error": "^3.0.3"
   },
   "peerDependencies": {
     "@chakra-ui/react": "^2.8.2",
@@ -118,42 +115,40 @@
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.0.10",
-    "@storybook/addon-interactions": "^8.0.10",
-    "@storybook/addon-links": "^8.0.10",
-    "@storybook/addon-storysource": "^8.0.10",
-    "@storybook/manager-api": "^8.0.10",
-    "@storybook/react": "^8.0.10",
-    "@storybook/react-vite": "^8.0.10",
-    "@storybook/theming": "^8.0.10",
+    "@storybook/addon-essentials": "^8.0.4",
+    "@storybook/addon-interactions": "^8.0.4",
+    "@storybook/addon-links": "^8.0.4",
+    "@storybook/addon-storysource": "^8.0.4",
+    "@storybook/manager-api": "^8.0.4",
+    "@storybook/react": "^8.0.4",
+    "@storybook/react-vite": "^8.0.4",
+    "@storybook/theming": "^8.0.4",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.12.10",
-    "@types/react": "^18.3.1",
-    "@types/react-dom": "^18.3.0",
+    "@types/node": "^20.11.30",
+    "@types/react": "^18.2.73",
+    "@types/react-dom": "^18.2.22",
     "@types/uuid": "^9.0.8",
     "@vitejs/plugin-react-swc": "^3.6.0",
-    "@vitest/coverage-v8": "^1.5.0",
-    "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.0",
     "eslint-plugin-i18next": "^6.0.3",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.12.3",
+    "knip": "^5.6.1",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.7.5",
     "prettier": "^3.2.5",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.0.10",
+    "storybook": "^8.0.4",
     "ts-toolbelt": "^9.6.0",
     "tsafe": "^1.6.6",
-    "typescript": "^5.4.5",
-    "vite": "^5.2.11",
-    "vite-plugin-css-injected-by-js": "^3.5.1",
-    "vite-plugin-dts": "^3.9.1",
+    "typescript": "^5.4.3",
+    "vite": "^5.2.6",
+    "vite-plugin-css-injected-by-js": "^3.5.0",
+    "vite-plugin-dts": "^3.8.0",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.3.2",
-    "vitest": "^1.6.0"
+    "vitest": "^1.4.0"
   }
 }

invokeai/frontend/web/public/locales/de.json

@@ -76,9 +76,7 @@
         "aboutHeading": "Nutzen Sie Ihre kreative Energie",
         "toResolve": "Lösen",
         "add": "Hinzufügen",
-        "loglevel": "Protokoll Stufe",
-        "selected": "Ausgewählt",
-        "beta": "Beta"
+        "loglevel": "Protokoll Stufe"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -88,7 +86,7 @@
         "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
         "deleteImage_one": "Lösche Bild",
-        "deleteImage_other": "Lösche {{count}} Bilder",
+        "deleteImage_other": "",
         "copy": "Kopieren",
         "download": "Runterladen",
         "setCurrentImage": "Setze aktuelle Bild",
@@ -399,14 +397,7 @@
         "cancel": "Stornieren",
         "defaultSettingsSaved": "Standardeinstellungen gespeichert",
         "addModels": "Model hinzufügen",
-        "deleteModelImage": "Lösche Model Bild",
-        "hfTokenInvalidErrorMessage": "Falscher oder fehlender HuggingFace Schlüssel.",
-        "huggingFaceRepoID": "HuggingFace Repo ID",
-        "hfToken": "HuggingFace Schlüssel",
-        "hfTokenInvalid": "Falscher oder fehlender HF Schlüssel",
-        "huggingFacePlaceholder": "besitzer/model-name",
-        "hfTokenSaved": "HF Schlüssel gespeichert",
-        "hfTokenUnableToVerify": "Konnte den HF Schlüssel nicht validieren"
+        "deleteModelImage": "Lösche Model Bild"
     },
     "parameters": {
         "images": "Bilder",
@@ -695,11 +686,7 @@
         "hands": "Hände",
         "dwOpenpose": "DW Openpose",
         "dwOpenposeDescription": "Posenschätzung mit DW Openpose",
-        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus",
-        "ipAdapterMethod": "Methode",
-        "composition": "Nur Komposition",
-        "full": "Voll",
-        "style": "Nur Style"
+        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus"
     },
     "queue": {
         "status": "Status",
@@ -730,6 +717,7 @@
         "resume": "Wieder aufnehmen",
         "item": "Auftrag",
         "notReady": "Warteschlange noch nicht bereit",
+        "queueCountPrediction": "{{promptsCount}} Prompts × {{iterations}} Iterationen -> {{count}} Generationen",
         "clearQueueAlertDialog": "\"Die Warteschlange leeren\" stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
         "completedIn": "Fertig in",
         "cancelBatchSucceeded": "Stapel abgebrochen",

invokeai/frontend/web/public/locales/en.json

@@ -2,7 +2,6 @@
     "accessibility": {
         "about": "About",
         "createIssue": "Create Issue",
-        "submitSupportTicket": "Submit Support Ticket",
         "invokeProgressBar": "Invoke progress bar",
         "menu": "Menu",
         "mode": "Mode",
@@ -89,13 +88,11 @@
         "negativePrompt": "Negative Prompt",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
-        "editor": "Editor",
         "error": "Error",
         "file": "File",
         "folder": "Folder",
         "format": "format",
         "githubLabel": "Github",
-        "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
@@ -143,13 +140,7 @@
         "blue": "Blue",
         "alpha": "Alpha",
         "selected": "Selected",
-        "tab": "Tab",
-        "viewing": "Viewing",
-        "viewingDesc": "Review images in a large gallery view",
-        "editing": "Editing",
-        "editingDesc": "Edit on the Control Layers canvas",
-        "enabled": "Enabled",
-        "disabled": "Disabled"
+        "viewer": "Viewer"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -165,7 +156,6 @@
         "balanced": "Balanced",
         "base": "Base",
         "beginEndStepPercent": "Begin / End Step Percentage",
-        "beginEndStepPercentShort": "Begin/End %",
         "bgth": "bg_th",
         "canny": "Canny",
         "cannyDescription": "Canny edge detection",
@@ -234,11 +224,10 @@
         "composition": "Composition Only",
         "safe": "Safe",
         "saveControlImage": "Save Control Image",
-        "scribble": "Scribble",
+        "scribble": "scribble",
         "selectModel": "Select a model",
         "selectCLIPVisionModel": "Select a CLIP Vision model",
-        "setControlImageDimensions": "Copy size to W/H (optimize for model)",
-        "setControlImageDimensionsForce": "Copy size to W/H (ignore model)",
+        "setControlImageDimensions": "Set Control Image Dimensions To W/H",
         "showAdvanced": "Show Advanced",
         "small": "Small",
         "toggleControlNet": "Toggle this ControlNet",
@@ -264,6 +253,7 @@
         "queue": "Queue",
         "queueFront": "Add to Front of Queue",
         "queueBack": "Add to Queue",
+        "queueCountPrediction": "{{promptsCount}} prompts \u00d7 {{iterations}} iterations -> {{count}} generations",
         "queueEmpty": "Queue Empty",
         "enqueueing": "Queueing Batch",
         "resume": "Resume",
@@ -316,13 +306,7 @@
         "batchFailedToQueue": "Failed to Queue Batch",
         "graphQueued": "Graph queued",
         "graphFailedToQueue": "Failed to queue graph",
-        "openQueue": "Open Queue",
-        "prompts_one": "Prompt",
-        "prompts_other": "Prompts",
-        "iterations_one": "Iteration",
-        "iterations_other": "Iterations",
-        "generations_one": "Generation",
-        "generations_other": "Generations"
+        "openQueue": "Open Queue"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -598,10 +582,6 @@
         "upscale": {
             "desc": "Upscale the current image",
             "title": "Upscale"
-        },
-        "toggleViewer": {
-            "desc": "Switches between the Image Viewer and workspace for the current tab.",
-            "title": "Toggle Image Viewer"
         }
     },
     "metadata": {
@@ -777,15 +757,10 @@
         "cannotConnectOutputToOutput": "Cannot connect output to output",
         "cannotConnectToSelf": "Cannot connect to self",
         "cannotDuplicateConnection": "Cannot create duplicate connections",
-        "cannotMixAndMatchCollectionItemTypes": "Cannot mix and match collection item types",
-        "missingNode": "Missing invocation node",
-        "missingInvocationTemplate": "Missing invocation template",
-        "missingFieldTemplate": "Missing field template",
         "nodePack": "Node pack",
         "collection": "Collection",
-        "singleFieldType": "{{name}} (Single)",
-        "collectionFieldType": "{{name}} (Collection)",
-        "collectionOrScalarFieldType": "{{name}} (Single or Collection)",
+        "collectionFieldType": "{{name}} Collection",
+        "collectionOrScalarFieldType": "{{name}} Collection|Scalar",
         "colorCodeEdges": "Color-Code Edges",
         "colorCodeEdgesHelp": "Color-code edges according to their connected fields",
         "connectionWouldCreateCycle": "Connection would create a cycle",
@@ -887,7 +862,6 @@
         "versionUnknown": " Version Unknown",
         "workflow": "Workflow",
         "graph": "Graph",
-        "noGraph": "No Graph",
         "workflowAuthor": "Author",
         "workflowContact": "Contact",
         "workflowDescription": "Short Description",
@@ -900,10 +874,7 @@
         "zoomInNodes": "Zoom In",
         "zoomOutNodes": "Zoom Out",
         "betaDesc": "This invocation is in beta. Until it is stable, it may have breaking changes during app updates. We plan to support this invocation long-term.",
-        "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time.",
-        "imageAccessError": "Unable to find image {{image_name}}, resetting to default",
-        "boardAccessError": "Unable to find board {{board_id}}, resetting to default",
-        "modelAccessError": "Unable to find model {{key}}, resetting to default"
+        "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time."
     },
     "parameters": {
         "aspect": "Aspect",
@@ -944,37 +915,23 @@
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} missing input",
             "missingNodeTemplate": "Missing node template",
             "noControlImageForControlAdapter": "Control Adapter #{{number}} has no control image",
-            "imageNotProcessedForControlAdapter": "Control Adapter #{{number}}'s image is not processed",
             "noInitialImageSelected": "No initial image selected",
             "noModelForControlAdapter": "Control Adapter #{{number}} has no model selected.",
             "incompatibleBaseModelForControlAdapter": "Control Adapter #{{number}} model is incompatible with main model.",
             "noModelSelected": "No model selected",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected",
-            "layer": {
-                "initialImageNoImageSelected": "no initial image selected",
-                "controlAdapterNoModelSelected": "no Control Adapter model selected",
-                "controlAdapterIncompatibleBaseModel": "incompatible Control Adapter base model",
-                "controlAdapterNoImageSelected": "no Control Adapter image selected",
-                "controlAdapterImageNotProcessed": "Control Adapter image not processed",
-                "t2iAdapterIncompatibleDimensions": "T2I Adapter requires image dimension to be multiples of {{multiple}}",
-                "ipAdapterNoModelSelected": "no IP adapter selected",
-                "ipAdapterIncompatibleBaseModel": "incompatible IP Adapter base model",
-                "ipAdapterNoImageSelected": "no IP Adapter image selected",
-                "rgNoPromptsOrIPAdapters": "no text prompts or IP Adapters",
-                "rgNoRegion": "no region selected"
-            }
+            "systemDisconnected": "System disconnected"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
-        "globalNegativePromptPlaceholder": "Global Negative Prompt",
         "noiseThreshold": "Noise Threshold",
         "patchmatchDownScaleSize": "Downscale",
         "perlinNoise": "Perlin Noise",
         "positivePromptPlaceholder": "Positive Prompt",
-        "globalPositivePromptPlaceholder": "Global Positive Prompt",
         "iterations": "Iterations",
+        "iterationsWithCount_one": "{{count}} Iteration",
+        "iterationsWithCount_other": "{{count}} Iterations",
         "scale": "Scale",
         "scaleBeforeProcessing": "Scale Before Processing",
         "scaledHeight": "Scaled H",
@@ -1076,9 +1033,8 @@
     },
     "toast": {
         "addedToBoard": "Added to board",
-        "baseModelChanged": "Base Model Changed",
-        "baseModelChangedCleared_one": "Cleared or disabled {{count}} incompatible submodel",
-        "baseModelChangedCleared_other": "Cleared or disabled {{count}} incompatible submodels",
+        "baseModelChangedCleared_one": "Base model changed, cleared or disabled {{count}} incompatible submodel",
+        "baseModelChangedCleared_other": "Base model changed, cleared or disabled {{count}} incompatible submodels",
         "canceled": "Processing Canceled",
         "canvasCopiedClipboard": "Canvas Copied to Clipboard",
         "canvasDownloaded": "Canvas Downloaded",
@@ -1099,17 +1055,10 @@
         "metadataLoadFailed": "Failed to load metadata",
         "modelAddedSimple": "Model Added to Queue",
         "modelImportCanceled": "Model Import Canceled",
-        "outOfMemoryError": "Out of Memory Error",
-        "outOfMemoryErrorDesc": "Your current generation settings exceed system capacity. Please adjust your settings and try again.",
         "parameters": "Parameters",
-        "parameterSet": "Parameter Recalled",
-        "parameterSetDesc": "Recalled {{parameter}}",
-        "parameterNotSet": "Parameter Recalled",
-        "parameterNotSetDesc": "Unable to recall {{parameter}}",
-        "parameterNotSetDescWithMessage": "Unable to recall {{parameter}}: {{message}}",
-        "parametersSet": "Parameters Recalled",
-        "parametersNotSet": "Parameters Not Recalled",
-        "errorCopied": "Error Copied",
+        "parameterNotSet": "{{parameter}} not set",
+        "parameterSet": "{{parameter}} set",
+        "parametersNotSet": "Parameters Not Set",
         "problemCopyingCanvas": "Problem Copying Canvas",
         "problemCopyingCanvasDesc": "Unable to export base layer",
         "problemCopyingImage": "Unable to Copy Image",
@@ -1129,13 +1078,11 @@
         "sentToImageToImage": "Sent To Image To Image",
         "sentToUnifiedCanvas": "Sent to Unified Canvas",
         "serverError": "Server Error",
-        "sessionRef": "Session: {{sessionId}}",
         "setAsCanvasInitialImage": "Set as canvas initial image",
         "setCanvasInitialImage": "Set canvas initial image",
         "setControlImage": "Set as control image",
         "setInitialImage": "Set as initial image",
         "setNodeField": "Set as node field",
-        "somethingWentWrong": "Something Went Wrong",
         "uploadFailed": "Upload failed",
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "uploadInitialImage": "Upload Initial Image",
@@ -1564,7 +1511,7 @@
     "app": {
         "storeNotInitialized": "Store is not initialized"
     },
-    "controlLayers": {
+    "regionalPrompts": {
         "deleteAll": "Delete All",
         "addLayer": "Add Layer",
         "moveToFront": "Move to Front",
@@ -1572,9 +1519,11 @@
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "controlLayers": "Control Layers",
+        "regionalControl": "Regional Control (ALPHA)",
+        "enableRegionalPrompts": "Enable $t(regionalPrompts.regionalPrompts)",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
+        "toggleVisibility": "Toggle Layer Visibility",
         "deletePrompt": "Delete Prompt",
         "resetRegion": "Reset Region",
         "debugLayers": "Debug Layers",
@@ -1582,35 +1531,6 @@
         "maskPreviewColor": "Mask Preview Color",
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
-        "addIPAdapter": "Add $t(common.ipAdapter)",
-        "regionalGuidance": "Regional Guidance",
-        "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "opacity": "Opacity",
-        "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
-        "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
-        "globalIPAdapter": "Global $t(common.ipAdapter)",
-        "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
-        "globalInitialImage": "Global Initial Image",
-        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "opacityFilter": "Opacity Filter",
-        "clearProcessor": "Clear Processor",
-        "resetProcessor": "Reset Processor to Defaults",
-        "noLayersAdded": "No Layers Added",
-        "layers_one": "Layer",
-        "layers_other": "Layers"
-    },
-    "ui": {
-        "tabs": {
-            "generation": "Generation",
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "canvas": "Canvas",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "workflows": "Workflows",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "models": "Models",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Queue",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)"
-        }
+        "addIPAdapter": "Add $t(common.ipAdapter)"
     }
 }

