remove deprecated binary installer

.coveragerc

@@ -1,6 +0,0 @@
-[run]
-omit='.env/*'
-source='.'
-
-[report]
-show_missing = true

.editorconfig

@@ -1,8 +1,5 @@
-root = true
-
 # All files
 [*]
-max_line_length = 80
 charset = utf-8
 end_of_line = lf
 indent_size = 2
@@ -13,18 +10,3 @@ trim_trailing_whitespace = true
 # Python
 [*.py]
 indent_size = 4
-max_line_length = 120
-
-# css
-[*.css]
-indent_size = 4
-
-# flake8
-[.flake8]
-indent_size = 4
-
-# Markdown MkDocs
-[docs/**/*.md]
-max_line_length = 80
-indent_size = 4
-indent_style = unset

.flake8

@@ -1,37 +0,0 @@
-[flake8]
-max-line-length = 120
-extend-ignore =
-    # See https://github.com/PyCQA/pycodestyle/issues/373
-    E203,
-    # use Bugbear's B950 instead
-    E501,
-    # from black repo https://github.com/psf/black/blob/main/.flake8
-    E266, W503, B907
-extend-select =
-    # Bugbear line length
-    B950
-extend-exclude =
-    scripts/orig_scripts/*
-    ldm/models/*
-    ldm/modules/*
-    ldm/data/*
-    ldm/generate.py
-    ldm/util.py
-    ldm/simplet2i.py
-per-file-ignores =
-    # B950 line too long
-    # W605 invalid escape sequence
-    # F841 assigned to but never used
-    # F401 imported but unused
-    tests/test_prompt_parser.py: B950, W605, F401
-    tests/test_textual_inversion.py: F841, B950
-    # B023 Function definition does not bind loop variable
-    scripts/legacy_api.py: F401, B950, B023, F841
-    ldm/invoke/__init__.py: F401
-    # B010 Do not call setattr with a constant attribute value
-    ldm/invoke/server_legacy.py: B010
-# =====================
-# flake-quote settings:
-# =====================
-# Set this to match black style:
-inline-quotes = double

.github/CODEOWNERS

@@ -2,60 +2,50 @@
 /.github/workflows/ @mauwii @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @mauwii @blessedcoolant
-mkdocs.yml @mauwii @lstein
+/docs/ @lstein @mauwii @tildebyte @blessedcoolant
+mkdocs.yml @lstein @mauwii @blessedcoolant
 
 # installation and configuration
-/pyproject.toml @mauwii @lstein @ebr
-/docker/ @mauwii
+/pyproject.toml @mauwii @lstein @ebr @blessedcoolant
+/docker/ @mauwii @lstein @blessedcoolant
 /scripts/ @ebr @lstein @blessedcoolant
-/installer/ @ebr @lstein 
-ldm/invoke/config @lstein @ebr
-invokeai/assets @lstein @blessedcoolant
+/installer/ @ebr @lstein @tildebyte  @blessedcoolant
+ldm/invoke/config @lstein @ebr  @blessedcoolant
+invokeai/assets @lstein @ebr  @blessedcoolant
 invokeai/configs @lstein @ebr  @blessedcoolant
 /ldm/invoke/_version.py @lstein @blessedcoolant
 
 # web ui
-/invokeai/frontend @blessedcoolant @psychedelicious
-/invokeai/backend @blessedcoolant @psychedelicious
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein
+/invokeai/backend @blessedcoolant @psychedelicious @lstein
 
 # generation and model management
 /ldm/*.py @lstein @blessedcoolant
-/ldm/generate.py @lstein @keturn
+/ldm/generate.py @lstein @keturn @blessedcoolant
 /ldm/invoke/args.py @lstein @blessedcoolant
 /ldm/invoke/ckpt* @lstein @blessedcoolant
 /ldm/invoke/ckpt_generator @lstein @blessedcoolant
 /ldm/invoke/CLI.py @lstein @blessedcoolant
 /ldm/invoke/config @lstein @ebr @mauwii @blessedcoolant
-/ldm/invoke/generator @keturn @damian0815
+/ldm/invoke/generator @keturn @damian0815 @blessedcoolant
 /ldm/invoke/globals.py @lstein @blessedcoolant 
 /ldm/invoke/merge_diffusers.py @lstein @blessedcoolant
 /ldm/invoke/model_manager.py @lstein @blessedcoolant
 /ldm/invoke/txt2mask.py @lstein @blessedcoolant
-/ldm/invoke/patchmatch.py @Kyle0654 @lstein
+/ldm/invoke/patchmatch.py @Kyle0654 @blessedcoolant @lstein
 /ldm/invoke/restoration @lstein @blessedcoolant
 
 # attention, textual inversion, model configuration
-/ldm/models @damian0815 @keturn @blessedcoolant
-/ldm/modules/textual_inversion_manager.py @lstein @blessedcoolant
-/ldm/modules/attention.py @damian0815 @keturn
-/ldm/modules/diffusionmodules @damian0815 @keturn
-/ldm/modules/distributions @damian0815 @keturn
-/ldm/modules/ema.py  @damian0815 @keturn
-/ldm/modules/embedding_manager.py @lstein
-/ldm/modules/encoders @damian0815 @keturn
-/ldm/modules/image_degradation @damian0815 @keturn
-/ldm/modules/losses  @damian0815 @keturn
-/ldm/modules/x_transformer.py @damian0815 @keturn
+/ldm/models @damian0815 @keturn @lstein @blessedcoolant
+/ldm/modules @damian0815 @keturn @lstein @blessedcoolant
 
 # Nodes
-apps/ @Kyle0654 @jpphoto
+apps/ @Kyle0654 @lstein @blessedcoolant
 
 # legacy REST API
-# these are dead code
-#/ldm/invoke/pngwriter.py @CapableWeb
-#/ldm/invoke/server_legacy.py @CapableWeb
-#/scripts/legacy_api.py @CapableWeb
-#/tests/legacy_tests.sh @CapableWeb
-
+# is CapableWeb still engaged?
+/ldm/invoke/pngwriter.py @CapableWeb @lstein @blessedcoolant
+/ldm/invoke/server_legacy.py @CapableWeb @lstein @blessedcoolant
+/scripts/legacy_api.py @CapableWeb @lstein @blessedcoolant
+/tests/legacy_tests.sh @CapableWeb @lstein @blessedcoolant
 

.github/workflows/mkdocs-material.yml

@@ -9,10 +9,6 @@ jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
-    env:
-      REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
-      REPO_NAME: '${{ github.repository }}'
-      SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
     steps:
       - name: checkout sources
         uses: actions/checkout@v3
@@ -23,15 +19,11 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
 
       - name: install requirements
-        env:
-          PIP_USE_PEP517: 1
         run: |
           python -m \
-            pip install ".[docs]"
+            pip install -r docs/requirements-mkdocs.txt
 
       - name: confirm buildability
         run: |

.gitignore

@@ -68,7 +68,6 @@ htmlcov/
 .cache
 nosetests.xml
 coverage.xml
-cov.xml
 *.cover
 *.py,cover
 .hypothesis/

.pre-commit-config.yaml

@@ -1,41 +0,0 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
-repos:
-  - repo: https://github.com/psf/black
-    rev: 23.1.0
-    hooks:
-      - id: black
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-
-  - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
-    hooks:
-      - id: flake8
-        additional_dependencies:
-          - flake8-black
-          - flake8-bugbear
-          - flake8-comprehensions
-          - flake8-simplify
-
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: 'v3.0.0-alpha.4'
-    hooks:
-      - id: prettier
-
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
-    hooks:
-      - id: check-added-large-files
-      - id: check-executables-have-shebangs
-      - id: check-shebang-scripts-are-executable
-      - id: check-merge-conflict
-      - id: check-symlinks
-      - id: check-toml
-      - id: end-of-file-fixer
-      - id: no-commit-to-branch
-        args: ['--branch', 'main']
-      - id: trailing-whitespace

.prettierignore

@@ -1,14 +0,0 @@
-invokeai/frontend/.husky
-invokeai/frontend/patches
-
-# Ignore artifacts:
-build
-coverage
-static
-invokeai/frontend/dist
-
-# Ignore all HTML files:
-*.html
-
-# Ignore deprecated docs
-docs/installation/deprecated_documentation

.prettierrc.yaml

@@ -1,9 +1,9 @@
-embeddedLanguageFormatting: auto
 endOfLine: lf
-singleQuote: true
-semi: true
-trailingComma: es5
+tabWidth: 2
 useTabs: false
+singleQuote: true
+quoteProps: as-needed
+embeddedLanguageFormatting: auto
 overrides:
   - files: '*.md'
     options:
@@ -11,9 +11,3 @@ overrides:
       printWidth: 80
       parser: markdown
       cursorOffset: -1
-  - files: docs/**/*.md
-    options:
-      tabWidth: 4
-  - files: 'invokeai/frontend/public/locales/*.json'
-    options:
-      tabWidth: 4

.pytest.ini

@@ -1,5 +0,0 @@
-[pytest]
-DJANGO_SETTINGS_MODULE = webtas.settings
-; python_files = tests.py test_*.py *_tests.py
-
-addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

README.md

@@ -145,7 +145,7 @@ not supported.
     _For Linux with an AMD GPU:_
 
     ```sh
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
     _For Macintoshes, either Intel or M1/M2:_

docs/.markdownlint.jsonc

@@ -1,5 +0,0 @@
-{
-  "MD046": false,
-  "MD007": false,
-  "MD030": false
-}

docs/contributing/ARCHITECTURE.md

@@ -1,93 +0,0 @@
-# Invoke.AI Architecture
-
-```mermaid
-flowchart TB
-
-  subgraph apps[Applications]
-    webui[WebUI]
-    cli[CLI]
-
-  subgraph webapi[Web API]
-    api[HTTP API]
-    sio[Socket.IO]
-  end
-
-  end
-
-  subgraph invoke[Invoke]
-    direction LR
-    invoker
-    services
-    sessions
-    invocations
-  end
-
-  subgraph core[AI Core]
-    Generate
-  end
-
-  webui --> webapi
-  webapi --> invoke
-  cli --> invoke
-
-  invoker --> services & sessions
-  invocations --> services
-  sessions --> invocations
-
-  services --> core
-
-  %% Styles
-  classDef sg fill:#5028C8,font-weight:bold,stroke-width:2,color:#fff,stroke:#14141A
-  classDef default stroke-width:2px,stroke:#F6B314,color:#fff,fill:#14141A
-
-  class apps,webapi,invoke,core sg
-
-```
-
-## Applications
-
-Applications are built on top of the invoke framework. They should construct `invoker` and then interact through it. They should avoid interacting directly with core code in order to support a variety of configurations.
-
-### Web UI
-
-The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
-
-| Component | Description |
-| --- | --- |
-| api_app.py | Sets up the API app, annotates the OpenAPI spec with additional data, and runs the API |
-| dependencies | Creates all invoker services and the invoker, and provides them to the API |
-| events | An eventing system that could in the future be adapted to support horizontal scale-out |
-| sockets | The Socket.IO interface - handles listening to and emitting session events (events are defined in the events service module) |
-| routers | API definitions for different areas of API functionality |
-
-### CLI
-
-The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
-
-## Invoke
-
-The Invoke framework provides the interface to the underlying AI systems and is built with flexibility and extensibility in mind. There are four major concepts: invoker, sessions, invocations, and services.
-
-### Invoker
-
-The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
-- **invocation services**, which are used by invocations to interact with core functionality.
-- **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
-
-### Sessions
-
-Invocations and links between them form a graph, which is maintained in a session. Sessions can be queued for invocation, which will execute their graph (either the next ready invocation, or all invocations). Sessions also maintain execution history for the graph (including storage of any outputs). An invocation may be added to a session at any time, and there is capability to add and entire graph at once, as well as to automatically link new invocations to previous invocations. Invocations can not be deleted or modified once added.
-
-The session graph does not support looping. This is left as an application problem to prevent additional complexity in the graph.
-
-### Invocations
-
-Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
-
-### Services
-
-Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
-
-## AI Core
-
-The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/INVOCATIONS.md

@@ -1,105 +0,0 @@
-# Invocations
-
-Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
-
-## Creating a new invocation
-
-To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
-
-An invocation looks like this:
-
-```py
-class UpscaleInvocation(BaseInvocation):
-    """Upscales an image."""
-    type: Literal['upscale'] = 'upscale'
-
-    # Inputs
-    image: Union[ImageField,None] = Field(description="The input image")
-    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2,4]           = Field(default=2, description = "The upscale level")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
-        results = context.services.generate.upscale_and_reconstruct(
-            image_list     = [[image, 0]],
-            upscale        = (self.level, self.strength),
-            strength       = 0.0, # GFPGAN strength
-            save_original  = False,
-            image_callback = None,
-        )
-
-        # Results are image and seed, unwrap for now
-        # TODO: can this return multiple results?
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
-        context.services.images.save(image_type, image_name, results[0][0])
-        return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
-        )
-```
-
-Each portion is important to implement correctly.
-
-### Class definition and type
-```py
-class UpscaleInvocation(BaseInvocation):
-    """Upscales an image."""
-    type: Literal['upscale'] = 'upscale'
-```
-All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
-
-### Inputs
-```py
-    # Inputs
-    image: Union[ImageField,None] = Field(description="The input image")
-    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2,4]           = Field(default=2, description="The upscale level")
-```
-Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
-| Part | Value | Description |
-| ---- | ----- | ----------- |
-| Name | `strength` | This field is referred to as `strength` |
-| Type Hint | `float` | This field must be of type `float` |
-| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
-
-Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
-
-The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
-
-Finally, note that for all linking, the `type` of the linked fields must match. If the `name` also matches, then the field can be **automatically linked** to a previous invocation by name and matching.
-
-### Invoke Function
-```py
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
-        results = context.services.generate.upscale_and_reconstruct(
-            image_list     = [[image, 0]],
-            upscale        = (self.level, self.strength),
-            strength       = 0.0, # GFPGAN strength
-            save_original  = False,
-            image_callback = None,
-        )
-
-        # Results are image and seed, unwrap for now
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
-        context.services.images.save(image_type, image_name, results[0][0])
-        return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
-        )
-```
-The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
-
-Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
-
-Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
-
-### Outputs
-```py
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-    type: Literal['image'] = 'image'
-
-    image: ImageField = Field(default=None, description="The output image")
-```
-Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

docs/features/CONCEPTS.md

@@ -1,5 +1,5 @@
 ---
-title: Styles and Subjects
+title: Concepts Library
 ---
 
 # :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
@@ -25,14 +25,10 @@ library which downloads and merges TI files automatically upon request. You can
 also install your own or others' TI files by placing them in a designated
 directory.
 
-You may also be interested in using [LoRA Models](LORAS.md) to
-generate images with specialized styles and subjects.
-
 ### An Example
 
-Here are a few examples to illustrate how Textual Inversion works. All
-these images were generated using the command-line client and the
-Stable Diffusion 1.5 model:
+Here are a few examples to illustrate how it works. All these images were
+generated using the command-line client and the Stable Diffusion 1.5 model:
 
 |         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
 | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
@@ -113,50 +109,21 @@ For example, TI files generated by the Hugging Face toolkit share the named
 `learned_embedding.bin`. You can use subdirectories to keep them distinct.
 
 At startup time, InvokeAI will scan the `embeddings` directory and load any TI
-files it finds there. At startup you will see messages similar to these:
+files it finds there. At startup you will see a message similar to this one:
 
 ```bash
->> Loading embeddings from /data/lstein/invokeai-2.3/embeddings
-   | Loading v1 embedding file: style-hamunaptra
-   | Loading v4 embedding file: embeddings/learned_embeds-steps-500.bin
-   | Loading v2 embedding file: lfa
-   | Loading v3 embedding file: easynegative
-   | Loading v1 embedding file: rem_rezero
-   | Loading v2 embedding file: midj-strong
-   | Loading v4 embedding file: anime-background-style-v2/learned_embeds.bin
-   | Loading v4 embedding file: kamon-style/learned_embeds.bin
-   ** Notice: kamon-style/learned_embeds.bin was trained on a model with an incompatible token dimension: 768 vs 1024.
->> Textual inversion triggers: <anime-background-style-v2>, <easynegative>, <lfa>, <midj-strong>, <milo>, Rem3-2600, Style-Hamunaptra
+>> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
 ```
 
-Textual Inversion embeddings trained on version 1.X stable diffusion
-models are incompatible with version 2.X models and vice-versa.
+Note the `*` trigger term. This is a placeholder term that many early TI
+tutorials taught people to use rather than a more descriptive term.
+Unfortunately, if you have multiple TI files that all use this term, only the
+first one loaded will be triggered by use of the term.
 
-After the embeddings load, InvokeAI will print out a list of all the
-recognized trigger terms. To trigger the term, include it in the
-prompt exactly as written, including angle brackets if any and
-respecting the capitalization.
-
-There are at least four different embedding file formats, and each uses
-a different convention for the trigger terms. In some cases, the
-trigger term is specified in the file contents and may or may not be
-surrounded by angle brackets. In the example above, `Rem3-2600`,
-`Style-Hamunaptra`, and `<midj-strong>` were specified this way and
-there is no easy way to change the term.
-
-In other cases the trigger term is not contained within the embedding
-file. In this case, InvokeAI constructs a trigger term consisting of
-the base name of the file (without the file extension) surrounded by
-angle brackets. In the example above `<easynegative`> is such a file
-(the filename was `easynegative.safetensors`). In such cases, you can
-change the trigger term simply by renaming the file.
-
-## Training your own Textual Inversion models
-
-InvokeAI provides a script that lets you train your own Textual
-Inversion embeddings using a small number (about a half-dozen) images
-of your desired style or subject. Please see [Textual
-Inversion](TEXTUAL_INVERSION.md) for details.
+To avoid this problem, you can use the `merge_embeddings.py` script to merge two
+or more TI files together. If it encounters a collision of terms, the script
+will prompt you to select new terms that do not collide. See
+[Textual Inversion](TEXTUAL_INVERSION.md) for details.
 
 ## Further Reading
 

docs/features/LORAS.md

@@ -1,100 +0,0 @@
----
-title: Low-Rank Adaptation (LoRA) Models
----
-
-# :material-library-shelves: Using Low-Rank Adaptation (LoRA) Models
-
-## Introduction
-
-LoRA is a technique for fine-tuning Stable Diffusion models using much
-less time and memory than traditional training techniques. The
-resulting model files are much smaller than full model files, and can
-be used to generate specialized styles and subjects.
-
-LoRAs are built on top of Stable Diffusion v1.x or 2.x checkpoint or
-diffusers models. To load a LoRA, you include its name in the text
-prompt using a simple syntax described below. While you will generally
-get the best results when you use the same model the LoRA was trained
-on, they will work to a greater or lesser extent with other models.
-The major caveat is that a LoRA built on top of a SD v1.x model cannot
-be used with a v2.x model, and vice-versa. If you try, you will get an
-error! You may refer to multiple LoRAs in your prompt.
-
-When you apply a LoRA in a prompt you can specify a weight. The higher
-the weight, the more influence it will have on the image. Useful
-ranges for weights are usually in the 0.0 to 1.0 range (with ranges
-between 0.5 and 1.0 being most typical). However you can specify a
-higher weight if you wish. Like models, each LoRA has a slightly
-different useful weight range and will interact with other generation
-parameters such as the CFG, step count and sampler. The author of the
-LoRA will often provide guidance on the best settings, but feel free
-to experiment. Be aware that it often helps to reduce the CFG value
-when using LoRAs.
-
-## Installing LoRAs
-
-This is very easy! Download a LoRA model file from your favorite site
-(e.g. [CIVITAI](https://civitai.com) and place it in the `loras`
-folder in the InvokeAI root directory (usually `~invokeai/loras` on
-Linux/Macintosh machines, and `C:\Users\your-name\invokeai/loras` on
-Windows systems). If the `loras` folder does not already exist, just
-create it. The vast majority of LoRA models use the Kohya file format,
-which is a type of `.safetensors` file.
-
-You may change where InvokeAI looks for the `loras` folder by passing the
-`--lora_directory` option to the `invoke.sh`/`invoke.bat` launcher, or
-by placing the option in `invokeai.init`. For example:
-
-```
-invoke.sh --lora_directory=C:\Users\your-name\SDModels\lora
-```
-
-## Using a LoRA in your prompt
-
-To activate a LoRA use the syntax `withLora(my-lora-name,weight)`
-somewhere in the text of the prompt. The position doesn't matter; use
-whatever is most comfortable for you.
-
-For example, if you have a LoRA named `parchment_people.safetensors`
-in your `loras` directory, you can load it with a weight of 0.9 with a
-prompt like this one:
-
-```
-family sitting at dinner table withLora(parchment_people,0.9)
-```
-
-Add additional `withLora()` phrases to load more LoRAs.
-
-You may omit the weight entirely to default to a weight of 1.0:
-
-```
-family sitting at dinner table withLora(parchment_people)
-```
-
-If you watch the console as your prompt executes, you will see
-messages relating to the loading and execution of the LoRA. If things
-don't work as expected, note down the console messages and report them
-on the InvokeAI Issues pages or Discord channel.
-
-That's pretty much all you need to know!
-
-## Training Kohya Models
-
-InvokeAI cannot currently train LoRA models, but it can load and use
-existing LoRA ones to generate images. While there are several LoRA
-model file formats, the predominant one is ["Kohya"
-format](https://github.com/kohya-ss/sd-scripts), written by [Kohya
-S.](https://github.com/kohya-ss). InvokeAI provides support for this
-format. For creating your own Kohya models, we recommend the Windows
-GUI written by former InvokeAI-team member
-[bmaltais](https://github.com/bmaltais), which can be found at
-[kohya_ss](https://github.com/bmaltais/kohya_ss).
-
-We can also recommend the [HuggingFace DreamBooth Training
-UI](https://huggingface.co/spaces/lora-library/LoRA-DreamBooth-Training-UI),
-a paid service that supports both Textual Inversion and LoRA training.
-
-You may also be interested in [Textual
-Inversion](TEXTUAL_INVERSION.md) training, which is supported by
-InvokeAI as a text console and command-line tool.
-

docs/features/TEXTUAL_INVERSION.md

@@ -154,11 +154,8 @@ training sets will converge with 2000-3000 steps.
 
 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 is selected based on
-whether you have the `xformers` memory-efficient attention library
-installed. If `xformers` is available, the batch size will be 8,
-otherwise 3.  These values were chosen to allow training to run with
-GPUs with as little as 12 GB VRAM.
+quickly, but use more memory. The default size will run with GPUs with
+as little as 12 GB.
 
 ### Learning rate
 
@@ -175,10 +172,8 @@ learning rate to improve performance.
 
 ### Use xformers acceleration
 
-This will activate XFormers memory-efficient attention, which will
-reduce memory requirements by half or more and allow you to select a
-higher batch size. You need to have XFormers installed for this to
-have an effect.
+This will activate XFormers memory-efficient attention. You need to
+have XFormers installed for this to have an effect.
 