invokeai/frontend/web/public/locales/es.json

@@ -25,24 +25,7 @@
         "areYouSure": "¿Estas seguro?",
         "batch": "Administrador de lotes",
         "modelManager": "Administrador de modelos",
-        "communityLabel": "Comunidad",
-        "direction": "Dirección",
-        "ai": "Ia",
-        "add": "Añadir",
-        "auto": "Automático",
-        "copyError": "Error $t(gallery.copy)",
-        "details": "Detalles",
-        "or": "o",
-        "checkpoint": "Punto de control",
-        "controlNet": "ControlNet",
-        "aboutHeading": "Sea dueño de su poder creativo",
-        "advanced": "Avanzado",
-        "data": "Fecha",
-        "delete": "Borrar",
-        "copy": "Copiar",
-        "beta": "Beta",
-        "on": "En",
-        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:"
+        "communityLabel": "Comunidad"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -382,7 +365,7 @@
         "canvasMerged": "Lienzo consolidado",
         "sentToImageToImage": "Enviar hacia Imagen a Imagen",
         "sentToUnifiedCanvas": "Enviar hacia Lienzo Consolidado",
-        "parametersNotSet": "Parámetros no recuperados",
+        "parametersNotSet": "Parámetros no establecidos",
         "metadataLoadFailed": "Error al cargar metadatos",
         "serverError": "Error en el servidor",
         "canceled": "Procesando la cancelación",
@@ -390,8 +373,7 @@
         "uploadFailedInvalidUploadDesc": "Debe ser una sola imagen PNG o JPEG",
         "parameterSet": "Conjunto de parámetros",
         "parameterNotSet": "Parámetro no configurado",
-        "problemCopyingImage": "No se puede copiar la imagen",
-        "errorCopied": "Error al copiar"
+        "problemCopyingImage": "No se puede copiar la imagen"
     },
     "tooltip": {
         "feature": {
@@ -461,13 +443,7 @@
         "previousImage": "Imagen anterior",
         "nextImage": "Siguiente imagen",
         "showOptionsPanel": "Mostrar el panel lateral",
-        "menu": "Menú",
-        "showGalleryPanel": "Mostrar panel de galería",
-        "loadMore": "Cargar más",
-        "about": "Acerca de",
-        "createIssue": "Crear un problema",
-        "resetUI": "Interfaz de usuario $t(accessibility.reset)",
-        "mode": "Modo"
+        "menu": "Menú"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -480,68 +456,5 @@
         "reloadNodeTemplates": "Recargar las plantillas de nodos",
         "loadWorkflow": "Cargar el flujo de trabajo",
         "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON"
-    },
-    "boards": {
-        "autoAddBoard": "Agregar panel automáticamente",
-        "changeBoard": "Cambiar el panel",
-        "clearSearch": "Borrar la búsqueda",
-        "deleteBoard": "Borrar el panel",
-        "selectBoard": "Seleccionar un panel",
-        "uncategorized": "Sin categoría",
-        "cancel": "Cancelar",
-        "addBoard": "Agregar un panel",
-        "movingImagesToBoard_one": "Moviendo {{count}} imagen al panel:",
-        "movingImagesToBoard_many": "Moviendo {{count}} imágenes al panel:",
-        "movingImagesToBoard_other": "Moviendo {{count}} imágenes al panel:",
-        "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
-        "deleteBoardAndImages": "Borrar el panel y las imágenes",
-        "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar",
-        "move": "Mover",
-        "menuItemAutoAdd": "Agregar automáticamente a este panel",
-        "searchBoard": "Buscando paneles…",
-        "topMessage": "Este panel contiene imágenes utilizadas en las siguientes funciones:",
-        "downloadBoard": "Descargar panel",
-        "deleteBoardOnly": "Borrar solo el panel",
-        "myBoard": "Mi panel",
-        "noMatching": "No hay paneles que coincidan"
-    },
-    "accordions": {
-        "compositing": {
-            "title": "Composición",
-            "infillTab": "Relleno"
-        },
-        "generation": {
-            "title": "Generación"
-        },
-        "image": {
-            "title": "Imagen"
-        },
-        "control": {
-            "title": "Control"
-        },
-        "advanced": {
-            "options": "$t(accordions.advanced.title) opciones",
-            "title": "Avanzado"
-        }
-    },
-    "ui": {
-        "tabs": {
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "canvas": "Lienzo",
-            "generation": "Generación",
-            "queue": "Cola",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "workflows": "Flujos de trabajo",
-            "models": "Modelos",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)"
-        }
-    },
-    "controlLayers": {
-        "layers_one": "Capa",
-        "layers_many": "Capas",
-        "layers_other": "Capas"
     }
 }

invokeai/frontend/web/public/locales/it.json

@@ -5,7 +5,7 @@
         "reportBugLabel": "Segnala un errore",
         "settingsLabel": "Impostazioni",
         "img2img": "Immagine a Immagine",
-        "unifiedCanvas": "Tela",
+        "unifiedCanvas": "Tela unificata",
         "nodes": "Flussi di lavoro",
         "upload": "Caricamento",
         "load": "Carica",
@@ -74,18 +74,7 @@
         "file": "File",
         "toResolve": "Da risolvere",
         "add": "Aggiungi",
-        "loglevel": "Livello di log",
-        "beta": "Beta",
-        "positivePrompt": "Prompt positivo",
-        "negativePrompt": "Prompt negativo",
-        "selected": "Selezionato",
-        "goTo": "Vai a",
-        "editor": "Editor",
-        "tab": "Scheda",
-        "viewing": "Visualizza",
-        "viewingDesc": "Rivedi le immagini in un'ampia vista della galleria",
-        "editing": "Modifica",
-        "editingDesc": "Modifica nell'area Livelli di controllo"
+        "loglevel": "Livello di log"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -191,8 +180,8 @@
             "desc": "Mostra le informazioni sui metadati dell'immagine corrente"
         },
         "sendToImageToImage": {
-            "title": "Invia a Generazione da immagine",
-            "desc": "Invia l'immagine corrente a Generazione da immagine"
+            "title": "Invia a Immagine a Immagine",
+            "desc": "Invia l'immagine corrente a da Immagine a Immagine"
         },
         "deleteImage": {
             "title": "Elimina immagine",
@@ -345,10 +334,6 @@
         "remixImage": {
             "desc": "Utilizza tutti i parametri tranne il seme dell'immagine corrente",
             "title": "Remixa l'immagine"
-        },
-        "toggleViewer": {
-            "title": "Attiva/disattiva il visualizzatore di immagini",
-            "desc": "Passa dal Visualizzatore immagini all'area di lavoro per la scheda corrente."
         }
     },
     "modelManager": {
@@ -486,8 +471,8 @@
         "scaledHeight": "Altezza ridimensionata",
         "infillMethod": "Metodo di riempimento",
         "tileSize": "Dimensione piastrella",
-        "sendToImg2Img": "Invia a Generazione da immagine",
-        "sendToUnifiedCanvas": "Invia alla Tela",
+        "sendToImg2Img": "Invia a Immagine a Immagine",
+        "sendToUnifiedCanvas": "Invia a Tela Unificata",
         "downloadImage": "Scarica l'immagine",
         "usePrompt": "Usa Prompt",
         "useSeed": "Usa Seme",
@@ -523,24 +508,13 @@
             "incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo #{{number}} non è compatibile con il modello principale.",
             "missingNodeTemplate": "Modello di nodo mancante",
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante",
-            "missingFieldTemplate": "Modello di campo mancante",
-            "imageNotProcessedForControlAdapter": "L'immagine dell'adattatore di controllo #{{number}} non è stata elaborata",
-            "layer": {
-                "initialImageNoImageSelected": "Nessuna immagine iniziale selezionata",
-                "t2iAdapterIncompatibleDimensions": "L'adattatore T2I richiede che la dimensione dell'immagine sia un multiplo di {{multiple}}",
-                "controlAdapterNoModelSelected": "Nessun modello di Adattatore di Controllo selezionato",
-                "controlAdapterIncompatibleBaseModel": "Il modello base dell'adattatore di controllo non è compatibile",
-                "controlAdapterNoImageSelected": "Nessuna immagine dell'adattatore di controllo selezionata",
-                "controlAdapterImageNotProcessed": "Immagine dell'adattatore di controllo non elaborata",
-                "ipAdapterNoModelSelected": "Nessun adattatore IP selezionato",
-                "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
-                "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
-                "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
-                "rgNoRegion": "Nessuna regione selezionata"
-            }
+            "missingFieldTemplate": "Modello di campo mancante"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
+        "iterationsWithCount_one": "{{count}} Iterazione",
+        "iterationsWithCount_many": "{{count}} Iterazioni",
+        "iterationsWithCount_other": "{{count}} Iterazioni",
         "isAllowedToUpscale": {
             "useX2Model": "L'immagine è troppo grande per l'ampliamento con il modello x4, utilizza il modello x2",
             "tooLarge": "L'immagine è troppo grande per l'ampliamento, seleziona un'immagine più piccola"
@@ -560,10 +534,7 @@
         "infillMosaicMinColor": "Colore minimo",
         "infillMosaicMaxColor": "Colore massimo",
         "infillMosaicTileHeight": "Altezza piastrella",
-        "infillColorValue": "Colore di riempimento",
-        "globalSettings": "Impostazioni globali",
-        "globalPositivePromptPlaceholder": "Prompt positivo globale",
-        "globalNegativePromptPlaceholder": "Prompt negativo globale"
+        "infillColorValue": "Colore di riempimento"
     },
     "settings": {
         "models": "Modelli",
@@ -588,7 +559,7 @@
         "intermediatesCleared_one": "Cancellata {{count}} immagine intermedia",
         "intermediatesCleared_many": "Cancellate {{count}} immagini intermedie",
         "intermediatesCleared_other": "Cancellate {{count}} immagini intermedie",
-        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato della Tela e degli Adattatori di Controllo.",
+        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato di Tela Unificata e ControlNet.",
         "intermediatesClearedFailed": "Problema con la cancellazione delle immagini intermedie",
         "clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
         "clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
@@ -604,8 +575,8 @@
         "imageCopied": "Immagine copiata",
         "imageNotLoadedDesc": "Impossibile trovare l'immagine",
         "canvasMerged": "Tela unita",
-        "sentToImageToImage": "Inviato a Generazione da immagine",
-        "sentToUnifiedCanvas": "Inviato alla Tela",
+        "sentToImageToImage": "Inviato a Immagine a Immagine",
+        "sentToUnifiedCanvas": "Inviato a Tela Unificata",
         "parametersNotSet": "Parametri non impostati",
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "serverError": "Errore del Server",
@@ -824,7 +795,7 @@
         "float": "In virgola mobile",
         "currentImageDescription": "Visualizza l'immagine corrente nell'editor dei nodi",
         "fieldTypesMustMatch": "I tipi di campo devono corrispondere",
-        "edge": "Collegamento",
+        "edge": "Bordo",
         "currentImage": "Immagine corrente",
         "integer": "Numero Intero",
         "inputMayOnlyHaveOneConnection": "L'ingresso può avere solo una connessione",
@@ -837,8 +808,8 @@
         "unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi",
         "addLinearView": "Aggiungi alla vista Lineare",
         "unknownErrorValidatingWorkflow": "Errore sconosciuto durante la convalida del flusso di lavoro",
-        "collectionFieldType": "{{name}} (Raccolta)",
-        "collectionOrScalarFieldType": "{{name}} (Singola o Raccolta)",
+        "collectionFieldType": "{{name}} Raccolta",
+        "collectionOrScalarFieldType": "{{name}} Raccolta|Scalare",
         "nodeVersion": "Versione Nodo",
         "inputFieldTypeParseError": "Impossibile analizzare il tipo di campo di input {{node}}.{{field}} ({{message}})",
         "unsupportedArrayItemType": "Tipo di elemento dell'array non supportato \"{{type}}\"",
@@ -874,15 +845,7 @@
         "resetToDefaultValue": "Ripristina il valore predefinito",
         "noFieldsViewMode": "Questo flusso di lavoro non ha campi selezionati da visualizzare. Visualizza il flusso di lavoro completo per configurare i valori.",
         "edit": "Modifica",
-        "graph": "Grafico",
-        "showEdgeLabelsHelp": "Mostra etichette sui collegamenti, che indicano i nodi collegati",
-        "showEdgeLabels": "Mostra le etichette del collegamento",
-        "cannotMixAndMatchCollectionItemTypes": "Impossibile combinare e abbinare i tipi di elementi della raccolta",
-        "noGraph": "Nessun grafico",
-        "missingNode": "Nodo di invocazione mancante",
-        "missingInvocationTemplate": "Modello di invocazione mancante",
-        "missingFieldTemplate": "Modello di campo mancante",
-        "singleFieldType": "{{name}} (Singola)"
+        "graph": "Grafico"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -959,7 +922,7 @@
         "colorMapTileSize": "Dimensione piastrella",
         "mediapipeFaceDescription": "Rilevamento dei volti tramite Mediapipe",
         "hedDescription": "Rilevamento dei bordi nidificati olisticamente",
-        "setControlImageDimensions": "Copia le dimensioni in L/A (ottimizza per il modello)",
+        "setControlImageDimensions": "Imposta le dimensioni dell'immagine di controllo su L/A",
         "maxFaces": "Numero massimo di volti",
         "addT2IAdapter": "Aggiungi $t(common.t2iAdapter)",
         "addControlNet": "Aggiungi $t(common.controlNet)",
@@ -988,17 +951,12 @@
         "mediapipeFace": "Mediapipe Volto",
         "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
         "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
-        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision",
-        "ipAdapterMethod": "Metodo",
-        "full": "Completo",
-        "composition": "Solo la composizione",
-        "style": "Solo lo stile",
-        "beginEndStepPercentShort": "Inizio/Fine %",
-        "setControlImageDimensionsForce": "Copia le dimensioni in L/A (ignora il modello)"
+        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
         "queueBack": "Aggiungi alla coda",
+        "queueCountPrediction": "{{promptsCount}} prompt × {{iterations}} iterazioni -> {{count}} generazioni",
         "queue": "Coda",
         "status": "Stato",
         "pruneSucceeded": "Rimossi {{item_count}} elementi completati dalla coda",
@@ -1035,7 +993,7 @@
         "cancelBatchSucceeded": "Lotto annullato",
         "clearTooltip": "Annulla e cancella tutti gli elementi",
         "current": "Attuale",
-        "pauseTooltip": "Sospendi l'elaborazione",
+        "pauseTooltip": "Sospende l'elaborazione",
         "failed": "Falliti",
         "cancelItem": "Annulla l'elemento",
         "next": "Prossimo",
@@ -1053,16 +1011,7 @@
         "graphFailedToQueue": "Impossibile mettere in coda il grafico",
         "batchFieldValues": "Valori Campi Lotto",
         "time": "Tempo",
-        "openQueue": "Apri coda",
-        "iterations_one": "Iterazione",
-        "iterations_many": "Iterazioni",
-        "iterations_other": "Iterazioni",
-        "prompts_one": "Prompt",
-        "prompts_many": "Prompt",
-        "prompts_other": "Prompt",
-        "generations_one": "Generazione",
-        "generations_many": "Generazioni",
-        "generations_other": "Generazioni"
+        "openQueue": "Apri coda"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1445,12 +1394,6 @@
             "paragraphs": [
                 "La dimensione del bordo del passaggio di coerenza."
             ]
-        },
-        "ipAdapterMethod": {
-            "heading": "Metodo",
-            "paragraphs": [
-                "Metodo con cui applicare l'adattatore IP corrente."
-            ]
         }
     },
     "sdxl": {
@@ -1579,55 +1522,5 @@
         "compatibleEmbeddings": "Incorporamenti compatibili",
         "addPromptTrigger": "Aggiungi Trigger nel prompt",
         "noMatchingTriggers": "Nessun Trigger corrispondente"
-    },
-    "controlLayers": {
-        "opacityFilter": "Filtro opacità",
-        "deleteAll": "Cancella tutto",
-        "addLayer": "Aggiungi Livello",
-        "moveToFront": "Sposta in primo piano",
-        "moveToBack": "Sposta in fondo",
-        "moveForward": "Sposta avanti",
-        "moveBackward": "Sposta indietro",
-        "brushSize": "Dimensioni del pennello",
-        "globalMaskOpacity": "Opacità globale della maschera",
-        "autoNegative": "Auto Negativo",
-        "deletePrompt": "Cancella il prompt",
-        "debugLayers": "Debug dei Livelli",
-        "rectangle": "Rettangolo",
-        "maskPreviewColor": "Colore anteprima maschera",
-        "addPositivePrompt": "Aggiungi $t(common.positivePrompt)",
-        "addNegativePrompt": "Aggiungi $t(common.negativePrompt)",
-        "addIPAdapter": "Aggiungi $t(common.ipAdapter)",
-        "regionalGuidance": "Guida regionale",
-        "regionalGuidanceLayer": "$t(unifiedCanvas.layer) $t(controlLayers.regionalGuidance)",
-        "opacity": "Opacità",
-        "globalControlAdapter": "$t(controlnet.controlAdapter_one) Globale",
-        "globalControlAdapterLayer": "$t(controlnet.controlAdapter_one) - $t(unifiedCanvas.layer) Globale",
-        "globalIPAdapter": "$t(common.ipAdapter) Globale",
-        "globalIPAdapterLayer": "$t(common.ipAdapter) - $t(unifiedCanvas.layer) Globale",
-        "globalInitialImage": "Immagine iniziale",
-        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) - $t(unifiedCanvas.layer) Globale",
-        "clearProcessor": "Cancella processore",
-        "resetProcessor": "Ripristina il processore alle impostazioni predefinite",
-        "noLayersAdded": "Nessun livello aggiunto",
-        "resetRegion": "Reimposta la regione",
-        "controlLayers": "Livelli di controllo",
-        "layers_one": "Livello",
-        "layers_many": "Livelli",
-        "layers_other": "Livelli"
-    },
-    "ui": {
-        "tabs": {
-            "generation": "Generazione",
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "canvas": "Tela",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "workflows": "Flussi di lavoro",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "models": "Modelli",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Coda",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)"
-        }
     }
 }

invokeai/frontend/web/public/locales/ja.json

@@ -570,6 +570,7 @@
         "pauseSucceeded": "処理が一時停止されました",
         "queueFront": "キューの先頭へ追加",
         "queueBack": "キューに追加",
+        "queueCountPrediction": "{{promptsCount}} プロンプト × {{iterations}} イテレーション -> {{count}} 枚生成",
         "pause": "一時停止",
         "queue": "キュー",
         "pauseTooltip": "処理を一時停止",

invokeai/frontend/web/public/locales/ko.json

@@ -505,6 +505,7 @@
         "completed": "완성된",
         "queueBack": "Queue에 추가",
         "cancelFailed": "항목 취소 중 발생한 문제",
+        "queueCountPrediction": "Queue에 {{predicted}} 추가",
         "batchQueued": "Batch Queued",
         "pauseFailed": "프로세서 중지 중 발생한 문제",
         "clearFailed": "Queue 제거 중 발생한 문제",

invokeai/frontend/web/public/locales/nl.json

@@ -6,7 +6,7 @@
         "settingsLabel": "Instellingen",
         "img2img": "Afbeelding naar afbeelding",
         "unifiedCanvas": "Centraal canvas",
-        "nodes": "Werkstromen",
+        "nodes": "Werkstroom-editor",
         "upload": "Upload",
         "load": "Laad",
         "statusDisconnected": "Niet verbonden",
@@ -34,60 +34,7 @@
         "controlNet": "ControlNet",
         "imageFailedToLoad": "Kan afbeelding niet laden",
         "learnMore": "Meer informatie",
-        "advanced": "Uitgebreid",
-        "file": "Bestand",
-        "installed": "Geïnstalleerd",
-        "notInstalled": "Niet $t(common.installed)",
-        "simple": "Eenvoudig",
-        "somethingWentWrong": "Er ging iets mis",
-        "add": "Voeg toe",
-        "checkpoint": "Checkpoint",
-        "details": "Details",
-        "outputs": "Uitvoeren",
-        "save": "Bewaar",
-        "nextPage": "Volgende pagina",
-        "blue": "Blauw",
-        "alpha": "Alfa",
-        "red": "Rood",
-        "editor": "Editor",
-        "folder": "Map",
-        "format": "structuur",
-        "goTo": "Ga naar",
-        "template": "Sjabloon",
-        "input": "Invoer",
-        "loglevel": "Logboekniveau",
-        "safetensors": "Safetensors",
-        "saveAs": "Bewaar als",
-        "created": "Gemaakt",
-        "green": "Groen",
-        "tab": "Tab",
-        "positivePrompt": "Positieve prompt",
-        "negativePrompt": "Negatieve prompt",
-        "selected": "Geselecteerd",
-        "orderBy": "Sorteer op",
-        "prevPage": "Vorige pagina",
-        "beta": "Bèta",
-        "copyError": "$t(gallery.copy) Fout",
-        "toResolve": "Op te lossen",
-        "aboutDesc": "Gebruik je Invoke voor het werk? Kijk dan naar:",
-        "aboutHeading": "Creatieve macht voor jou",
-        "copy": "Kopieer",
-        "data": "Gegevens",
-        "or": "of",
-        "updated": "Bijgewerkt",
-        "outpaint": "outpainten",
-        "viewing": "Bekijken",
-        "viewingDesc": "Beoordeel afbeelding in een grote galerijweergave",
-        "editing": "Bewerken",
-        "editingDesc": "Bewerk op het canvas Stuurlagen",
-        "ai": "ai",
-        "inpaint": "inpainten",
-        "unknown": "Onbekend",
-        "delete": "Verwijder",
-        "direction": "Richting",
-        "error": "Fout",
-        "localSystem": "Lokaal systeem",
-        "unknownError": "Onbekende fout"
+        "advanced": "Uitgebreid"
     },
     "gallery": {
         "galleryImageSize": "Afbeeldingsgrootte",
@@ -363,41 +310,10 @@
         "modelSyncFailed": "Synchronisatie modellen mislukt",
         "modelDeleteFailed": "Model kon niet verwijderd worden",
         "convertingModelBegin": "Model aan het converteren. Even geduld.",
-        "predictionType": "Soort voorspelling",
+        "predictionType": "Soort voorspelling (voor Stable Diffusion 2.x-modellen en incidentele Stable Diffusion 1.x-modellen)",
         "advanced": "Uitgebreid",
         "modelType": "Soort model",
-        "vaePrecision": "Nauwkeurigheid VAE",
-        "loraTriggerPhrases": "LoRA-triggerzinnen",
-        "urlOrLocalPathHelper": "URL's zouden moeten wijzen naar een los bestand. Lokale paden kunnen wijzen naar een los bestand of map voor een individueel Diffusers-model.",
-        "modelName": "Modelnaam",
-        "path": "Pad",
-        "triggerPhrases": "Triggerzinnen",
-        "typePhraseHere": "Typ zin hier in",
-        "useDefaultSettings": "Gebruik standaardinstellingen",
-        "modelImageDeleteFailed": "Fout bij verwijderen modelafbeelding",
-        "modelImageUpdated": "Modelafbeelding bijgewerkt",
-        "modelImageUpdateFailed": "Fout bij bijwerken modelafbeelding",
-        "noMatchingModels": "Geen overeenkomende modellen",
-        "scanPlaceholder": "Pad naar een lokale map",
-        "noModelsInstalled": "Geen modellen geïnstalleerd",
-        "noModelsInstalledDesc1": "Installeer modellen met de",
-        "noModelSelected": "Geen model geselecteerd",
-        "starterModels": "Beginnermodellen",
-        "textualInversions": "Tekstuele omkeringen",
-        "upcastAttention": "Upcast-aandacht",
-        "uploadImage": "Upload afbeelding",
-        "mainModelTriggerPhrases": "Triggerzinnen hoofdmodel",
-        "urlOrLocalPath": "URL of lokaal pad",
-        "scanFolderHelper": "De map zal recursief worden ingelezen voor modellen. Dit kan enige tijd in beslag nemen voor erg grote mappen.",
-        "simpleModelPlaceholder": "URL of pad naar een lokaal pad of Diffusers-map",
-        "modelSettings": "Modelinstellingen",
-        "pathToConfig": "Pad naar configuratie",
-        "prune": "Snoei",
-        "pruneTooltip": "Snoei voltooide importeringen uit wachtrij",
-        "repoVariant": "Repovariant",
-        "scanFolder": "Lees map in",
-        "scanResults": "Resultaten inlezen",
-        "source": "Bron"
+        "vaePrecision": "Nauwkeurigheid VAE"
     },
     "parameters": {
         "images": "Afbeeldingen",
@@ -437,13 +353,13 @@
         "copyImage": "Kopieer afbeelding",
         "denoisingStrength": "Sterkte ontruisen",
         "scheduler": "Planner",
-        "seamlessXAxis": "Naadloze tegels in x-as",
-        "seamlessYAxis": "Naadloze tegels in y-as",
+        "seamlessXAxis": "X-as",
+        "seamlessYAxis": "Y-as",
         "clipSkip": "Overslaan CLIP",
         "negativePromptPlaceholder": "Negatieve prompt",
         "controlNetControlMode": "Aansturingsmodus",
         "positivePromptPlaceholder": "Positieve prompt",
-        "maskBlur": "Vervaging van masker",
+        "maskBlur": "Vervaag",
         "invoke": {
             "noNodesInGraph": "Geen knooppunten in graaf",
             "noModelSelected": "Geen model ingesteld",
@@ -453,25 +369,11 @@
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} invoer ontbreekt",
             "noControlImageForControlAdapter": "Controle-adapter #{{number}} heeft geen controle-afbeelding",
             "noModelForControlAdapter": "Control-adapter #{{number}} heeft geen model ingesteld staan.",
-            "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is niet compatibel met het hoofdmodel.",
+            "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is ongeldig in combinatie met het hoofdmodel.",
             "systemDisconnected": "Systeem is niet verbonden",
             "missingNodeTemplate": "Knooppuntsjabloon ontbreekt",
             "missingFieldTemplate": "Veldsjabloon ontbreekt",
-            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan",
-            "layer": {
-                "initialImageNoImageSelected": "geen initiële afbeelding geselecteerd",
-                "controlAdapterNoModelSelected": "geen controle-adaptermodel geselecteerd",
-                "controlAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor controle-adapter",
-                "controlAdapterNoImageSelected": "geen afbeelding voor controle-adapter geselecteerd",
-                "controlAdapterImageNotProcessed": "Afbeelding voor controle-adapter niet verwerkt",
-                "ipAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor IP-adapter",
-                "ipAdapterNoImageSelected": "geen afbeelding voor IP-adapter geselecteerd",
-                "rgNoRegion": "geen gebied geselecteerd",
-                "rgNoPromptsOrIPAdapters": "geen tekstprompts of IP-adapters",
-                "t2iAdapterIncompatibleDimensions": "T2I-adapter vereist een afbeelding met afmetingen met een veelvoud van 64",
-                "ipAdapterNoModelSelected": "geen IP-adapter geselecteerd"
-            },
-            "imageNotProcessedForControlAdapter": "De afbeelding van controle-adapter #{{number}} is niet verwerkt"
+            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Afbeelding is te groot om te vergroten met het x4-model. Gebruik hiervoor het x2-model",
@@ -481,26 +383,9 @@
         "useCpuNoise": "Gebruik CPU-ruis",
         "imageActions": "Afbeeldingshandeling",
         "iterations": "Iteraties",
-        "coherenceMode": "Modus",
-        "infillColorValue": "Vulkleur",
-        "remixImage": "Meng afbeelding opnieuw",
-        "setToOptimalSize": "Optimaliseer grootte voor het model",
-        "setToOptimalSizeTooSmall": "$t(parameters.setToOptimalSize) (is mogelijk te klein)",
-        "aspect": "Beeldverhouding",
-        "infillMosaicTileWidth": "Breedte tegel",
-        "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (is mogelijk te groot)",
-        "lockAspectRatio": "Zet beeldverhouding vast",
-        "infillMosaicTileHeight": "Hoogte tegel",
-        "globalNegativePromptPlaceholder": "Globale negatieve prompt",
-        "globalPositivePromptPlaceholder": "Globale positieve prompt",
-        "useSize": "Gebruik grootte",
-        "swapDimensions": "Wissel afmetingen om",
-        "globalSettings": "Globale instellingen",
-        "coherenceEdgeSize": "Randgrootte",
-        "coherenceMinDenoise": "Min. ontruising",
-        "infillMosaicMinColor": "Min. kleur",
-        "infillMosaicMaxColor": "Max. kleur",
-        "cfgRescaleMultiplier": "Vermenigvuldiger voor CFG-herschaling"
+        "iterationsWithCount_one": "{{count}} iteratie",
+        "iterationsWithCount_other": "{{count}} iteraties",
+        "coherenceMode": "Modus"
     },
     "settings": {
         "models": "Modellen",
@@ -527,12 +412,7 @@
         "intermediatesCleared_one": "{{count}} tussentijdse afbeelding gewist",
         "intermediatesCleared_other": "{{count}} tussentijdse afbeeldingen gewist",
         "clearIntermediatesDesc1": "Als je tussentijdse afbeeldingen wist, dan wordt de staat hersteld van je canvas en van ControlNet.",
-        "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen",
-        "clearIntermediatesDisabled": "Wachtrij moet leeg zijn om tussentijdse afbeeldingen te kunnen leegmaken",
-        "enableInformationalPopovers": "Schakel informatieve hulpballonnen in",
-        "enableInvisibleWatermark": "Schakel onzichtbaar watermerk in",
-        "enableNSFWChecker": "Schakel NSFW-controle in",
-        "reloadingIn": "Opnieuw laden na"
+        "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen"
     },
     "toast": {
         "uploadFailed": "Upload mislukt",
@@ -547,8 +427,8 @@
         "connected": "Verbonden met server",
         "canceled": "Verwerking geannuleerd",
         "uploadFailedInvalidUploadDesc": "Moet een enkele PNG- of JPEG-afbeelding zijn",
-        "parameterNotSet": "{{parameter}} niet ingesteld",
-        "parameterSet": "{{parameter}} ingesteld",
+        "parameterNotSet": "Parameter niet ingesteld",
+        "parameterSet": "Instellen parameters",
         "problemCopyingImage": "Kan Afbeelding Niet Kopiëren",
         "baseModelChangedCleared_one": "Basismodel is gewijzigd: {{count}} niet-compatibel submodel weggehaald of uitgeschakeld",
         "baseModelChangedCleared_other": "Basismodel is gewijzigd: {{count}} niet-compatibele submodellen weggehaald of uitgeschakeld",
@@ -565,11 +445,11 @@
         "maskSavedAssets": "Masker bewaard in Assets",
         "problemDownloadingCanvas": "Fout bij downloaden van canvas",
         "problemMergingCanvas": "Fout bij samenvoegen canvas",
-        "setCanvasInitialImage": "Initiële canvasafbeelding ingesteld",
+        "setCanvasInitialImage": "Ingesteld als initiële canvasafbeelding",
         "imageUploaded": "Afbeelding geüpload",
         "addedToBoard": "Toegevoegd aan bord",
         "workflowLoaded": "Werkstroom geladen",
-        "modelAddedSimple": "Model toegevoegd aan wachtrij",
+        "modelAddedSimple": "Model toegevoegd",
         "problemImportingMaskDesc": "Kan masker niet exporteren",
         "problemCopyingCanvas": "Fout bij kopiëren canvas",
         "problemSavingCanvas": "Fout bij bewaren canvas",
@@ -581,18 +461,7 @@
         "maskSentControlnetAssets": "Masker gestuurd naar ControlNet en Assets",
         "canvasSavedGallery": "Canvas bewaard in galerij",
         "imageUploadFailed": "Fout bij uploaden afbeelding",
-        "problemImportingMask": "Fout bij importeren masker",
-        "workflowDeleted": "Werkstroom verwijderd",