 ### Learning rate scheduler
 
@@ -255,49 +250,6 @@ invokeai-ti \
        --only_save_embeds
 ```
 
-## Using Distributed Training
-
-If you have multiple GPUs on one machine, or a cluster of GPU-enabled
-machines, you can activate distributed training. See the [HuggingFace
-Accelerate pages](https://huggingface.co/docs/accelerate/index) for
-full information, but the basic recipe is:
-
-1. Enter the InvokeAI developer's console command line by selecting
-option [8] from the `invoke.sh`/`invoke.bat` script.
-
-2. Configurate Accelerate using `accelerate config`:
-```sh
-accelerate config
-```
-This will guide you through the configuration process, including
-specifying how many machines you will run training on and the number
-of GPUs pe rmachine.
-
-You only need to do this once.
-
-3. Launch training from the command line using `accelerate launch`. Be sure
-that your current working directory is the InvokeAI root directory (usually
-named `invokeai` in your home directory):
-
-```sh
-accelerate launch .venv/bin/invokeai-ti \
-       --model=stable-diffusion-1.5 \
-       --resolution=512 \
-       --learnable_property=object \
-       --initializer_token='*' \
-       --placeholder_token='<shraddha>' \
-       --train_data_dir=/home/lstein/invokeai/text-inversion-training-data/shraddha \
-       --output_dir=/home/lstein/invokeai/text-inversion-training/shraddha \
-       --scale_lr \
-       --train_batch_size=10 \
-       --gradient_accumulation_steps=4 \
-       --max_train_steps=2000 \
-       --learning_rate=0.0005 \
-       --lr_scheduler=constant \
-       --mixed_precision=fp16 \
-       --only_save_embeds
-```
-
 ## Using Embeddings
 
 After training completes, the resultant embeddings will be saved into your `$INVOKEAI_ROOT/embeddings/<trigger word>/learned_embeds.bin`.

docs/features/index.md

@@ -2,84 +2,62 @@
 title: Overview
 ---
 
--   The Basics
+Here you can find the documentation for InvokeAI's various features.
 
-    -   The [Web User Interface](WEB.md)
+## The Basics
+### * The [Web User Interface](WEB.md)
+Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
 
-        Guide to the Web interface. Also see the
-        [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
+### * The [Unified Canvas](UNIFIED_CANVAS.md)
+Build complex scenes by combine and modifying multiple images in a stepwise
+fashion. This feature combines img2img, inpainting and outpainting in
+a single convenient digital artist-optimized user interface.
 
-    -   The [Unified Canvas](UNIFIED_CANVAS.md)
+### * The [Command Line Interface (CLI)](CLI.md)
+Scriptable access to InvokeAI's features.
 
-        Build complex scenes by combine and modifying multiple images in a
-        stepwise fashion. This feature combines img2img, inpainting and
-        outpainting in a single convenient digital artist-optimized user
-        interface.
+## Image Generation
+### * [Prompt Engineering](PROMPTS.md)
+Get the images you want with the InvokeAI  prompt engineering language.
 
-    -   The [Command Line Interface (CLI)](CLI.md)
+## * [Post-Processing](POSTPROCESS.md)
+Restore mangled faces and make images larger with upscaling. Also see the [Embiggen Upscaling Guide](EMBIGGEN.md).
 
-        Scriptable access to InvokeAI's features.
+## * The [Concepts Library](CONCEPTS.md)
+Add custom subjects and styles using HuggingFace's repository of embeddings.
 
-    -   [Visual Manual for InvokeAI](https://docs.google.com/presentation/d/e/2PACX-1vSE90aC7bVVg0d9KXVMhy-Wve-wModgPFp7AGVTOCgf4xE03SnV24mjdwldolfCr59D_35oheHe4Cow/pub?start=false&loop=true&delayms=60000) (contributed by Statcomm)
+### * [Image-to-Image Guide for the CLI](IMG2IMG.md)
+Use a seed image to build new creations in the CLI.
 
--   Image Generation
+### * [Inpainting Guide for the CLI](INPAINTING.md)
+Selectively erase and replace portions of an existing image in the CLI.
 
-    -   [Prompt Engineering](PROMPTS.md)
+### * [Outpainting Guide for the CLI](OUTPAINTING.md)
+Extend the borders of the image with an "outcrop" function within the CLI.
 
-        Get the images you want with the InvokeAI prompt engineering language.
+### * [Generating Variations](VARIATIONS.md)
+Have an image you like and want to generate many more like it? Variations
+are the ticket.
 
-    -   [Post-Processing](POSTPROCESS.md)
+## Model Management
 
-        Restore mangled faces and make images larger with upscaling. Also see
-        the [Embiggen Upscaling Guide](EMBIGGEN.md).
+## * [Model Installation](../installation/050_INSTALLING_MODELS.md)
+Learn how to import third-party models and switch among them. This
+guide also covers optimizing models to load quickly.
 
-    -   The [Concepts Library](CONCEPTS.md)
+## * [Merging Models](MODEL_MERGING.md)
+Teach an old model new tricks. Merge 2-3 models together to create a
+new model that combines characteristics of the originals.
 
-        Add custom subjects and styles using HuggingFace's repository of
-        embeddings.
+## * [Textual Inversion](TEXTUAL_INVERSION.md)
+Personalize models by adding your own style or subjects.
 
-    -   [Image-to-Image Guide for the CLI](IMG2IMG.md)
+# Other Features
 
-        Use a seed image to build new creations in the CLI.
+## * [The NSFW Checker](NSFW.md)
+Prevent InvokeAI from displaying unwanted racy images.
 
-    -   [Inpainting Guide for the CLI](INPAINTING.md)
-
-        Selectively erase and replace portions of an existing image in the CLI.
-
-    -   [Outpainting Guide for the CLI](OUTPAINTING.md)
-
-        Extend the borders of the image with an "outcrop" function within the
-        CLI.
-
-    -   [Generating Variations](VARIATIONS.md)
-
-        Have an image you like and want to generate many more like it?
-        Variations are the ticket.
-
--   Model Management
-
-    -   [Model Installation](../installation/050_INSTALLING_MODELS.md)
-
-        Learn how to import third-party models and switch among them. This guide
-        also covers optimizing models to load quickly.
-
-    -   [Merging Models](MODEL_MERGING.md)
-
-        Teach an old model new tricks. Merge 2-3 models together to create a new
-        model that combines characteristics of the originals.
-
-    -   [Textual Inversion](TEXTUAL_INVERSION.md)
-
-        Personalize models by adding your own style or subjects.
-
--   Other Features
-
-    -   [The NSFW Checker](NSFW.md)
-
-        Prevent InvokeAI from displaying unwanted racy images.
-
-    -   [Miscellaneous](OTHER.md)
-
-        Run InvokeAI on Google Colab, generate images with repeating patterns,
-        batch process a file of prompts, increase the "creativity" of image
-        generation by adding initial noise, and more!
+## * [Miscellaneous](OTHER.md)
+Run InvokeAI on Google Colab, generate images with repeating patterns,
+batch process a file of prompts, increase the "creativity" of image
+generation by adding initial noise, and more!

docs/help/IDE-Settings/index.md

@@ -1,4 +0,0 @@
-# :octicons-file-code-16: IDE-Settings
-
-Here we will share settings for IDEs used by our developers, maybe you can find
-something interestening which will help to boost your development efficency 🔥

docs/help/IDE-Settings/vs-code.md

@@ -1,250 +0,0 @@
----
-title: Visual Studio Code
----
-
-# :material-microsoft-visual-studio-code:Visual Studio Code
-
-The Workspace Settings are stored in the project (repository) root and get
-higher priorized than your user settings.
-
-This helps to have different settings for different projects, while the user
-settings get used as a default value if no workspace settings are provided.
-
-## tasks.json
-
-First we will create a task configuration which will create a virtual
-environment and update the deps (pip, setuptools and wheel).
-
-Into this venv we will then install the pyproject.toml in editable mode with
-dev, docs and test dependencies.
-
-```json title=".vscode/tasks.json"
-{
-    // See https://go.microsoft.com/fwlink/?LinkId=733558
-    // for the documentation about the tasks.json format
-    "version": "2.0.0",
-    "tasks": [
-        {
-            "label": "Create virtual environment",
-            "detail": "Create .venv and upgrade pip, setuptools and wheel",
-            "command": "python3",
-            "args": [
-                "-m",
-                "venv",
-                ".venv",
-                "--prompt",
-                "InvokeAI",
-                "--upgrade-deps"
-            ],
-            "runOptions": {
-                "instanceLimit": 1,
-                "reevaluateOnRerun": true
-            },
-            "group": {
-                "kind": "build"
-            },
-            "presentation": {
-                "echo": true,
-                "reveal": "always",
-                "focus": false,
-                "panel": "shared",
-                "showReuseMessage": true,
-                "clear": false
-            }
-        },
-        {
-            "label": "build InvokeAI",
-            "detail": "Build pyproject.toml with extras dev, docs and test",
-            "command": "${workspaceFolder}/.venv/bin/python3",
-            "args": [
-                "-m",
-                "pip",
-                "install",
-                "--use-pep517",
-                "--editable",
-                ".[dev,docs,test]"
-            ],
-            "dependsOn": "Create virtual environment",
-            "dependsOrder": "sequence",
-            "group": {
-                "kind": "build",
-                "isDefault": true
-            },
-            "presentation": {
-                "echo": true,
-                "reveal": "always",
-                "focus": false,
-                "panel": "shared",
-                "showReuseMessage": true,
-                "clear": false
-            }
-        }
-    ]
-}
-```
-
-The fastest way to build InvokeAI now is ++cmd+shift+b++
-
-## launch.json
-
-This file is used to define debugger configurations, so that you can one-click
-launch and monitor the application, set halt points to inspect specific states,
-...
-
-```json title=".vscode/launch.json"
-{
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "invokeai web",
-            "type": "python",
-            "request": "launch",
-            "program": ".venv/bin/invokeai",
-            "justMyCode": true
-        },
-        {
-            "name": "invokeai cli",
-            "type": "python",
-            "request": "launch",
-            "program": ".venv/bin/invokeai",
-            "justMyCode": true
-        },
-        {
-            "name": "mkdocs serve",
-            "type": "python",
-            "request": "launch",
-            "program": ".venv/bin/mkdocs",
-            "args": ["serve"],
-            "justMyCode": true
-        }
-    ]
-}
-```
-
-Then you only need to hit ++f5++ and the fun begins :nerd: (It is asumed that
-you have created a virtual environment via the [tasks](#tasksjson) from the
-previous step.)
-
-## extensions.json
-
-A list of recommended vscode-extensions to make your life easier:
-
-```json title=".vscode/extensions.json"
-{
-    "recommendations": [
-        "editorconfig.editorconfig",
-        "github.vscode-pull-request-github",
-        "ms-python.black-formatter",
-        "ms-python.flake8",
-        "ms-python.isort",
-        "ms-python.python",
-        "ms-python.vscode-pylance",
-        "redhat.vscode-yaml",
-        "tamasfe.even-better-toml",
-        "eamodio.gitlens",
-        "foxundermoon.shell-format",
-        "timonwong.shellcheck",
-        "esbenp.prettier-vscode",
-        "davidanson.vscode-markdownlint",
-        "yzhang.markdown-all-in-one",
-        "bierner.github-markdown-preview",
-        "ms-azuretools.vscode-docker",
-        "mads-hartmann.bash-ide-vscode"
-    ]
-}
-```
-
-## settings.json
-
-With bellow settings your files already get formated when you save them (only
-your modifications if available), which will help you to not run into trouble
-with the pre-commit hooks. If the hooks fail, they will prevent you from
-commiting, but most hooks directly add a fixed version, so that you just need to
-stage and commit them:
-
-```json title=".vscode/settings.json"
-{
-    "[json]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.quickSuggestions": {
-            "comments": false,
-            "strings": true,
-            "other": true
-        },
-        "editor.suggest.insertMode": "replace",
-        "gitlens.codeLens.scopes": ["document"]
-    },
-    "[jsonc]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[python]": {
-        "editor.defaultFormatter": "ms-python.black-formatter",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "file"
-    },
-    "[toml]": {
-        "editor.defaultFormatter": "tamasfe.even-better-toml",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[yaml]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[markdown]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.rulers": [80],
-        "editor.unicodeHighlight.ambiguousCharacters": false,
-        "editor.unicodeHighlight.invisibleCharacters": false,
-        "diffEditor.ignoreTrimWhitespace": false,
-        "editor.wordWrap": "on",
-        "editor.quickSuggestions": {
-            "comments": "off",
-            "strings": "off",
-            "other": "off"
-        },
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[shellscript]": {
-        "editor.defaultFormatter": "foxundermoon.shell-format"
-    },
-    "[ignore]": {
-        "editor.defaultFormatter": "foxundermoon.shell-format"
-    },
-    "editor.rulers": [88],
-    "evenBetterToml.formatter.alignEntries": false,
-    "evenBetterToml.formatter.allowedBlankLines": 1,
-    "evenBetterToml.formatter.arrayAutoExpand": true,
-    "evenBetterToml.formatter.arrayTrailingComma": true,
-    "evenBetterToml.formatter.arrayAutoCollapse": true,
-    "evenBetterToml.formatter.columnWidth": 88,
-    "evenBetterToml.formatter.compactArrays": true,
-    "evenBetterToml.formatter.compactInlineTables": true,
-    "evenBetterToml.formatter.indentEntries": false,
-    "evenBetterToml.formatter.inlineTableExpand": true,
-    "evenBetterToml.formatter.reorderArrays": true,
-    "evenBetterToml.formatter.reorderKeys": true,
-    "evenBetterToml.formatter.compactEntries": false,
-    "evenBetterToml.schema.enabled": true,
-    "python.analysis.typeCheckingMode": "basic",
-    "python.formatting.provider": "black",
-    "python.languageServer": "Pylance",
-    "python.linting.enabled": true,
-    "python.linting.flake8Enabled": true,
-    "python.testing.unittestEnabled": false,
-    "python.testing.pytestEnabled": true,
-    "python.testing.pytestArgs": [
-        "tests",
-        "--cov=ldm",
-        "--cov-branch",
-        "--cov-report=term:skip-covered"
-    ],
-    "yaml.schemas": {
-        "https://json.schemastore.org/prettierrc.json": "${workspaceFolder}/.prettierrc.yaml"
-    }
-}
-```

docs/help/contributing/010_PULL_REQUEST.md

@@ -1,135 +0,0 @@
----
-title: Pull-Request
----
-
-# :octicons-git-pull-request-16: Pull-Request
-
-## pre-requirements
-
-To follow the steps in this tutorial you will need:
-
--   [GitHub](https://github.com) account
--   [git](https://git-scm.com/downloads) source controll
--   Text / Code Editor (personally I preffer
-    [Visual Studio Code](https://code.visualstudio.com/Download))
--   Terminal:
-    -   If you are on Linux/MacOS you can use bash or zsh
-    -   for Windows Users the commands are written for PowerShell
-
-## Fork Repository
-
-The first step to be done if you want to contribute to InvokeAI, is to fork the
-rpeository.
-
-Since you are already reading this doc, the easiest way to do so is by clicking
-[here](https://github.com/invoke-ai/InvokeAI/fork). You could also open
-[InvokeAI](https://github.com/invoke-ai/InvoekAI) and click on the "Fork" Button
-in the top right.
-
-## Clone your fork
-
-After you forked the Repository, you should clone it to your dev machine:
-
-=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
-
-    ``` sh
-    git clone https://github.com/<github username>/InvokeAI \
-    && cd InvokeAI
-    ```
-
-=== ":fontawesome-brands-windows:Windows"
-
-    ``` powershell
-    git clone https://github.com/<github username>/InvokeAI `
-    && cd InvokeAI
-    ```
-
-## Install in Editable Mode
-
-To install InvokeAI in editable mode, (as always) we recommend to create and
-activate a venv first. Afterwards you can install the InvokeAI Package,
-including dev and docs extras in editable mode, follwed by the installation of
-the pre-commit hook:
-
-=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
-
-    ``` sh
-    python -m venv .venv \
-      --prompt InvokeAI \
-      --upgrade-deps \
-    && source .venv/bin/activate \
-    && pip install \
-      --upgrade-deps \
-      --use-pep517 \
-      --editable=".[dev,docs]" \
-    && pre-commit install
-    ```
-
-=== ":fontawesome-brands-windows:Windows"
-
-    ``` powershell
-    python -m venv .venv `
-      --prompt InvokeAI `
-      --upgrade-deps `
-    && .venv/scripts/activate.ps1 `
-    && pip install `
-      --upgrade `
-      --use-pep517 `
-      --editable=".[dev,docs]" `
-    && pre-commit install
-    ```
-
-## Create a branch
-
-Make sure you are on main branch, from there create your feature branch:
-
-=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
-
-    ``` sh
-    git checkout main \
-    && git pull \
-    && git checkout -B <branch name>
-    ```
-
-=== ":fontawesome-brands-windows:Windows"
-
-    ``` powershell
-    git checkout main `
-    && git pull `
-    && git checkout -B <branch name>
-    ```
-
-## Commit your changes
-
-When you are done with adding / updating content, you need to commit those
-changes to your repository before you can actually open an PR:
-
-```{ .sh .annotate }
-git add <files you have changed> # (1)!
-git commit -m "A commit message which describes your change"
-git push
-```
-
-1. Replace this with a space seperated list of the files you changed, like:
-   `README.md foo.sh bar.json baz`
-
-## Create a Pull Request
-
-After pushing your changes, you are ready to create a Pull Request. just head
-over to your fork on [GitHub](https://github.com), which should already show you
-a message that there have been recent changes on your feature branch and a green
-button which you could use to create the PR.
-
-The default target for your PRs would be the main branch of
-[invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)
-
-Another way would be to create it in VS-Code or via the GitHub CLI (or even via
-the GitHub CLI in a VS-Code Terminal Window 🤭):
-
-```sh
-gh pr create
-```
-
-The CLI will inform you if there are still unpushed commits on your branch. It
-will also prompt you for things like the the Title and the Body (Description) if
-you did not already pass them as arguments.

docs/help/contributing/020_ISSUES.md

@@ -1,26 +0,0 @@
----
-title: Issues
----
-
-# :octicons-issue-opened-16: Issues
-
-## :fontawesome-solid-bug: Report a bug
-
-If you stumbled over a bug while using InvokeAI, we would apreciate it a lot if
-you
-[open a issue](https://github.com/invoke-ai/InvokeAI/issues/new?assignees=&labels=bug&template=BUG_REPORT.yml&title=%5Bbug%5D%3A+)
-to inform us about the details so that our developers can look into it.
-
-If you also know how to fix the bug, take a look [here](010_PULL_REQUEST.md) to
-find out how to create a Pull Request.
-
-## Request a feature
-
-If you have a idea for a new feature on your mind which you would like to see in
-InvokeAI, there is a
-[feature request](https://github.com/invoke-ai/InvokeAI/issues/new?assignees=&labels=bug&template=BUG_REPORT.yml&title=%5Bbug%5D%3A+)
-available in the issues section of the repository.
-
-If you are just curious which features already got requested you can find the
-overview of open requests
-[here](https://github.com/invoke-ai/InvokeAI/labels/enhancement)

docs/help/contributing/030_DOCS.md

@@ -1,32 +0,0 @@
----
-title: docs
----
-
-# :simple-readthedocs: MkDocs-Material
-
-If you want to contribute to the docs, there is a easy way to verify the results
-of your changes before commiting them.
-
-Just follow the steps in the [Pull-Requests](010_PULL_REQUEST.md) docs, there we
-already
-[create a venv and install the docs extras](010_PULL_REQUEST.md#install-in-editable-mode).
-When installed it's as simple as:
-
-```sh
-mkdocs serve
-```
-
-This will build the docs locally and serve them on your local host, even
-auto-refresh is included, so you can just update a doc, save it and tab to the
-browser, without the needs of restarting the `mkdocs serve`.
-
-More information about the "mkdocs flavored markdown syntax" can be found
-[here](https://squidfunk.github.io/mkdocs-material/reference/).
-
-## :material-microsoft-visual-studio-code:VS-Code
-
-We also provide a
-[launch configuration for VS-Code](../IDE-Settings/vs-code.md#launchjson) which
-includes a `mkdocs serve` entrypoint as well. You also don't have to worry about
-the formatting since this is automated via prettier, but this is of course not
-limited to VS-Code.

docs/help/contributing/090_NODE_TRANSFORMATION.md

@@ -1,76 +0,0 @@
-# Tranformation to nodes
-
-## Current state
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img| generate(generate);
-    web --> |txt2img| generate(generate);
-    cli --> |txt2img| generate(generate);
-    cli --> |img2img| generate(generate);
-    generate --> model_manager;
-    generate --> generators;
-    generate --> ti_manager[TI Manager];
-    generate --> etc;
-```
-
-## Transitional Architecture
-
-### first step
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img| img2img_node(Img2img node);
-    web --> |txt2img| generate(generate);
-    img2img_node --> model_manager;
-    img2img_node --> generators;
-    cli --> |txt2img| generate;
-    cli --> |img2img| generate;
-    generate --> model_manager;
-    generate --> generators;
-    generate --> ti_manager[TI Manager];
-    generate --> etc;
-```
-
-### second step
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img| img2img_node(img2img node);
-    img2img_node --> model_manager;
-    img2img_node --> generators;
-    web --> |txt2img| txt2img_node(txt2img node);
-    cli --> |txt2img| txt2img_node;
-    cli --> |img2img| generate(generate);
-    generate --> model_manager;
-    generate --> generators;
-    generate --> ti_manager[TI Manager];
-    generate --> etc;
-    txt2img_node --> model_manager;
-    txt2img_node --> generators;
-    txt2img_node --> ti_manager[TI Manager];
-```
-
-## Final Architecture
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img|img2img_node(img2img node);
-    cli --> |img2img|img2img_node;
-    web --> |txt2img|txt2img_node(txt2img node);
-    cli --> |txt2img|txt2img_node;
-    img2img_node --> model_manager;
-    txt2img_node --> model_manager;
-    img2img_node --> generators;
-    txt2img_node --> generators;
-    img2img_node --> ti_manager[TI Manager];
-    txt2img_node --> ti_manager[TI Manager];
-```

docs/help/contributing/index.md

@@ -1,16 +0,0 @@
----
-title: Contributing
----
-
-# :fontawesome-solid-code-commit: Contributing
-
-There are different ways how you can contribute to
-[InvokeAI](https://github.com/invoke-ai/InvokeAI), like Translations, opening
-Issues for Bugs or ideas how to improve.
-
-This Section of the docs will explain some of the different ways of how you can
-contribute to make it easier for newcommers as well as advanced users :nerd:
-
-If you want to contribute code, but you do not have an exact idea yet, take a
-look at the currently open
-[:fontawesome-solid-bug: Bug Reports](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)

docs/help/index.md

@@ -1,12 +0,0 @@
-# :material-help:Help
-
-If you are looking for help with the installation of InvokeAI, please take a
-look into the [Installation](../installation/index.md) section of the docs.
-
-Here you will find help to topics like
-
--   how to contribute
--   configuration recommendation for IDEs
-
-If you have an Idea about what's missing and aren't scared from contributing,
-just take a look at [DOCS](./contributing/030_DOCS.md) to find out how to do so.

docs/index.md

@@ -2,8 +2,6 @@
 title: Home
 ---
 
-# :octicons-home-16: Home
-
 <!--
   The Docs you find here (/docs/*) are built and deployed via mkdocs. If you want to run a local version to verify your changes, it's as simple as::
 
@@ -31,36 +29,36 @@ title: Home
 [![github open prs badge]][github open prs link]
 
 [ci checks on dev badge]:
-    https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
+  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
 [ci checks on dev link]:
-    https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
+  https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
 [ci checks on main badge]:
-    https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
+  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
 [ci checks on main link]:
-    https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
+  https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
 [github forks badge]:
-    https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
 [github forks link]:
-    https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
+  https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
 [github open issues badge]:
-    https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
 [github open issues link]:
-    https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
+  https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
 [github open prs badge]:
-    https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
 [github open prs link]:
-    https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
+  https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
 [github stars badge]:
-    https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
 [github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
 [latest commit to dev badge]:
-    https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
+  https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
 [latest commit to dev link]:
-    https://github.com/invoke-ai/InvokeAI/commits/development
+  https://github.com/invoke-ai/InvokeAI/commits/development
 [latest release badge]:
-    https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
+  https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 
 </div>
@@ -89,24 +87,24 @@ Q&A</a>]
 
 You wil need one of the following:
 
--   :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
--   :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
-    only)
--   :fontawesome-brands-apple: An Apple computer with an M1 chip.
+- :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
+- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
+  only)
+- :fontawesome-brands-apple: An Apple computer with an M1 chip.
 
 We do **not recommend** the following video cards due to issues with their
 running in half-precision mode and having insufficient VRAM to render 512x512
 images in full-precision mode:
 
--   NVIDIA 10xx series cards such as the 1080ti
--   GTX 1650 series cards
--   GTX 1660 series cards
+- NVIDIA 10xx series cards such as the 1080ti
+- GTX 1650 series cards
+- GTX 1660 series cards
 
 ### :fontawesome-solid-memory: Memory and Disk
 
--   At least 12 GB Main Memory RAM.
--   At least 18 GB of free disk space for the machine learning model, Python,
-    and all its dependencies.
+- At least 12 GB Main Memory RAM.
+- At least 18 GB of free disk space for the machine learning model, Python, and
+  all its dependencies.
 
 ## :octicons-package-dependencies-24: Installation
 
@@ -115,407 +113,133 @@ either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
 ### [Installation Getting Started Guide](installation)
-
 #### [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
-
 This method is recommended for 1st time users
-
 #### [Manual Installation](installation/020_INSTALL_MANUAL.md)
-
 This method is recommended for experienced users and developers
-
 #### [Docker Installation](installation/040_INSTALL_DOCKER.md)
-
 This method is recommended for those familiar with running Docker containers
-
 ### Other Installation Guides
-
--   [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
--   [XFormers](installation/070_INSTALL_XFORMERS.md)
--   [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
--   [Installing New Models](installation/050_INSTALLING_MODELS.md)
+  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
+  - [XFormers](installation/070_INSTALL_XFORMERS.md)
+  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
+  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
 
 ## :octicons-gift-24: InvokeAI Features
 
 ### The InvokeAI Web Interface
-
--   [WebUI overview](features/WEB.md)
--   [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
--   [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
--   [Visual Manual for InvokeAI v2.3.1](https://docs.google.com/presentation/d/e/2PACX-1vSE90aC7bVVg0d9KXVMhy-Wve-wModgPFp7AGVTOCgf4xE03SnV24mjdwldolfCr59D_35oheHe4Cow/pub?start=false&loop=true&delayms=60000) (contributed by Statcomm)
-
+- [WebUI overview](features/WEB.md)
+- [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
+- [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
 <!-- separator -->
-
-<!-- separator -->
-
 ### The InvokeAI Command Line Interface
-
--   [Command Line Interace Reference Guide](features/CLI.md)
+- [Command Line Interace Reference Guide](features/CLI.md)
 <!-- separator -->
-
 ### Image Management
-
--   [Image2Image](features/IMG2IMG.md)
--   [Inpainting](features/INPAINTING.md)
--   [Outpainting](features/OUTPAINTING.md)
--   [Adding custom styles and subjects](features/CONCEPTS.md)
--   [Using LoRA models](features/LORAS.md)
--   [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
--   [Embiggen upscaling](features/EMBIGGEN.md)
--   [Other Features](features/OTHER.md)
+- [Image2Image](features/IMG2IMG.md)
+- [Inpainting](features/INPAINTING.md)
+- [Outpainting](features/OUTPAINTING.md)
+- [Adding custom styles and subjects](features/CONCEPTS.md)
+- [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
+- [Embiggen upscaling](features/EMBIGGEN.md)
+- [Other Features](features/OTHER.md)
 
 <!-- separator -->
-
 ### Model Management
-
--   [Installing](installation/050_INSTALLING_MODELS.md)
--   [Model Merging](features/MODEL_MERGING.md)
--   [Adding custom styles and subjects via embeddings](features/CONCEPTS.md)
--   [Textual Inversion](features/TEXTUAL_INVERSION.md)
--   [Not Safe for Work (NSFW) Checker](features/NSFW.md)
+- [Installing](installation/050_INSTALLING_MODELS.md)
+- [Model Merging](features/MODEL_MERGING.md)
+- [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
+- [Textual Inversion](features/TEXTUAL_INVERSION.md)
+- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
 <!-- seperator -->
-
 ### Prompt Engineering
-
--   [Prompt Syntax](features/PROMPTS.md)
--   [Generating Variations](features/VARIATIONS.md)
+- [Prompt Syntax](features/PROMPTS.md)
+- [Generating Variations](features/VARIATIONS.md)
 
 ## :octicons-log-16: Latest Changes
 
-### v2.3.3 <small>(29 March 2023)</small>
-
-#### Bug Fixes
-1. When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
-2. Textual inversion will select an appropriate batchsize based on whether `xformers` is active, and will default to `xformers` enabled if the library is detected.
-3. The batch script log file names have been fixed to be compatible with Windows.
-4. Occasional corruption of the `.next_prefix` file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
-5. An infinite loop when opening the developer's console from within the `invoke.sh` script has been corrected.
-
-#### Enhancements
-1. It is now possible to load and run several community-contributed SD-2.0 based models, including the infamous "Illuminati" model.
-2. The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI `embeddings` directory.
-3. If no `--model` is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
-4. On Linux systems, the `invoke.sh` launcher now uses a prettier console-based interface. To take advantage of it, install the `dialog` package using your package manager (e.g. `sudo apt install dialog`).
-5. When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
-```
-my-favorite-model.ckpt
-my-favorite-model.yaml
-my-favorite-model.vae.pt      # or my-favorite-model.vae.safetensors
-```
-
-### v2.3.2 <small>(13 March 2023)</small>
-
-#### Bugfixes
-
-Since version 2.3.1 the following bugs have been fixed:
-
-1. Black images appearing for potential NSFW images when generating with legacy checkpoint models and both `--no-nsfw_checker` and `--ckpt_convert` turned on.
-2. Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
-3. The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
-4. When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
-5. Crashes that occurred during model merging.
-6. Restore previous naming of Stable Diffusion base and 768 models.
-7. Upgraded to latest versions of `diffusers`, `transformers`, `safetensors` and `accelerate` libraries upstream. We hope that this will fix the `assertion NDArray > 2**32` issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
-
-As part of the upgrade to `diffusers`, the location of the diffusers-based models has changed from `models/diffusers` to `models/hub`. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your `models/diffusers` directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
-
-#### New "Invokeai-batch" script
-
-2.3.2 introduces a new command-line only script called
-`invokeai-batch` that can be used to generate hundreds of images from
-prompts and settings that vary systematically. This can be used to try
-the same prompt across multiple combinations of models, steps, CFG
-settings and so forth. It also allows you to template prompts and
-generate a combinatorial list like: ``` a shack in the mountains,
-photograph a shack in the mountains, watercolor a shack in the
-mountains, oil painting a chalet in the mountains, photograph a chalet
-in the mountains, watercolor a chalet in the mountains, oil painting a
-shack in the desert, photograph ...  ```
-
-If you have a system with multiple GPUs, or a single GPU with lots of
-VRAM, you can parallelize generation across the combinatorial set,
-reducing wait times and using your system's resources efficiently
-(make sure you have good GPU cooling).
-
-To try `invokeai-batch` out. Launch the "developer's console" using
-the `invoke` launcher script, or activate the invokeai virtual
-environment manually. From the console, give the command
-`invokeai-batch --help` in order to learn how the script works and
-create your first template file for dynamic prompt generation.
-
-### v2.3.1 <small>(26 February 2023)</small>
-
-This is primarily a bugfix release, but it does provide several new features that will improve the user experience. 
-
-#### Enhanced support for model management
-
-InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
-
-There are three ways of accessing the model management features:
-
-1. ***From the WebUI***, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
-
-![image](https://user-images.githubusercontent.com/111189/220638091-918492cc-0719-4194-b033-3741e8289b30.png)
-
-2. **Using the Model Installer App**
-
-Choose option (5) _download and install models_ from the `invoke` launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
-
-Command-line users can start this app using the command `invokeai-model-install`.
-
-![image](https://user-images.githubusercontent.com/111189/220660363-22ff3a2e-8082-410e-a818-d2b3a0529bac.png)
-
-3. **Using the Command Line Client (CLI)**
-
-The `!install_model` and `!convert_model` commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
-
-Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do **not** need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
-
-Please see [INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/) for more information on model management.
-
-#### An Improved Installer Experience
-
-The installer now launches a console-based UI for setting and changing commonly-used startup options:
-
-![image](https://user-images.githubusercontent.com/111189/220644777-3d3a90ca-f9e2-4e6d-93da-cbdd66bf12f3.png)
-
-After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time,  you can edit the startup options by launching `invoke.sh`/`invoke.bat` and entering option (6) _change InvokeAI startup options_
-
-Command-line users can launch the new configure app using `invokeai-configure`.
-
-This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch `invoke.sh` or `invoke.bat` and choose option (9) _update InvokeAI_ . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
-
-![image](https://user-images.githubusercontent.com/111189/220650124-30a77137-d9cd-406e-a87d-d8283f99a4b3.png)
-
-Command-line users can run this interface by typing `invokeai-configure`
-
-#### Image Symmetry Options
-
-There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting _Symmetry_ from the image generation settings, or within the CLI by using the options `--h_symmetry_time_pct` and `--v_symmetry_time_pct` (these can be abbreviated to `--h_sym` and `--v_sym` like all other options).
-
-![image](https://user-images.githubusercontent.com/111189/220658687-47fd0f2c-7069-4d95-aec9-7196fceb360d.png)
-
-#### A New Unified Canvas Look
-
-This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select _Use Canvas Beta Layout_:
-
-![image](https://user-images.githubusercontent.com/111189/220646958-b7eca95e-dc39-4cd2-b277-63eac98ed446.png)
-
-Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
-
-![image](https://user-images.githubusercontent.com/111189/220647560-4a9265a1-6926-44f9-9d08-e1ef2ce61ff8.png)
-
-#### Model conversion and merging within the WebUI
-
-The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the `invoke.sh`/`invoke.bat` scripts.
-
-#### An easier way to contribute translations to the WebUI
-
-We have migrated our translation efforts to [Weblate](https://hosted.weblate.org/engage/invokeai/), a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief [translation guide](https://github.com/invoke-ai/InvokeAI/blob/v2.3.1/docs/other/TRANSLATION.md) for more information on how to contribute.
-
-#### Numerous internal bugfixes and performance issues
-
-This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to `diffusers 0.13.0`, and using the `compel` library for prompt parsing. See [Detailed Change Log](#full-change-log) for a detailed list of bugs caught and squished.
-
-#### Summary of InvokeAI command line scripts (all accessible via the launcher menu)
-
-| Command                  | Description                                                         |
-|--------------------------|---------------------------------------------------------------------|
-| `invokeai`               | Command line interface                                              |
-| `invokeai --web`         | Web interface                                                       |
-| `invokeai-model-install` | Model installer with console forms-based front end                  |
-| `invokeai-ti --gui`      | Textual inversion, with a console forms-based front end             |
-| `invokeai-merge --gui`   | Model merging, with a console forms-based front end                 |
-| `invokeai-configure`     | Startup configuration; can also be used to reinstall support models |
-| `invokeai-update`        | InvokeAI software updater                                           |
-
-
 ### v2.3.0 <small>(9 February 2023)</small>
 
 #### Migration to Stable Diffusion `diffusers` models
 
-Previous versions of InvokeAI supported the original model file format
-introduced with Stable Diffusion 1.4. In the original format, known variously as
-"checkpoint", or "legacy" format, there is a single large weights file ending
-with `.ckpt` or `.safetensors`. Though this format has served the community
-well, it has a number of disadvantages, including file size, slow loading times,
-and a variety of non-standard variants that require special-case code to handle.
-In addition, because checkpoint files are actually a bundle of multiple machine
-learning sub-models, it is hard to swap different sub-models in and out, or to
-share common sub-models. A new format, introduced by the StabilityAI company in
-collaboration with HuggingFace, is called `diffusers` and consists of a
-directory of individual models. The most immediate benefit of `diffusers` is
-that they load from disk very quickly. A longer term benefit is that in the near
-future `diffusers` models will be able to share common sub-models, dramatically
-reducing disk space when you have multiple fine-tune models derived from the
-same base.
+Previous versions of InvokeAI supported the original model file format introduced with Stable Diffusion 1.4. In the original format, known variously as "checkpoint", or "legacy" format, there is a single large weights file ending with `.ckpt` or `.safetensors`. Though this format has served the community well, it has a number of disadvantages, including file size, slow loading times, and a variety of non-standard variants that require special-case code to handle. In addition, because checkpoint files are actually a bundle of multiple machine learning sub-models, it is hard to swap different sub-models in and out, or to share common sub-models. A new format, introduced by the StabilityAI company in collaboration with HuggingFace, is called `diffusers` and consists of a directory of individual models. The most immediate benefit of `diffusers` is that they load from disk very quickly. A longer term benefit is that in the near future `diffusers` models will be able to share common sub-models, dramatically reducing disk space when you have multiple fine-tune models derived from the same base.
 
-When you perform a new install of version 2.3.0, you will be offered the option
-to install the `diffusers` versions of a number of popular SD models, including
-Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of
-2.1). These will act and work just like the checkpoint versions. Do not be
-concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk!
-InvokeAI 2.3.0 can still load these and generate images from them without any
-extra intervention on your part.
+When you perform a new install of version 2.3.0, you will be offered the option to install the `diffusers` versions of a number of popular SD models, including Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of 2.1). These will act and work just like the checkpoint versions. Do not be concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk! InvokeAI 2.3.0 can still load these and generate images from them without any extra intervention on your part.
 
-To take advantage of the optimized loading times of `diffusers` models, InvokeAI
-offers options to convert legacy checkpoint models into optimized `diffusers`
-models. If you use the `invokeai` command line interface, the relevant commands
-are:
+To take advantage of the optimized loading times of `diffusers` models, InvokeAI offers options to convert legacy checkpoint models into optimized `diffusers` models. If you use the `invokeai` command line interface, the relevant commands are:
 
--   `!convert_model` -- Take the path to a local checkpoint file or a URL that
-    is pointing to one, convert it into a `diffusers` model, and import it into
-    InvokeAI's models registry file.
--   `!optimize_model` -- If you already have a checkpoint model in your InvokeAI
-    models file, this command will accept its short name and convert it into a
-    like-named `diffusers` model, optionally deleting the original checkpoint
-    file.
--   `!import_model` -- Take the local path of either a checkpoint file or a
-    `diffusers` model directory and import it into InvokeAI's registry file. You
-    may also provide the ID of any diffusers model that has been published on
-    the
-    [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads)
-    and it will be downloaded and installed automatically.
+* `!convert_model` -- Take the path to a local checkpoint file or a URL that is pointing to one, convert it into a `diffusers` model, and import it into InvokeAI's models registry file.
+* `!optimize_model` -- If you already have a checkpoint model in your InvokeAI models file, this command will accept its short name and convert it into a like-named `diffusers` model, optionally deleting the original checkpoint file.
+* `!import_model` -- Take the local path of either a checkpoint file or a `diffusers` model directory and import it into InvokeAI's registry file. You may also provide the ID of any diffusers model that has been published on the [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads) and it will be downloaded and installed automatically.
 
 The WebGUI offers similar functionality for model management.
 
-For advanced users, new command-line options provide additional functionality.
-Launching `invokeai` with the argument `--autoconvert <path to directory>` takes
-the path to a directory of checkpoint files, automatically converts them into
-`diffusers` models and imports them. Each time the script is launched, the
-directory will be scanned for new checkpoint files to be loaded. Alternatively,
-the `--ckpt_convert` argument will cause any checkpoint or safetensors model
-that is already registered with InvokeAI to be converted into a `diffusers`
-model on the fly, allowing you to take advantage of future diffusers-only
-features without explicitly converting the model and saving it to disk.
+For advanced users, new command-line options provide additional functionality. Launching `invokeai` with the argument `--autoconvert <path to directory>` takes the path to a directory of checkpoint files, automatically converts them into `diffusers` models and imports them. Each time the script is launched, the directory will be scanned for new checkpoint files to be loaded. Alternatively, the `--ckpt_convert` argument will cause any checkpoint or safetensors model that is already registered with InvokeAI to be converted into a `diffusers` model on the fly, allowing you to take advantage of future diffusers-only features without explicitly converting the model and saving it to disk.
 
-Please see
-[INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/)
-for more information on model management in both the command-line and Web
-interfaces.
+Please see [INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/) for more information on model management in both the command-line and Web interfaces.
 
 #### Support for the `XFormers` Memory-Efficient Crossattention Package
 
-On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once
-installed, the`xformers` package dramatically reduces the memory footprint of
-loaded Stable Diffusion models files and modestly increases image generation
-speed. `xformers` will be installed and activated automatically if you specify a
-CUDA system at install time.
+On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once installed,  the`xformers` package dramatically reduces the memory footprint of loaded Stable Diffusion models files and modestly increases image generation speed. `xformers` will be installed and activated automatically if you specify a CUDA system at install time.
 
-The caveat with using `xformers` is that it introduces slightly
-non-deterministic behavior, and images generated using the same seed and other
-settings will be subtly different between invocations. Generally the changes are
-unnoticeable unless you rapidly shift back and forth between images, but to
-disable `xformers` and restore fully deterministic behavior, you may launch
-InvokeAI using the `--no-xformers` option. This is most conveniently done by
-opening the file `invokeai/invokeai.init` with a text editor, and adding the
-line `--no-xformers` at the bottom.
+The caveat with using `xformers` is that it introduces slightly non-deterministic behavior, and images generated using the same seed and other settings will be subtly different between invocations. Generally the changes are unnoticeable unless you rapidly shift back and forth between images, but to disable `xformers` and restore fully deterministic behavior, you may launch InvokeAI using the `--no-xformers` option. This is most conveniently done by opening the file `invokeai/invokeai.init` with a text editor, and adding the line `--no-xformers` at the bottom.
 
 #### A Negative Prompt Box in the WebUI
 
-There is now a separate text input box for negative prompts in the WebUI. This
-is convenient for stashing frequently-used negative prompts ("mangled limbs, bad
-anatomy"). The `[negative prompt]` syntax continues to work in the main prompt
-box as well.
+There is now a separate text input box for negative prompts in the WebUI. This is convenient for stashing frequently-used negative prompts ("mangled limbs, bad anatomy"). The `[negative prompt]` syntax continues to work in the main prompt box as well.
 
-To see exactly how your prompts are being parsed, launch `invokeai` with the
-`--log_tokenization` option. The console window will then display the
-tokenization process for both positive and negative prompts.
+To see exactly how your prompts are being parsed, launch `invokeai` with the `--log_tokenization` option. The console window will then display the tokenization process for both positive and negative prompts.
 
 #### Model Merging
 
-Version 2.3.0 offers an intuitive user interface for merging up to three Stable
-Diffusion models using an intuitive user interface. Model merging allows you to
-mix the behavior of models to achieve very interesting effects. To use this,
-each of the models must already be imported into InvokeAI and saved in
-`diffusers` format, then launch the merger using a new menu item in the InvokeAI
-launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line
-with `invokeai-merge --gui`. You will be prompted to select the models to merge,
-the proportions in which to mix them, and the mixing algorithm. The script will
-create a new merged `diffusers` model and import it into InvokeAI for your use.
+Version 2.3.0 offers an intuitive user interface for merging up to three Stable Diffusion models using an intuitive user interface. Model merging allows you to mix the behavior of models to achieve very interesting effects. To use this, each of the models must already be imported into InvokeAI and saved in `diffusers` format, then launch the merger using a new menu item in the InvokeAI launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line with `invokeai-merge --gui`. You will be prompted to select the models to merge, the proportions in which to mix them, and the mixing algorithm. The script will create a new merged `diffusers` model and import it into InvokeAI for your use.
 
-See
-[MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/)
-for more details.
+See [MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/) for more details.
 
 #### Textual Inversion Training
 
-Textual Inversion (TI) is a technique for training a Stable Diffusion model to
-emit a particular subject or style when triggered by a keyword phrase. You can
-perform TI training by placing a small number of images of the subject or style
-in a directory, and choosing a distinctive trigger phrase, such as
-"pointillist-style". After successful training, The subject or style will be
-activated by including `<pointillist-style>` in your prompt.
+Textual Inversion (TI) is a technique for training a Stable Diffusion model to emit a particular subject or style when triggered by a keyword phrase. You can perform TI training by placing a small number of images of the subject or style in a directory, and choosing a distinctive trigger phrase, such as "pointillist-style". After successful training, The subject or style will be activated by including `<pointillist-style>` in your prompt.
 
-Previous versions of InvokeAI were able to perform TI, but it required using a
-command-line script with dozens of obscure command-line arguments. Version 2.3.0
-features an intuitive TI frontend that will build a TI model on top of any
-`diffusers` model. To access training you can launch from a new item in the
-launcher script or from the command line using `invokeai-ti --gui`.
+Previous versions of InvokeAI were able to perform TI, but it required using a command-line script with dozens of obscure command-line arguments. Version 2.3.0 features an intuitive TI frontend that will build a TI model on top of any `diffusers` model. To access training you can launch from a new item in the launcher script or from the command line using `invokeai-ti --gui`.
 
-See
-[TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/)
-for further details.
+See [TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/) for further details.
 
 #### A New Installer Experience
 
-The InvokeAI installer has been upgraded in order to provide a smoother and
-hopefully more glitch-free experience. In addition, InvokeAI is now packaged as
-a PyPi project, allowing developers and power-users to install InvokeAI with the
-command `pip install InvokeAI  --use-pep517`. Please see
-[Installation](#installation) for details.
+The InvokeAI installer has been upgraded in order to provide a smoother and hopefully more glitch-free experience. In addition, InvokeAI is now packaged as a PyPi project, allowing developers and power-users to install InvokeAI with the command `pip install InvokeAI  --use-pep517`. Please see [Installation](#installation) for details.
 
-Developers should be aware that the `pip` installation procedure has been
-simplified and that the `conda` method is no longer supported at all.
-Accordingly, the `environments_and_requirements` directory has been deleted from
-the repository.
+Developers should be aware that the `pip` installation procedure has been simplified and that the `conda` method is no longer supported at all. Accordingly, the `environments_and_requirements` directory has been deleted from the repository.
 
 #### Command-line name changes
 
-All of InvokeAI's functionality, including the WebUI, command-line interface,
-textual inversion training and model merging, can all be accessed from the
-`invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been
-expanded to add the new functionality. For the convenience of developers and
-power users, we have normalized the names of the InvokeAI command-line scripts:
+All of InvokeAI's functionality, including the WebUI, command-line interface, textual inversion training and model merging, can all be accessed from the `invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been expanded to add the new functionality. For the convenience of developers and power users, we have normalized the names of the InvokeAI command-line scripts:
 
--   `invokeai` -- Command-line client
--   `invokeai --web` -- Web GUI
--   `invokeai-merge --gui` -- Model merging script with graphical front end
--   `invokeai-ti --gui` -- Textual inversion script with graphical front end
--   `invokeai-configure` -- Configuration tool for initializing the `invokeai`
-    directory and selecting popular starter models.
+* `invokeai` -- Command-line client
+* `invokeai --web` -- Web GUI
+* `invokeai-merge --gui` -- Model merging script with graphical front end
+* `invokeai-ti --gui` -- Textual inversion script with graphical front end
+* `invokeai-configure` -- Configuration tool for initializing the `invokeai` directory and selecting popular starter models.
 
-For backward compatibility, the old command names are also recognized, including
-`invoke.py` and `configure-invokeai.py`. However, these are deprecated and will
-eventually be removed.
+For backward compatibility, the old command names are also recognized, including `invoke.py` and `configure-invokeai.py`. However, these are deprecated and will eventually be removed.
 
-Developers should be aware that the locations of the script's source code has
-been moved. The new locations are:
+Developers should be aware that the locations of the script's source code has been moved. The new locations are:
+* `invokeai` =>  `ldm/invoke/CLI.py`
+* `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
+* `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
+* `invokeai-merge` => `ldm/invoke/merge_diffusers`
 
--   `invokeai` => `ldm/invoke/CLI.py`
--   `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
--   `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
--   `invokeai-merge` => `ldm/invoke/merge_diffusers`
+Developers are strongly encouraged to perform an "editable" install of InvokeAI using `pip install -e .  --use-pep517` in the Git repository, and then to call the scripts using their 2.3.0 names, rather than executing the scripts directly. Developers should also be aware that the several important data files have been relocated into a new directory named `invokeai`. This includes the WebGUI's `frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used by the installer to select starter models. Eventually all InvokeAI modules will be in subdirectories of `invokeai`.
 
-Developers are strongly encouraged to perform an "editable" install of InvokeAI
-using `pip install -e .  --use-pep517` in the Git repository, and then to call
-the scripts using their 2.3.0 names, rather than executing the scripts directly.
-Developers should also be aware that the several important data files have been
-relocated into a new directory named `invokeai`. This includes the WebGUI's
-`frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used
-by the installer to select starter models. Eventually all InvokeAI modules will
-be in subdirectories of `invokeai`.
-
-Please see
-[2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0)
-for further details. For older changelogs, please visit the
+Please see [2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0) for further details.
+For older changelogs, please visit the
 **[CHANGELOG](CHANGELOG/#v223-2-december-2022)**.
 
 ## :material-target: Troubleshooting
 
-Please check out our
-**[:material-frequently-asked-questions: Troubleshooting Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)**
-to get solutions for common installation problems and other issues.
+Please check out our **[:material-frequently-asked-questions:
+Troubleshooting
+Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)** to
+get solutions for common installation problems and other issues.
 
 ## :octicons-repo-push-24: Contributing
 
@@ -541,8 +265,8 @@ thank them for their time, hard work and effort.
 For support, please use this repository's GitHub Issues tracking service. Feel
 free to send me an email if you use and like the script.
 
-Original portions of the software are Copyright (c) 2022-23 by
-[The InvokeAI Team](https://github.com/invoke-ai).
+Original portions of the software are Copyright (c) 2022-23
+by [The InvokeAI Team](https://github.com/invoke-ai).
 
 ## :octicons-book-24: Further Reading
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -417,7 +417,7 @@ Then type the following commands:
 
 === "AMD System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
 ### Corrupted configuration file

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -77,7 +77,7 @@ machine. To test, open up a terminal window and issue the following
 command:
 
 ```
-rocminfo
+rocm-smi
 ```
 
 If you get a table labeled "ROCm System Management Interface" the
@@ -95,17 +95,9 @@ recent version of Ubuntu, 22.04. However, this [community-contributed
 recipe](https://novaspirit.github.io/amdgpu-rocm-ubu22/) is reported
 to work well.
 
-After installation, please run `rocminfo` a second time to confirm
+After installation, please run `rocm-smi` a second time to confirm
 that the driver is present and the GPU is recognized. You may need to
-do a reboot in order to load the driver. In addition, if you see
-errors relating to your username not being a member of the `render`
-group, you may fix this by adding yourself to this group with the command:
-
-```
-sudo usermod -a -G render myUserName
-```
-
-(Thanks to @EgoringKosmos for the usermod recipe.)
+do a reboot in order to load the driver.
 
 ### Linux Install with a ROCm-docker Container
 
@@ -118,7 +110,7 @@ recipes are available
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
+https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 This will be done automatically for you if you use the installer

docs/installation/050_INSTALLING_MODELS.md

@@ -11,7 +11,7 @@ The model checkpoint files ('\*.ckpt') are the Stable Diffusion
 captioned images gathered from multiple sources.
 
 Originally there was only a single Stable Diffusion weights file,
-which many people named `model.ckpt`. Now there are hundreds
+which many people named `model.ckpt`. Now there are dozens or more
 that have been fine tuned to provide particulary styles, genres, or
 other features. In addition, there are several new formats that
 improve on the original checkpoint format: a `.safetensors` format
@@ -29,10 +29,9 @@ and performance are being made at a rapid pace. Among other features
 is the ability to download and install a `diffusers` model just by
 providing its HuggingFace repository ID.
 
-While InvokeAI will continue to support legacy `.ckpt` and `.safetensors`
+While InvokeAI will continue to support `.ckpt` and `.safetensors`
 models for the near future, these are deprecated and support will
-be withdrawn in version 3.0, after which all legacy models will be
-converted into diffusers at the time they are loaded.
+likely be withdrawn at some point in the not-too-distant future.
 
 This manual will guide you through installing and configuring model
 weight files and converting legacy `.ckpt` and `.safetensors` files
@@ -90,18 +89,15 @@ aware that CIVITAI hosts many models that generate NSFW content.
 !!! note
 
     InvokeAI 2.3.x does not support directly importing and
-    running Stable Diffusion version 2 checkpoint models. If you
-    try to import them, they will be automatically
-    converted into `diffusers` models on the fly. This adds about 20s
-    to loading time. To avoid this overhead, you are encouraged to
-    use one of the conversion methods described below to convert them
-    permanently.
+    running Stable Diffusion version 2 checkpoint models. You may instead
+    convert them into `diffusers` models using the conversion methods
+    described below.
 
 ## Installation
 
 There are multiple ways to install and manage models:
 
-1. The `invokeai-model-install` script which will download and install them for you.
+1. The `invokeai-configure` script which will download and install them for you.
 
 2. The command-line tool (CLI) has commands that allows you to import, configure and modify
    models files.
@@ -109,41 +105,14 @@ There are multiple ways to install and manage models:
 3. The web interface (WebUI) has a GUI for importing and managing
    models.
 
-### Installation via `invokeai-model-install`
+### Installation via `invokeai-configure`
 
-From the `invoke` launcher, choose option (5) "Download and install
-models."  This will launch the same script that prompted you to select
-models at install time. You can use this to add models that you
-skipped the first time around. It is all right to specify a model that
-was previously downloaded; the script will just confirm that the files
-are complete.
-
-This script allows you to load 3d party models. Look for a large text
-entry box labeled "IMPORT LOCAL AND REMOTE MODELS." In this box, you
-can cut and paste one or more of any of the following:
-
-1. A URL that points to a downloadable .ckpt or .safetensors file.
-2. A file path pointing to a .ckpt or .safetensors  file.
-3. A diffusers model repo_id (from HuggingFace) in the format
-   "owner/repo_name".
-4. A directory path pointing to a diffusers model directory.
-5. A directory path pointing to a directory containing a bunch of
-   .ckpt and .safetensors files. All will be imported.
-
-You can enter multiple items into the textbox, each one on a separate
-line. You can paste into the textbox using ctrl-shift-V or by dragging
-and dropping a file/directory from the desktop into the box.
-
-The script also lets you designate a directory that will be scanned
-for new model files each time InvokeAI starts up. These models will be
-added automatically.
-
-Lastly, the script gives you a checkbox option to convert legacy models
-into diffusers, or to run the legacy model directly. If you choose to
-convert, the original .ckpt/.safetensors file will **not** be deleted,
-but a new diffusers directory will be created, using twice your disk
-space. However, the diffusers version will load faster, and will be
-compatible with InvokeAI 3.0.
+From the `invoke` launcher, choose option (6) "re-run the configure
+script to download new models." This will launch the same script that
+prompted you to select models at install time. You can use this to add
+models that you skipped the first time around. It is all right to
+specify a model that was previously downloaded; the script will just
+confirm that the files are complete.
 
 ### Installation via the CLI
 
@@ -175,15 +144,19 @@ invoke> !import_model https://example.org/sd_models/martians.safetensors
 For this to work, the URL must not be password-protected. Otherwise
 you will receive a 404 error.
 
-When you import a legacy model, the CLI will try to figure out what
-type of model it is and select the correct load configuration file.
-However, one thing it can't do is to distinguish between Stable
-Diffusion 2.x models trained on 512x512 vs 768x768 images. In this
-case, the CLI will pop up a menu of choices, asking you to select
-which type of model it is. Please consult the model documentation to
-identify the correct answer, as loading with the wrong configuration
-will lead to black images. You can correct the model type after the
-fact using the `!edit_model` command.
+When you import a legacy model, the CLI will first ask you what type
+of model this is. You can indicate whether it is a model based on
+Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
+or a 1.x inpainting model. Be careful to indicate the correct model
+type, or it will not load correctly. You can correct the model type
+after the fact using the `!edit_model` command.
+
+The system will then ask you a few other questions about the model,
+including what size image it was trained on (usually 512x512), what
+name and description you wish to use for it, and whether you would
+like to install a custom VAE (variable autoencoder) file for the
+model. For recent models, the answer to the VAE question is usually
+"no," but it won't hurt to answer "yes".
 
 After importing, the model will load. If this is successful, you will
 be asked if you want to keep the model loaded in memory to start
@@ -238,6 +211,109 @@ description for the model, whether to make this the default model that
 is loaded at InvokeAI startup time, and whether to replace its
 VAE. Generally the answer to the latter question is "no".
 
+### Converting legacy models into `diffusers`
+
+The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
+models file into `diffusers` and install it.This will enable the model
+to load and run faster without loss of image quality.
+
+The usage is identical to `!import_model`. You may point the command
+to either a downloaded model file on disk, or to a (non-password
+protected) URL:
+
+```bash
+invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
+```
+
+After a successful conversion, the CLI will offer you the option of
+deleting the original `.ckpt` or `.safetensors` file.
+
+### Optimizing a previously-installed model
+
+Lastly, if you have previously installed a `.ckpt` or `.safetensors`
+file and wish to convert it into a `diffusers` model, you can do this
+without re-downloading and converting the original file using the
+`!optimize_model` command. Simply pass the short name of an existing
+installed model:
+
+```bash
+invoke> !optimize_model martians-v1.0
+```
+
+The model will be converted into `diffusers` format and replace the
+previously installed version. You will again be offered the
+opportunity to delete the original `.ckpt` or `.safetensors` file.
+
+### Related CLI Commands
+
+There are a whole series of additional model management commands in
+the CLI that you can read about in [Command-Line
+Interface](../features/CLI.md). These include:
+
+* `!models` - List all installed models
+* `!switch <model name>` - Switch to the indicated model
+* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
+* `!del_model <model name>` - Delete the indicated model
+
+### Manually editing `configs/models.yaml`
+
+
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
+
+You will need to download the desired `.ckpt/.safetensors` file and
+place it somewhere on your machine's filesystem. Alternatively, for a
+`diffusers` model, record the repo_id or download the whole model
+directory. Then using a **text** editor (e.g. the Windows Notepad
+application), open the file `configs/models.yaml`, and add a new
+stanza that follows this model:
+
+#### A legacy model
+
+A legacy `.ckpt` or `.safetensors` entry will look like this:
+
+```yaml
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./path/to/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  format: ckpt
+  width: 512
+  height: 512
+  default: false
+```
+
+Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
+
+#### A diffusers model
+
+A stanza for a `diffusers` model will look like this for a HuggingFace
+model with a repository ID:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  repo_id: captahab/arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+And for a downloaded directory:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  path: /path/to/captahab-arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+There is additional syntax for indicating an external VAE to use with
+this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
+
+After you save the modified `models.yaml` file relaunch
+`invokeai`. The new model will now be available for your use.
+
 ### Installation via the WebUI
 
 To access the WebUI Model Manager, click on the button that looks like
@@ -317,143 +393,3 @@ And here is what the same argument looks like in `invokeai.init`:
 --no-nsfw_checker
 --autoconvert /home/fred/stable-diffusion-checkpoints
 ```
-
-### Specifying a configuration file for legacy checkpoints
-
-Some checkpoint files come with instructions to use a specific .yaml
-configuration file. For InvokeAI load this file correctly, please put
-the config file in the same directory as the corresponding `.ckpt` or
-`.safetensors` file and make sure the file has the same basename as
-the model file. Here is an example:
-
-```bash
-wonderful-model-v2.ckpt
-wonderful-model-v2.yaml
-```
-
-This is not needed for `diffusers` models, which come with their own
-pre-packaged configuration.
-
-### Specifying a custom VAE file for legacy checkpoints
-
-To associate a custom VAE with a legacy file, place the VAE file in
-the same directory as the corresponding `.ckpt` or
-`.safetensors` file and make sure the file has the same basename as
-the model file. Use the suffix `.vae.pt` for VAE checkpoint files, and
-`.vae.safetensors` for VAE safetensors files. There is no requirement
-that both the model and the VAE follow the same format.
-
-Example:
-
-```bash
-wonderful-model-v2.pt
-wonderful-model-v2.vae.safetensors
-```
-
-### Converting legacy models into `diffusers`
-
-The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
-models file into `diffusers` and install it.This will enable the model
-to load and run faster without loss of image quality.
-
-The usage is identical to `!import_model`. You may point the command
-to either a downloaded model file on disk, or to a (non-password
-protected) URL:
-
-```bash
-invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
-```
-
-After a successful conversion, the CLI will offer you the option of
-deleting the original `.ckpt` or `.safetensors` file.
-
-### Optimizing a previously-installed model
-
-Lastly, if you have previously installed a `.ckpt` or `.safetensors`
-file and wish to convert it into a `diffusers` model, you can do this
-without re-downloading and converting the original file using the
-`!optimize_model` command. Simply pass the short name of an existing
-installed model:
-
-```bash
-invoke> !optimize_model martians-v1.0
-```
-
-The model will be converted into `diffusers` format and replace the
-previously installed version. You will again be offered the
-opportunity to delete the original `.ckpt` or `.safetensors` file.
-
-Alternatively you can use the WebUI's model manager to handle diffusers
-optimization. Select the legacy model you wish to convert, and then
-look for a button labeled "Convert to Diffusers" in the upper right of
-the window.
-
-### Related CLI Commands
-
-There are a whole series of additional model management commands in
-the CLI that you can read about in [Command-Line
-Interface](../features/CLI.md). These include:
-
-* `!models` - List all installed models
-* `!switch <model name>` - Switch to the indicated model
-* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
-* `!del_model <model name>` - Delete the indicated model
-
-### Manually editing `configs/models.yaml`
-
-If you are comfortable with a text editor then you may simply edit `models.yaml`
-directly.
-
-You will need to download the desired `.ckpt/.safetensors` file and
-place it somewhere on your machine's filesystem. Alternatively, for a
-`diffusers` model, record the repo_id or download the whole model
-directory. Then using a **text** editor (e.g. the Windows Notepad
-application), open the file `configs/models.yaml`, and add a new
-stanza that follows this model:
-
-#### A legacy model
-
-A legacy `.ckpt` or `.safetensors` entry will look like this:
-
-```yaml
-arabian-nights-1.0:
-  description: A great fine-tune in Arabian Nights style
-  weights: ./path/to/arabian-nights-1.0.ckpt
-  config: ./configs/stable-diffusion/v1-inference.yaml
-  format: ckpt
-  width: 512
-  height: 512
-  default: false
-```
-
-Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
-
-#### A diffusers model
-
-A stanza for a `diffusers` model will look like this for a HuggingFace
-model with a repository ID:
-
-```yaml
-arabian-nights-1.1:
-  description: An even better fine-tune of the Arabian Nights
-  repo_id: captahab/arabian-nights-1.1
-  format: diffusers
-  default: true
-```
-
-And for a downloaded directory:
-
-```yaml
-arabian-nights-1.1:
-  description: An even better fine-tune of the Arabian Nights
-  path: /path/to/captahab-arabian-nights-1.1
-  format: diffusers
-  default: true
-```
-
-There is additional syntax for indicating an external VAE to use with
-this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
-
-After you save the modified `models.yaml` file relaunch
-`invokeai`. The new model will now be available for your use.
-

docs/other/CONTRIBUTORS.md

@@ -23,16 +23,14 @@ We thank them for all of their time and hard work.
 * @damian0815 - Attention Systems and Gameplay Engineer
 * @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
 * @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
+* @tildebyte - General gadfly and resident (self-appointed) know-it-all
 * @keturn - Lead for Diffusers port
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
 * @jpphoto (Jonathan Pollack) - Inference and rendering engine optimization
 * @genomancer (Gregg Helt) - Model training and merging
-* @gogurtenjoyer - User support and testing
-* @whosawwhatsis - User support and testing
 