-        "invalidUpload": "Ongeldige upload",
-        "uploadInitialImage": "Initiële afbeelding uploaden",
-        "setAsCanvasInitialImage": "Ingesteld als initiële afbeelding voor canvas",
-        "problemRetrievingWorkflow": "Fout bij ophalen van werkstroom",
-        "parameters": "Parameters",
-        "modelImportCanceled": "Importeren model geannuleerd",
-        "problemDeletingWorkflow": "Fout bij verwijderen van werkstroom",
-        "prunedQueue": "Wachtrij gesnoeid",
-        "problemDownloadingImage": "Fout bij downloaden afbeelding",
-        "resetInitialImage": "Initiële afbeelding hersteld"
+        "problemImportingMask": "Fout bij importeren masker"
     },
     "tooltip": {
         "feature": {
@@ -666,11 +535,7 @@
         "showOptionsPanel": "Toon zijscherm",
         "menu": "Menu",
         "showGalleryPanel": "Toon deelscherm Galerij",
-        "loadMore": "Laad meer",
-        "about": "Over",
-        "mode": "Modus",
-        "resetUI": "$t(accessibility.reset) UI",
-        "createIssue": "Maak probleem aan"
+        "loadMore": "Laad meer"
     },
     "nodes": {
         "zoomOutNodes": "Uitzoomen",
@@ -684,7 +549,7 @@
         "loadWorkflow": "Laad werkstroom",
         "downloadWorkflow": "Download JSON van werkstroom",
         "scheduler": "Planner",
-        "missingTemplate": "Ongeldig knooppunt: knooppunt {{node}} van het soort {{type}} heeft een ontbrekend sjabloon (niet geïnstalleerd?)",
+        "missingTemplate": "Ontbrekende sjabloon",
         "workflowDescription": "Korte beschrijving",
         "versionUnknown": " Versie onbekend",
         "noNodeSelected": "Geen knooppunt gekozen",
@@ -700,7 +565,7 @@
         "integer": "Geheel getal",
         "nodeTemplate": "Sjabloon knooppunt",
         "nodeOpacity": "Dekking knooppunt",
-        "unableToLoadWorkflow": "Fout bij laden werkstroom",
+        "unableToLoadWorkflow": "Kan werkstroom niet valideren",
         "snapToGrid": "Lijn uit op raster",
         "noFieldsLinearview": "Geen velden toegevoegd aan lineaire weergave",
         "nodeSearch": "Zoek naar knooppunten",
@@ -751,56 +616,11 @@
         "unknownField": "Onbekend veld",
         "colorCodeEdges": "Kleurgecodeerde randen",
         "unknownNode": "Onbekend knooppunt",
-        "mismatchedVersion": "Ongeldig knooppunt: knooppunt {{node}} van het soort {{type}} heeft een niet-overeenkomende versie (probeer het bij te werken?)",
+        "mismatchedVersion": "Heeft niet-overeenkomende versie",
         "addNodeToolTip": "Voeg knooppunt toe (Shift+A, spatie)",
         "loadingNodes": "Bezig met laden van knooppunten...",
         "snapToGridHelp": "Lijn knooppunten uit op raster bij verplaatsing",
-        "workflowSettings": "Instellingen werkstroomeditor",
-        "addLinearView": "Voeg toe aan lineaire weergave",
-        "nodePack": "Knooppuntpakket",
-        "unknownInput": "Onbekende invoer: {{name}}",
-        "sourceNodeFieldDoesNotExist": "Ongeldige rand: bron-/uitvoerveld {{node}}.{{field}} bestaat niet",
-        "collectionFieldType": "Verzameling {{name}}",
-        "deletedInvalidEdge": "Ongeldige hoek {{source}} -> {{target}} verwijderd",
-        "graph": "Grafiek",
-        "targetNodeDoesNotExist": "Ongeldige rand: doel-/invoerknooppunt {{node}} bestaat niet",
-        "resetToDefaultValue": "Herstel naar standaardwaarden",
-        "editMode": "Bewerk in Werkstroom-editor",
-        "showEdgeLabels": "Toon randlabels",
-        "showEdgeLabelsHelp": "Toon labels aan randen, waarmee de verbonden knooppunten mee worden aangegeven",
-        "clearWorkflowDesc2": "Je huidige werkstroom heeft niet-bewaarde wijzigingen.",
-        "unableToParseFieldType": "fout bij bepalen soort veld",
-        "sourceNodeDoesNotExist": "Ongeldige rand: bron-/uitvoerknooppunt {{node}} bestaat niet",
-        "unsupportedArrayItemType": "niet-ondersteunde soort van het array-onderdeel \"{{type}}\"",
-        "targetNodeFieldDoesNotExist": "Ongeldige rand: doel-/invoerveld {{node}}.{{field}} bestaat niet",
-        "reorderLinearView": "Herorden lineaire weergave",
-        "newWorkflowDesc": "Een nieuwe werkstroom aanmaken?",
-        "collectionOrScalarFieldType": "Verzameling|scalair {{name}}",
-        "newWorkflow": "Nieuwe werkstroom",
-        "unknownErrorValidatingWorkflow": "Onbekende fout bij valideren werkstroom",
-        "unsupportedAnyOfLength": "te veel union-leden ({{count}})",
-        "unknownOutput": "Onbekende uitvoer: {{name}}",
-        "viewMode": "Gebruik in lineaire weergave",
-        "unableToExtractSchemaNameFromRef": "fout bij het extraheren van de schemanaam via de ref",
-        "unsupportedMismatchedUnion": "niet-overeenkomende soort CollectionOrScalar met basissoorten {{firstType}} en {{secondType}}",
-        "unknownNodeType": "Onbekend soort knooppunt",
-        "edit": "Bewerk",
-        "updateAllNodes": "Werk knooppunten bij",
-        "allNodesUpdated": "Alle knooppunten bijgewerkt",
-        "nodeVersion": "Knooppuntversie",
-        "newWorkflowDesc2": "Je huidige werkstroom heeft niet-bewaarde wijzigingen.",
-        "clearWorkflow": "Maak werkstroom leeg",
-        "clearWorkflowDesc": "Deze werkstroom leegmaken en met een nieuwe beginnen?",
-        "inputFieldTypeParseError": "Fout bij bepalen van het soort invoerveld {{node}}.{{field}} ({{message}})",
-        "outputFieldTypeParseError": "Fout bij het bepalen van het soort uitvoerveld {{node}}.{{field}} ({{message}})",
-        "unableToExtractEnumOptions": "fout bij extraheren enumeratie-opties",
-        "unknownFieldType": "Soort $t(nodes.unknownField): {{type}}",
-        "unableToGetWorkflowVersion": "Fout bij ophalen schemaversie van werkstroom",
-        "betaDesc": "Deze uitvoering is in bèta. Totdat deze stabiel is kunnen er wijzigingen voorkomen gedurende app-updates die zaken kapotmaken. We zijn van plan om deze uitvoering op lange termijn te gaan ondersteunen.",
-        "prototypeDesc": "Deze uitvoering is een prototype. Er kunnen wijzigingen voorkomen gedurende app-updates die zaken kapotmaken. Deze kunnen op een willekeurig moment verwijderd worden.",
-        "noFieldsViewMode": "Deze werkstroom heeft geen geselecteerde velden om te tonen. Bekijk de volledige werkstroom om de waarden te configureren.",
-        "unableToUpdateNodes_one": "Fout bij bijwerken van {{count}} knooppunt",
-        "unableToUpdateNodes_other": "Fout bij bijwerken van {{count}} knooppunten"
+        "workflowSettings": "Instellingen werkstroomeditor"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -873,28 +693,9 @@
         "canny": "Canny",
         "depthZoeDescription": "Genereer diepteblad via Zoe",
         "hedDescription": "Herkenning van holistisch-geneste randen",
-        "setControlImageDimensions": "Kopieer grootte naar B/H (optimaliseer voor model)",
+        "setControlImageDimensions": "Stel afmetingen controle-afbeelding in op B/H",
         "scribble": "Krabbel",
-        "maxFaces": "Max. gezichten",
-        "dwOpenpose": "DW Openpose",
-        "depthAnything": "Depth Anything",
-        "base": "Basis",
-        "hands": "Handen",
-        "selectCLIPVisionModel": "Selecteer een CLIP Vision-model",
-        "modelSize": "Modelgrootte",
-        "small": "Klein",
-        "large": "Groot",
-        "resizeSimple": "Wijzig grootte (eenvoudig)",
-        "beginEndStepPercentShort": "Begin-/eind-%",
-        "depthAnythingDescription": "Genereren dieptekaart d.m.v. de techniek Depth Anything",
-        "face": "Gezicht",
-        "body": "Lichaam",
-        "dwOpenposeDescription": "Schatting menselijke pose d.m.v. DW Openpose",
-        "ipAdapterMethod": "Methode",
-        "full": "Volledig",
-        "style": "Alleen stijl",
-        "composition": "Alleen samenstelling",
-        "setControlImageDimensionsForce": "Kopieer grootte naar B/H (negeer model)"
+        "maxFaces": "Max. gezichten"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -907,10 +708,7 @@
         "maxPrompts": "Max. prompts",
         "promptsWithCount_one": "{{count}} prompt",
         "promptsWithCount_other": "{{count}} prompts",
-        "dynamicPrompts": "Dynamische prompts",
-        "showDynamicPrompts": "Toon dynamische prompts",
-        "loading": "Genereren van dynamische prompts...",
-        "promptsPreview": "Voorvertoning prompts"
+        "dynamicPrompts": "Dynamische prompts"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -923,7 +721,7 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "De planner gebruikt gedurende het genereringsproces."
+                "De planner bepaalt hoe ruis per iteratie wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model."
             ],
             "heading": "Planner"
         },
@@ -1010,8 +808,8 @@
         },
         "clipSkip": {
             "paragraphs": [
-                "Aantal over te slaan CLIP-modellagen.",
-                "Bepaalde modellen zijn beter geschikt met bepaalde Overslaan CLIP-instellingen."
+                "Kies hoeveel CLIP-modellagen je wilt overslaan.",
+                "Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen."
             ],
             "heading": "Overslaan CLIP"
         },
@@ -1142,6 +940,7 @@
         "completed": "Voltooid",
         "queueBack": "Voeg toe aan wachtrij",
         "cancelFailed": "Fout bij annuleren onderdeel",
+        "queueCountPrediction": "Voeg {{predicted}} toe aan wachtrij",
         "batchQueued": "Reeks in wachtrij geplaatst",
         "pauseFailed": "Fout bij onderbreken verwerker",
         "clearFailed": "Fout bij wissen van wachtrij",
@@ -1195,26 +994,17 @@
         "denoisingStrength": "Sterkte ontruising",
         "refinermodel": "Verfijningsmodel",
         "posAestheticScore": "Positieve esthetische score",
-        "concatPromptStyle": "Koppelen van prompt en stijl",
+        "concatPromptStyle": "Plak prompt- en stijltekst aan elkaar",
         "loading": "Bezig met laden...",
         "steps": "Stappen",
-        "posStylePrompt": "Positieve-stijlprompt",
-        "freePromptStyle": "Handmatige stijlprompt",
-        "refinerSteps": "Aantal stappen verfijner"
+        "posStylePrompt": "Positieve-stijlprompt"
     },
     "models": {
         "noMatchingModels": "Geen overeenkomend modellen",
         "loading": "bezig met laden",
         "noMatchingLoRAs": "Geen overeenkomende LoRA's",
         "noModelsAvailable": "Geen modellen beschikbaar",
-        "selectModel": "Kies een model",
-        "noLoRAsInstalled": "Geen LoRA's geïnstalleerd",
-        "noRefinerModelsInstalled": "Geen SDXL-verfijningsmodellen geïnstalleerd",
-        "defaultVAE": "Standaard-VAE",
-        "lora": "LoRA",
-        "esrganModel": "ESRGAN-model",
-        "addLora": "Voeg LoRA toe",
-        "concepts": "Concepten"
+        "selectModel": "Kies een model"
     },
     "boards": {
         "autoAddBoard": "Voeg automatisch bord toe",
@@ -1232,13 +1022,7 @@
         "downloadBoard": "Download bord",
         "changeBoard": "Wijzig bord",
         "loading": "Bezig met laden...",
-        "clearSearch": "Maak zoekopdracht leeg",
-        "deleteBoard": "Verwijder bord",
-        "deleteBoardAndImages": "Verwijder bord en afbeeldingen",
-        "deleteBoardOnly": "Verwijder alleen bord",
-        "deletedBoardsCannotbeRestored": "Verwijderde borden kunnen niet worden hersteld",
-        "movingImagesToBoard_one": "Verplaatsen van {{count}} afbeelding naar bord:",
-        "movingImagesToBoard_other": "Verplaatsen van {{count}} afbeeldingen naar bord:"
+        "clearSearch": "Maak zoekopdracht leeg"
     },
     "invocationCache": {
         "disable": "Schakel uit",
@@ -1255,39 +1039,5 @@
         "clear": "Wis",
         "maxCacheSize": "Max. grootte cache",
         "cacheSize": "Grootte cache"
-    },
-    "accordions": {
-        "generation": {
-            "title": "Genereren"
-        },
-        "image": {
-            "title": "Afbeelding"
-        },
-        "advanced": {
-            "title": "Geavanceerd",
-            "options": "$t(accordions.advanced.title) Opties"
-        },
-        "control": {
-            "title": "Besturing"
-        },
-        "compositing": {
-            "title": "Samenstellen",
-            "coherenceTab": "Coherentiefase",
-            "infillTab": "Invullen"
-        }
-    },
-    "hrf": {
-        "upscaleMethod": "Opschaalmethode",
-        "metadata": {
-            "strength": "Sterkte oplossing voor hoge resolutie",
-            "method": "Methode oplossing voor hoge resolutie",
-            "enabled": "Oplossing voor hoge resolutie ingeschakeld"
-        },
-        "hrf": "Oplossing voor hoge resolutie",
-        "enableHrf": "Schakel oplossing in voor hoge resolutie"
-    },
-    "prompt": {
-        "addPromptTrigger": "Voeg prompttrigger toe",
-        "compatibleEmbeddings": "Compatibele embeddings"
     }
 }