 ## **Contributions by**
 
-- [tildebyte](https://github.com/tildebyte)
 - [Sean McLellan](https://github.com/Oceanswave)
 - [Kevin Gibbons](https://github.com/bakkot)
 - [Tesseract Cat](https://github.com/TesseractCat)
@@ -80,7 +78,6 @@ We thank them for all of their time and hard work.
 - [psychedelicious](https://github.com/psychedelicious)
 - [damian0815](https://github.com/damian0815)
 - [Eugene Brodsky](https://github.com/ebr)
-- [Statcomm](https://github.com/statcomm)
 
 ## **Original CompVis Authors**
 

docs/requirements-mkdocs.txt

@@ -0,0 +1,5 @@
+mkdocs
+mkdocs-material>=8, <9
+mkdocs-git-revision-date-localized-plugin
+mkdocs-redirects==1.2.0
+

installer/lib/installer.py

@@ -144,8 +144,8 @@ class Installer:
 
             from plumbum import FG, local
 
-            python = local[get_python_from_venv(venv_dir)]
-            python[ "-m", "pip", "install", "--upgrade", "pip"] & FG
+            pip = local[get_pip_from_venv(venv_dir)]
+            pip[ "install", "--upgrade", "pip"] & FG
 
         return venv_dir
 
@@ -241,18 +241,14 @@ class InvokeAiInstance:
 
         from plumbum import FG, local
 
-        # Note that we're installing pinned versions of torch and
-        # torchvision here, which *should* correspond to what is
-        # in pyproject.toml. This is to prevent torch 2.0 from
-        # being installed and immediately uninstalled and replaced with 1.13
         pip = local[self.pip]
 
         (
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch~=1.13.1",
-                "torchvision~=0.14.1",
+                "torch",
+                "torchvision",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,
@@ -383,9 +379,6 @@ class InvokeAiInstance:
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
 
-        if OS == "Linux":
-            shutil.copy(Path(__file__).parents[1] / "templates" / "dialogrc", self.runtime / '.dialogrc')
-
     def update(self):
         pass
 
@@ -412,22 +405,6 @@ def get_pip_from_venv(venv_path: Path) -> str:
     return str(venv_path.expanduser().resolve() / pip)
 
 
-def get_python_from_venv(venv_path: Path) -> str:
-    """
-    Given a path to a virtual environment, get the absolute path to the `python` executable
-    in a cross-platform fashion. Does not validate that the python executable
-    actually exists in the virtualenv.
-
-    :param venv_path: Path to the virtual environment
-    :type venv_path: Path
-    :return: Absolute path to the python executable
-    :rtype: str
-    """
-
-    python = "Scripts\python.exe" if OS == "Windows" else "bin/python"
-    return str(venv_path.expanduser().resolve() / python)
-
-
 def set_sys_path(venv_path: Path) -> None:
     """
     Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,

installer/templates/dialogrc

@@ -1,27 +0,0 @@
-# Screen 
-use_shadow = OFF
-use_colors = ON
-screen_color = (BLACK, BLACK, ON)
-
-# Box 
-dialog_color = (YELLOW, BLACK , ON) 
-title_color = (YELLOW, BLACK, ON) 
-border_color = (YELLOW, BLACK, OFF) 
-border2_color = (YELLOW, BLACK, OFF) 
-
-# Button 
-button_active_color = (RED, BLACK, OFF) 
-button_inactive_color = (YELLOW, BLACK, OFF) 
-button_label_active_color = (YELLOW,BLACK,ON) 
-button_label_inactive_color = (YELLOW,BLACK,ON)
- 
-# Menu box 
-menubox_color = (BLACK, BLACK, ON) 
-menubox_border_color = (YELLOW, BLACK, OFF) 
-menubox_border2_color = (YELLOW, BLACK, OFF) 
-
-# Menu window 
-item_color = (YELLOW, BLACK, OFF) 
-item_selected_color = (BLACK, YELLOW, OFF) 
-tag_key_color = (YELLOW, BLACK, OFF) 
-tag_key_selected_color = (BLACK, YELLOW, OFF)

installer/templates/invoke.sh.in

@@ -1,10 +1,5 @@
 #!/bin/bash
 
-# MIT License
-
-# Coauthored by Lincoln Stein, Eugene Brodsky and Joshua Kimsey
-# Copyright 2023, The InvokeAI Development Team
-
 ####
 # This launch script assumes that:
 # 1. it is located in the runtime directory,
@@ -16,168 +11,85 @@
 
 set -eu
 
-# Ensure we're in the correct folder in case user's CWD is somewhere else
+# ensure we're in the correct folder in case user's CWD is somewhere else
 scriptdir=$(dirname "$0")
 cd "$scriptdir"
 
 . .venv/bin/activate
 
 export INVOKEAI_ROOT="$scriptdir"
-PARAMS=$@
 
-# Check to see if dialog is installed (it seems to be fairly standard, but good to check regardless) and if the user has passed the --no-tui argument to disable the dialog TUI
-tui=true
-if command -v dialog &>/dev/null; then
-    # This must use $@ to properly loop through the arguments passed by the user
-    for arg in "$@"; do
-        if [ "$arg" == "--no-tui" ]; then
-            tui=false
-            # Remove the --no-tui argument to avoid errors later on when passing arguments to InvokeAI
-            PARAMS=$(echo "$PARAMS" | sed 's/--no-tui//')
-            break
-        fi
-    done
-else
-    tui=false
-fi
-
-# Set required env var for torch on mac MPS
+# set required env var for torch on mac MPS
 if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
-# Primary function for the case statement to determine user input
-do_choice() {
-    case $1 in
-    1)
-        clear
-        printf "Generate images with a browser-based interface\n"
-        invokeai --web $PARAMS
-        ;;
-    2)
-        clear
-        printf "Generate images using a command-line interface\n"
-        invokeai $PARAMS
-        ;;
-    3)
-        clear
-        printf "Textual inversion training\n"
-        invokeai-ti --gui $PARAMS
-        ;;
-    4)
-        clear
-        printf "Merge models (diffusers type only)\n"
-        invokeai-merge --gui $PARAMS
-        ;;
-    5)
-        clear
-        printf "Download and install models\n"
-        invokeai-model-install --root ${INVOKEAI_ROOT}
-        ;;
-    6)
-        clear
-        printf "Change InvokeAI startup options\n"
-        invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
-        ;;
-    7)
-        clear
-        printf "Re-run the configure script to fix a broken install\n"
-        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
-        ;;
-    8)
-        clear
-        printf "Open the developer console\n"
-        file_name=$(basename "${BASH_SOURCE[0]}")
-        bash --init-file "$file_name"
-        ;;
-    9)
-        clear
-        printf "Update InvokeAI\n"
-        invokeai-update
-        ;;
-    10)
-        clear
-        printf "Command-line help\n"
-        invokeai --help
-        ;;
-    "HELP 1")
-        clear
-        printf "Command-line help\n"
-        invokeai --help
-        ;;
-    *)
-        clear
-        printf "Exiting...\n"
-        exit
-        ;;
-    esac
-    clear
-}
-
-# Dialog-based TUI for launcing Invoke functions
-do_dialog() {
-    options=(
-        1 "Generate images with a browser-based interface"
-        2 "Generate images 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"
-        8 "Open the developer console"
-        9 "Update InvokeAI")
-
-    choice=$(dialog --clear \
-        --backtitle "\Zb\Zu\Z3InvokeAI" \
-        --colors \
-        --title "What would you like to run?" \
-        --ok-label "Run" \
-        --cancel-label "Exit" \
-        --help-button \
-        --help-label "CLI Help" \
-        --menu "Select an option:" \
-        0 0 0 \
-        "${options[@]}" \
-        2>&1 >/dev/tty) || clear
-    do_choice "$choice"
-    clear
-}
-
-# Command-line interface for launching Invoke functions
-do_line_input() {
-    clear
-    printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
-    printf "Do you want to generate images using the\n"
-    printf "1: Browser-based UI\n"
-    printf "2: Command-line interface\n"
-    printf "3: Run textual inversion training\n"
-    printf "4: Merge models (diffusers type only)\n"
-    printf "5: Download and install models\n"
-    printf "6: Change InvokeAI startup options\n"
-    printf "7: Re-run the configure script to fix a broken install\n"
-    printf "8: Open the developer console\n"
-    printf "9: Update InvokeAI\n"
-    printf "10: Command-line help\n"
-    printf "Q: Quit\n\n"
-    read -p "Please enter 1-10, Q: [1] " yn
-    choice=${yn:='1'}
-    do_choice $choice
-    clear
-}
-
-# Main IF statement for launching Invoke with either the TUI or CLI, and for checking if the user is in the developer console
+while true
+do
 if [ "$0" != "bash" ]; then
-    while true; do
-        if $tui; then
-            # .dialogrc must be located in the same directory as the invoke.sh script
-            export DIALOGRC="./.dialogrc"
-            do_dialog
-        else
-            do_line_input
-        fi
-    done
+    echo "Do you want to generate images using the"
+    echo "1. command-line interface"
+    echo "2. browser-based UI"
+    echo "3. run textual inversion training"
+    echo "4. merge models (diffusers type only)"
+    echo "5. download and install models"
+    echo "6. change InvokeAI startup options"
+    echo "7. re-run the configure script to fix a broken install"
+    echo "8. open the developer console"
+    echo "9. update InvokeAI"
+    echo "10. command-line help"
+    echo "Q - Quit"
+    echo ""
+    read -p "Please enter 1-10, Q: [2] " yn
+    choice=${yn:='2'}
+    case $choice in
+        1)
+            echo "Starting the InvokeAI command-line..."
+            invokeai $@
+            ;;
+        2)
+            echo "Starting the InvokeAI browser-based UI..."
+            invokeai --web $@
+            ;;
+        3)
+            echo "Starting Textual Inversion:"
+            invokeai-ti --gui $@
+            ;;
+        4)
+            echo "Merging Models:"
+            invokeai-merge --gui $@
+            ;;
+        5)
+            invokeai-model-install --root ${INVOKEAI_ROOT}
+            ;;
+        6)
+            invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
+            ;;
+        7)
+            invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
+	    ;;
+	8)
+	    echo "Developer Console:"
+            file_name=$(basename "${BASH_SOURCE[0]}")
+            bash --init-file "$file_name"
+            ;;
+        9)
+	    echo "Update:"
+            invokeai-update
+            ;;
+        10)
+            invokeai --help
+            ;;
+	[qQ])
+            exit 0
+            ;;
+        *)
+            echo "Invalid selection"
+            exit;;
+    esac
 else # in developer console
     python --version
-    printf "Press ^D to exit\n"
+    echo "Press ^D to exit"
     export PS1="(InvokeAI) \u@\h \w> "
 fi
+done

invokeai/backend/invoke_ai_web_server.py

@@ -25,23 +25,14 @@ from invokeai.backend.modules.parameters import parameters_to_command
 import invokeai.frontend.dist as frontend
 from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
-from ldm.invoke.conditioning import (
-    get_tokens_for_prompt_object,
-    get_prompt_structure,
-    split_weighted_subprompts,
-    get_tokenizer,
-)
+from ldm.invoke.conditioning import get_tokens_for_prompt_object, get_prompt_structure, split_weighted_subprompts, \
+    get_tokenizer
 from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
 from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import (
-    Globals,
-    global_converted_ckpts_dir,
-    global_models_dir,
-    global_lora_models_dir,
-)
+from ldm.invoke.globals import Globals, global_converted_ckpts_dir
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
 from compel.prompt_parser import Blend
+from ldm.invoke.globals import global_models_dir
 from ldm.invoke.merge_diffusers import merge_diffusion_models
 
 # Loading Arguments
@@ -202,7 +193,8 @@ class InvokeAIWebServer:
                 (width, height) = pil_image.size
 
                 thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(file_path), self.thumbnail_image_path
+                    pil_image, os.path.basename(
+                        file_path), self.thumbnail_image_path
                 )
 
                 response = {
@@ -232,7 +224,7 @@ class InvokeAIWebServer:
                     server="flask_socketio",
                     width=1600,
                     height=1000,
-                    port=self.port,
+                    port=self.port
                 ).run()
             except KeyboardInterrupt:
                 import sys
@@ -273,14 +265,16 @@ class InvokeAIWebServer:
         # location for "finished" images
         self.result_path = args.outdir
         # temporary path for intermediates
-        self.intermediate_path = os.path.join(self.result_path, "intermediates/")
+        self.intermediate_path = os.path.join(
+            self.result_path, "intermediates/")
         # path for user-uploaded init images and masks
         self.init_image_path = os.path.join(self.result_path, "init-images/")
         self.mask_image_path = os.path.join(self.result_path, "mask-images/")
         # path for temp images e.g. gallery generations which are not committed
         self.temp_image_path = os.path.join(self.result_path, "temp-images/")
         # path for thumbnail images
-        self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/")
+        self.thumbnail_image_path = os.path.join(
+            self.result_path, "thumbnails/")
         # txt log
         self.log_path = os.path.join(self.result_path, "invoke_log.txt")
         # make all output paths
@@ -305,22 +299,21 @@ class InvokeAIWebServer:
             config["infill_methods"] = infill_methods()
             socketio.emit("systemConfig", config)
 
-        @socketio.on("searchForModels")
+        @socketio.on('searchForModels')
         def handle_search_models(search_folder: str):
             try:
                 if not search_folder:
                     socketio.emit(
                         "foundModels",
-                        {"search_folder": None, "found_models": None},
+                        {'search_folder': None, 'found_models': None},
                     )
                 else:
-                    (
-                        search_folder,
-                        found_models,
-                    ) = self.generate.model_manager.search_models(search_folder)
+                    search_folder, found_models = self.generate.model_manager.search_models(
+                        search_folder)
                     socketio.emit(
                         "foundModels",
-                        {"search_folder": search_folder, "found_models": found_models},
+                        {'search_folder': search_folder,
+                            'found_models': found_models},
                     )
             except Exception as e:
                 self.handle_exceptions(e)
@@ -329,11 +322,11 @@ class InvokeAIWebServer:
         @socketio.on("addNewModel")
         def handle_add_model(new_model_config: dict):
             try:
-                model_name = new_model_config["name"]
-                del new_model_config["name"]
+                model_name = new_model_config['name']
+                del new_model_config['name']
                 model_attributes = new_model_config
-                if len(model_attributes["vae"]) == 0:
-                    del model_attributes["vae"]
+                if len(model_attributes['vae']) == 0:
+                    del model_attributes['vae']
                 update = False
                 current_model_list = self.generate.model_manager.list_models()
                 if model_name in current_model_list:
@@ -342,20 +335,14 @@ class InvokeAIWebServer:
                 print(f">> Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
-                    model_name=model_name,
-                    model_attributes=model_attributes,
-                    clobber=True,
-                )
+                    model_name=model_name, model_attributes=model_attributes, clobber=True)
                 self.generate.model_manager.commit(opt.conf)
 
                 new_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "newModelAdded",
-                    {
-                        "new_model_name": model_name,
-                        "model_list": new_model_list,
-                        "update": update,
-                    },
+                    {"new_model_name": model_name,
+                     "model_list": new_model_list, 'update': update},
                 )
                 print(f">> New Model Added: {model_name}")
             except Exception as e:
@@ -370,10 +357,8 @@ class InvokeAIWebServer:
                 updated_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "modelDeleted",
-                    {
-                        "deleted_model_name": model_name,
-                        "model_list": updated_model_list,
-                    },
+                    {"deleted_model_name": model_name,
+                     "model_list": updated_model_list},
                 )
                 print(f">> Model Deleted: {model_name}")
             except Exception as e:
@@ -398,48 +383,41 @@ class InvokeAIWebServer:
             except Exception as e:
                 self.handle_exceptions(e)
 
-        @socketio.on("convertToDiffusers")
+        @socketio.on('convertToDiffusers')
         def convert_to_diffusers(model_to_convert: dict):
             try:
-                if model_info := self.generate.model_manager.model_info(
-                    model_name=model_to_convert["model_name"]
-                ):
-                    if "weights" in model_info:
-                        ckpt_path = Path(model_info["weights"])
-                        original_config_file = Path(model_info["config"])
-                        model_name = model_to_convert["model_name"]
-                        model_description = model_info["description"]
+                if (model_info := self.generate.model_manager.model_info(model_name=model_to_convert['model_name'])):
+                    if 'weights' in model_info:
+                        ckpt_path = Path(model_info['weights'])
+                        original_config_file = Path(model_info['config'])
+                        model_name = model_to_convert['model_name']
+                        model_description = model_info['description']
                     else:
                         self.socketio.emit(
-                            "error", {"message": "Model is not a valid checkpoint file"}
-                        )
+                            "error", {"message": "Model is not a valid checkpoint file"})
                 else:
                     self.socketio.emit(
-                        "error", {"message": "Could not retrieve model info."}
-                    )
+                        "error", {"message": "Could not retrieve model info."})
 
                 if not ckpt_path.is_absolute():
                     ckpt_path = Path(Globals.root, ckpt_path)
 
                 if original_config_file and not original_config_file.is_absolute():
-                    original_config_file = Path(Globals.root, original_config_file)
+                    original_config_file = Path(
+                        Globals.root, original_config_file)
 
                 diffusers_path = Path(
-                    ckpt_path.parent.absolute(), f"{model_name}_diffusers"
+                    ckpt_path.parent.absolute(),
+                    f'{model_name}_diffusers'
                 )
 
-                if model_to_convert["save_location"] == "root":
+                if model_to_convert['save_location'] == 'root':
                     diffusers_path = Path(
-                        global_converted_ckpts_dir(), f"{model_name}_diffusers"
-                    )
+                        global_converted_ckpts_dir(), f'{model_name}_diffusers')
 
-                if (
-                    model_to_convert["save_location"] == "custom"
-                    and model_to_convert["custom_location"] is not None
-                ):
+                if model_to_convert['save_location'] == 'custom' and model_to_convert['custom_location'] is not None:
                     diffusers_path = Path(
-                        model_to_convert["custom_location"], f"{model_name}_diffusers"
-                    )
+                        model_to_convert['custom_location'], f'{model_name}_diffusers')
 
                 if diffusers_path.exists():
                     shutil.rmtree(diffusers_path)
@@ -457,99 +435,54 @@ class InvokeAIWebServer:
                 new_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "modelConverted",
-                    {
-                        "new_model_name": model_name,
-                        "model_list": new_model_list,
-                        "update": True,
-                    },
+                    {"new_model_name": model_name,
+                     "model_list": new_model_list, 'update': True},
                 )
                 print(f">> Model Converted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
-        @socketio.on("mergeDiffusersModels")
+        @socketio.on('mergeDiffusersModels')
         def merge_diffusers_models(model_merge_info: dict):
             try:
-                models_to_merge = model_merge_info["models_to_merge"]
+                models_to_merge = model_merge_info['models_to_merge']
                 model_ids_or_paths = [
-                    self.generate.model_manager.model_name_or_path(x)
-                    for x in models_to_merge
-                ]
+                    self.generate.model_manager.model_name_or_path(x) for x in models_to_merge]
                 merged_pipe = merge_diffusion_models(
-                    model_ids_or_paths,
-                    model_merge_info["alpha"],
-                    model_merge_info["interp"],
-                    model_merge_info["force"],
-                )
+                    model_ids_or_paths, model_merge_info['alpha'], model_merge_info['interp'], model_merge_info['force'])
 
-                dump_path = global_models_dir() / "merged_models"
-                if model_merge_info["model_merge_save_path"] is not None:
-                    dump_path = Path(model_merge_info["model_merge_save_path"])
+                dump_path = global_models_dir() / 'merged_models'
+                if model_merge_info['model_merge_save_path'] is not None:
+                    dump_path = Path(model_merge_info['model_merge_save_path'])
 
                 os.makedirs(dump_path, exist_ok=True)
-                dump_path = dump_path / model_merge_info["merged_model_name"]
+                dump_path = dump_path / model_merge_info['merged_model_name']
                 merged_pipe.save_pretrained(dump_path, safe_serialization=1)
 
                 merged_model_config = dict(
-                    model_name=model_merge_info["merged_model_name"],
+                    model_name=model_merge_info['merged_model_name'],
                     description=f'Merge of models {", ".join(models_to_merge)}',
-                    commit_to_conf=opt.conf,
+                    commit_to_conf=opt.conf
                 )
 
-                if vae := self.generate.model_manager.config[models_to_merge[0]].get(
-                    "vae", None
-                ):
-                    print(f">> Using configured VAE assigned to {models_to_merge[0]}")
+                if vae := self.generate.model_manager.config[models_to_merge[0]].get("vae", None):
+                    print(
+                        f">> Using configured VAE assigned to {models_to_merge[0]}")
                     merged_model_config.update(vae=vae)
 
                 self.generate.model_manager.import_diffuser_model(
-                    dump_path, **merged_model_config
-                )
+                    dump_path, **merged_model_config)
                 new_model_list = self.generate.model_manager.list_models()
 
                 socketio.emit(
                     "modelsMerged",
-                    {
-                        "merged_models": models_to_merge,
-                        "merged_model_name": model_merge_info["merged_model_name"],
-                        "model_list": new_model_list,
-                        "update": True,
-                    },
+                    {"merged_models": models_to_merge,
+                     "merged_model_name": model_merge_info['merged_model_name'],
+                     "model_list": new_model_list, 'update': True},
                 )
                 print(f">> Models Merged: {models_to_merge}")
-                print(f">> New Model Added: {model_merge_info['merged_model_name']}")
-            except Exception as e:
-                self.handle_exceptions(e)
-
-        @socketio.on("getLoraModels")
-        def get_lora_models():
-            try:
-                lora_path = global_lora_models_dir()
-                loras = []
-                for root, _, files in os.walk(lora_path):
-                    models = [
-                        Path(root, x)
-                        for x in files
-                        if Path(x).suffix in [".ckpt", ".pt", ".safetensors"]
-                    ]
-                    loras = loras + models
-
-                found_loras = []
-                for lora in sorted(loras, key=lambda s: s.stem.lower()):
-                    location = str(lora.resolve()).replace("\\", "/")
-                    found_loras.append({"name": lora.stem, "location": location})
-                socketio.emit("foundLoras", found_loras)
-            except Exception as e:
-                self.handle_exceptions(e)
-
-        @socketio.on("getTextualInversionTriggers")
-        def get_ti_triggers():
-            try:
-                local_triggers = self.generate.model.textual_inversion_manager.get_all_trigger_strings()
-                locals = [{'name': x} for x in sorted(local_triggers, key=str.casefold)]
-                concepts = HuggingFaceConceptsLibrary().list_concepts(minimum_likes=5)
-                concepts = [{'name': f'<{x}>'} for x in sorted(concepts, key=str.casefold) if f'<{x}>' not in local_triggers]
-                socketio.emit("foundTextualInversionTriggers", {'local_triggers': locals, 'huggingface_concepts': concepts})
+                print(
+                    f">> New Model Added: {model_merge_info['merged_model_name']}")
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -567,8 +500,7 @@ class InvokeAIWebServer:
                         os.remove(thumbnail_path)
                     except Exception as e:
                         socketio.emit(
-                            "error", {"message": f"Unable to delete {f}: {str(e)}"}
-                        )
+                            "error", {"message": f"Unable to delete {f}: {str(e)}"})
                         pass
 
                 socketio.emit("tempFolderEmptied")
@@ -579,7 +511,8 @@ class InvokeAIWebServer:
         def save_temp_image_to_gallery(url):
             try:
                 image_path = self.get_image_path_from_url(url)
-                new_path = os.path.join(self.result_path, os.path.basename(image_path))
+                new_path = os.path.join(
+                    self.result_path, os.path.basename(image_path))
                 shutil.copy2(image_path, new_path)
 
                 if os.path.splitext(new_path)[1] == ".png":
@@ -592,7 +525,8 @@ class InvokeAIWebServer:
                 (width, height) = pil_image.size
 
                 thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(new_path), self.thumbnail_image_path
+                    pil_image, os.path.basename(
+                        new_path), self.thumbnail_image_path
                 )
 
                 image_array = [
@@ -651,7 +585,8 @@ class InvokeAIWebServer:
                         (width, height) = pil_image.size
 