invokeai/frontend/web/public/locales/ru.json

@@ -76,18 +76,7 @@
         "localSystem": "Локальная система",
         "aboutDesc": "Используя Invoke для работы? Проверьте это:",
         "add": "Добавить",
-        "loglevel": "Уровень логов",
-        "beta": "Бета",
-        "selected": "Выбрано",
-        "positivePrompt": "Позитивный запрос",
-        "negativePrompt": "Негативный запрос",
-        "editor": "Редактор",
-        "goTo": "Перейти к",
-        "tab": "Вкладка",
-        "viewing": "Просмотр",
-        "editing": "Редактирование",
-        "viewingDesc": "Просмотр изображений в режиме большой галереи",
-        "editingDesc": "Редактировать на холсте слоёв управления"
+        "loglevel": "Уровень логов"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -98,8 +87,8 @@
         "deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
         "deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
         "deleteImage_one": "Удалить изображение",
-        "deleteImage_few": "Удалить {{count}} изображения",
-        "deleteImage_many": "Удалить {{count}} изображений",
+        "deleteImage_few": "",
+        "deleteImage_many": "",
         "assets": "Ресурсы",
         "autoAssignBoardOnClick": "Авто-назначение доски по клику",
         "deleteSelection": "Удалить выделенное",
@@ -347,10 +336,6 @@
         "remixImage": {
             "desc": "Используйте все параметры, кроме сида из текущего изображения",
             "title": "Ремикс изображения"
-        },
-        "toggleViewer": {
-            "title": "Переключить просмотр изображений",
-            "desc": "Переключение между средством просмотра изображений и рабочей областью для текущей вкладки."
         }
     },
     "modelManager": {
@@ -527,8 +512,7 @@
             "missingNodeTemplate": "Отсутствует шаблон узла",
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
-            "invoke": "Создать",
-            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
+            "invoke": "Создать"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -539,6 +523,9 @@
         "useCpuNoise": "Использовать шум CPU",
         "imageActions": "Действия с изображениями",
         "iterations": "Кол-во",
+        "iterationsWithCount_one": "{{count}} Интеграция",
+        "iterationsWithCount_few": "{{count}} Итерации",
+        "iterationsWithCount_many": "{{count}} Итераций",
         "useSize": "Использовать размер",
         "coherenceMode": "Режим",
         "aspect": "Соотношение",
@@ -554,10 +541,7 @@
         "infillMosaicTileHeight": "Высота плиток",
         "infillMosaicMinColor": "Мин цвет",
         "infillMosaicMaxColor": "Макс цвет",
-        "infillColorValue": "Цвет заливки",
-        "globalSettings": "Глобальные настройки",
-        "globalNegativePromptPlaceholder": "Глобальный негативный запрос",
-        "globalPositivePromptPlaceholder": "Глобальный запрос"
+        "infillColorValue": "Цвет заливки"
     },
     "settings": {
         "models": "Модели",
@@ -722,9 +706,7 @@
         "coherenceModeBoxBlur": "коробчатое размытие",
         "discardCurrent": "Отбросить текущее",
         "invertBrushSizeScrollDirection": "Инвертировать прокрутку для размера кисти",
-        "initialFitImageSize": "Подогнать размер изображения при перебросе",
-        "hideBoundingBox": "Скрыть ограничительную рамку",
-        "showBoundingBox": "Показать ограничительную рамку"
+        "initialFitImageSize": "Подогнать размер изображения при перебросе"
     },
     "accessibility": {
         "uploadImage": "Загрузить изображение",
@@ -867,10 +849,7 @@
         "editMode": "Открыть в редакторе узлов",
         "resetToDefaultValue": "Сбросить к стандартному значкнию",
         "edit": "Редактировать",
-        "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений.",
-        "graph": "График",
-        "showEdgeLabels": "Показать метки на ребрах",
-        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы"
+        "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений."
     },
     "controlnet": {
         "amult": "a_mult",
@@ -938,8 +917,8 @@
         "lineartAnime": "Контурный рисунок в стиле аниме",
         "mediapipeFaceDescription": "Обнаружение лиц с помощью Mediapipe",
         "hedDescription": "Целостное обнаружение границ",
-        "setControlImageDimensions": "Скопируйте размер в Ш/В (оптимизируйте для модели)",
-        "scribble": "Штрихи",
+        "setControlImageDimensions": "Установите размеры контрольного изображения на Ш/В",
+        "scribble": "каракули",
         "maxFaces": "Макс Лица",
         "mlsdDescription": "Минималистичный детектор отрезков линии",
         "resizeSimple": "Изменить размер (простой)",
@@ -954,18 +933,7 @@
         "small": "Маленький",
         "body": "Тело",
         "hands": "Руки",
-        "selectCLIPVisionModel": "Выбрать модель CLIP Vision",
-        "ipAdapterMethod": "Метод",
-        "full": "Всё",
-        "mlsd": "M-LSD",
-        "h": "H",
-        "style": "Только стиль",
-        "dwOpenpose": "DW Openpose",
-        "pidi": "PIDI",
-        "composition": "Только композиция",
-        "hed": "HED",
-        "beginEndStepPercentShort": "Начало/конец %",
-        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)"
+        "selectCLIPVisionModel": "Выбрать модель CLIP Vision"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1344,12 +1312,6 @@
             "paragraphs": [
                 "Плавно укладывайте изображение вдоль вертикальной оси."
             ]
-        },
-        "ipAdapterMethod": {
-            "heading": "Метод",
-            "paragraphs": [
-                "Метод, с помощью которого применяется текущий IP-адаптер."
-            ]
         }
     },
     "metadata": {
@@ -1397,6 +1359,7 @@
         "completed": "Выполнено",
         "queueBack": "Добавить в очередь",
         "cancelFailed": "Проблема с отменой элемента",
+        "queueCountPrediction": "{{promptsCount}} запросов × {{iterations}} изображений -> {{count}} генераций",
         "batchQueued": "Пакетная очередь",
         "pauseFailed": "Проблема с приостановкой рендеринга",
         "clearFailed": "Проблема с очисткой очереди",
@@ -1512,11 +1475,7 @@
         "projectWorkflows": "Рабочие процессы проекта",
         "defaultWorkflows": "Стандартные рабочие процессы",
         "name": "Имя",
-        "noRecentWorkflows": "Нет последних рабочих процессов",
-        "loadWorkflow": "Рабочий процесс $t(common.load)",
-        "convertGraph": "Конвертировать график",
-        "loadFromGraph": "Загрузка рабочего процесса из графика",
-        "autoLayout": "Автоматическое расположение"
+        "noRecentWorkflows": "Нет последних рабочих процессов"
     },
     "hrf": {
         "enableHrf": "Включить исправление высокого разрешения",
@@ -1569,55 +1528,5 @@
         "addPromptTrigger": "Добавить триггер запроса",
         "compatibleEmbeddings": "Совместимые встраивания",
         "noMatchingTriggers": "Нет соответствующих триггеров"
-    },
-    "controlLayers": {
-        "moveToBack": "На задний план",
-        "moveForward": "Переместить вперёд",
-        "moveBackward": "Переместить назад",
-        "brushSize": "Размер кисти",
-        "controlLayers": "Слои управления",
-        "globalMaskOpacity": "Глобальная непрозрачность маски",
-        "autoNegative": "Авто негатив",
-        "deletePrompt": "Удалить запрос",
-        "resetRegion": "Сбросить регион",
-        "debugLayers": "Слои отладки",
-        "rectangle": "Прямоугольник",
-        "maskPreviewColor": "Цвет предпросмотра маски",
-        "addNegativePrompt": "Добавить $t(common.negativePrompt)",
-        "regionalGuidance": "Региональная точность",
-        "opacity": "Непрозрачность",
-        "globalControlAdapter": "Глобальный $t(controlnet.controlAdapter_one)",
-        "globalControlAdapterLayer": "Глобальный $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
-        "globalIPAdapter": "Глобальный $t(common.ipAdapter)",
-        "globalIPAdapterLayer": "Глобальный $t(common.ipAdapter) $t(unifiedCanvas.layer)",
-        "opacityFilter": "Фильтр непрозрачности",
-        "deleteAll": "Удалить всё",
-        "addLayer": "Добавить слой",
-        "moveToFront": "На передний план",
-        "addPositivePrompt": "Добавить $t(common.positivePrompt)",
-        "addIPAdapter": "Добавить $t(common.ipAdapter)",
-        "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "resetProcessor": "Сброс процессора по умолчанию",
-        "clearProcessor": "Чистый процессор",
-        "globalInitialImage": "Глобальное исходное изображение",
-        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "noLayersAdded": "Без слоев",
-        "layers_one": "Слой",
-        "layers_few": "Слоя",
-        "layers_many": "Слоев"
-    },
-    "ui": {
-        "tabs": {
-            "generation": "Генерация",
-            "canvas": "Холст",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "models": "Модели",
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "workflows": "Рабочие процессы",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Очередь"
-        }
     }
 }

invokeai/frontend/web/public/locales/zh_CN.json

@@ -66,7 +66,7 @@
         "saveAs": "保存为",
         "ai": "ai",
         "or": "或",
-        "aboutDesc": "使用 Invoke 工作？来看看：",
+        "aboutDesc": "使用 Invoke 工作？查看：",
         "add": "添加",
         "loglevel": "日志级别",
         "copy": "复制",
@@ -445,6 +445,7 @@
             "useX2Model": "图像太大，无法使用 x4 模型，使用 x2 模型作为替代",
             "tooLarge": "图像太大无法进行放大，请选择更小的图像"
         },
+        "iterationsWithCount_other": "{{count}} 次迭代生成",
         "cfgRescaleMultiplier": "CFG 重缩放倍数",
         "useSize": "使用尺寸",
         "setToOptimalSize": "优化模型大小",
@@ -852,6 +853,7 @@
         "pruneSucceeded": "从队列修剪 {{item_count}} 个已完成的项目",
         "notReady": "无法排队",
         "batchFailedToQueue": "批次加入队列失败",
+        "queueCountPrediction": "{{promptsCount}} 提示词 × {{iterations}} 迭代次数 -> {{count}} 次生成",
         "batchQueued": "加入队列的批次",
         "front": "前",
         "pruneTooltip": "修剪 {{item_count}} 个已完成的项目",

invokeai/frontend/web/scripts/typegen.js

@@ -1,4 +1,3 @@
-/* eslint-disable no-console */
 import fs from 'node:fs';
 
 import openapiTS from 'openapi-typescript';

invokeai/frontend/web/src/app/components/App.tsx

@@ -21,10 +21,10 @@ import i18n from 'i18n';
 import { size } from 'lodash-es';
 import { memo, useCallback, useEffect } from 'react';
 import { ErrorBoundary } from 'react-error-boundary';
-import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
 import PreselectedImage from './PreselectedImage';
+import Toaster from './Toaster';
 
 const DEFAULT_CONFIG = {};
 
@@ -46,7 +46,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
   useSocketIO();
   useGlobalModifiersInit();
   useGlobalHotkeys();
-  useGetOpenAPISchemaQuery();
 
   const { dropzone, isHandlingUpload, setIsHandlingUpload } = useFullscreenDropzone();
 
@@ -95,6 +94,7 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
       <DeleteImageModal />
       <ChangeBoardModal />
       <DynamicPromptsModal />
+      <Toaster />
       <PreselectedImage selectedImage={selectedImage} />
     </ErrorBoundary>
   );

invokeai/frontend/web/src/app/components/AppErrorBoundaryFallback.tsx

@@ -1,8 +1,5 @@
-import { Button, Flex, Heading, Image, Link, Text } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { toast } from 'features/toast/toast';
+import { Button, Flex, Heading, Link, Text, useToast } from '@invoke-ai/ui-library';
 import newGithubIssueUrl from 'new-github-issue-url';
-import InvokeLogoYellow from 'public/assets/images/invoke-symbol-ylw-lrg.svg';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold, PiArrowSquareOutBold, PiCopyBold } from 'react-icons/pi';
@@ -14,39 +11,31 @@ type Props = {
 };
 
 const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
+  const toast = useToast();
   const { t } = useTranslation();
-  const isLocal = useAppSelector((s) => s.config.isLocal);
 
   const handleCopy = useCallback(() => {
     const text = JSON.stringify(serializeError(error), null, 2);
     navigator.clipboard.writeText(`\`\`\`\n${text}\n\`\`\``);
     toast({
-      id: 'ERROR_COPIED',
-      title: t('toast.errorCopied'),
+      title: 'Error Copied',
     });
-  }, [error, t]);
+  }, [error, toast]);
 
-  const url = useMemo(() => {
-    if (isLocal) {
-      return newGithubIssueUrl({
+  const url = useMemo(
+    () =>
+      newGithubIssueUrl({
         user: 'invoke-ai',
         repo: 'InvokeAI',
         template: 'BUG_REPORT.yml',
         title: `[bug]: ${error.name}: ${error.message}`,
-      });
-    } else {
-      return 'https://support.invoke.ai/support/tickets/new';
-    }
-  }, [error.message, error.name, isLocal]);
-
+      }),
+    [error.message, error.name]
+  );
   return (
     <Flex layerStyle="body" w="100vw" h="100vh" alignItems="center" justifyContent="center" p={4}>
       <Flex layerStyle="first" flexDir="column" borderRadius="base" justifyContent="center" gap={8} p={16}>
-        <Flex alignItems="center" gap="2">
-          <Image src={InvokeLogoYellow} alt="invoke-logo" w="24px" h="24px" minW="24px" minH="24px" userSelect="none" />
-          <Heading fontSize="2xl">{t('common.somethingWentWrong')}</Heading>
-        </Flex>
-
+        <Heading>{t('common.somethingWentWrong')}</Heading>
         <Flex
           layerStyle="second"
           px={8}
@@ -68,9 +57,7 @@ const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
             {t('common.copyError')}
           </Button>
           <Link href={url} isExternal>
-            <Button leftIcon={<PiArrowSquareOutBold />}>
-              {isLocal ? t('accessibility.createIssue') : t('accessibility.submitSupportTicket')}
-            </Button>
+            <Button leftIcon={<PiArrowSquareOutBold />}>{t('accessibility.createIssue')}</Button>
           </Link>
         </Flex>
       </Flex>

invokeai/frontend/web/src/app/components/Toaster.ts

@@ -0,0 +1,44 @@
+import { useToast } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
+import type { MakeToastArg } from 'features/system/util/makeToast';
+import { makeToast } from 'features/system/util/makeToast';
+import { memo, useCallback, useEffect } from 'react';
+
+/**
+ * Logical component. Watches the toast queue and makes toasts when the queue is not empty.
+ * @returns null
+ */
+const Toaster = () => {
+  const dispatch = useAppDispatch();
+  const toastQueue = useAppSelector((s) => s.system.toastQueue);
+  const toast = useToast();
+  useEffect(() => {
+    toastQueue.forEach((t) => {
+      toast(t);
+    });
+    toastQueue.length > 0 && dispatch(clearToastQueue());
+  }, [dispatch, toast, toastQueue]);
+
+  return null;
+};
+
+/**
+ * Returns a function that can be used to make a toast.
+ * @example
+ * const toaster = useAppToaster();
+ * toaster('Hello world!');
+ * toaster({ title: 'Hello world!', status: 'success' });
+ * @returns A function that can be used to make a toast.
+ * @see makeToast
+ * @see MakeToastArg
+ * @see UseToastOptions
+ */
+export const useAppToaster = () => {
+  const dispatch = useAppDispatch();
+  const toaster = useCallback((arg: MakeToastArg) => dispatch(addToast(makeToast(arg))), [dispatch]);
+
+  return toaster;
+};
+
+export default memo(Toaster);

invokeai/frontend/web/src/app/hooks/useSocketIO.ts

@@ -67,8 +67,6 @@ export const useSocketIO = () => {
 
     if ($isDebugging.get() || import.meta.env.MODE === 'development') {
       window.$socketOptions = $socketOptions;
-      // This is only enabled manually for debugging, console is allowed.
-      /* eslint-disable-next-line no-console */
       console.log('Socket initialized', socket);
     }
 
@@ -77,8 +75,6 @@ export const useSocketIO = () => {
     return () => {
       if ($isDebugging.get() || import.meta.env.MODE === 'development') {
         window.$socketOptions = undefined;
-        // This is only enabled manually for debugging, console is allowed.
-        /* eslint-disable-next-line no-console */
         console.log('Socket teardown', socket);
       }
       socket.disconnect();

invokeai/frontend/web/src/app/logging/logger.ts

@@ -20,14 +20,15 @@ export type LoggerNamespace =
   | 'models'
   | 'config'
   | 'canvas'
-  | 'generation'
+  | 'txt2img'
+  | 'img2img'
   | 'nodes'
   | 'system'
   | 'socketio'
   | 'session'
   | 'queue'
   | 'dnd'
-  | 'controlLayers';
+  | 'regionalPrompts';
 
 export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 

invokeai/frontend/web/src/app/store/middleware/debugLoggerMiddleware.ts

@@ -1,6 +1,3 @@
-/* eslint-disable no-console */
-// This is only enabled manually for debugging, console is allowed.
-
 import type { Middleware, MiddlewareAPI } from '@reduxjs/toolkit';
 import { diff } from 'jsondiffpatch';
 