                         thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(path), self.thumbnail_image_path
+                            pil_image, os.path.basename(
+                                path), self.thumbnail_image_path
                         )
 
                         image_array.append(
@@ -670,8 +605,7 @@ class InvokeAIWebServer:
                         )
                     except Exception as e:
                         socketio.emit(
-                            "error", {"message": f"Unable to load {path}: {str(e)}"}
-                        )
+                            "error", {"message": f"Unable to load {path}: {str(e)}"})
                         pass
 
                 socketio.emit(
@@ -721,7 +655,8 @@ class InvokeAIWebServer:
                         (width, height) = pil_image.size
 
                         thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(path), self.thumbnail_image_path
+                            pil_image, os.path.basename(
+                                path), self.thumbnail_image_path
                         )
 
                         image_array.append(
@@ -741,8 +676,7 @@ class InvokeAIWebServer:
                     except Exception as e:
                         print(f">> Unable to load {path}")
                         socketio.emit(
-                            "error", {"message": f"Unable to load {path}: {str(e)}"}
-                        )
+                            "error", {"message": f"Unable to load {path}: {str(e)}"})
                         pass
 
                 socketio.emit(
@@ -776,9 +710,10 @@ class InvokeAIWebServer:
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
-                print(f">> ESRGAN Parameters: {esrgan_parameters}")
-                print(f">> Facetool Parameters: {facetool_parameters}")
+                print(
+                    f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
+                print(f'>> ESRGAN Parameters: {esrgan_parameters}')
+                print(f'>> Facetool Parameters: {facetool_parameters}')
 
                 self.generate_images(
                     generation_parameters,
@@ -815,9 +750,11 @@ class InvokeAIWebServer:
                 if postprocessing_parameters["type"] == "esrgan":
                     progress.set_current_status("common.statusUpscalingESRGAN")
                 elif postprocessing_parameters["type"] == "gfpgan":
-                    progress.set_current_status("common.statusRestoringFacesGFPGAN")
+                    progress.set_current_status(
+                        "common.statusRestoringFacesGFPGAN")
                 elif postprocessing_parameters["type"] == "codeformer":
-                    progress.set_current_status("common.statusRestoringFacesCodeFormer")
+                    progress.set_current_status(
+                        "common.statusRestoringFacesCodeFormer")
 
                 socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
@@ -982,7 +919,8 @@ class InvokeAIWebServer:
 
                 init_img_url = generation_parameters["init_img"]
 
-                original_bounding_box = generation_parameters["bounding_box"].copy()
+                original_bounding_box = generation_parameters["bounding_box"].copy(
+                )
 
                 initial_image = dataURL_to_image(
                     generation_parameters["init_img"]
@@ -1059,9 +997,8 @@ class InvokeAIWebServer:
             elif generation_parameters["generation_mode"] == "img2img":
                 init_img_url = generation_parameters["init_img"]
                 init_img_path = self.get_image_path_from_url(init_img_url)
-                generation_parameters["init_img"] = Image.open(init_img_path).convert(
-                    "RGB"
-                )
+                generation_parameters["init_img"] = Image.open(
+                    init_img_path).convert('RGB')
 
             def image_progress(sample, step):
                 if self.canceled.is_set():
@@ -1121,7 +1058,8 @@ class InvokeAIWebServer:
                     )
 
                 if generation_parameters["progress_latents"]:
-                    image = self.generate.sample_to_lowres_estimated_image(sample)
+                    image = self.generate.sample_to_lowres_estimated_image(
+                        sample)
                     (width, height) = image.size
                     width *= 8
                     height *= 8
@@ -1140,7 +1078,8 @@ class InvokeAIWebServer:
                         },
                     )
 
-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
             def image_done(image, seed, first_seed, attention_maps_image=None):
@@ -1167,7 +1106,8 @@ class InvokeAIWebServer:
 
                 progress.set_current_status("common.statusGenerationComplete")
 
-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
                 all_parameters = generation_parameters
@@ -1178,7 +1118,8 @@ class InvokeAIWebServer:
                     and all_parameters["variation_amount"] > 0
                 ):
                     first_seed = first_seed or seed
-                    this_variation = [[seed, all_parameters["variation_amount"]]]
+                    this_variation = [
+                        [seed, all_parameters["variation_amount"]]]
                     all_parameters["with_variations"] = (
                         prior_variations + this_variation
                     )
@@ -1194,13 +1135,14 @@ class InvokeAIWebServer:
                 if esrgan_parameters:
                     progress.set_current_status("common.statusUpscaling")
                     progress.set_current_status_has_steps(False)
-                    self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                    self.socketio.emit(
+                        "progressUpdate", progress.to_formatted_dict())
                     eventlet.sleep(0)
 
                     image = self.esrgan.process(
                         image=image,
                         upsampler_scale=esrgan_parameters["level"],
-                        denoise_str=esrgan_parameters["denoise_str"],
+                        denoise_str=esrgan_parameters['denoise_str'],
                         strength=esrgan_parameters["strength"],
                         seed=seed,
                     )
@@ -1208,7 +1150,7 @@ class InvokeAIWebServer:
                     postprocessing = True
                     all_parameters["upscale"] = [
                         esrgan_parameters["level"],
-                        esrgan_parameters["denoise_str"],
+                        esrgan_parameters['denoise_str'],
                         esrgan_parameters["strength"],
                     ]
 
@@ -1217,14 +1159,15 @@ class InvokeAIWebServer:
 
                 if facetool_parameters:
                     if facetool_parameters["type"] == "gfpgan":
-                        progress.set_current_status("common.statusRestoringFacesGFPGAN")
+                        progress.set_current_status(
+                            "common.statusRestoringFacesGFPGAN")
                     elif facetool_parameters["type"] == "codeformer":
                         progress.set_current_status(
-                            "common.statusRestoringFacesCodeFormer"
-                        )
+                            "common.statusRestoringFacesCodeFormer")
 
                     progress.set_current_status_has_steps(False)
-                    self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                    self.socketio.emit(
+                        "progressUpdate", progress.to_formatted_dict())
                     eventlet.sleep(0)
 
                     if facetool_parameters["type"] == "gfpgan":
@@ -1254,7 +1197,8 @@ class InvokeAIWebServer:
                     all_parameters["facetool_type"] = facetool_parameters["type"]
 
                 progress.set_current_status("common.statusSavingImage")
-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
                 # restore the stashed URLS and discard the paths, we are about to send the result to client
@@ -1271,7 +1215,8 @@ class InvokeAIWebServer:
                 if generation_parameters["generation_mode"] == "unifiedCanvas":
                     all_parameters["bounding_box"] = original_bounding_box
 
-                metadata = self.parameters_to_generated_image_metadata(all_parameters)
+                metadata = self.parameters_to_generated_image_metadata(
+                    all_parameters)
 
                 command = parameters_to_command(all_parameters)
 
@@ -1301,27 +1246,22 @@ class InvokeAIWebServer:
 
                 if progress.total_iterations > progress.current_iteration:
                     progress.set_current_step(1)
-                    progress.set_current_status("common.statusIterationComplete")
+                    progress.set_current_status(
+                        "common.statusIterationComplete")
                     progress.set_current_status_has_steps(False)
                 else:
                     progress.mark_complete()
 
-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
-                parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"])
-                tokens = (
-                    None
-                    if type(parsed_prompt) is Blend
-                    else get_tokens_for_prompt_object(
-                        get_tokenizer(self.generate.model), parsed_prompt
-                    )
-                )
-                attention_maps_image_base64_url = (
-                    None
-                    if attention_maps_image is None
+                parsed_prompt, _ = get_prompt_structure(
+                    generation_parameters["prompt"])
+                tokens = None if type(parsed_prompt) is Blend else \
+                    get_tokens_for_prompt_object(get_tokenizer(self.generate.model), parsed_prompt)
+                attention_maps_image_base64_url = None if attention_maps_image is None \
                     else image_to_dataURL(attention_maps_image)
-                )
 
                 self.socketio.emit(
                     "generationResult",
@@ -1353,7 +1293,7 @@ class InvokeAIWebServer:
             self.generate.prompt2image(
                 **generation_parameters,
                 step_callback=diffusers_step_callback_adapter,
-                image_callback=image_done,
+                image_callback=image_done
             )
 
         except KeyboardInterrupt:
@@ -1476,7 +1416,8 @@ class InvokeAIWebServer:
         self, parameters, original_image_path
     ):
         try:
-            current_metadata = retrieve_metadata(original_image_path)["sd-metadata"]
+            current_metadata = retrieve_metadata(
+                original_image_path)["sd-metadata"]
             postprocessing_metadata = {}
 
             """
@@ -1516,7 +1457,8 @@ class InvokeAIWebServer:
                     postprocessing_metadata
                 )
             else:
-                current_metadata["image"]["postprocessing"] = [postprocessing_metadata]
+                current_metadata["image"]["postprocessing"] = [
+                    postprocessing_metadata]
 
             return current_metadata
 
@@ -1612,7 +1554,8 @@ class InvokeAIWebServer:
                 )
             elif "thumbnails" in url:
                 return os.path.abspath(
-                    os.path.join(self.thumbnail_image_path, os.path.basename(url))
+                    os.path.join(self.thumbnail_image_path,
+                                 os.path.basename(url))
                 )
             else:
                 return os.path.abspath(
@@ -1658,7 +1601,7 @@ class InvokeAIWebServer:
         except Exception as e:
             self.handle_exceptions(e)
 
-    def handle_exceptions(self, exception, emit_key: str = "error"):
+    def handle_exceptions(self, exception, emit_key: str = 'error'):
         self.socketio.emit(emit_key, {"message": (str(exception))})
         print("\n")
         traceback.print_exc()

invokeai/configs/stable-diffusion/v2-inference.yaml

@@ -1,67 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"

invokeai/frontend/.prettierrc.js

@@ -1,7 +1,6 @@
 module.exports = {
   trailingComma: 'es5',
   tabWidth: 2,
-  endOfLine: 'auto',
   semi: true,
   singleQuote: true,
   overrides: [

invokeai/frontend/dist/index.html

@@ -5,8 +5,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>InvokeAI - A Stable Diffusion Toolkit</title>
     <link rel="shortcut icon" type="icon" href="./assets/favicon-0d253ced.ico" />
-    <script type="module" crossorigin src="./assets/index-f56b39bc.js"></script>
-    <link rel="stylesheet" href="./assets/index-2ab0eb58.css">
+    <script type="module" crossorigin src="./assets/index-c33fa9da.js"></script>
+    <link rel="stylesheet" href="./assets/index-14cb2922.css">
   </head>
 
   <body>

invokeai/frontend/dist/locales/en.json

@@ -327,13 +327,6 @@
         "addModel": "Add Model",
         "updateModel": "Update Model",
         "availableModels": "Available Models",
-        "addLora": "Add Lora",
-        "clearLoras": "Clear Loras",
-        "noLoraModels": "No Loras Found",
-        "addTextualInversionTrigger": "Add Textual Inversion",
-        "addTIToNegative": "Add To Negative",
-        "clearTextualInversions": "Clear Textual Inversions",
-        "noTextualInversionTriggers": "No Textual Inversions Found",
         "search": "Search",
         "load": "Load",
         "active": "active",
@@ -372,8 +365,7 @@
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
         "v1": "v1",
-        "v2_base": "v2 (512px)",
-        "v2_768": "v2 (768px)",
+        "v2": "v2",
         "inpainting": "v1 Inpainting",
         "customConfig": "Custom Config",
         "pathToCustomConfig": "Path To Custom Config",
@@ -490,7 +482,6 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
-        "showHuggingFaceConcepts": "Show Textual Inversions from HF Concepts Library",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",

invokeai/frontend/package.json

@@ -15,8 +15,8 @@
     "postinstall": "patch-package"
   },
   "dependencies": {
-    "@chakra-ui/icons": "^2.0.18",
-    "@chakra-ui/react": "^2.5.5",
+    "@chakra-ui/icons": "^2.0.17",
+    "@chakra-ui/react": "^2.5.1",
     "@emotion/cache": "^11.10.5",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
@@ -52,7 +52,6 @@
     "redux-persist": "^6.0.0",
     "socket.io": "^4.6.0",
     "socket.io-client": "^4.6.0",
-    "typescript": "^5.0.3",
     "use-image": "^1.1.0",
     "uuid": "^9.0.0",
     "yarn": "^1.22.19"
@@ -62,8 +61,8 @@
     "@types/react": "^18.0.28",
     "@types/react-dom": "^18.0.11",
     "@types/react-transition-group": "^4.4.5",
-    "@typescript-eslint/eslint-plugin": "^5.57.0",
-    "@typescript-eslint/parser": "^5.57.0",
+    "@typescript-eslint/eslint-plugin": "^5.52.0",
+    "@typescript-eslint/parser": "^5.52.0",
     "babel-plugin-transform-imports": "^2.0.0",
     "eslint": "^8.34.0",
     "eslint-config-prettier": "^8.6.0",

invokeai/frontend/public/locales/en.json

@@ -327,13 +327,6 @@
         "addModel": "Add Model",
         "updateModel": "Update Model",
         "availableModels": "Available Models",
-        "addLora": "Add Lora",
-        "clearLoras": "Clear Loras",
-        "noLoraModels": "No Loras Found",
-        "addTextualInversionTrigger": "Add Textual Inversion",
-        "addTIToNegative": "Add To Negative",
-        "clearTextualInversions": "Clear Textual Inversions",
-        "noTextualInversionTriggers": "No Textual Inversions Found",
         "search": "Search",
         "load": "Load",
         "active": "active",
@@ -372,8 +365,7 @@
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
         "v1": "v1",
-        "v2_base": "v2 (512px)",
-        "v2_768": "v2 (768px)",
+        "v2": "v2",
         "inpainting": "v1 Inpainting",
         "customConfig": "Custom Config",
         "pathToCustomConfig": "Path To Custom Config",
@@ -490,7 +482,6 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
-        "showHuggingFaceConcepts": "Show Textual Inversions from HF Concepts Library",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",

invokeai/frontend/src/app/invokeai.d.ts

@@ -271,23 +271,6 @@ export declare type FoundModelResponse = {
   found_models: FoundModel[];
 };
 
-export declare type FoundLora = {
-  name: string;
-  location: string;
-};
-
-export declare type FoundTextualInversionTriggers = {
-  name: string;
-  location: string;
-};
-
-export declare type FoundLorasRsponse = FoundLora[];
-
-export declare type FoundTextualInversionTriggersResponse = {
-  local_triggers: FoundTextualInversionTriggers[];
-  huggingface_concepts: FoundTextualInversionTriggers[];
-};
-
 export declare type SystemStatusResponse = SystemStatus;
 
 export declare type SystemConfigResponse = SystemConfig;

invokeai/frontend/src/app/socketio/actions.ts

@@ -52,12 +52,6 @@ export const requestModelChange = createAction<string>(
   'socketio/requestModelChange'
 );
 
-export const getLoraModels = createAction<undefined>('socketio/getLoraModels');
-
-export const getTextualInversionTriggers = createAction<undefined>(
-  'socketio/getTextualInversionTriggers'
-);
-
 export const saveStagingAreaImageToGallery = createAction<string>(
   'socketio/saveStagingAreaImageToGallery'
 );

invokeai/frontend/src/app/socketio/emitters.ts

@@ -196,12 +196,6 @@ const makeSocketIOEmitters = (
       dispatch(modelChangeRequested());
       socketio.emit('requestModelChange', modelName);
     },
-    emitGetLoraModels: () => {
-      socketio.emit('getLoraModels');
-    },
-    emitGetTextualInversionTriggers: () => {
-      socketio.emit('getTextualInversionTriggers');
-    },
     emitSaveStagingAreaImageToGallery: (url: string) => {
       socketio.emit('requestSaveStagingAreaImageToGallery', url);
     },

invokeai/frontend/src/app/socketio/listeners.ts

@@ -11,7 +11,6 @@ import {
   errorOccurred,
   processingCanceled,
   setCurrentStatus,
-  setFoundLoras,
   setFoundModels,
   setIsCancelable,
   setIsConnected,
@@ -20,8 +19,6 @@ import {
   setSearchFolder,
   setSystemConfig,
   setSystemStatus,
-  setFoundLocalTextualInversionTriggers,
-  setFoundHuggingFaceTextualInversionTriggers,
 } from 'features/system/store/systemSlice';
 
 import {
@@ -37,10 +34,8 @@ import type { RootState } from 'app/store';
 import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
 import {
   clearInitialImage,
-  setHuggingFaceTextualInversionConcepts,
   setInfillMethod,
   setInitialImage,
-  setLocalTextualInversionTriggers,
   setMaskPath,
 } from 'features/parameters/store/generationSlice';
 import { tabMap } from 'features/ui/store/tabMap';
@@ -487,37 +482,6 @@ const makeSocketIOListeners = (
         })
       );
     },
-    onFoundLoras: (data: InvokeAI.FoundLorasRsponse) => {
-      dispatch(setFoundLoras(data));
-    },
-    onFoundTextualInversionTriggers: (
-      data: InvokeAI.FoundTextualInversionTriggersResponse
-    ) => {
-      const localTriggers = data.local_triggers;
-      const huggingFaceConcepts = data.huggingface_concepts;
-
-      dispatch(setFoundLocalTextualInversionTriggers(localTriggers));
-      dispatch(
-        setFoundHuggingFaceTextualInversionTriggers(huggingFaceConcepts)
-      );
-
-      // Assign Local TI's
-      const foundLocalTINames: string[] = [];
-      localTriggers.forEach((textualInversion) => {
-        foundLocalTINames.push(textualInversion.name);
-      });
-      dispatch(setLocalTextualInversionTriggers(foundLocalTINames));
-
-      // Assign HuggingFace Concepts
-      const foundHuggingFaceConceptNames: string[] = [];
-      huggingFaceConcepts.forEach((concept) => {
-        foundHuggingFaceConceptNames.push(concept.name);
-      });
-      dispatch(
-        setHuggingFaceTextualInversionConcepts(foundHuggingFaceConceptNames)
-      );
-    },
-
     onTempFolderEmptied: () => {
       dispatch(
         addToast({

invokeai/frontend/src/app/socketio/middleware.ts

@@ -51,8 +51,6 @@ export const socketioMiddleware = () => {
       onModelConverted,
       onModelsMerged,
       onModelChangeFailed,
-      onFoundLoras,
-      onFoundTextualInversionTriggers,
       onTempFolderEmptied,
     } = makeSocketIOListeners(store);
 
@@ -71,8 +69,6 @@ export const socketioMiddleware = () => {
       emitConvertToDiffusers,
       emitMergeDiffusersModels,
       emitRequestModelChange,
-      emitGetLoraModels,
-      emitGetTextualInversionTriggers,
       emitSaveStagingAreaImageToGallery,
       emitRequestEmptyTempFolder,
     } = makeSocketIOEmitters(store, socketio);
@@ -149,17 +145,6 @@ export const socketioMiddleware = () => {
         onModelChangeFailed(data);
       });
 
-      socketio.on('foundLoras', (data: InvokeAI.FoundLorasRsponse) => {
-        onFoundLoras(data);
-      });
-
-      socketio.on(
-        'foundTextualInversionTriggers',
-        (data: InvokeAI.FoundTextualInversionTriggersResponse) => {
-          onFoundTextualInversionTriggers(data);
-        }
-      );
-
       socketio.on('tempFolderEmptied', () => {
         onTempFolderEmptied();
       });
@@ -241,16 +226,6 @@ export const socketioMiddleware = () => {
         break;
       }
 
-      case 'socketio/getLoraModels': {
-        emitGetLoraModels();
-        break;
-      }
-
-      case 'socketio/getTextualInversionTriggers': {
-        emitGetTextualInversionTriggers();
-        break;
-      }
-
       case 'socketio/saveStagingAreaImageToGallery': {
         emitSaveStagingAreaImageToGallery(action.payload);
         break;

invokeai/frontend/src/common/components/IAISimpleMenu.tsx

@@ -7,14 +7,13 @@ import {
   MenuButtonProps,
   MenuListProps,
   MenuItemProps,
-  Text,
 } from '@chakra-ui/react';
 import { MouseEventHandler, ReactNode } from 'react';
 import { MdArrowDropDown, MdArrowDropUp } from 'react-icons/md';
 import IAIButton from './IAIButton';
 import IAIIconButton from './IAIIconButton';
 
-export interface IAIMenuItem {
+interface IAIMenuItem {
   item: ReactNode | string;
   onClick: MouseEventHandler<HTMLButtonElement> | undefined;
 }
@@ -44,7 +43,6 @@ export default function IAISimpleMenu(props: IAIMenuProps) {
 
   const renderMenuItems = () => {
     const menuItemsToRender: ReactNode[] = [];
-
     menuItems.forEach((menuItem, index) => {
       menuItemsToRender.push(
         <MenuItem
@@ -84,17 +82,12 @@ export default function IAISimpleMenu(props: IAIMenuProps) {
             fontSize="1.5rem"
             {...menuButtonProps}
           >
-            {menuType === 'regular' && (
-              <Text fontSize="0.9rem">{buttonText}</Text>
-            )}
+            {menuType === 'regular' && buttonText}
           </MenuButton>
           <MenuList
             zIndex={15}
             padding={0}
             borderRadius="0.5rem"
-            overflow="scroll"
-            maxWidth={'22.5rem'}
-            maxHeight={500}
             backgroundColor="var(--background-color-secondary)"
             color="var(--text-color-secondary)"
             borderColor="var(--border-color)"

invokeai/frontend/src/features/gallery/components/CurrentImageButtons.tsx

@@ -21,7 +21,6 @@ import {
   setInitialImage,
   setSeed,
 } from 'features/parameters/store/generationSlice';
-import { setAllPostProcessingParameters } from 'features/parameters/store/postprocessingSlice';
 import { postprocessingSelector } from 'features/parameters/store/postprocessingSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { SystemState } from 'features/system/store/systemSlice';
@@ -190,12 +189,11 @@ const CurrentImageButtons = () => {
   );
 
   const handleClickUseAllParameters = () => {
-    if (!currentImage?.metadata) return;
-    dispatch(setAllParameters(currentImage.metadata));
-    dispatch(setAllPostProcessingParameters(currentImage.metadata));
-    if (currentImage.metadata.image.type === 'img2img') {
+    if (!currentImage) return;
+    currentImage.metadata && dispatch(setAllParameters(currentImage.metadata));
+    if (currentImage.metadata?.image.type === 'img2img') {
       dispatch(setActiveTab('img2img'));
-    } else if (currentImage.metadata.image.type === 'txt2img') {
+    } else if (currentImage.metadata?.image.type === 'txt2img') {
       dispatch(setActiveTab('txt2img'));
     }
   };

invokeai/frontend/src/features/gallery/components/HoverableImage.tsx

@@ -10,7 +10,6 @@ import {
   setInitialImage,
   setSeed,
 } from 'features/parameters/store/generationSlice';
-import { setAllPostProcessingParameters } from 'features/parameters/store/postprocessingSlice';
 import { DragEvent, memo, useState } from 'react';
 import { FaCheck, FaTrashAlt } from 'react-icons/fa';
 import DeleteImageModal from './DeleteImageModal';
@@ -115,10 +114,7 @@ const HoverableImage = memo((props: HoverableImageProps) => {
   };
 
   const handleUseAllParameters = () => {
-    if (metadata) {
-      dispatch(setAllParameters(metadata));
-      dispatch(setAllPostProcessingParameters(metadata));
-    }
+    metadata && dispatch(setAllParameters(metadata));
     toast({
       title: t('toast.parametersSet'),
       status: 'success',

invokeai/frontend/src/features/parameters/components/MainParameters/MainWidth.tsx

@@ -34,6 +34,7 @@ export default function MainWidth() {
       withSliderMarks
       sliderMarkRightOffset={-8}
       inputWidth="6.2rem"
+      inputReadOnly
       sliderNumberInputProps={{ max: 15360 }}
     />
   ) : (

invokeai/frontend/src/features/parameters/components/PromptInput/Extras/LoraManager/LoraManager.tsx

@@ -1,86 +0,0 @@
-import { Box, Flex } from '@chakra-ui/react';
-import { getLoraModels } from 'app/socketio/actions';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import IAIIconButton from 'common/components/IAIIconButton';
-import IAISimpleMenu, { IAIMenuItem } from 'common/components/IAISimpleMenu';
-import {
-  setClearLoras,
-  setLorasInUse,
-} from 'features/parameters/store/generationSlice';
-import { useEffect } from 'react';
-import { useTranslation } from 'react-i18next';
-import { MdClear } from 'react-icons/md';
-
-export default function LoraManager() {
-  const dispatch = useAppDispatch();
-  const foundLoras = useAppSelector((state) => state.system.foundLoras);
-  const lorasInUse = useAppSelector((state) => state.generation.lorasInUse);
-
-  const { t } = useTranslation();
-
-  const handleLora = (lora: string) => {
-    dispatch(setLorasInUse(lora));
-  };
-
-  useEffect(() => {
-    dispatch(getLoraModels());
-  }, [dispatch]);
-
-  const renderLoraOption = (lora: string) => {
-    const thisloraExists = lorasInUse.includes(lora);
-    const loraExistsStyle = {
-      fontWeight: 'bold',
-      color: 'var(--context-menu-active-item)',
-    };
-    return <Box style={thisloraExists ? loraExistsStyle : {}}>{lora}</Box>;
-  };
-
-  const numOfActiveLoras = () => {
-    const foundLoraNames: string[] = [];
-    foundLoras?.forEach((lora) => {
-      foundLoraNames.push(lora.name);
-    });
-    return foundLoraNames.filter((lora) => lorasInUse.includes(lora)).length;
-  };
-
-  const makeLoraItems = () => {
-    const lorasFound: IAIMenuItem[] = [];
-    foundLoras?.forEach((lora) => {
-      if (lora.name !== ' ') {
-        const newLoraItem: IAIMenuItem = {
-          item: renderLoraOption(lora.name),
-          onClick: () => handleLora(lora.name),
-        };
-        lorasFound.push(newLoraItem);
-      }
-    });
-    return lorasFound;
-  };
-
-  return foundLoras && foundLoras?.length > 0 ? (
-    <Flex columnGap={2}>
-      <IAISimpleMenu
-        menuItems={makeLoraItems()}
-        menuType="regular"
-        buttonText={`${t('modelManager.addLora')} (${numOfActiveLoras()})`}
-        menuButtonProps={{ width: '100%', padding: '0 1rem' }}
-      />
-      <IAIIconButton
-        icon={<MdClear />}
-        tooltip={t('modelManager.clearLoras')}
-        aria-label={t('modelManager.clearLoras')}
-        onClick={() => dispatch(setClearLoras())}
-      />
-    </Flex>
-  ) : (
-    <Box
-      background="var(--btn-base-color)"
-      padding={2}
-      textAlign="center"
-      borderRadius={4}
-      fontWeight="bold"
-    >
-      {t('modelManager.noLoraModels')}
-    </Box>
-  );
-}

invokeai/frontend/src/features/parameters/components/PromptInput/Extras/PromptExtras.tsx

@@ -1,12 +0,0 @@
-import { Flex } from '@chakra-ui/react';
-import LoraManager from './LoraManager/LoraManager';
-import TextualInversionManager from './TextualInversionManager/TextualInversionManager';
-
-export default function PromptExtras() {
-  return (
-    <Flex flexDir="column" rowGap={2}>
-      <LoraManager />
-      <TextualInversionManager />
-    </Flex>
-  );
-}

invokeai/frontend/src/features/parameters/components/PromptInput/Extras/TextualInversionManager/TextualInversionManager.tsx