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,6 +1,7 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
 import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
@@ -24,6 +25,13 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     };
   }
 
+  if (nodeTemplatesBuilt.match(action)) {
+    return {
+      ...action,
+      payload: '<Node templates omitted>',
+    };
+  }
+
   if (socketGeneratorProgress.match(action)) {
     const sanitized = deepClone(action);
     if (sanitized.payload.data.progress_image) {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -16,7 +16,6 @@ import { addCanvasMaskSavedToGalleryListener } from 'app/store/middleware/listen
 import { addCanvasMaskToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet';
 import { addCanvasMergedListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMerged';
 import { addCanvasSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery';
-import { addControlAdapterPreprocessor } from 'app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor';
 import { addControlNetAutoProcessListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess';
 import { addControlNetImageProcessedListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed';
 import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas';
@@ -32,6 +31,7 @@ import { addImagesStarredListener } from 'app/store/middleware/listenerMiddlewar
 import { addImagesUnstarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesUnstarred';
 import { addImageToDeleteSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected';
 import { addImageUploadedFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageUploaded';
+import { addInitialImageSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/initialImageSelected';
 import { addModelSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelSelected';
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
@@ -41,10 +41,12 @@ import { addGeneratorProgressEventListener } from 'app/store/middleware/listener
 import { addGraphExecutionStateCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGraphExecutionStateComplete';
 import { addInvocationCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete';
 import { addInvocationErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError';
+import { addInvocationRetrievalErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationRetrievalError';
 import { addInvocationStartedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted';
 import { addModelInstallEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelInstall';
 import { addModelLoadEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelLoad';
 import { addSocketQueueItemStatusChangedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketQueueItemStatusChanged';
+import { addSessionRetrievalErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketSessionRetrievalError';
 import { addSocketSubscribedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketSubscribed';
 import { addSocketUnsubscribedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketUnsubscribed';
 import { addStagingAreaImageSavedListener } from 'app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved';
@@ -70,6 +72,9 @@ const startAppListening = listenerMiddleware.startListening as AppStartListening
 // Image uploaded
 addImageUploadedFulfilledListener(startAppListening);
 
+// Image selected
+addInitialImageSelectedListener(startAppListening);
+
 // Image deleted
 addRequestedSingleImageDeletionListener(startAppListening);
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
@@ -112,6 +117,8 @@ addSocketSubscribedEventListener(startAppListening);
 addSocketUnsubscribedEventListener(startAppListening);
 addModelLoadEventListener(startAppListening);
 addModelInstallEventListener(startAppListening);
+addSessionRetrievalErrorEventListener(startAppListening);
+addInvocationRetrievalErrorEventListener(startAppListening);
 addSocketQueueItemStatusChangedEventListener(startAppListening);
 addBulkDownloadListeners(startAppListening);
 
@@ -150,4 +157,3 @@ addUpscaleRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
-addControlAdapterPreprocessor(startAppListening);

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener.ts

@@ -8,7 +8,7 @@ import {
   resetCanvas,
   setInitialCanvasImage,
 } from 'features/canvas/store/canvasSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 
@@ -30,20 +30,22 @@ export const addCommitStagingAreaImageListener = (startAppListening: AppStartLis
         req.reset();
         if (canceled > 0) {
           log.debug(`Canceled ${canceled} canvas batches`);
-          toast({
-            id: 'CANCEL_BATCH_SUCCEEDED',
-            title: t('queue.cancelBatchSucceeded'),
-            status: 'success',
-          });
+          dispatch(
+            addToast({
+              title: t('queue.cancelBatchSucceeded'),
+              status: 'success',
+            })
+          );
         }
         dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
-        toast({
-          id: 'CANCEL_BATCH_FAILED',
-          title: t('queue.cancelBatchFailed'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('queue.cancelBatchFailed'),
+            status: 'error',
+          })
+        );
       }
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/batchEnqueued.ts

@@ -1,8 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
+import { toast } from 'common/util/toast';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
-import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -16,15 +16,18 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
       const arg = action.meta.arg.originalArgs;
       logger('queue').debug({ enqueueResult: parseify(response) }, 'Batch enqueued');
 
-      toast({
-        id: 'QUEUE_BATCH_SUCCEEDED',
-        title: t('queue.batchQueued'),
-        status: 'success',
-        description: t('queue.batchQueuedDesc', {
-          count: response.enqueued,
-          direction: arg.prepend ? t('queue.front') : t('queue.back'),
-        }),
-      });
+      if (!toast.isActive('batch-queued')) {
+        toast({
+          id: 'batch-queued',
+          title: t('queue.batchQueued'),
+          description: t('queue.batchQueuedDesc', {
+            count: response.enqueued,
+            direction: arg.prepend ? t('queue.front') : t('queue.back'),
+          }),
+          duration: 1000,
+          status: 'success',
+        });
+      }
     },
   });
 
@@ -37,10 +40,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
 
       if (!response) {
         toast({
-          id: 'QUEUE_BATCH_FAILED',
           title: t('queue.batchFailedToQueue'),
           status: 'error',
-          description: t('common.unknownError'),
+          description: 'Unknown Error',
         });
         logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
         return;
@@ -50,7 +52,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
       if (result.success) {
         result.data.data.detail.map((e) => {
           toast({
-            id: 'QUEUE_BATCH_FAILED',
+            id: 'batch-failed-to-queue',
             title: truncate(upperFirst(e.msg), { length: 128 }),
             status: 'error',
             description: truncate(
@@ -62,10 +64,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
         });
       } else if (response.status !== 403) {
         toast({
-          id: 'QUEUE_BATCH_FAILED',
           title: t('queue.batchFailedToQueue'),
-          status: 'error',
           description: t('common.unknownError'),
+          status: 'error',
         });
       }
       logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted.ts

@@ -1,9 +1,9 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { allLayersDeleted } from 'features/controlLayers/store/controlLayersSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppStartListening) => {
@@ -14,14 +14,19 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
 
       // Remove all deleted images from the UI
 
+      let wasInitialImageReset = false;
       let wasCanvasReset = false;
       let wasNodeEditorReset = false;
       let wereControlAdaptersReset = false;
-      let wereControlLayersReset = false;
 
-      const { canvas, nodes, controlAdapters, controlLayers } = getState();
+      const { generation, canvas, nodes, controlAdapters } = getState();
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
+        const imageUsage = getImageUsage(generation, canvas, nodes, controlAdapters, image_name);
+
+        if (imageUsage.isInitialImage && !wasInitialImageReset) {
+          dispatch(clearInitialImage());
+          wasInitialImageReset = true;
+        }
 
         if (imageUsage.isCanvasImage && !wasCanvasReset) {
           dispatch(resetCanvas());
@@ -37,11 +42,6 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
           dispatch(controlAdaptersReset());
           wereControlAdaptersReset = true;
         }
-
-        if (imageUsage.isControlLayerImage && !wereControlLayersReset) {
-          dispatch(allLayersDeleted());
-          wereControlLayersReset = true;
-        }
       });
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/bulkDownload.tsx

@@ -1,7 +1,8 @@
+import type { UseToastOptions } from '@invoke-ai/ui-library';
 import { ExternalLink } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { toast } from 'features/toast/toast';
+import { toast } from 'common/util/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 import {
@@ -27,6 +28,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
         // Show the response message if it exists, otherwise show the default message
         description: action.payload.response || t('gallery.bulkDownloadRequestedDesc'),
         duration: null,
+        isClosable: true,
       });
     },
   });
@@ -38,9 +40,9 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
       // There isn't any toast to update if we get this event.
       toast({
-        id: 'BULK_DOWNLOAD_REQUEST_FAILED',
         title: t('gallery.bulkDownloadRequestFailed'),
-        status: 'error',
+        status: 'success',
+        isClosable: true,
       });
     },
   });
@@ -63,7 +65,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
       // TODO(psyche): This URL may break in in some environments (e.g. Nvidia workbench) but we need to test it first
       const url = `/api/v1/images/download/${bulk_download_item_name}`;
 
-      toast({
+      const toastOptions: UseToastOptions = {
         id: bulk_download_item_name,
         title: t('gallery.bulkDownloadReady', 'Download ready'),
         status: 'success',
@@ -75,7 +77,14 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
           />
         ),
         duration: null,
-      });
+        isClosable: true,
+      };
+
+      if (toast.isActive(bulk_download_item_name)) {
+        toast.update(bulk_download_item_name, toastOptions);
+      } else {
+        toast(toastOptions);
+      }
     },
   });
 
@@ -86,13 +95,20 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
       const { bulk_download_item_name } = action.payload.data;
 
-      toast({
+      const toastOptions: UseToastOptions = {
         id: bulk_download_item_name,
         title: t('gallery.bulkDownloadFailed'),
         status: 'error',
         description: action.payload.data.error,
         duration: null,
-      });
+        isClosable: true,
+      };
+
+      if (toast.isActive(bulk_download_item_name)) {
+        toast.update(bulk_download_item_name, toastOptions);
+      } else {
+        toast(toastOptions);
+      }
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasCopiedToClipboard.ts

@@ -2,14 +2,14 @@ import { $logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
 import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
-import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 
 export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: canvasCopiedToClipboard,
-    effect: async (action, { getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
       const state = getState();
 
@@ -19,20 +19,22 @@ export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartLi
         copyBlobToClipboard(blob);
       } catch (err) {
         moduleLog.error(String(err));
-        toast({
-          id: 'CANVAS_COPY_FAILED',
-          title: t('toast.problemCopyingCanvas'),
-          description: t('toast.problemCopyingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemCopyingCanvas'),
+            description: t('toast.problemCopyingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
-      toast({
-        id: 'CANVAS_COPY_SUCCEEDED',
-        title: t('toast.canvasCopiedClipboard'),
-        status: 'success',
-      });
+      dispatch(
+        addToast({
+          title: t('toast.canvasCopiedClipboard'),
+          status: 'success',
+        })
+      );
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasDownloadedAsImage.ts

@@ -3,13 +3,13 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
 import { downloadBlob } from 'features/canvas/util/downloadBlob';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 
 export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: canvasDownloadedAsImage,
-    effect: async (action, { getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const moduleLog = $logger.get().child({ namespace: 'canvasSavedToGalleryListener' });
       const state = getState();
 
@@ -18,17 +18,18 @@ export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartLi
         blob = await getBaseLayerBlob(state);
       } catch (err) {
         moduleLog.error(String(err));
-        toast({
-          id: 'CANVAS_DOWNLOAD_FAILED',
-          title: t('toast.problemDownloadingCanvas'),
-          description: t('toast.problemDownloadingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemDownloadingCanvas'),
+            description: t('toast.problemDownloadingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
       downloadBlob(blob, 'canvas.png');
-      toast({ id: 'CANVAS_DOWNLOAD_SUCCEEDED', title: t('toast.canvasDownloaded'), status: 'success' });
+      dispatch(addToast({ title: t('toast.canvasDownloaded'), status: 'success' }));
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasImageToControlNet.ts

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasImageToControlAdapter } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
 import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -20,12 +20,13 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
         blob = await getBaseLayerBlob(state, true);
       } catch (err) {
         log.error(String(err));
-        toast({
-          id: 'PROBLEM_SAVING_CANVAS',
-          title: t('toast.problemSavingCanvas'),
-          description: t('toast.problemSavingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemSavingCanvas'),
+            description: t('toast.problemSavingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -42,7 +43,7 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
           crop_visible: false,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.canvasSentControlnetAssets'),
+            toastOptions: { title: t('toast.canvasSentControlnetAssets') },
           },
         })
       ).unwrap();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMaskSavedToGallery.ts

@@ -2,7 +2,7 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { canvasMaskSavedToGallery } from 'features/canvas/store/actions';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -29,12 +29,13 @@ export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartL
 
       if (!maskBlob) {
         log.error('Problem getting mask layer blob');
-        toast({
-          id: 'PROBLEM_SAVING_MASK',
-          title: t('toast.problemSavingMask'),
-          description: t('toast.problemSavingMaskDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemSavingMask'),
+            description: t('toast.problemSavingMaskDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -51,7 +52,7 @@ export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartL
           crop_visible: true,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.maskSavedAssets'),
+            toastOptions: { title: t('toast.maskSavedAssets') },
           },
         })
       );

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet.ts

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasMaskToControlAdapter } from 'features/canvas/store/actions';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
 import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -30,12 +30,13 @@ export const addCanvasMaskToControlNetListener = (startAppListening: AppStartLis
 
       if (!maskBlob) {
         log.error('Problem getting mask layer blob');
-        toast({
-          id: 'PROBLEM_IMPORTING_MASK',
-          title: t('toast.problemImportingMask'),
-          description: t('toast.problemImportingMaskDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemImportingMask'),
+            description: t('toast.problemImportingMaskDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -52,7 +53,7 @@ export const addCanvasMaskToControlNetListener = (startAppListening: AppStartLis
           crop_visible: false,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.maskSentControlnetAssets'),
+            toastOptions: { title: t('toast.maskSentControlnetAssets') },
           },
         })
       ).unwrap();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts

@@ -4,7 +4,7 @@ import { canvasMerged } from 'features/canvas/store/actions';
 import { $canvasBaseLayer } from 'features/canvas/store/canvasNanostore';
 import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
 import { getFullBaseLayerBlob } from 'features/canvas/util/getFullBaseLayerBlob';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -17,12 +17,13 @@ export const addCanvasMergedListener = (startAppListening: AppStartListening) =>
 
       if (!blob) {
         moduleLog.error('Problem getting base layer blob');
-        toast({
-          id: 'PROBLEM_MERGING_CANVAS',
-          title: t('toast.problemMergingCanvas'),
-          description: t('toast.problemMergingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemMergingCanvas'),
+            description: t('toast.problemMergingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -30,12 +31,13 @@ export const addCanvasMergedListener = (startAppListening: AppStartListening) =>
 
       if (!canvasBaseLayer) {
         moduleLog.error('Problem getting canvas base layer');
-        toast({
-          id: 'PROBLEM_MERGING_CANVAS',
-          title: t('toast.problemMergingCanvas'),
-          description: t('toast.problemMergingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemMergingCanvas'),
+            description: t('toast.problemMergingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -52,7 +54,7 @@ export const addCanvasMergedListener = (startAppListening: AppStartListening) =>
           is_intermediate: true,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.canvasMerged'),
+            toastOptions: { title: t('toast.canvasMerged') },
           },
         })
       ).unwrap();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts

@@ -1,9 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
 import { canvasSavedToGallery } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -19,12 +18,13 @@ export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListe
         blob = await getBaseLayerBlob(state);
       } catch (err) {
         log.error(String(err));
-        toast({
-          id: 'CANVAS_SAVE_FAILED',
-          title: t('toast.problemSavingCanvas'),
-          description: t('toast.problemSavingCanvasDesc'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('toast.problemSavingCanvas'),
+            description: t('toast.problemSavingCanvasDesc'),
+            status: 'error',
+          })
+        );
         return;
       }
 
@@ -41,10 +41,7 @@ export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListe
           crop_visible: true,
           postUploadAction: {
             type: 'TOAST',
-            title: t('toast.canvasSavedGallery'),
-          },
-          metadata: {
-            _canvas_objects: parseify(state.canvas.layerState.objects),
+            toastOptions: { title: t('toast.canvasSavedGallery') },
           },
         })
       );