@@ -1,163 +0,0 @@
-import { Box, Flex } from '@chakra-ui/react';
-import { getTextualInversionTriggers } from 'app/socketio/actions';
-import { RootState } from 'app/store';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import IAIIconButton from 'common/components/IAIIconButton';
-import IAISimpleMenu, { IAIMenuItem } from 'common/components/IAISimpleMenu';
-import {
-  setAddTIToNegative,
-  setClearTextualInversions,
-  setTextualInversionsInUse,
-} from 'features/parameters/store/generationSlice';
-import { useEffect } from 'react';
-import { useTranslation } from 'react-i18next';
-import { MdArrowDownward, MdClear } from 'react-icons/md';
-
-export default function TextualInversionManager() {
-  const dispatch = useAppDispatch();
-  const textualInversionsInUse = useAppSelector(
-    (state: RootState) => state.generation.textualInversionsInUse
-  );
-
-  const negativeTextualInversionsInUse = useAppSelector(
-    (state: RootState) => state.generation.negativeTextualInversionsInUse
-  );
-
-  const foundLocalTextualInversionTriggers = useAppSelector(
-    (state) => state.system.foundLocalTextualInversionTriggers
-  );
-  const foundHuggingFaceTextualInversionTriggers = useAppSelector(
-    (state) => state.system.foundHuggingFaceTextualInversionTriggers
-  );
-
-  const localTextualInversionTriggers = useAppSelector(
-    (state) => state.generation.localTextualInversionTriggers
-  );
-
-  const huggingFaceTextualInversionConcepts = useAppSelector(
-    (state) => state.generation.huggingFaceTextualInversionConcepts
-  );
-
-  const shouldShowHuggingFaceConcepts = useAppSelector(
-    (state) => state.ui.shouldShowHuggingFaceConcepts
-  );
-
-  const addTIToNegative = useAppSelector(
-    (state) => state.generation.addTIToNegative
-  );
-
-  const { t } = useTranslation();
-
-  useEffect(() => {
-    dispatch(getTextualInversionTriggers());
-  }, [dispatch]);
-
-  const handleTextualInversion = (textual_inversion: string) => {
-    dispatch(setTextualInversionsInUse(textual_inversion));
-  };
-
-  const TIPip = ({ color }: { color: string }) => {
-    return (
-      <Box width={2} height={2} borderRadius={9999} backgroundColor={color}>
-        {' '}
-      </Box>
-    );
-  };
-
-  const renderTextualInversionOption = (textual_inversion: string) => {
-    return (
-      <Flex alignItems="center" columnGap={1}>
-        {textual_inversion}
-        {textualInversionsInUse.includes(textual_inversion) && (
-          <TIPip color="var(--context-menu-active-item)" />
-        )}
-        {negativeTextualInversionsInUse.includes(textual_inversion) && (
-          <TIPip color="var(--status-bad-color)" />
-        )}
-      </Flex>
-    );
-  };
-
-  const numOfActiveTextualInversions = () => {
-    const allTextualInversions = localTextualInversionTriggers.concat(
-      huggingFaceTextualInversionConcepts
-    );
-    return allTextualInversions.filter(
-      (ti) =>
-        textualInversionsInUse.includes(ti) ||
-        negativeTextualInversionsInUse.includes(ti)
-    ).length;
-  };
-
-  const makeTextualInversionItems = () => {
-    const textualInversionsFound: IAIMenuItem[] = [];
-    foundLocalTextualInversionTriggers?.forEach((textualInversion) => {
-      if (textualInversion.name !== ' ') {
-        const newTextualInversionItem: IAIMenuItem = {
-          item: renderTextualInversionOption(textualInversion.name),
-          onClick: () => handleTextualInversion(textualInversion.name),
-        };
-        textualInversionsFound.push(newTextualInversionItem);
-      }
-    });
-
-    if (shouldShowHuggingFaceConcepts) {
-      foundHuggingFaceTextualInversionTriggers?.forEach((textualInversion) => {
-        if (textualInversion.name !== ' ') {
-          const newTextualInversionItem: IAIMenuItem = {
-            item: renderTextualInversionOption(textualInversion.name),
-            onClick: () => handleTextualInversion(textualInversion.name),
-          };
-          textualInversionsFound.push(newTextualInversionItem);
-        }
-      });
-    }
-
-    return textualInversionsFound;
-  };
-
-  return foundLocalTextualInversionTriggers &&
-    (foundLocalTextualInversionTriggers?.length > 0 ||
-      (foundHuggingFaceTextualInversionTriggers &&
-        foundHuggingFaceTextualInversionTriggers?.length > 0 &&
-        shouldShowHuggingFaceConcepts)) ? (
-    <Flex columnGap={2}>
-      <IAISimpleMenu
-        menuItems={makeTextualInversionItems()}
-        menuType="regular"
-        buttonText={`${t(
-          'modelManager.addTextualInversionTrigger'
-        )} (${numOfActiveTextualInversions()})`}
-        menuButtonProps={{
-          width: '100%',
-          padding: '0 1rem',
-        }}
-      />
-      <IAIIconButton
-        icon={<MdArrowDownward />}
-        style={{
-          backgroundColor: addTIToNegative ? 'var(--btn-delete-image)' : '',
-        }}
-        tooltip={t('modelManager.addTIToNegative')}
-        aria-label={t('modelManager.addTIToNegative')}
-        onClick={() => dispatch(setAddTIToNegative(!addTIToNegative))}
-      />
-      <IAIIconButton
-        icon={<MdClear />}
-        tooltip={t('modelManager.clearTextualInversions')}
-        aria-label={t('modelManager.clearTextualInversions')}
-        onClick={() => dispatch(setClearTextualInversions())}
-      />
-    </Flex>
-  ) : (
-    <Box
-      background="var(--btn-base-color)"
-      padding={2}
-      textAlign="center"
-      borderRadius={4}
-      fontWeight="bold"
-    >
-      {t('modelManager.noTextualInversionTriggers')}
-    </Box>
-  );
-}

invokeai/frontend/src/features/parameters/components/PromptInput/NegativePromptInput.tsx

@@ -1,43 +1,24 @@
 import { FormControl, Textarea } from '@chakra-ui/react';
 import type { RootState } from 'app/store';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import {
-  handlePromptCheckers,
-  setNegativePrompt,
-} from 'features/parameters/store/generationSlice';
+import { setNegativePrompt } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
-import { ChangeEvent, useState } from 'react';
 
 const NegativePromptInput = () => {
   const negativePrompt = useAppSelector(
     (state: RootState) => state.generation.negativePrompt
   );
 
-  const [promptTimer, setPromptTimer] = useState<number | undefined>(undefined);
-
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const handleNegativeChangePrompt = (e: ChangeEvent<HTMLTextAreaElement>) => {
-    dispatch(setNegativePrompt(e.target.value));
-
-    // Debounce Prompt UI Checking
-    clearTimeout(promptTimer);
-    const newPromptTimer = window.setTimeout(() => {
-      dispatch(
-        handlePromptCheckers({ prompt: e.target.value, toNegative: true })
-      );
-    }, 500);
-    setPromptTimer(newPromptTimer);
-  };
-
   return (
     <FormControl>
       <Textarea
         id="negativePrompt"
         name="negativePrompt"
         value={negativePrompt}
-        onChange={handleNegativeChangePrompt}
+        onChange={(e) => dispatch(setNegativePrompt(e.target.value))}
         background="var(--prompt-bg-color)"
         placeholder={t('parameters.negativePrompts')}
         _placeholder={{ fontSize: '0.8rem' }}

invokeai/frontend/src/features/parameters/components/PromptInput/PromptInput.tsx

@@ -2,13 +2,12 @@ import { FormControl, Textarea } from '@chakra-ui/react';
 import { generateImage } from 'app/socketio/actions';
 import { RootState } from 'app/store';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import { ChangeEvent, KeyboardEvent, useRef, useState } from 'react';
+import { ChangeEvent, KeyboardEvent, useRef } from 'react';
 
 import { createSelector } from '@reduxjs/toolkit';
 import { readinessSelector } from 'app/selectors/readinessSelector';
 import {
   GenerationState,
-  handlePromptCheckers,
   setPrompt,
 } from 'features/parameters/store/generationSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
@@ -41,21 +40,11 @@ const PromptInput = () => {
   const { isReady } = useAppSelector(readinessSelector);
 
   const promptRef = useRef<HTMLTextAreaElement>(null);
-  const [promptTimer, setPromptTimer] = useState<number | undefined>(undefined);
 
   const { t } = useTranslation();
 
   const handleChangePrompt = (e: ChangeEvent<HTMLTextAreaElement>) => {
     dispatch(setPrompt(e.target.value));
-
-    // Debounce Prompt UI Checking
-    clearTimeout(promptTimer);
-    const newPromptTimer = window.setTimeout(() => {
-      dispatch(
-        handlePromptCheckers({ prompt: e.target.value, toNegative: false })
-      );
-    }, 500);
-    setPromptTimer(newPromptTimer);
   };
 
   useHotkeys(

invokeai/frontend/src/features/parameters/hooks/usePrompt.ts

@@ -3,11 +3,7 @@ import { getPromptAndNegative } from 'common/util/getPromptAndNegative';
 import * as InvokeAI from 'app/invokeai';
 import promptToString from 'common/util/promptToString';
 import { useAppDispatch } from 'app/storeHooks';
-import {
-  handlePromptCheckers,
-  setNegativePrompt,
-  setPrompt,
-} from '../store/generationSlice';
+import { setNegativePrompt, setPrompt } from '../store/generationSlice';
 
 // TECHDEBT: We have two metadata prompt formats and need to handle recalling either of them.
 // This hook provides a function to do that.
@@ -24,10 +20,6 @@ const useSetBothPrompts = () => {
 
     dispatch(setPrompt(prompt));
     dispatch(setNegativePrompt(negativePrompt));
-    dispatch(handlePromptCheckers({ prompt: prompt, toNegative: false }));
-    dispatch(
-      handlePromptCheckers({ prompt: negativePrompt, toNegative: true })
-    );
   };
 };
 

invokeai/frontend/src/features/parameters/store/generationSlice.ts

@@ -1,4 +1,4 @@
-import { PayloadAction } from '@reduxjs/toolkit';
+import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import * as InvokeAI from 'app/invokeai';
 import { getPromptAndNegative } from 'common/util/getPromptAndNegative';
@@ -17,12 +17,6 @@ export interface GenerationState {
   perlin: number;
   prompt: string;
   negativePrompt: string;
-  lorasInUse: string[];
-  huggingFaceTextualInversionConcepts: string[];
-  localTextualInversionTriggers: string[];
-  textualInversionsInUse: string[];
-  negativeTextualInversionsInUse: string[];
-  addTIToNegative: boolean;
   sampler: string;
   seamBlur: number;
   seamless: boolean;
@@ -54,12 +48,6 @@ const initialGenerationState: GenerationState = {
   perlin: 0,
   prompt: '',
   negativePrompt: '',
-  lorasInUse: [],
-  huggingFaceTextualInversionConcepts: [],
-  localTextualInversionTriggers: [],
-  textualInversionsInUse: [],
-  negativeTextualInversionsInUse: [],
-  addTIToNegative: false,
   sampler: 'k_lms',
   seamBlur: 16,
   seamless: false,
@@ -83,99 +71,12 @@ const initialGenerationState: GenerationState = {
 
 const initialState: GenerationState = initialGenerationState;
 
-const loraExists = (state: GenerationState, lora: string) => {
-  const loraRegex = new RegExp(`withLora\\(${lora},?\\s*([^\\)]+)?\\)`);
-  if (state.prompt.match(loraRegex)) return true;
-  return false;
-};
-
-const getTIRegex = (textualInversion: string) => {
-  if (textualInversion.includes('<' || '>')) {
-    return new RegExp(`${textualInversion}`);
-  } else {
-    return new RegExp(`\\b${textualInversion}\\b`);
-  }
-};
-
-const textualInversionExists = (
-  state: GenerationState,
-  textualInversion: string
-) => {
-  const textualInversionRegex = getTIRegex(textualInversion);
-
-  if (!state.addTIToNegative) {
-    if (state.prompt.match(textualInversionRegex)) return true;
-  } else {
-    if (state.negativePrompt.match(textualInversionRegex)) return true;
-  }
-  return false;
-};
-
-const handleTypedTICheck = (
-  state: GenerationState,
-  newPrompt: string,
-  toNegative: boolean
-) => {
-  let textualInversionsInUse = !toNegative
-    ? [...state.textualInversionsInUse]
-    : [...state.negativeTextualInversionsInUse]; // Get Words In Prompt
-
-  const textualInversionRegex = /([\w<>!@%&*_-]+)/g; // Scan For Each Word
-
-  const textualInversionMatches = [
-    ...newPrompt.matchAll(textualInversionRegex),
-  ]; // Match All Words
-
-  if (textualInversionMatches.length > 0) {
-    textualInversionsInUse = []; // Reset Textual Inversions In Use
-
-    textualInversionMatches.forEach((textualInversionMatch) => {
-      const textualInversionName = textualInversionMatch[0];
-      if (
-        (!textualInversionsInUse.includes(textualInversionName) &&
-          state.localTextualInversionTriggers.includes(textualInversionName)) ||
-        state.huggingFaceTextualInversionConcepts.includes(textualInversionName)
-      ) {
-        textualInversionsInUse.push(textualInversionName); // Add Textual Inversions In Prompt
-      }
-    });
-  } else {
-    textualInversionsInUse = []; // If No Matches, Remove Textual Inversions In Use
-  }
-
-  if (!toNegative) {
-    state.textualInversionsInUse = textualInversionsInUse;
-  } else {
-    state.negativeTextualInversionsInUse = textualInversionsInUse;
-  }
-};
-
-const handleTypedLoraCheck = (state: GenerationState, newPrompt: string) => {
-  let lorasInUse = [...state.lorasInUse]; // Get Loras In Prompt
-
-  const loraRegex = /withLora\(([^\\)]+)\)/g; // Scan For Lora Syntax
-  const loraMatches = [...newPrompt.matchAll(loraRegex)]; // Match All Lora Syntaxes
-
-  if (loraMatches.length > 0) {
-    lorasInUse = []; // Reset Loras In Use
-    loraMatches.forEach((loraMatch) => {
-      const loraName = loraMatch[1].split(',')[0];
-      if (!lorasInUse.includes(loraName)) lorasInUse.push(loraName); // Add Loras In Prompt
-    });
-  } else {
-    lorasInUse = []; // If No Matches, Remove Loras In Use
-  }
-
-  state.lorasInUse = lorasInUse;
-};
-
 export const generationSlice = createSlice({
   name: 'generation',
   initialState,
   reducers: {
     setPrompt: (state, action: PayloadAction<string | InvokeAI.Prompt>) => {
       const newPrompt = action.payload;
-
       if (typeof newPrompt === 'string') {
         state.prompt = newPrompt;
       } else {
@@ -193,136 +94,6 @@ export const generationSlice = createSlice({
         state.negativePrompt = promptToString(newPrompt);
       }
     },
-    handlePromptCheckers: (
-      state,
-      action: PayloadAction<{
-        prompt: string | InvokeAI.Prompt;
-        toNegative: boolean;
-      }>
-    ) => {
-      const newPrompt = action.payload.prompt;
-
-      if (typeof newPrompt === 'string') {
-        if (!action.payload.toNegative) handleTypedLoraCheck(state, newPrompt);
-        handleTypedTICheck(state, newPrompt, action.payload.toNegative);
-      }
-    },
-    setLorasInUse: (state, action: PayloadAction<string>) => {
-      const newLora = action.payload;
-      const loras = [...state.lorasInUse];
-
-      if (loraExists(state, newLora)) {
-        const loraRegex = new RegExp(
-          `withLora\\(${newLora},?\\s*([^\\)]+)?\\)`,
-          'g'
-        );
-        const newPrompt = state.prompt.replaceAll(loraRegex, '');
-        state.prompt = newPrompt.trim();
-
-        if (loras.includes(newLora)) {
-          const newLoraIndex = loras.indexOf(newLora);
-          if (newLoraIndex > -1) loras.splice(newLoraIndex, 1);
-        }
-      } else {
-        state.prompt = `${state.prompt.trim()} withLora(${newLora},0.75)`;
-        if (!loras.includes(newLora)) loras.push(newLora);
-      }
-      state.lorasInUse = loras;
-    },
-    setClearLoras: (state) => {
-      const lorasInUse = [...state.lorasInUse];
-
-      lorasInUse.forEach((lora) => {
-        const loraRegex = new RegExp(
-          `withLora\\(${lora},?\\s*([^\\)]+)?\\)`,
-          'g'
-        );
-        const newPrompt = state.prompt.replaceAll(loraRegex, '');
-        state.prompt = newPrompt.trim();
-      });
-
-      state.lorasInUse = [];
-    },
-    setTextualInversionsInUse: (state, action: PayloadAction<string>) => {
-      const newTextualInversion = action.payload;
-
-      const textualInversions = [...state.textualInversionsInUse];
-      const negativeTextualInversions = [
-        ...state.negativeTextualInversionsInUse,
-      ];
-
-      if (textualInversionExists(state, newTextualInversion)) {
-        const textualInversionRegex = getTIRegex(newTextualInversion);
-
-        if (!state.addTIToNegative) {
-          const newPrompt = state.prompt.replace(textualInversionRegex, '');
-          state.prompt = newPrompt.trim();
-
-          const newTIIndex = textualInversions.indexOf(newTextualInversion);
-          if (newTIIndex > -1) textualInversions.splice(newTIIndex, 1);
-        } else {
-          const newPrompt = state.negativePrompt.replace(
-            textualInversionRegex,
-            ''
-          );
-          state.negativePrompt = newPrompt.trim();
-
-          const newTIIndex =
-            negativeTextualInversions.indexOf(newTextualInversion);
-          if (newTIIndex > -1) negativeTextualInversions.splice(newTIIndex, 1);
-        }
-      } else {
-        if (!state.addTIToNegative) {
-          state.prompt = `${state.prompt.trim()} ${newTextualInversion}`;
-          textualInversions.push(newTextualInversion);
-        } else {
-          state.negativePrompt = `${state.negativePrompt.trim()} ${newTextualInversion}`;
-          negativeTextualInversions.push(newTextualInversion);
-        }
-      }
-
-      state.textualInversionsInUse = textualInversions;
-      state.negativeTextualInversionsInUse = negativeTextualInversions;
-    },
-    setClearTextualInversions: (state) => {
-      const textualInversions = [...state.textualInversionsInUse];
-      const negativeTextualInversions = [
-        ...state.negativeTextualInversionsInUse,
-      ];
-
-      textualInversions.forEach((ti) => {
-        const textualInversionRegex = getTIRegex(ti);
-        const newPrompt = state.prompt.replace(textualInversionRegex, '');
-        state.prompt = newPrompt.trim();
-      });
-
-      negativeTextualInversions.forEach((ti) => {
-        const textualInversionRegex = getTIRegex(ti);
-        const newPrompt = state.negativePrompt.replace(
-          textualInversionRegex,
-          ''
-        );
-        state.negativePrompt = newPrompt.trim();
-      });
-
-      state.textualInversionsInUse = [];
-      state.negativeTextualInversionsInUse = [];
-    },
-    setAddTIToNegative: (state, action: PayloadAction<boolean>) => {
-      state.addTIToNegative = action.payload;
-    },
-    setLocalTextualInversionTriggers: (
-      state,
-      action: PayloadAction<string[]>
-    ) => {
-      state.localTextualInversionTriggers = action.payload;
-    },
-    setHuggingFaceTextualInversionConcepts: (
-      state,
-      action: PayloadAction<string[]>
-    ) => {
-      state.huggingFaceTextualInversionConcepts = action.payload;
-    },
     setIterations: (state, action: PayloadAction<number>) => {
       state.iterations = action.payload;
     },
@@ -530,6 +301,7 @@ export const generationSlice = createSlice({
         state.perlin = perlin;
       }
       if (typeof seamless === 'boolean') state.seamless = seamless;
+      // if (typeof hires_fix === 'boolean') state.hiresFix = hires_fix; // TODO: Needs to be fixed after reorg
       if (width) state.width = width;
       if (height) state.height = height;
 
@@ -603,14 +375,6 @@ export const {
   setPerlin,
   setPrompt,
   setNegativePrompt,
-  handlePromptCheckers,
-  setLorasInUse,
-  setClearLoras,
-  setHuggingFaceTextualInversionConcepts,
-  setLocalTextualInversionTriggers,
-  setTextualInversionsInUse,
-  setAddTIToNegative,
-  setClearTextualInversions,
   setSampler,
   setSeamBlur,
   setSeamless,

invokeai/frontend/src/features/parameters/store/postprocessingSlice.ts

@@ -1,6 +1,5 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
-import * as InvokeAI from 'app/invokeai';
 import { FACETOOL_TYPES } from 'app/constants';
 
 export type UpscalingLevel = 2 | 4;
@@ -41,17 +40,6 @@ export const postprocessingSlice = createSlice({
   name: 'postprocessing',
   initialState,
   reducers: {
-    setAllPostProcessingParameters: (
-      state,
-      action: PayloadAction<InvokeAI.Metadata>
-    ) => {
-      const { type, hires_fix } = action.payload.image;
-
-      if (type === 'txt2img') {
-        state.hiresFix = Boolean(hires_fix);
-        // Strength of img2img used in hires_fix is not currently exposed in the Metadata for the final image.
-      }
-    },
     setFacetoolStrength: (state, action: PayloadAction<number>) => {
       state.facetoolStrength = action.payload;
     },
@@ -95,7 +83,6 @@ export const postprocessingSlice = createSlice({
 });
 
 export const {
-  setAllPostProcessingParameters,
   resetPostprocessingState,
   setCodeformerFidelity,
   setFacetoolStrength,

invokeai/frontend/src/features/system/components/ModelManager/SearchModels.tsx

@@ -181,8 +181,7 @@ export default function SearchModels() {
 
     const configFiles = {
       v1: 'configs/stable-diffusion/v1-inference.yaml',
-      v2_base: 'configs/stable-diffusion/v2-inference-v.yaml',
-      v2_768: 'configs/stable-diffusion/v2-inference-v.yaml',
+      v2: 'configs/stable-diffusion/v2-inference-v.yaml',
       inpainting: 'configs/stable-diffusion/v1-inpainting-inference.yaml',
       custom: pathToConfig,
     };
@@ -386,8 +385,7 @@ export default function SearchModels() {
               >
                 <Flex gap={4}>
                   <Radio value="v1">{t('modelManager.v1')}</Radio>
-                  <Radio value="v2_base">{t('modelManager.v2_base')}</Radio>
-                  <Radio value="v2_768">{t('modelManager.v2_768')}</Radio>
+                  <Radio value="v2">{t('modelManager.v2')}</Radio>
                   <Radio value="inpainting">
                     {t('modelManager.inpainting')}
                   </Radio>

invokeai/frontend/src/features/system/components/SettingsModal/SettingsModal.tsx

@@ -31,7 +31,6 @@ import {
 } from 'features/system/store/systemSlice';
 import { uiSelector } from 'features/ui/store/uiSelectors';
 import {
-  setShouldShowHuggingFaceConcepts,
   setShouldUseCanvasBetaLayout,
   setShouldUseSliders,
 } from 'features/ui/store/uiSlice';
@@ -53,11 +52,7 @@ const selector = createSelector(
       enableImageDebugging,
     } = system;
 
-    const {
-      shouldUseCanvasBetaLayout,
-      shouldUseSliders,
-      shouldShowHuggingFaceConcepts,
-    } = ui;
+    const { shouldUseCanvasBetaLayout, shouldUseSliders } = ui;
 
     return {
       shouldDisplayInProgressType,
@@ -68,7 +63,6 @@ const selector = createSelector(
       enableImageDebugging,
       shouldUseCanvasBetaLayout,
       shouldUseSliders,
-      shouldShowHuggingFaceConcepts,
     };
   },
   {
@@ -113,7 +107,6 @@ const SettingsModal = ({ children }: SettingsModalProps) => {
     enableImageDebugging,
     shouldUseCanvasBetaLayout,
     shouldUseSliders,
-    shouldShowHuggingFaceConcepts,
   } = useAppSelector(selector);
 
   /**
@@ -213,14 +206,6 @@ const SettingsModal = ({ children }: SettingsModalProps) => {
                   dispatch(setShouldUseSliders(e.target.checked))
                 }
               />
-              <IAISwitch
-                styleClass="settings-modal-item"
-                label={t('settings.showHuggingFaceConcepts')}
-                isChecked={shouldShowHuggingFaceConcepts}
-                onChange={(e: ChangeEvent<HTMLInputElement>) =>
-                  dispatch(setShouldShowHuggingFaceConcepts(e.target.checked))
-                }
-              />
             </div>
 
             <div className="settings-modal-items">

invokeai/frontend/src/features/system/store/systemSlice.ts

@@ -51,13 +51,6 @@ export interface SystemState
   toastQueue: UseToastOptions[];
   searchFolder: string | null;
   foundModels: InvokeAI.FoundModel[] | null;
-  foundLoras: InvokeAI.FoundLora[] | null;
-  foundLocalTextualInversionTriggers:
-    | InvokeAI.FoundTextualInversionTriggers[]
-    | null;
-  foundHuggingFaceTextualInversionTriggers:
-    | InvokeAI.FoundTextualInversionTriggers[]
-    | null;
   openModel: string | null;
   cancelOptions: {
     cancelType: CancelType;
@@ -100,9 +93,6 @@ const initialSystemState: SystemState = {
   toastQueue: [],
   searchFolder: null,
   foundModels: null,
-  foundLoras: null,
-  foundLocalTextualInversionTriggers: null,
-  foundHuggingFaceTextualInversionTriggers: null,
   openModel: null,
   cancelOptions: {
     cancelType: 'immediate',
@@ -272,24 +262,6 @@ export const systemSlice = createSlice({
     ) => {
       state.foundModels = action.payload;
     },
-    setFoundLoras: (
-      state,
-      action: PayloadAction<InvokeAI.FoundLora[] | null>
-    ) => {
-      state.foundLoras = action.payload;
-    },
-    setFoundLocalTextualInversionTriggers: (
-      state,
-      action: PayloadAction<InvokeAI.FoundTextualInversionTriggers[] | null>
-    ) => {
-      state.foundLocalTextualInversionTriggers = action.payload;
-    },
-    setFoundHuggingFaceTextualInversionTriggers: (
-      state,
-      action: PayloadAction<InvokeAI.FoundTextualInversionTriggers[] | null>
-    ) => {
-      state.foundHuggingFaceTextualInversionTriggers = action.payload;
-    },
     setOpenModel: (state, action: PayloadAction<string | null>) => {
       state.openModel = action.payload;
     },
@@ -331,9 +303,6 @@ export const {
   setProcessingIndeterminateTask,
   setSearchFolder,
   setFoundModels,
-  setFoundLoras,
-  setFoundLocalTextualInversionTriggers,
-  setFoundHuggingFaceTextualInversionTriggers,
   setOpenModel,
   setCancelType,
   setCancelAfter,

invokeai/frontend/src/features/ui/components/ImageToImage/ImageToImagePanel.tsx

@@ -18,7 +18,6 @@ import PromptInput from 'features/parameters/components/PromptInput/PromptInput'
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
 import { useTranslation } from 'react-i18next';
 import ImageToImageOptions from './ImageToImageOptions';
-import PromptExtras from 'features/parameters/components/PromptInput/Extras/PromptExtras';
 
 export default function ImageToImagePanel() {
   const { t } = useTranslation();
@@ -64,7 +63,6 @@ export default function ImageToImagePanel() {
       <Flex flexDir="column" rowGap="0.5rem">
         <PromptInput />
         <NegativePromptInput />
-        <PromptExtras />
       </Flex>
       <ProcessButtons />
       <MainSettings />

invokeai/frontend/src/features/ui/components/TextToImage/TextToImagePanel.tsx

@@ -17,7 +17,6 @@ import NegativePromptInput from 'features/parameters/components/PromptInput/Nega
 import PromptInput from 'features/parameters/components/PromptInput/PromptInput';
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
 import { useTranslation } from 'react-i18next';
-import PromptExtras from 'features/parameters/components/PromptInput/Extras/PromptExtras';
 
 export default function TextToImagePanel() {
   const { t } = useTranslation();
@@ -63,7 +62,6 @@ export default function TextToImagePanel() {
       <Flex flexDir="column" rowGap="0.5rem">
         <PromptInput />
         <NegativePromptInput />
-        <PromptExtras />
       </Flex>
       <ProcessButtons />
       <MainSettings />

invokeai/frontend/src/features/ui/components/UnifiedCanvas/UnifiedCanvasPanel.tsx