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor.ts

@@ -1,188 +0,0 @@
-import { isAnyOf } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { AppDispatch } from 'app/store/store';
-import { parseify } from 'common/util/serialize';
-import {
-  caLayerImageChanged,
-  caLayerModelChanged,
-  caLayerProcessedImageChanged,
-  caLayerProcessorConfigChanged,
-  caLayerProcessorPendingBatchIdChanged,
-  caLayerRecalled,
-  isControlAdapterLayer,
-} from 'features/controlLayers/store/controlLayersSlice';
-import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
-import { isImageOutput } from 'features/nodes/types/common';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { isEqual } from 'lodash-es';
-import { getImageDTO } from 'services/api/endpoints/images';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { BatchConfig } from 'services/api/types';
-import { socketInvocationComplete } from 'services/events/actions';
-import { assert } from 'tsafe';
-
-const matcher = isAnyOf(caLayerImageChanged, caLayerProcessorConfigChanged, caLayerModelChanged, caLayerRecalled);
-
-const DEBOUNCE_MS = 300;
-const log = logger('session');
-
-/**
- * Simple helper to cancel a batch and reset the pending batch ID
- */
-const cancelProcessorBatch = async (dispatch: AppDispatch, layerId: string, batchId: string) => {
-  const req = dispatch(queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: [batchId] }));
-  log.trace({ batchId }, 'Cancelling existing preprocessor batch');
-  try {
-    await req.unwrap();
-  } catch {
-    // no-op
-  } finally {
-    req.reset();
-    // Always reset the pending batch ID - the cancel req could fail if the batch doesn't exist
-    dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
-  }
-};
-
-export const addControlAdapterPreprocessor = (startAppListening: AppStartListening) => {
-  startAppListening({
-    matcher,
-    effect: async (action, { dispatch, getState, getOriginalState, cancelActiveListeners, delay, take, signal }) => {
-      const layerId = caLayerRecalled.match(action) ? action.payload.id : action.payload.layerId;
-      const state = getState();
-      const originalState = getOriginalState();
-
-      // Cancel any in-progress instances of this listener
-      cancelActiveListeners();
-      log.trace('Control Layer CA auto-process triggered');
-
-      // Delay before starting actual work
-      await delay(DEBOUNCE_MS);
-
-      const layer = state.controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
-
-      if (!layer) {
-        return;
-      }
-
-      // We should only process if the processor settings or image have changed
-      const originalLayer = originalState.controlLayers.present.layers
-        .filter(isControlAdapterLayer)
-        .find((l) => l.id === layerId);
-      const originalImage = originalLayer?.controlAdapter.image;
-      const originalConfig = originalLayer?.controlAdapter.processorConfig;
-
-      const image = layer.controlAdapter.image;
-      const config = layer.controlAdapter.processorConfig;
-
-      if (isEqual(config, originalConfig) && isEqual(image, originalImage)) {
-        // Neither config nor image have changed, we can bail
-        return;
-      }
-
-      if (!image || !config) {
-        // - If we have no image, we have nothing to process
-        // - If we have no processor config, we have nothing to process
-        // Clear the processed image and bail
-        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
-        return;
-      }
-
-      // At this point, the user has stopped fiddling with the processor settings and there is a processor selected.
-
-      // If there is a pending processor batch, cancel it.
-      if (layer.controlAdapter.processorPendingBatchId) {
-        cancelProcessorBatch(dispatch, layerId, layer.controlAdapter.processorPendingBatchId);
-      }
-
-      // TODO(psyche): I can't get TS to be happy, it thinkgs `config` is `never` but it should be inferred from the generic... I'll just cast it for now
-      const processorNode = CA_PROCESSOR_DATA[config.type].buildNode(image, config as never);
-      const enqueueBatchArg: BatchConfig = {
-        prepend: true,
-        batch: {
-          graph: {
-            nodes: {
-              [processorNode.id]: {
-                ...processorNode,
-                // Control images are always intermediate - do not save to gallery
-                is_intermediate: true,
-              },
-            },
-            edges: [],
-          },
-          runs: 1,
-        },
-      };
-
-      // Kick off the processor batch
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
-          fixedCacheKey: 'enqueueBatch',
-        })
-      );
-
-      try {
-        const enqueueResult = await req.unwrap();
-        // TODO(psyche): Update the pydantic models, pretty sure we will _always_ have a batch_id here, but the model says it's optional
-        assert(enqueueResult.batch.batch_id, 'Batch ID not returned from queue');
-        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: enqueueResult.batch.batch_id }));
-        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
-
-        // Wait for the processor node to complete
-        const [invocationCompleteAction] = await take(
-          (action): action is ReturnType<typeof socketInvocationComplete> =>
-            socketInvocationComplete.match(action) &&
-            action.payload.data.queue_batch_id === enqueueResult.batch.batch_id &&
-            action.payload.data.source_node_id === processorNode.id
-        );
-
-        // We still have to check the output type
-        assert(
-          isImageOutput(invocationCompleteAction.payload.data.result),
-          `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
-        );
-        const { image_name } = invocationCompleteAction.payload.data.result.image;
-
-        const imageDTO = await getImageDTO(image_name);
-        assert(imageDTO, "Failed to fetch processor output's image DTO");
-
-        // Whew! We made it. Update the layer with the processed image
-        log.debug({ layerId, imageDTO }, 'ControlNet image processed');
-        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO }));
-        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
-      } catch (error) {
-        if (signal.aborted) {
-          // The listener was canceled - we need to cancel the pending processor batch, if there is one (could have changed by now).
-          const pendingBatchId = getState()
-            .controlLayers.present.layers.filter(isControlAdapterLayer)
-            .find((l) => l.id === layerId)?.controlAdapter.processorPendingBatchId;
-          if (pendingBatchId) {
-            cancelProcessorBatch(dispatch, layerId, pendingBatchId);
-          }
-          log.trace('Control Adapter preprocessor cancelled');
-        } else {
-          // Some other error condition...
-          log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
-
-          if (error instanceof Object) {
-            if ('data' in error && 'status' in error) {
-              if (error.status === 403) {
-                dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
-                return;
-              }
-            }
-          }
-
-          toast({
-            id: 'GRAPH_QUEUE_FAILED',
-            title: t('queue.graphFailedToQueue'),
-            status: 'error',
-          });
-        }
-      } finally {
-        req.reset();
-      }
-    },
-  });
-};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts

@@ -10,7 +10,7 @@ import {
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
 import { isImageOutput } from 'features/nodes/types/common';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -108,11 +108,12 @@ export const addControlNetImageProcessedListener = (startAppListening: AppStartL
           }
         }
 
-        toast({
-          id: 'GRAPH_QUEUE_FAILED',
-          title: t('queue.graphFailedToQueue'),
-          status: 'error',
-        });
+        dispatch(
+          addToast({
+            title: t('queue.graphFailedToQueue'),
+            status: 'error',
+          })
+        );
       }
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas.ts

@@ -8,8 +8,8 @@ import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
 import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
 import { canvasGraphBuilt } from 'features/nodes/store/actions';
+import { buildCanvasGraph } from 'features/nodes/util/graph/buildCanvasGraph';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildCanvasGraph } from 'features/nodes/util/graph/canvas/buildCanvasGraph';
 import { imagesApi } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
@@ -30,7 +30,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'canvas',
+      enqueueRequested.match(action) && action.payload.tabName === 'unifiedCanvas',
     effect: async (action, { getState, dispatch }) => {
       const log = logger('queue');
       const { prepend } = action.payload;

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear.ts

@@ -1,27 +1,35 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildGenerationTabGraph } from 'features/nodes/util/graph/generation/buildGenerationTabGraph';
-import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/generation/buildGenerationTabSDXLGraph';
+import { buildLinearImageToImageGraph } from 'features/nodes/util/graph/buildLinearImageToImageGraph';
+import { buildLinearSDXLImageToImageGraph } from 'features/nodes/util/graph/buildLinearSDXLImageToImageGraph';
+import { buildLinearSDXLTextToImageGraph } from 'features/nodes/util/graph/buildLinearSDXLTextToImageGraph';
+import { buildLinearTextToImageGraph } from 'features/nodes/util/graph/buildLinearTextToImageGraph';
 import { queueApi } from 'services/api/endpoints/queue';
 
 export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'generation',
+      enqueueRequested.match(action) && (action.payload.tabName === 'txt2img' || action.payload.tabName === 'img2img'),
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { shouldShowProgressInViewer } = state.ui;
       const model = state.generation.model;
       const { prepend } = action.payload;
 
       let graph;
 
-      if (model?.base === 'sdxl') {
-        graph = await buildGenerationTabSDXLGraph(state);
+      if (model && model.base === 'sdxl') {
+        if (action.payload.tabName === 'txt2img') {
+          graph = await buildLinearSDXLTextToImageGraph(state);
+        } else {
+          graph = await buildLinearSDXLImageToImageGraph(state);
+        }
       } else {
-        graph = await buildGenerationTabGraph(state);
+        if (action.payload.tabName === 'txt2img') {
+          graph = await buildLinearTextToImageGraph(state);
+        } else {
+          graph = await buildLinearImageToImageGraph(state);
+        }
       }
 
       const batchConfig = prepareLinearUIBatch(state, graph, prepend);
@@ -31,14 +39,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      try {
-        await req.unwrap();
-        if (shouldShowProgressInViewer) {
-          dispatch(isImageViewerOpenChanged(true));
-        }
-      } finally {
-        req.reset();
-      }
+      req.reset();
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedNodes.ts

@@ -8,12 +8,12 @@ import type { BatchConfig } from 'services/api/types';
 export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'workflows',
+      enqueueRequested.match(action) && action.payload.tabName === 'nodes',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { nodes, edges } = state.nodes.present;
+      const { nodes, edges } = state.nodes;
       const workflow = state.workflow;
-      const graph = buildNodesGraph(state.nodes.present);
+      const graph = buildNodesGraph(state.nodes);
       const builtWorkflow = buildWorkflowWithValidation({
         nodes,
         edges,
@@ -39,11 +39,7 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      try {
-        await req.unwrap();
-      } finally {
-        req.reset();
-      }
+      req.reset();
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema.ts

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
-import { $templates } from 'features/nodes/store/nodesSlice';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
@@ -9,7 +9,7 @@ import { appInfoApi } from 'services/api/endpoints/appInfo';
 export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
-    effect: (action, { getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
@@ -20,7 +20,7 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
 
       log.debug({ nodeTemplates: parseify(nodeTemplates) }, `Built ${size(nodeTemplates)} node templates`);
 
-      $templates.set(nodeTemplates);
+      dispatch(nodeTemplatesBuilt(nodeTemplates));
     },
   });
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts

@@ -1,6 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { AppDispatch, RootState } from 'app/store/store';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import {
   controlAdapterImageChanged,
@@ -8,13 +7,6 @@ import {
   selectControlAdapterAll,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-import {
-  isControlAdapterLayer,
-  isInitialImageLayer,
-  isIPAdapterLayer,
-  isRegionalGuidanceLayer,
-  layerDeleted,
-} from 'features/controlLayers/store/controlLayersSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
@@ -22,82 +14,12 @@ import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { isImageFieldInputInstance } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { clamp, forEach } from 'lodash-es';
 import { api } from 'services/api';
 import { imagesApi } from 'services/api/endpoints/images';
-import type { ImageDTO } from 'services/api/types';
 import { imagesSelectors } from 'services/api/util';
 
-const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  state.nodes.present.nodes.forEach((node) => {
-    if (!isInvocationNode(node)) {
-      return;
-    }
-
-    forEach(node.data.inputs, (input) => {
-      if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
-        dispatch(
-          fieldImageValueChanged({
-            nodeId: node.data.id,
-            fieldName: input.name,
-            value: undefined,
-          })
-        );
-      }
-    });
-  });
-};
-
-const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  forEach(selectControlAdapterAll(state.controlAdapters), (ca) => {
-    if (
-      ca.controlImage === imageDTO.image_name ||
-      (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
-    ) {
-      dispatch(
-        controlAdapterImageChanged({
-          id: ca.id,
-          controlImage: null,
-        })
-      );
-      dispatch(
-        controlAdapterProcessedImageChanged({
-          id: ca.id,
-          processedControlImage: null,
-        })
-      );
-    }
-  });
-};
-
-const deleteControlLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  state.controlLayers.present.layers.forEach((l) => {
-    if (isRegionalGuidanceLayer(l)) {
-      if (l.ipAdapters.some((ipa) => ipa.image?.name === imageDTO.image_name)) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-    if (isControlAdapterLayer(l)) {
-      if (
-        l.controlAdapter.image?.name === imageDTO.image_name ||
-        l.controlAdapter.processedImage?.name === imageDTO.image_name
-      ) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-    if (isIPAdapterLayer(l)) {
-      if (l.ipAdapter.image?.name === imageDTO.image_name) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-    if (isInitialImageLayer(l)) {
-      if (l.image?.name === imageDTO.image_name) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-  });
-};
-
 export const addRequestedSingleImageDeletionListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: imageDeletionConfirmed,
@@ -151,9 +73,50 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
       }
 
       imageDTOs.forEach((imageDTO) => {
-        deleteControlAdapterImages(state, dispatch, imageDTO);
-        deleteNodesImages(state, dispatch, imageDTO);
-        deleteControlLayerImages(state, dispatch, imageDTO);
+        // reset init image if we deleted it
+        if (getState().generation.initialImage?.imageName === imageDTO.image_name) {
+          dispatch(clearInitialImage());
+        }
+
+        // reset control adapters that use the deleted images
+        forEach(selectControlAdapterAll(getState().controlAdapters), (ca) => {
+          if (
+            ca.controlImage === imageDTO.image_name ||
+            (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+          ) {
+            dispatch(
+              controlAdapterImageChanged({
+                id: ca.id,
+                controlImage: null,
+              })
+            );
+            dispatch(
+              controlAdapterProcessedImageChanged({
+                id: ca.id,
+                processedControlImage: null,
+              })
+            );
+          }
+        });
+
+        // reset nodes that use the deleted images
+        getState().nodes.nodes.forEach((node) => {
+          if (!isInvocationNode(node)) {
+            return;
+          }
+
+          forEach(node.data.inputs, (input) => {
+            if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
+              dispatch(
+                fieldImageValueChanged({
+                  nodeId: node.data.id,
+                  fieldName: input.name,
+                  value: undefined,
+                })
+              );
+            }
+          });
+        });
       });
 
       // Delete from server
@@ -205,9 +168,50 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
         }
 
         imageDTOs.forEach((imageDTO) => {
-          deleteControlAdapterImages(state, dispatch, imageDTO);
-          deleteNodesImages(state, dispatch, imageDTO);
-          deleteControlLayerImages(state, dispatch, imageDTO);
+          // reset init image if we deleted it
+          if (getState().generation.initialImage?.imageName === imageDTO.image_name) {
+            dispatch(clearInitialImage());
+          }
+
+          // reset control adapters that use the deleted images
+          forEach(selectControlAdapterAll(getState().controlAdapters), (ca) => {
+            if (
+              ca.controlImage === imageDTO.image_name ||
+              (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+            ) {
+              dispatch(
+                controlAdapterImageChanged({
+                  id: ca.id,
+                  controlImage: null,
+                })
+              );
+              dispatch(
+                controlAdapterProcessedImageChanged({
+                  id: ca.id,
+                  processedControlImage: null,
+                })
+              );
+            }
+          });
+
+          // reset nodes that use the deleted images
+          getState().nodes.nodes.forEach((node) => {
+            if (!isInvocationNode(node)) {
+              return;
+            }
+
+            forEach(node.data.inputs, (input) => {
+              if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
+                dispatch(
+                  fieldImageValueChanged({
+                    nodeId: node.data.id,
+                    fieldName: input.name,
+                    value: undefined,
+                  })
+                );
+              }
+            });
+          });
         });
       } catch {
         // no-op