@@ -17,7 +17,6 @@ import NegativePromptInput from 'features/parameters/components/PromptInput/Nega
 import PromptInput from 'features/parameters/components/PromptInput/PromptInput';
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
 import { useTranslation } from 'react-i18next';
-import PromptExtras from 'features/parameters/components/PromptInput/Extras/PromptExtras';
 
 export default function UnifiedCanvasPanel() {
   const { t } = useTranslation();
@@ -74,7 +73,6 @@ export default function UnifiedCanvasPanel() {
       <Flex flexDir="column" rowGap="0.5rem">
         <PromptInput />
         <NegativePromptInput />
-        <PromptExtras />
       </Flex>
       <ProcessButtons />
       <MainSettings />

invokeai/frontend/src/features/ui/store/uiSlice.ts

@@ -15,7 +15,6 @@ const initialtabsState: UIState = {
   shouldUseCanvasBetaLayout: false,
   shouldShowExistingModelsInSearch: false,
   shouldUseSliders: false,
-  shouldShowHuggingFaceConcepts: false,
   addNewModelUIOption: null,
 };
 
@@ -71,12 +70,6 @@ export const uiSlice = createSlice({
     setShouldUseSliders: (state, action: PayloadAction<boolean>) => {
       state.shouldUseSliders = action.payload;
     },
-    setShouldShowHuggingFaceConcepts: (
-      state,
-      action: PayloadAction<boolean>
-    ) => {
-      state.shouldShowHuggingFaceConcepts = action.payload;
-    },
     setAddNewModelUIOption: (state, action: PayloadAction<AddNewModelType>) => {
       state.addNewModelUIOption = action.payload;
     },
@@ -95,7 +88,6 @@ export const {
   setShouldUseCanvasBetaLayout,
   setShouldShowExistingModelsInSearch,
   setShouldUseSliders,
-  setShouldShowHuggingFaceConcepts,
   setAddNewModelUIOption,
 } = uiSlice.actions;
 

invokeai/frontend/src/features/ui/store/uiTypes.ts

@@ -12,6 +12,5 @@ export interface UIState {
   shouldUseCanvasBetaLayout: boolean;
   shouldShowExistingModelsInSearch: boolean;
   shouldUseSliders: boolean;
-  shouldShowHuggingFaceConcepts: boolean;
   addNewModelUIOption: AddNewModelType;
 }

invokeai/frontend/src/styles/Themes/_Colors_Dark.scss

@@ -8,6 +8,7 @@
   --accent-color-bright: rgb(104, 60, 230);
   --accent-color-hover: var(--accent-color-bright);
 
+  // App Colors
   --root-bg-color: rgb(10, 10, 10);
   --background-color: rgb(26, 26, 32);
   --background-color-light: rgb(40, 44, 48);
@@ -118,7 +119,6 @@
   --context-menu-bg-color: rgb(46, 48, 58);
   --context-menu-box-shadow: none;
   --context-menu-bg-color-hover: rgb(30, 32, 42);
-  --context-menu-active-item: var(--accent-color-bright);
 
   // Shadows
   --floating-button-drop-shadow-color: var(--accent-color);

invokeai/frontend/src/styles/Themes/_Colors_Green.scss

@@ -117,7 +117,6 @@
   --context-menu-bg-color: rgb(46, 48, 58);
   --context-menu-box-shadow: none;
   --context-menu-bg-color-hover: rgb(30, 32, 42);
-  --context-menu-active-item: var(--accent-color-bright);
 
   // Shadows
   --floating-button-drop-shadow-color: var(--accent-color);

invokeai/frontend/src/styles/Themes/_Colors_Light.scss

@@ -114,7 +114,6 @@
   --context-menu-box-shadow: 0px 10px 38px -10px rgba(22, 23, 24, 0.35),
     0px 10px 20px -15px rgba(22, 23, 24, 0.2);
   --context-menu-bg-color-hover: var(--background-color-secondary);
-  --context-menu-active-item: rgb(0, 0, 0);
 
   // Shadows
   --floating-button-drop-shadow-color: rgba(0, 0, 0, 0.7);

invokeai/frontend/yarn.lock

@@ -56,26 +56,26 @@
     "@babel/helper-validator-identifier" "^7.19.1"
     to-fast-properties "^2.0.0"
 
-"@chakra-ui/accordion@2.1.11":
-  version "2.1.11"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.1.11.tgz#c6df0100c543645d0631df3aefde2ea2b8ed6313"
-  integrity sha512-mfVPmqETp9pyRDHJ33AdF19oHv/LyxVzQJtlxUByuvs8Cj9QQZ2LQLg5kejm+b3mj03A7A6yfbuo3RNaI4Bhsg==
+"@chakra-ui/accordion@2.1.9":
+  version "2.1.9"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.1.9.tgz#20fa86d94dc034251df2f7c8595ae4dd541a29d9"
+  integrity sha512-a9CKIAUHezc0f5FR/SQ4GVxnWuIb2HbDTxTEKTp58w/J9pecIbJaNrJ5TUZ0MVbDU9jkgO9RsZ29jkja8PomAw==
   dependencies:
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/descendant" "3.0.13"
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/transition" "2.0.15"
 
-"@chakra-ui/alert@2.1.0":
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.1.0.tgz#7a234ac6426231b39243088648455cbcf1cbdf24"
-  integrity sha512-OcfHwoXI5VrmM+tHJTHT62Bx6TfyfCxSa0PWUOueJzSyhlUOKBND5we6UtrOB7D0jwX45qKKEDJOLG5yCG21jQ==
+"@chakra-ui/alert@2.0.17":
+  version "2.0.17"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.0.17.tgz#b129732ec308db6a6a1afa7c06a6595ad853c967"
+  integrity sha512-0Y5vw+HkeXpwbL1roVpSSNM6luMRmUbwduUSHEA4OnX1ismvsDb1ZBfpi4Vxp6w8euJ2Uj6df3krbd5tbCP6tg==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/spinner" "2.0.13"
 
@@ -84,23 +84,23 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.1.2.tgz#ea66b1841e7195da08ddc862daaa3f3e56e565f5"
   integrity sha512-pKfOS/mztc4sUXHNc8ypJ1gPWSolWT770jrgVRfolVbYlki8y5Y+As996zMF6k5lewTu6j9DQequ7Cc9a69IVQ==
 
-"@chakra-ui/avatar@2.2.8":
-  version "2.2.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/avatar/-/avatar-2.2.8.tgz#a6e16accb2bb9c879f197090ccc9df1ff42992a6"
-  integrity sha512-uBs9PMrqyK111tPIYIKnOM4n3mwgKqGpvYmtwBnnbQLTNLg4gtiWWVbpTuNMpyu1av0xQYomjUt8Doed8w6p8g==
+"@chakra-ui/avatar@2.2.5":
+  version "2.2.5"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/avatar/-/avatar-2.2.5.tgz#50eb7cc5a172d394b301fa0abd5f607b7f5d3563"
+  integrity sha512-TEHXuGE79+fEn61qJ7J/A0Ec+WjyNwobrDTATcLg9Zx2/WEMmZNfrWIAlI5ANQAwVbdSWeGVbyoLAK5mbcrE0A==
   dependencies:
     "@chakra-ui/image" "2.0.15"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/breadcrumb@2.1.5":
-  version "2.1.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.1.5.tgz#a43b22cc8005291a615696a8c88efc37064562f3"
-  integrity sha512-p3eQQrHQBkRB69xOmNyBJqEdfCrMt+e0eOH+Pm/DjFWfIVIbnIaFbmDCeWClqlLa21Ypc6h1hR9jEmvg8kmOog==
+"@chakra-ui/breadcrumb@2.1.4":
+  version "2.1.4"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.1.4.tgz#0d249dc2a92639bd2bf46d097dd5445112bd2367"
+  integrity sha512-vyBx5TAxPnHhb0b8nyRGfqyjleD//9mySFhk96c9GL+T6YDO4swHw5y/kvDv3Ngc/iRwJ9hdI49PZKwPxLqsEg==
   dependencies:
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/breakpoint-utils@2.0.8":
@@ -110,12 +110,12 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/button@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/button/-/button-2.0.18.tgz#c13d2e404e22a9873ba5373fde494bedafe32fdd"
-  integrity sha512-E3c99+lOm6ou4nQVOTLkG+IdOPMjsQK+Qe7VyP8A/xeAMFONuibrWPRPpprr4ZkB4kEoLMfNuyH2+aEza3ScUA==
+"@chakra-ui/button@2.0.16":
+  version "2.0.16"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/button/-/button-2.0.16.tgz#ff315b57ee47c3511a6507fcfb6f00bb93e2ac7d"
+  integrity sha512-NjuTKa7gNhnGSUutKuTc8HoAOe9WWIigpciBG7yj3ok67kg8bXtSzPyQFZlgTY6XGdAckWTT+Do4tvhwa5LA+g==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/spinner" "2.0.13"
@@ -127,13 +127,13 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/checkbox@2.2.14":
-  version "2.2.14"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.2.14.tgz#902acc99a9a80c1c304788a230cf36f8116e8260"
-  integrity sha512-uqo6lFWLqYBujPglrvRhTAErtuIXpmdpc5w0W4bjK7kyvLhxOpUh1hlDb2WoqlNpfRn/OaNeF6VinPnf9BJL8w==
+"@chakra-ui/checkbox@2.2.10":
+  version "2.2.10"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.2.10.tgz#e4f773e7d2464f1d6e9d18dd88b679290cb33171"
+  integrity sha512-vzxEjw99qj7loxAdP1WuHNt4EAvj/t6cc8oxyOB2mEvkAzhxI34rLR+3zWDuHWsmhyUO+XEDh4FiWdR+DK5Siw==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
@@ -142,7 +142,7 @@
     "@chakra-ui/react-use-update-effect" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/visually-hidden" "2.0.15"
-    "@zag-js/focus-visible" "0.2.2"
+    "@zag-js/focus-visible" "0.2.1"
 
 "@chakra-ui/clickable@2.0.14":
   version "2.0.14"
@@ -180,17 +180,17 @@
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/css-reset@2.1.1":
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.1.1.tgz#c61f3d2103c13e62a86fd2d359682092e961852c"
-  integrity sha512-jwEOfIAWmQsnChHQTW/eRE+dfE4MjmhvSvoUug5nkV1pI7veC/20noFlIZxzi82EbiQI8Fs0+Jnusgxr2yaOHA==
+"@chakra-ui/css-reset@2.0.12":
+  version "2.0.12"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.0.12.tgz#6eebcbe9e971facd215e174e063ace29f647a045"
+  integrity sha512-Q5OYIMvqTl2vZ947kIYxcS5DhQXeStB84BzzBd6C10wOx1gFUu9pL+jLpOnHR3hhpWRMdX5o7eT+gMJWIYUZ0Q==
 
-"@chakra-ui/descendant@3.0.14":
-  version "3.0.14"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/descendant/-/descendant-3.0.14.tgz#fe8bac3f0e1ffe562e3e73eac393dbf222d57e13"
-  integrity sha512-+Ahvp9H4HMpfScIv9w1vaecGz7qWAaK1YFHHolz/SIsGLaLGlbdp+5UNabQC7L6TUnzzJDQDxzwif78rTD7ang==
+"@chakra-ui/descendant@3.0.13":
+  version "3.0.13"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/descendant/-/descendant-3.0.13.tgz#e883a2233ee07fe1ae6c014567824c0f79df11cf"
+  integrity sha512-9nzxZVxUSMc4xPL5fSaRkEOQjDQWUGjGvrZI7VzWk9eq63cojOtIxtWMSW383G9148PzWJjJYt30Eud5tdZzlg==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
 
 "@chakra-ui/dom-utils@2.0.6":
@@ -198,12 +198,12 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/dom-utils/-/dom-utils-2.0.6.tgz#68f49f3b4a0bdebd5e416d6fd2c012c9ad64b76a"
   integrity sha512-PVtDkPrDD5b8aoL6Atg7SLjkwhWb7BwMcLOF1L449L3nZN+DAO3nyAh6iUhZVJyunELj9d0r65CDlnMREyJZmA==
 
-"@chakra-ui/editable@2.0.21":
-  version "2.0.21"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/editable/-/editable-2.0.21.tgz#bc74510470d6d455844438e540851896d3879132"
-  integrity sha512-oYuXbHnggxSYJN7P9Pn0Scs9tPC91no4z1y58Oe+ILoJKZ+bFAEHtL7FEISDNJxw++MEukeFu7GU1hVqmdLsKQ==
+"@chakra-ui/editable@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/editable/-/editable-2.0.19.tgz#1af2fe3c215111f61f7872fb5f599f4d8da24e7d"
+  integrity sha512-YxRJsJ2JQd42zfPBgTKzIhg1HugT+gfQz1ZosmUN+IZT9YZXL2yodHTUz6Lee04Vc/CdEqgBFLuREXEUNBfGtA==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
@@ -226,13 +226,13 @@
     "@chakra-ui/dom-utils" "2.0.6"
     react-focus-lock "^2.9.2"
 
-"@chakra-ui/form-control@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/form-control/-/form-control-2.0.18.tgz#1923f293afde70b2b07ca731d98fef3660098c56"
-  integrity sha512-I0a0jG01IAtRPccOXSNugyRdUAe8Dy40ctqedZvznMweOXzbMCF1m+sHPLdWeWC/VI13VoAispdPY0/zHOdjsQ==
+"@chakra-ui/form-control@2.0.17":
+  version "2.0.17"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/form-control/-/form-control-2.0.17.tgz#2f710325e77ce35067337616d440f903b137bdd5"
+  integrity sha512-34ptCaJ2LNvQNOlB6MAKsmH1AkT1xo7E+3Vw10Urr81yTOjDTM/iU6vG3JKPfRDMyXeowPjXmutlnuk72SSjRg==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
@@ -254,10 +254,10 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/icons@^2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/icons/-/icons-2.0.18.tgz#6f859d2e0d8f31fea9cb2e6507d65eb65cb95cf5"
-  integrity sha512-E/+DF/jw7kdN4/XxCZRnr4FdMXhkl50Q34MVwN9rADWMwPK9uSZPGyC7HOx6rilo7q4bFjYDH3yRj9g+VfbVkg==
+"@chakra-ui/icons@^2.0.17":
+  version "2.0.17"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/icons/-/icons-2.0.17.tgz#625a46d169707aad36d65c04a4626a422f92e5ae"
+  integrity sha512-HMJP0WrJgAmFR9+Xh/CBH0nVnGMsJ4ZC8MK6tMgxPKd9/muvn0I4hsicHqdPlLpmB0TlxlhkBAKaVMtOdz6F0w==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
 
@@ -269,27 +269,27 @@
     "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/input@2.0.21":
-  version "2.0.21"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/input/-/input-2.0.21.tgz#a7e55ea6fa32ae39c0f6ec44ca2189933fda9eb5"
-  integrity sha512-AIWjjg6MgcOtlvKmVoZfPPfgF+sBSWL3Zq2HSCAMvS6h7jfxz/Xv0UTFGPk5F4Wt0YHT7qMySg0Jsm0b78HZJg==
+"@chakra-ui/input@2.0.20":
+  version "2.0.20"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/input/-/input-2.0.20.tgz#8db3ec46b52be901c94599b3659a9003bdb2dd07"
+  integrity sha512-ypmsy4n4uNBVgn6Gd24Zrpi+qRf/T9WEzWkysuYC9Qfxo+i7yuf3snp7XmBy8KSGVSiXE11eO8ZN5oCg6Xg0jg==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.0.17"
     "@chakra-ui/object-utils" "2.0.8"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/layout@2.1.18":
-  version "2.1.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/layout/-/layout-2.1.18.tgz#f5dba687dfced9145d495f3a21edb5672df6bb73"
-  integrity sha512-F4Gh2e+DGdaWdWT5NZduIFD9NM7Bnuh8sXARFHWPvIu7yvAwZ3ddqC9GK4F3qUngdmkJxDLWQqRSwSh96Lxbhw==
+"@chakra-ui/layout@2.1.16":
+  version "2.1.16"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/layout/-/layout-2.1.16.tgz#9d90f25cf9f0537d19cd36a417f7ddc1461e8591"
+  integrity sha512-QFS3feozIGsvB0H74lUocev55aRF26eNrdmhfJifwikZAiq+zzZAMdBdNU9UJhHClnMOU8/iGZ0MF7ti4zQS1A==
   dependencies:
     "@chakra-ui/breakpoint-utils" "2.0.8"
     "@chakra-ui/icon" "3.0.16"
     "@chakra-ui/object-utils" "2.0.8"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/lazy-utils@2.0.5":
@@ -311,17 +311,17 @@
     "@chakra-ui/react-env" "3.0.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/menu@2.1.12":
-  version "2.1.12"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/menu/-/menu-2.1.12.tgz#ab83b7a5165bd31a6c68328d7f65a79e3412c48d"
-  integrity sha512-ylNK1VJlr/3/EGg9dLPZ87cBJJjeiYXeU/gOAphsKXMnByrXWhbp4YVnyyyha2KZ0zEw0aPU4nCZ+A69aT9wrg==
+"@chakra-ui/menu@2.1.9":
+  version "2.1.9"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/menu/-/menu-2.1.9.tgz#2f3239a9b2855fd77fc317d9e6b904c1ad50d7c6"
+  integrity sha512-ue5nD4QJcl3H3UwN0zZNJmH89XUebnvEdW6THAUL41hDjJ0J/Fjpg9Sgzwug2aBbBXBNbVMsUuhcCj6x91d+IQ==
   dependencies:
     "@chakra-ui/clickable" "2.0.14"
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/descendant" "3.0.13"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/popper" "3.0.13"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-animation-state" "2.0.8"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-disclosure" "2.0.8"
@@ -330,33 +330,33 @@
     "@chakra-ui/react-use-outside-click" "2.0.7"
     "@chakra-ui/react-use-update-effect" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/transition" "2.0.15"
 
-"@chakra-ui/modal@2.2.11":
-  version "2.2.11"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/modal/-/modal-2.2.11.tgz#8a964288759f3d681e23bfc3a837a3e2c7523f8e"
-  integrity sha512-2J0ZUV5tEzkPiawdkgPz6bmex7NXAde1VXooMwdvK+vuT8PV3U61yorTJOZVLdw7TjjI1Yo94mzsp6UwBud43Q==
+"@chakra-ui/modal@2.2.9":
+  version "2.2.9"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/modal/-/modal-2.2.9.tgz#aad65a2c60aa974e023f8b3facc0e79eb742e006"
+  integrity sha512-nTfNp7XsVwn5+xJOtstoFA8j0kq/9sJj7KesyYzjEDaMKvCZvIOntRYowoydho43jb4+YC7ebKhp0KOIINS0gg==
   dependencies:
     "@chakra-ui/close-button" "2.0.17"
     "@chakra-ui/focus-lock" "2.0.16"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/portal" "2.0.15"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/transition" "2.0.15"
     aria-hidden "^1.2.2"
     react-remove-scroll "^2.5.5"
 
-"@chakra-ui/number-input@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/number-input/-/number-input-2.0.19.tgz#82d4522036904c04d07e7050822fc522f9b32233"
-  integrity sha512-HDaITvtMEqOauOrCPsARDxKD9PSHmhWywpcyCSOX0lMe4xx2aaGhU0QQFhsJsykj8Er6pytMv6t0KZksdDv3YA==
+"@chakra-ui/number-input@2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/number-input/-/number-input-2.0.18.tgz#072a00ef869ebafa4960cfdee8caae8208864289"
+  integrity sha512-cPkyAFFHHzeFBselrT1BtjlzMkJ6TKrTDUnHFlzqXy6aqeXuhrjFhMfXucjedSpOqedsP9ZbKFTdIAhu9DdL/A==
   dependencies:
     "@chakra-ui/counter" "2.0.14"
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.0.17"
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-event-listener" "2.0.7"
@@ -376,27 +376,27 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/object-utils/-/object-utils-2.0.8.tgz#307f927f6434f99feb32ba92bdf451a6b59a6199"
   integrity sha512-2upjT2JgRuiupdrtBWklKBS6tqeGMA77Nh6Q0JaoQuH/8yq+15CGckqn3IUWkWoGI0Fg3bK9LDlbbD+9DLw95Q==
 
-"@chakra-ui/pin-input@2.0.20":
-  version "2.0.20"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/pin-input/-/pin-input-2.0.20.tgz#5bf115bf4282b69fc6532a9c542cbf41f815d200"
-  integrity sha512-IHVmerrtHN8F+jRB3W1HnMir1S1TUCWhI7qDInxqPtoRffHt6mzZgLZ0izx8p1fD4HkW4c1d4/ZLEz9uH9bBRg==
+"@chakra-ui/pin-input@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/pin-input/-/pin-input-2.0.19.tgz#f9b196174f0518feec5c1ee3fcaf2134c301148a"
+  integrity sha512-6O7s4vWz4cqQ6zvMov9sYj6ZqWAsTxR/MNGe3DNgu1zWQg8veNCYtj1rNGhNS3eZNUMAa8uM2dXIphGTP53Xow==
   dependencies:
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/descendant" "3.0.13"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/popover@2.1.9":
-  version "2.1.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/popover/-/popover-2.1.9.tgz#890cc0dfc5022757715ccf772ec194e7a409275f"
-  integrity sha512-OMJ12VVs9N32tFaZSOqikkKPtwAVwXYsES/D1pff/amBrE3ngCrpxJSIp4uvTdORfIYDojJqrR52ZplDKS9hRQ==
+"@chakra-ui/popover@2.1.8":
+  version "2.1.8"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/popover/-/popover-2.1.8.tgz#e906ce0533693d735b6e13a3a6ffe16d8e0a9ab4"
+  integrity sha512-ob7fAz+WWmXIq7iGHVB3wDKzZTj+T+noYBT/U1Q+jIf+jMr2WOpJLTfb0HTZcfhvn4EBFlfBg7Wk5qbXNaOn7g==
   dependencies:
     "@chakra-ui/close-button" "2.0.17"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/popper" "3.0.13"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-animation-state" "2.0.8"
     "@chakra-ui/react-use-disclosure" "2.0.8"
@@ -414,53 +414,53 @@
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@popperjs/core" "^2.9.3"
 
-"@chakra-ui/portal@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/portal/-/portal-2.0.16.tgz#e5ce3f9d9e559f17a95276e0c006d0e9b7703442"
-  integrity sha512-bVID0qbQ0l4xq38LdqAN4EKD4/uFkDnXzFwOlviC9sl0dNhzICDb1ltuH/Adl1d2HTMqyN60O3GO58eHy7plnQ==
+"@chakra-ui/portal@2.0.15":
+  version "2.0.15"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/portal/-/portal-2.0.15.tgz#21e1f97c4407fc15df8c365cb5cf799dac73ce41"
+  integrity sha512-z8v7K3j1/nMuBzp2+wRIIw7s/eipVtnXLdjK5yqbMxMRa44E8Mu5VNJLz3aQFLHXEUST+ifqrjImQeli9do6LQ==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
 
-"@chakra-ui/progress@2.1.6":
-  version "2.1.6"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/progress/-/progress-2.1.6.tgz#398db20440979c37adb0a34821f805ae3471873b"
-  integrity sha512-hHh5Ysv4z6bK+j2GJbi/FT9CVyto2PtNUNwBmr3oNMVsoOUMoRjczfXvvYqp0EHr9PCpxqrq7sRwgQXUzhbDSw==
+"@chakra-ui/progress@2.1.5":
+  version "2.1.5"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/progress/-/progress-2.1.5.tgz#eb6a47adf2bff93971262d163461d390782a04ff"
+  integrity sha512-jj5Vp4lxUchuwp4RPCepM0yAyKi344bgsOd3Apd+ldxclDcewPc82fbwDu7g/Xv27LqJkT+7E/SlQy04wGrk0g==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
 
-"@chakra-ui/provider@2.2.2":
-  version "2.2.2"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/provider/-/provider-2.2.2.tgz#a798d1c243f33e00c85763834a7350e0d1c643ad"
-  integrity sha512-UVwnIDnAWq1aKroN5AF+OpNpUqLVeIUk7tKvX3z4CY9FsPFFi6LTEhRHdhpwaU1Tau3Tf9agEu5URegpY7S8BA==
+"@chakra-ui/provider@2.1.2":
+  version "2.1.2"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/provider/-/provider-2.1.2.tgz#b025cb718826b003b3c9535b6961e8f3be70ebd5"
+  integrity sha512-4lLlz8QuJv00BhfyKzWpzfoti9MDOdJ/MqXixJV/EZ02RMBOdE9qy9bSz/WckPC2MVhtRUuwMkxH+0QY21PXuw==
   dependencies:
-    "@chakra-ui/css-reset" "2.1.1"
-    "@chakra-ui/portal" "2.0.16"
+    "@chakra-ui/css-reset" "2.0.12"
+    "@chakra-ui/portal" "2.0.15"
     "@chakra-ui/react-env" "3.0.0"
-    "@chakra-ui/system" "2.5.5"
+    "@chakra-ui/system" "2.5.1"
     "@chakra-ui/utils" "2.0.15"
 
-"@chakra-ui/radio@2.0.22":
-  version "2.0.22"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/radio/-/radio-2.0.22.tgz#fad0ce7c9ba4051991ed517cac4cfe526d6d47d9"
-  integrity sha512-GsQ5WAnLwivWl6gPk8P1x+tCcpVakCt5R5T0HumF7DGPXKdJbjS+RaFySrbETmyTJsKY4QrfXn+g8CWVrMjPjw==
+"@chakra-ui/radio@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/radio/-/radio-2.0.19.tgz#8d5c02eae8eddbced4476b1b50921ade62f0a744"
+  integrity sha512-PlJiV59eGSmeKP4v/4+ccQUWGRd0cjPKkj/p3L+UbOf8pl9dWm8y9kIeL5TYbghQSDv0nzkrH4+yMnnDTZjdMQ==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@zag-js/focus-visible" "0.2.2"
+    "@zag-js/focus-visible" "0.2.1"
 
 "@chakra-ui/react-children-utils@2.0.6":
   version "2.0.6"
   resolved "https://registry.yarnpkg.com/@chakra-ui/react-children-utils/-/react-children-utils-2.0.6.tgz#6c480c6a60678fcb75cb7d57107c7a79e5179b92"
   integrity sha512-QVR2RC7QsOsbWwEnq9YduhpqSFnZGvjjGREV8ygKi8ADhXh93C8azLECCUVgRJF2Wc+So1fgxmjLcbZfY2VmBA==
 
-"@chakra-ui/react-context@2.0.8":
-  version "2.0.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-context/-/react-context-2.0.8.tgz#5e0ed33ac3995875a21dea0e12b0ee5fc4c2e3cc"
-  integrity sha512-tRTKdn6lCTXM6WPjSokAAKCw2ioih7Eg8cNgaYRSwKBck8nkz9YqxgIIEj3dJD7MGtpl24S/SNI98iRWkRwR/A==
+"@chakra-ui/react-context@2.0.7":
+  version "2.0.7"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-context/-/react-context-2.0.7.tgz#f79a2b072d04d4280ec8799dc03a8a1af521ca2e"
+  integrity sha512-i7EGmSU+h2GB30cwrKB4t1R5BMHyGoJM5L2Zz7b+ZUX4aAqyPcfe97wPiQB6Rgr1ImGXrUeov4CDVrRZ2FPgLQ==
 