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts

@@ -7,16 +7,10 @@ import {
   controlAdapterImageChanged,
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import {
-  caLayerImageChanged,
-  iiLayerImageChanged,
-  ipaLayerImageChanged,
-  rgLayerIPAdapterImageChanged,
-} from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const dndDropped = createAction<{
@@ -53,6 +47,18 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
+      /**
+       * Image dropped on initial image
+       */
+      if (
+        overData.actionType === 'SET_INITIAL_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        dispatch(initialImageChanged(activeData.payload.imageDTO));
+        return;
+      }
+
       /**
        * Image dropped on ControlNet
        */
@@ -77,79 +83,6 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
-      /**
-       * Image dropped on Control Adapter Layer
-       */
-      if (
-        overData.actionType === 'SET_CA_LAYER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId } = overData.context;
-        dispatch(
-          caLayerImageChanged({
-            layerId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on IP Adapter Layer
-       */
-      if (
-        overData.actionType === 'SET_IPA_LAYER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId } = overData.context;
-        dispatch(
-          ipaLayerImageChanged({
-            layerId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on RG Layer IP Adapter
-       */
-      if (
-        overData.actionType === 'SET_RG_LAYER_IP_ADAPTER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId, ipAdapterId } = overData.context;
-        dispatch(
-          rgLayerIPAdapterImageChanged({
-            layerId,
-            ipAdapterId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on II Layer Image
-       */
-      if (
-        overData.actionType === 'SET_II_LAYER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId } = overData.context;
-        dispatch(
-          iiLayerImageChanged({
-            layerId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
       /**
        * Image dropped on Canvas
        */

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected.ts

@@ -14,6 +14,7 @@ export const addImageToDeleteSelectedListener = (startAppListening: AppStartList
 
       const isImageInUse =
         imagesUsage.some((i) => i.isCanvasImage) ||
+        imagesUsage.some((i) => i.isInitialImage) ||
         imagesUsage.some((i) => i.isControlImage) ||
         imagesUsage.some((i) => i.isNodesImage);
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts

@@ -1,3 +1,4 @@
+import type { UseToastOptions } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
@@ -5,15 +6,9 @@ import {
   controlAdapterImageChanged,
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import {
-  caLayerImageChanged,
-  iiLayerImageChanged,
-  ipaLayerImageChanged,
-  rgLayerIPAdapterImageChanged,
-} from 'features/controlLayers/store/controlLayersSlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
-import { toast } from 'features/toast/toast';
+import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
@@ -41,17 +36,16 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
-      const DEFAULT_UPLOADED_TOAST = {
-        id: 'IMAGE_UPLOADED',
+      const DEFAULT_UPLOADED_TOAST: UseToastOptions = {
         title: t('toast.imageUploaded'),
         status: 'success',
-      } as const;
+      };
 
       // default action - just upload and alert user
       if (postUploadAction?.type === 'TOAST') {
+        const { toastOptions } = postUploadAction;
         if (!autoAddBoardId || autoAddBoardId === 'none') {
-          const title = postUploadAction.title || DEFAULT_UPLOADED_TOAST.title;
-          toast({ ...DEFAULT_UPLOADED_TOAST, title });
+          dispatch(addToast({ ...DEFAULT_UPLOADED_TOAST, ...toastOptions }));
         } else {
           // Add this image to the board
           dispatch(
@@ -70,20 +64,24 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
             ? `${t('toast.addedToBoard')} ${board.board_name}`
             : `${t('toast.addedToBoard')} ${autoAddBoardId}`;
 
-          toast({
-            ...DEFAULT_UPLOADED_TOAST,
-            description,
-          });
+          dispatch(
+            addToast({
+              ...DEFAULT_UPLOADED_TOAST,
+              description,
+            })
+          );
         }
         return;
       }
 
       if (postUploadAction?.type === 'SET_CANVAS_INITIAL_IMAGE') {
         dispatch(setInitialCanvasImage(imageDTO, selectOptimalDimension(state)));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setAsCanvasInitialImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setAsCanvasInitialImage'),
+          })
+        );
         return;
       }
 
@@ -101,56 +99,35 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
             controlImage: imageDTO.image_name,
           })
         );
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
         return;
       }
 
-      if (postUploadAction?.type === 'SET_CA_LAYER_IMAGE') {
-        const { layerId } = postUploadAction;
-        dispatch(caLayerImageChanged({ layerId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
-      }
-
-      if (postUploadAction?.type === 'SET_IPA_LAYER_IMAGE') {
-        const { layerId } = postUploadAction;
-        dispatch(ipaLayerImageChanged({ layerId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
-      }
-
-      if (postUploadAction?.type === 'SET_RG_LAYER_IP_ADAPTER_IMAGE') {
-        const { layerId, ipAdapterId } = postUploadAction;
-        dispatch(rgLayerIPAdapterImageChanged({ layerId, ipAdapterId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
-      }
-
-      if (postUploadAction?.type === 'SET_II_LAYER_IMAGE') {
-        const { layerId } = postUploadAction;
-        dispatch(iiLayerImageChanged({ layerId, imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: t('toast.setControlImage'),
-        });
+      if (postUploadAction?.type === 'SET_INITIAL_IMAGE') {
+        dispatch(initialImageChanged(imageDTO));
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setInitialImage'),
+          })
+        );
+        return;
       }
 
       if (postUploadAction?.type === 'SET_NODES_IMAGE') {
         const { nodeId, fieldName } = postUploadAction;
         dispatch(fieldImageValueChanged({ nodeId, fieldName, value: imageDTO }));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: `${t('toast.setNodeField')} ${fieldName}`,
-        });
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: `${t('toast.setNodeField')} ${fieldName}`,
+          })
+        );
         return;
       }
     },
@@ -158,7 +135,7 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
 
   startAppListening({
     matcher: imagesApi.endpoints.uploadImage.matchRejected,
-    effect: (action) => {
+    effect: (action, { dispatch }) => {
       const log = logger('images');
       const sanitizedData = {
         arg: {
@@ -167,11 +144,13 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         },
       };
       log.error({ ...sanitizedData }, 'Image upload failed');
-      toast({
-        title: t('toast.imageUploadFailed'),
-        description: action.error.message,
-        status: 'error',
-      });
+      dispatch(
+        addToast({
+          title: t('toast.imageUploadFailed'),
+          description: action.error.message,
+          status: 'error',
+        })
+      );
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/initialImageSelected.ts

@@ -0,0 +1,21 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { initialImageSelected } from 'features/parameters/store/actions';
+import { initialImageChanged } from 'features/parameters/store/generationSlice';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
+import { t } from 'i18next';
+
+export const addInitialImageSelectedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: initialImageSelected,
+    effect: (action, { dispatch }) => {
+      if (!action.payload) {
+        dispatch(addToast(makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })));
+        return;
+      }
+
+      dispatch(initialImageChanged(action.payload));
+      dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelSelected.ts

@@ -8,7 +8,8 @@ import { loraRemoved } from 'features/lora/store/loraSlice';
 import { modelSelected } from 'features/parameters/store/actions';
 import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { zParameterModel } from 'features/parameters/types/parameterSchemas';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
 import { forEach } from 'lodash-es';
 
@@ -59,14 +60,16 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
         });
 
         if (modelsCleared > 0) {
-          toast({
-            id: 'BASE_MODEL_CHANGED',
-            title: t('toast.baseModelChanged'),
-            description: t('toast.baseModelChangedCleared', {
-              count: modelsCleared,
-            }),
-            status: 'warning',
-          });
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('toast.baseModelChangedCleared', {
+                  count: modelsCleared,
+                }),
+                status: 'warning',
+              })
+            )
+          );
         }
       }
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts

@@ -6,10 +6,9 @@ import {
   controlAdapterModelCleared,
   selectControlAdapterAll,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
 import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
-import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
+import { heightChanged, modelChanged, vaeSelected, widthChanged } from 'features/parameters/store/generationSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice';
@@ -70,22 +69,16 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
       dispatch(modelChanged(defaultModelInList, currentModel));
 
       const optimalDimension = getOptimalDimension(defaultModelInList);
-      if (
-        getIsSizeOptimal(
-          state.controlLayers.present.size.width,
-          state.controlLayers.present.size.height,
-          optimalDimension
-        )
-      ) {
+      if (getIsSizeOptimal(state.generation.width, state.generation.height, optimalDimension)) {
         return;
       }
       const { width, height } = calculateNewSize(
-        state.controlLayers.present.size.aspectRatio.value,
+        state.generation.aspectRatio.value,
         optimalDimension * optimalDimension
       );
 
-      dispatch(widthChanged({ width }));
-      dispatch(heightChanged({ height }));
+      dispatch(widthChanged(width));
+      dispatch(heightChanged(height));
       return;
     }
   }

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/promptChanged.ts

@@ -1,6 +1,5 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { positivePromptChanged } from 'features/controlLayers/store/controlLayersSlice';
 import {
   combinatorialToggled,
   isErrorChanged,
@@ -11,16 +10,11 @@ import {
   promptsChanged,
 } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
+import { setPositivePrompt } from 'features/parameters/store/generationSlice';
 import { utilitiesApi } from 'services/api/endpoints/utilities';
 import { socketConnected } from 'services/events/actions';
 
-const matcher = isAnyOf(
-  positivePromptChanged,
-  combinatorialToggled,
-  maxPromptsChanged,
-  maxPromptsReset,
-  socketConnected
-);
+const matcher = isAnyOf(setPositivePrompt, combinatorialToggled, maxPromptsChanged, maxPromptsReset, socketConnected);
 
 export const addDynamicPromptsListener = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -28,7 +22,7 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
     effect: async (action, { dispatch, getState, cancelActiveListeners, delay }) => {
       cancelActiveListeners();
       const state = getState();
-      const { positivePrompt } = state.controlLayers.present;
+      const { positivePrompt } = state.generation;
       const { maxPrompts } = state.dynamicPrompts;
 
       if (state.config.disabledFeatures.includes('dynamicPrompting')) {
@@ -38,7 +32,7 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
       const cachedPrompts = utilitiesApi.endpoints.dynamicPrompts.select({
         prompt: positivePrompt,
         max_prompts: maxPrompts,
-      })(state).data;
+      })(getState()).data;
 
       if (cachedPrompts) {
         dispatch(promptsChanged(cachedPrompts.prompts));
@@ -46,8 +40,8 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
         return;
       }
 
-      if (!getShouldProcessPrompt(positivePrompt)) {
-        dispatch(promptsChanged([positivePrompt]));
+      if (!getShouldProcessPrompt(state.generation.positivePrompt)) {
+        dispatch(promptsChanged([state.generation.positivePrompt]));
         dispatch(parsingErrorChanged(undefined));
         dispatch(isErrorChanged(false));
         return;

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/setDefaultSettings.ts

@@ -1,13 +1,14 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { setDefaultSettings } from 'features/parameters/store/actions';
 import {
+  heightRecalled,
   setCfgRescaleMultiplier,
   setCfgScale,
   setScheduler,
   setSteps,
   vaePrecisionChanged,
   vaeSelected,
+  widthRecalled,
 } from 'features/parameters/store/generationSlice';
 import {
   isParameterCFGRescaleMultiplier,
@@ -19,7 +20,8 @@ import {
   isParameterWidth,
   zParameterVAEModel,
 } from 'features/parameters/types/parameterSchemas';
-import { toast } from 'features/toast/toast';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
 import { modelConfigsAdapterSelectors, modelsApi } from 'services/api/endpoints/models';
 import { isNonRefinerMainModelConfig } from 'services/api/types';
@@ -95,20 +97,20 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
             dispatch(setScheduler(scheduler));
           }
         }
-        const setSizeOptions = { updateAspectRatio: true, clamp: true };
+
         if (width) {
           if (isParameterWidth(width)) {
-            dispatch(widthChanged({ width, ...setSizeOptions }));
+            dispatch(widthRecalled(width));
           }
         }
 
         if (height) {
           if (isParameterHeight(height)) {
-            dispatch(heightChanged({ height, ...setSizeOptions }));
+            dispatch(heightRecalled(height));
           }
         }
 
-        toast({ id: 'PARAMETER_SET', title: t('toast.parameterSet', { parameter: 'Default settings' }) });
+        dispatch(addToast(makeToast({ title: t('toast.parameterSet', { parameter: 'Default settings' }) })));
       }
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketGeneratorProgress.ts

@@ -1,8 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
-import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketGeneratorProgress } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -12,14 +9,6 @@ export const addGeneratorProgressEventListener = (startAppListening: AppStartLis
     actionCreator: socketGeneratorProgress,
     effect: (action) => {
       log.trace(action.payload, `Generator progress`);
-      const { source_node_id, step, total_steps, progress_image } = action.payload.data;
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.IN_PROGRESS;
-        nes.progress = (step + 1) / total_steps;
-        nes.progressImage = progress_image ?? null;
-        upsertExecutionState(nes.nodeId, nes);
-      }
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts

@@ -1,18 +1,10 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
 import { parseify } from 'common/util/serialize';
 import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
-import {
-  boardIdSelected,
-  galleryViewChanged,
-  imageSelected,
-  isImageViewerOpenChanged,
-} from 'features/gallery/store/gallerySlice';
+import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { isImageOutput } from 'features/nodes/types/common';
-import { zNodeStatus } from 'features/nodes/types/invocation';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -31,7 +23,7 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
       const { data } = action.payload;
       log.debug({ data: parseify(data) }, `Invocation complete (${action.payload.data.node.type})`);
 
-      const { result, node, queue_batch_id, source_node_id } = data;
+      const { result, node, queue_batch_id } = data;
       // This complete event has an associated image output
       if (isImageOutput(result) && !nodeTypeDenylist.includes(node.type)) {
         const { image_name } = result.image;
@@ -109,20 +101,9 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
             }
 
             dispatch(imageSelected(imageDTO));
-            dispatch(isImageViewerOpenChanged(true));
           }
         }
       }
-
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.COMPLETED;
-        if (nes.progress !== null) {
-          nes.progress = 1;
-        }
-        nes.outputs.push(result);
-        upsertExecutionState(nes.nodeId, nes);
-      }
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError.ts

@@ -1,72 +1,14 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
-import { zNodeStatus } from 'features/nodes/types/invocation';
-import { toast } from 'features/toast/toast';
-import ToastWithSessionRefDescription from 'features/toast/ToastWithSessionRefDescription';
-import { t } from 'i18next';
-import { startCase } from 'lodash-es';
 import { socketInvocationError } from 'services/events/actions';
 
 const log = logger('socketio');
 
-const getTitle = (errorType: string) => {
-  if (errorType === 'OutOfMemoryError') {
-    return t('toast.outOfMemoryError');
-  }
-  return t('toast.serverError');
-};
-
-const getDescription = (errorType: string, sessionId: string, isLocal?: boolean) => {
-  if (!isLocal) {
-    if (errorType === 'OutOfMemoryError') {
-      return ToastWithSessionRefDescription({
-        message: t('toast.outOfMemoryDescription'),
-        sessionId,
-      });
-    }
-    return ToastWithSessionRefDescription({
-      message: errorType,
-      sessionId,
-    });
-  }
-  return errorType;
-};
-
 export const addInvocationErrorEventListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: socketInvocationError,
-    effect: (action, { getState }) => {
+    effect: (action) => {
       log.error(action.payload, `Invocation error (${action.payload.data.node.type})`);
-      const { source_node_id, error_type, error_message, error_traceback, graph_execution_state_id } =
-        action.payload.data;
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.FAILED;
-        nes.progress = null;
-        nes.progressImage = null;
-
-        nes.error = {
-          error_type,
-          error_message,
-          error_traceback,
-        };
-        upsertExecutionState(nes.nodeId, nes);
-      }
-
-      const errorType = startCase(error_type);
-      const sessionId = graph_execution_state_id;
-      const { isLocal } = getState().config;
-
-      toast({
-        id: `INVOCATION_ERROR_${errorType}`,
-        title: getTitle(errorType),
-        status: 'error',
-        duration: null,
-        description: getDescription(errorType, sessionId, isLocal),
-        updateDescription: isLocal ? true : false,
-      });
     },
   });
 };