 "@chakra-ui/react-env@3.0.0":
   version "3.0.0"
@@ -568,12 +568,12 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-safe-layout-effect/-/react-use-safe-layout-effect-2.0.5.tgz#6cf388c37fd2a42b5295a292e149b32f860a00a7"
   integrity sha512-MwAQBz3VxoeFLaesaSEN87reVNVbjcQBDex2WGexAg6hUB6n4gc1OWYH/iXp4tzp4kuggBNhEHkk9BMYXWfhJQ==
 
-"@chakra-ui/react-use-size@2.0.10":
-  version "2.0.10"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-size/-/react-use-size-2.0.10.tgz#6131950852490c06e5fb3760bf64097c8057391f"
-  integrity sha512-fdIkH14GDnKQrtQfxX8N3gxbXRPXEl67Y3zeD9z4bKKcQUAYIMqs0MsPZY+FMpGQw8QqafM44nXfL038aIrC5w==
+"@chakra-ui/react-use-size@2.0.9":
+  version "2.0.9"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-size/-/react-use-size-2.0.9.tgz#00717867b98a24c3bdcfaa0c3e70732404193486"
+  integrity sha512-Jce7QmO1jlQZq+Y77VKckWzroRnajChzUQ8xhLQZO6VbYvrpg3cu+X2QCz3G+MZzB+1/hnvvAqmZ+uJLd8rEJg==
   dependencies:
-    "@zag-js/element-size" "0.3.2"
+    "@zag-js/element-size" "0.3.1"
 
 "@chakra-ui/react-use-timeout@2.0.5":
   version "2.0.5"
@@ -594,69 +594,69 @@
   dependencies:
     "@chakra-ui/utils" "2.0.15"
 
-"@chakra-ui/react@^2.5.5":
-  version "2.5.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react/-/react-2.5.5.tgz#5ae2450ec0d10d63e1314747466f21cf542032ff"
-  integrity sha512-aBVMUtdWv2MrptD/tKSqICPsuJ+I+jvauegffO1qPUDlK3RrXIDeOHkLGWohgXNcjY5bGVWguFEzJm97//0ooQ==
+"@chakra-ui/react@^2.5.1":
+  version "2.5.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react/-/react-2.5.1.tgz#05414db2b512bd4402e42eecc6b915d85102c576"
+  integrity sha512-ugkaqfcNMb9L4TkalWiF3rnqfr0TlUUD46JZaDIZiORVisaSwXTZTQrVfG40VghhaJT28rnC5WtiE8kd567ZBQ==
   dependencies:
-    "@chakra-ui/accordion" "2.1.11"
-    "@chakra-ui/alert" "2.1.0"
-    "@chakra-ui/avatar" "2.2.8"
-    "@chakra-ui/breadcrumb" "2.1.5"
-    "@chakra-ui/button" "2.0.18"
+    "@chakra-ui/accordion" "2.1.9"
+    "@chakra-ui/alert" "2.0.17"
+    "@chakra-ui/avatar" "2.2.5"
+    "@chakra-ui/breadcrumb" "2.1.4"
+    "@chakra-ui/button" "2.0.16"
     "@chakra-ui/card" "2.1.6"
-    "@chakra-ui/checkbox" "2.2.14"
+    "@chakra-ui/checkbox" "2.2.10"
     "@chakra-ui/close-button" "2.0.17"
     "@chakra-ui/control-box" "2.0.13"
     "@chakra-ui/counter" "2.0.14"
-    "@chakra-ui/css-reset" "2.1.1"
-    "@chakra-ui/editable" "2.0.21"
+    "@chakra-ui/css-reset" "2.0.12"
+    "@chakra-ui/editable" "2.0.19"
     "@chakra-ui/focus-lock" "2.0.16"
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.0.17"
     "@chakra-ui/hooks" "2.1.6"
     "@chakra-ui/icon" "3.0.16"
     "@chakra-ui/image" "2.0.15"
-    "@chakra-ui/input" "2.0.21"
-    "@chakra-ui/layout" "2.1.18"
+    "@chakra-ui/input" "2.0.20"
+    "@chakra-ui/layout" "2.1.16"
     "@chakra-ui/live-region" "2.0.13"
     "@chakra-ui/media-query" "3.2.12"
-    "@chakra-ui/menu" "2.1.12"
-    "@chakra-ui/modal" "2.2.11"
-    "@chakra-ui/number-input" "2.0.19"
-    "@chakra-ui/pin-input" "2.0.20"
-    "@chakra-ui/popover" "2.1.9"
+    "@chakra-ui/menu" "2.1.9"
+    "@chakra-ui/modal" "2.2.9"
+    "@chakra-ui/number-input" "2.0.18"
+    "@chakra-ui/pin-input" "2.0.19"
+    "@chakra-ui/popover" "2.1.8"
     "@chakra-ui/popper" "3.0.13"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/progress" "2.1.6"
-    "@chakra-ui/provider" "2.2.2"
-    "@chakra-ui/radio" "2.0.22"
+    "@chakra-ui/portal" "2.0.15"
+    "@chakra-ui/progress" "2.1.5"
+    "@chakra-ui/provider" "2.1.2"
+    "@chakra-ui/radio" "2.0.19"
     "@chakra-ui/react-env" "3.0.0"
-    "@chakra-ui/select" "2.0.19"
+    "@chakra-ui/select" "2.0.18"
     "@chakra-ui/skeleton" "2.0.24"
-    "@chakra-ui/slider" "2.0.23"
+    "@chakra-ui/slider" "2.0.21"
     "@chakra-ui/spinner" "2.0.13"
-    "@chakra-ui/stat" "2.0.18"
-    "@chakra-ui/styled-system" "2.8.0"
-    "@chakra-ui/switch" "2.0.26"
-    "@chakra-ui/system" "2.5.5"
-    "@chakra-ui/table" "2.0.17"
-    "@chakra-ui/tabs" "2.1.9"
-    "@chakra-ui/tag" "3.0.0"
-    "@chakra-ui/textarea" "2.0.19"
-    "@chakra-ui/theme" "3.0.1"
-    "@chakra-ui/theme-utils" "2.0.15"
-    "@chakra-ui/toast" "6.1.1"
-    "@chakra-ui/tooltip" "2.2.7"
-    "@chakra-ui/transition" "2.0.16"
+    "@chakra-ui/stat" "2.0.17"
+    "@chakra-ui/styled-system" "2.6.1"
+    "@chakra-ui/switch" "2.0.22"
+    "@chakra-ui/system" "2.5.1"
+    "@chakra-ui/table" "2.0.16"
+    "@chakra-ui/tabs" "2.1.8"
+    "@chakra-ui/tag" "2.0.17"
+    "@chakra-ui/textarea" "2.0.18"
+    "@chakra-ui/theme" "2.2.5"
+    "@chakra-ui/theme-utils" "2.0.11"
+    "@chakra-ui/toast" "6.0.1"
+    "@chakra-ui/tooltip" "2.2.6"
+    "@chakra-ui/transition" "2.0.15"
     "@chakra-ui/utils" "2.0.15"
     "@chakra-ui/visually-hidden" "2.0.15"
 
-"@chakra-ui/select@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/select/-/select-2.0.19.tgz#957e95a17a890d8c0a851e2f00a8d8dd17932d66"
-  integrity sha512-eAlFh+JhwtJ17OrB6fO6gEAGOMH18ERNrXLqWbYLrs674Le7xuREgtuAYDoxUzvYXYYTTdOJtVbcHGriI3o6rA==
+"@chakra-ui/select@2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/select/-/select-2.0.18.tgz#4eb6092610067c1b4131353fe39b4466e251395b"
+  integrity sha512-1d2lUT5LM6oOs5x4lzBh4GFDuXX62+lr+sgV7099g951/5UNbb0CS2hSZHsO7yZThLNbr7QTWZvAOAayVcGzdw==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.0.17"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/shared-utils@2.0.5":
@@ -673,20 +673,20 @@
     "@chakra-ui/react-use-previous" "2.0.5"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/slider@2.0.23":
-  version "2.0.23"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/slider/-/slider-2.0.23.tgz#9130c7aee8ca876be64d1aeba6b84fe421c8207b"
-  integrity sha512-/eyRUXLla+ZdBUPXpakE3SAS2JS8mIJR6qcUYiPVKSpRAi6tMyYeQijAXn2QC1AUVd2JrG8Pz+1Jy7Po3uA7cA==
+"@chakra-ui/slider@2.0.21":
+  version "2.0.21"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/slider/-/slider-2.0.21.tgz#f65b15bf0d5f827699ff9a2d6faee35006e2bfce"
+  integrity sha512-Mm76yJxEqJl21+3waEcKg3tM8Y4elJ7mcViN6Brj35PTfzUJfSJxeBGo1nLPJ+X5jLj7o/L4kfBmUk3lY4QYEQ==
   dependencies:
     "@chakra-ui/number-utils" "2.0.7"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-latest-ref" "2.0.5"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/react-use-pan-event" "2.0.9"
-    "@chakra-ui/react-use-size" "2.0.10"
+    "@chakra-ui/react-use-size" "2.0.9"
     "@chakra-ui/react-use-update-effect" "2.0.7"
 
 "@chakra-ui/spinner@2.0.13":
@@ -696,82 +696,82 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/stat@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/stat/-/stat-2.0.18.tgz#9e5d21d162b7cf2cf92065c19291ead2d4660772"
-  integrity sha512-wKyfBqhVlIs9bkSerUc6F9KJMw0yTIEKArW7dejWwzToCLPr47u+CtYO6jlJHV6lRvkhi4K4Qc6pyvtJxZ3VpA==
+"@chakra-ui/stat@2.0.17":
+  version "2.0.17"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/stat/-/stat-2.0.17.tgz#2cd712cc7e0d58d9cbd542deea911f1b0925074f"
+  integrity sha512-PhD+5oVLWjQmGLfeZSmexp3AtLcaggWBwoMZ4z8QMZIQzf/fJJWMk0bMqxlpTv8ORDkfY/4ImuFB/RJHvcqlcA==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/styled-system@2.8.0":
-  version "2.8.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/styled-system/-/styled-system-2.8.0.tgz#c02aa7b4a15bd826c19d055cd226bd44f7470f26"
-  integrity sha512-bmRv/8ACJGGKGx84U1npiUddwdNifJ+/ETklGwooS5APM0ymwUtBYZpFxjYNJrqvVYpg3mVY6HhMyBVptLS7iA==
+"@chakra-ui/styled-system@2.6.1":
+  version "2.6.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/styled-system/-/styled-system-2.6.1.tgz#302d496d34c0b7b30c646a7e3c9b113a2f4588da"
+  integrity sha512-jy/1dVi1LxjoRCm+Eo5mqBgvPy5SCWMlIcz6GbIZBDpkGeKZwtqrZLjekxxLBCy8ORY+kJlUB0FT6AzVR/1tjw==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
     csstype "^3.0.11"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/switch@2.0.26":
-  version "2.0.26"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/switch/-/switch-2.0.26.tgz#b93eeafd788e47c21222524adceffe9ef62602d6"
-  integrity sha512-x62lF6VazSZJQuVxosChVR6+0lIJe8Pxgkl/C9vxjhp2yVYb3mew5tcX/sDOu0dYZy8ro/9hMfGkdN4r9xEU8A==
+"@chakra-ui/switch@2.0.22":
+  version "2.0.22"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/switch/-/switch-2.0.22.tgz#7b35e2b10ea4cf91fb49f5175b4335c61dcd25b3"
+  integrity sha512-+/Yy6y7VFD91uSPruF8ZvePi3tl5D8UNVATtWEQ+QBI92DLSM+PtgJ2F0Y9GMZ9NzMxpZ80DqwY7/kqcPCfLvw==
   dependencies:
-    "@chakra-ui/checkbox" "2.2.14"
+    "@chakra-ui/checkbox" "2.2.10"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/system@2.5.5":
-  version "2.5.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/system/-/system-2.5.5.tgz#b8b070d07ca9b0190363100396eea02cca754cec"
-  integrity sha512-52BIp/Zyvefgxn5RTByfkTeG4J+y81LWEjWm8jCaRFsLVm8IFgqIrngtcq4I7gD5n/UKbneHlb4eLHo4uc5yDQ==
+"@chakra-ui/system@2.5.1":
+  version "2.5.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/system/-/system-2.5.1.tgz#bc03a11ae31e795966c7618280548d5cd866f47e"
+  integrity sha512-4+86OrcSoq7lGkm5fh+sJ3IWXSTzjz+HOllRbCW2Rtnmcg7ritiXVNV2VygEg2DrCcx5+tNqRHDM764zW+AEug==
   dependencies:
     "@chakra-ui/color-mode" "2.1.12"
     "@chakra-ui/object-utils" "2.0.8"
     "@chakra-ui/react-utils" "2.0.12"
-    "@chakra-ui/styled-system" "2.8.0"
-    "@chakra-ui/theme-utils" "2.0.15"
+    "@chakra-ui/styled-system" "2.6.1"
+    "@chakra-ui/theme-utils" "2.0.11"
     "@chakra-ui/utils" "2.0.15"
-    react-fast-compare "3.2.1"
+    react-fast-compare "3.2.0"
 
-"@chakra-ui/table@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/table/-/table-2.0.17.tgz#ad394dc6dcbe5a8a9e6d899997ecca3471603977"
-  integrity sha512-OScheTEp1LOYvTki2NFwnAYvac8siAhW9BI5RKm5f5ORL2gVJo4I72RUqE0aKe1oboxgm7CYt5afT5PS5cG61A==
+"@chakra-ui/table@2.0.16":
+  version "2.0.16"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/table/-/table-2.0.16.tgz#e69736cba5cfb218c5e40592ad9280c6e32f6fe7"
+  integrity sha512-vWDXZ6Ad3Aj66curp1tZBHvCfQHX2FJ4ijLiqGgQszWFIchfhJ5vMgEBJaFMZ+BN1draAjuRTZqaQefOApzvRg==
   dependencies:
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/tabs@2.1.9":
-  version "2.1.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tabs/-/tabs-2.1.9.tgz#2e5214cb453c6cc0c240e82bd88af1042fc6fe0e"
-  integrity sha512-Yf8e0kRvaGM6jfkJum0aInQ0U3ZlCafmrYYni2lqjcTtThqu+Yosmo3iYlnullXxCw5MVznfrkb9ySvgQowuYg==
+"@chakra-ui/tabs@2.1.8":
+  version "2.1.8"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tabs/-/tabs-2.1.8.tgz#e83071380f9a3633810308d45de51be7a74f5eb9"
+  integrity sha512-B7LeFN04Ny2jsSy5TFOQxnbZ6ITxGxLxsB2PE0vvQjMSblBrUryOxdjw80HZhfiw6od0ikK9CeKQOIt9QCguSw==
   dependencies:
     "@chakra-ui/clickable" "2.0.14"
-    "@chakra-ui/descendant" "3.0.14"
+    "@chakra-ui/descendant" "3.0.13"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/tag@3.0.0":
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tag/-/tag-3.0.0.tgz#d86cdab59bb3ff7fc628c2dbe7a5ff1b36bd3e96"
-  integrity sha512-YWdMmw/1OWRwNkG9pX+wVtZio+B89odaPj6XeMn5nfNN8+jyhIEpouWv34+CO9G0m1lupJTxPSfgLAd7cqXZMA==
+"@chakra-ui/tag@2.0.17":
+  version "2.0.17"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tag/-/tag-2.0.17.tgz#97adb86db190ddb3526060b78c590392e0ac8b4c"
+  integrity sha512-A47zE9Ft9qxOJ+5r1cUseKRCoEdqCRzFm0pOtZgRcckqavglk75Xjgz8HbBpUO2zqqd49MlqdOwR8o87fXS1vg==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/react-context" "2.0.7"
 
-"@chakra-ui/textarea@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/textarea/-/textarea-2.0.19.tgz#470b459f9cb3255d2abbe07d46b0a5b60a6a32c5"
-  integrity sha512-adJk+qVGsFeJDvfn56CcJKKse8k7oMGlODrmpnpTdF+xvlsiTM+1GfaJvgNSpHHuQFdz/A0z1uJtfGefk0G2ZA==
+"@chakra-ui/textarea@2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/textarea/-/textarea-2.0.18.tgz#da6d629b465f65bbc7b48039c2e48a4ae1d853b4"
+  integrity sha512-aGHHb29vVifO0OtcK/k8cMykzjOKo/coDTU0NJqz7OOLAWIMNV2eGenvmO1n9tTZbmbqHiX+Sa1nPRX+pd14lg==
   dependencies:
-    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/form-control" "2.0.17"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/theme-tools@2.0.17":
@@ -783,57 +783,57 @@
     "@chakra-ui/shared-utils" "2.0.5"
     color2k "^2.0.0"
 
-"@chakra-ui/theme-utils@2.0.15":
-  version "2.0.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-utils/-/theme-utils-2.0.15.tgz#968a5e8c47bb403323fe67049c7b751a6e47f069"
-  integrity sha512-UuxtEgE7gwMTGDXtUpTOI7F5X0iHB9ekEOG5PWPn2wWBL7rlk2JtPI7UP5Um5Yg6vvBfXYGK1ySahxqsgf+87g==
+"@chakra-ui/theme-utils@2.0.11":
+  version "2.0.11"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-utils/-/theme-utils-2.0.11.tgz#c01b1d14fdd63326d1ad11fd8f0872921ea43872"
+  integrity sha512-lBAay6Sq3/fl7exd3mFxWAbzgdQowytor0fnlHrpNStn1HgFjXukwsf6356XQOie2Vd8qaMM7qZtMh4AiC0dcg==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/styled-system" "2.8.0"
-    "@chakra-ui/theme" "3.0.1"
+    "@chakra-ui/styled-system" "2.6.1"
+    "@chakra-ui/theme" "2.2.5"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/theme@3.0.1":
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme/-/theme-3.0.1.tgz#151fc5d1e23d0fd0cd29d28acf8f6017269c13fc"
-  integrity sha512-92kDm/Ux/51uJqhRKevQo/O/rdwucDYcpHg2QuwzdAxISCeYvgtl2TtgOOl5EnqEP0j3IEAvZHZUlv8TTbawaw==
+"@chakra-ui/theme@2.2.5":
+  version "2.2.5"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme/-/theme-2.2.5.tgz#18ed1755ff27c1ff1f1a77083ffc546c361c926e"
+  integrity sha512-hYASZMwu0NqEv6PPydu+F3I+kMNd44yR4TwjR/lXBz/LEh64L6UPY6kQjebCfgdVtsGdl3HKg+eLlfa7SvfRgw==
   dependencies:
     "@chakra-ui/anatomy" "2.1.2"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/theme-tools" "2.0.17"
 
-"@chakra-ui/toast@6.1.1":
-  version "6.1.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/toast/-/toast-6.1.1.tgz#7ca78f38069bc87fa75b64de76c8fc2758bdf419"
-  integrity sha512-JtjIKkPVjEu8okGGCipCxNVgK/15h5AicTATZ6RbG2MsHmr4GfKG3fUCvpbuZseArqmLqGLQZQJjVE9vJzaSkQ==
+"@chakra-ui/toast@6.0.1":
+  version "6.0.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/toast/-/toast-6.0.1.tgz#726b67a57cdd592320bb3f450c66d007a2a1d902"
+  integrity sha512-ej2kJXvu/d2h6qnXU5D8XTyw0qpsfmbiU7hUffo/sPxkz89AUOQ08RUuUmB1ssW/FZcQvNMJ5WgzCTKHGBxtxw==
   dependencies:
-    "@chakra-ui/alert" "2.1.0"
+    "@chakra-ui/alert" "2.0.17"
     "@chakra-ui/close-button" "2.0.17"
-    "@chakra-ui/portal" "2.0.16"
-    "@chakra-ui/react-context" "2.0.8"
+    "@chakra-ui/portal" "2.0.15"
+    "@chakra-ui/react-context" "2.0.7"
     "@chakra-ui/react-use-timeout" "2.0.5"
     "@chakra-ui/react-use-update-effect" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/styled-system" "2.8.0"
-    "@chakra-ui/theme" "3.0.1"
+    "@chakra-ui/styled-system" "2.6.1"
+    "@chakra-ui/theme" "2.2.5"
 
-"@chakra-ui/tooltip@2.2.7":
-  version "2.2.7"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tooltip/-/tooltip-2.2.7.tgz#7c305efb057a5fe4694b1b8d82395aec776d8f57"
-  integrity sha512-ImUJ6NnVqARaYqpgtO+kzucDRmxo8AF3jMjARw0bx2LxUkKwgRCOEaaRK5p5dHc0Kr6t5/XqjDeUNa19/sLauA==
+"@chakra-ui/tooltip@2.2.6":
+  version "2.2.6"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tooltip/-/tooltip-2.2.6.tgz#a38f9ff2dd8a574c8cf49526c3846533455f8ddd"
+  integrity sha512-4cbneidZ5+HCWge3OZzewRQieIvhDjSsl+scrl4Scx7E0z3OmqlTIESU5nGIZDBLYqKn/UirEZhqaQ33FOS2fw==
   dependencies:
     "@chakra-ui/popper" "3.0.13"
-    "@chakra-ui/portal" "2.0.16"
+    "@chakra-ui/portal" "2.0.15"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-disclosure" "2.0.8"
     "@chakra-ui/react-use-event-listener" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/transition@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/transition/-/transition-2.0.16.tgz#498c91e6835bb5d950fd1d1402f483b85f7dcd87"
-  integrity sha512-E+RkwlPc3H7P1crEXmXwDXMB2lqY2LLia2P5siQ4IEnRWIgZXlIw+8Em+NtHNgusel2N+9yuB0wT9SeZZeZ3CQ==
+"@chakra-ui/transition@2.0.15":
+  version "2.0.15"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/transition/-/transition-2.0.15.tgz#c640df2ea82f5ad58c55a6e1a7c338f377cb96d8"
+  integrity sha512-o9LBK/llQfUDHF/Ty3cQ6nShpekKTqHUoJlUOzNKhoTsNpoRerr9v0jwojrX1YI02KtVjfhFU6PiqXlDfREoNw==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
@@ -1081,18 +1081,6 @@
   resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.16.17.tgz#c5a1a4bfe1b57f0c3e61b29883525c6da3e5c091"
   integrity sha512-y+EHuSchhL7FjHgvQL/0fnnFmO4T1bhvWANX6gcnqTjtnKWbTvUMCpGnv2+t+31d7RzyEAYAd4u2fnIhHL6N/Q==
 
-"@eslint-community/eslint-utils@^4.2.0":
-  version "4.4.0"
-  resolved "https://registry.yarnpkg.com/@eslint-community/eslint-utils/-/eslint-utils-4.4.0.tgz#a23514e8fb9af1269d5f7788aa556798d61c6b59"
-  integrity sha512-1/sA4dwrzBAyeUoQ6oxahHKmrZvsnLCg4RfxW3ZFGGmQkSNQPFNLV9CUEFQP1x9EYXHTo5p6xdhZM1Ne9p/AfA==
-  dependencies:
-    eslint-visitor-keys "^3.3.0"
-
-"@eslint-community/regexpp@^4.4.0":
-  version "4.5.0"
-  resolved "https://registry.yarnpkg.com/@eslint-community/regexpp/-/regexpp-4.5.0.tgz#f6f729b02feee2c749f57e334b7a1b5f40a81724"
-  integrity sha512-vITaYzIcNmjn5tF5uxcZ/ft7/RXGrMUIS9HalWckEOF6ESiwXKoMzAQf2UW0aVd6rnOeExTJVd5hmWXucBKGXQ==
-
 "@eslint/eslintrc@^1.4.1":
   version "1.4.1"
   resolved "https://registry.yarnpkg.com/@eslint/eslintrc/-/eslintrc-1.4.1.tgz#af58772019a2d271b7e2d4c23ff4ddcba3ccfb3e"
@@ -1780,47 +1768,47 @@
   resolved "https://registry.yarnpkg.com/@types/uuid/-/uuid-9.0.0.tgz#53ef263e5239728b56096b0a869595135b7952d2"
   integrity sha512-kr90f+ERiQtKWMz5rP32ltJ/BtULDI5RVO0uavn1HQUOwjx0R1h0rnDYNL0CepF1zL5bSY6FISAfd9tOdDhU5Q==
 
-"@typescript-eslint/eslint-plugin@^5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-5.57.0.tgz#52c8a7a4512f10e7249ca1e2e61f81c62c34365c"
-  integrity sha512-itag0qpN6q2UMM6Xgk6xoHa0D0/P+M17THnr4SVgqn9Rgam5k/He33MA7/D7QoJcdMxHFyX7U9imaBonAX/6qA==
+"@typescript-eslint/eslint-plugin@^5.52.0":
+  version "5.52.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-5.52.0.tgz#5fb0d43574c2411f16ea80f5fc335b8eaa7b28a8"
+  integrity sha512-lHazYdvYVsBokwCdKOppvYJKaJ4S41CgKBcPvyd0xjZNbvQdhn/pnJlGtQksQ/NhInzdaeaSarlBjDXHuclEbg==
   dependencies:
-    "@eslint-community/regexpp" "^4.4.0"
-    "@typescript-eslint/scope-manager" "5.57.0"
-    "@typescript-eslint/type-utils" "5.57.0"
-    "@typescript-eslint/utils" "5.57.0"
+    "@typescript-eslint/scope-manager" "5.52.0"
+    "@typescript-eslint/type-utils" "5.52.0"
+    "@typescript-eslint/utils" "5.52.0"
     debug "^4.3.4"
     grapheme-splitter "^1.0.4"
     ignore "^5.2.0"
     natural-compare-lite "^1.4.0"
+    regexpp "^3.2.0"
     semver "^7.3.7"
     tsutils "^3.21.0"
 
-"@typescript-eslint/parser@^5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-5.57.0.tgz#f675bf2cd1a838949fd0de5683834417b757e4fa"
-  integrity sha512-orrduvpWYkgLCyAdNtR1QIWovcNZlEm6yL8nwH/eTxWLd8gsP+25pdLHYzL2QdkqrieaDwLpytHqycncv0woUQ==
+"@typescript-eslint/parser@^5.52.0":
+  version "5.52.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-5.52.0.tgz#73c136df6c0133f1d7870de7131ccf356f5be5a4"
+  integrity sha512-e2KiLQOZRo4Y0D/b+3y08i3jsekoSkOYStROYmPUnGMEoA0h+k2qOH5H6tcjIc68WDvGwH+PaOrP1XRzLJ6QlA==
   dependencies:
-    "@typescript-eslint/scope-manager" "5.57.0"
-    "@typescript-eslint/types" "5.57.0"
-    "@typescript-eslint/typescript-estree" "5.57.0"
+    "@typescript-eslint/scope-manager" "5.52.0"
+    "@typescript-eslint/types" "5.52.0"
+    "@typescript-eslint/typescript-estree" "5.52.0"
     debug "^4.3.4"
 
-"@typescript-eslint/scope-manager@5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-5.57.0.tgz#79ccd3fa7bde0758059172d44239e871e087ea36"
-  integrity sha512-NANBNOQvllPlizl9LatX8+MHi7bx7WGIWYjPHDmQe5Si/0YEYfxSljJpoTyTWFTgRy3X8gLYSE4xQ2U+aCozSw==
+"@typescript-eslint/scope-manager@5.52.0":
+  version "5.52.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-5.52.0.tgz#a993d89a0556ea16811db48eabd7c5b72dcb83d1"
+  integrity sha512-AR7sxxfBKiNV0FWBSARxM8DmNxrwgnYMPwmpkC1Pl1n+eT8/I2NAUPuwDy/FmDcC6F8pBfmOcaxcxRHspgOBMw==
   dependencies:
-    "@typescript-eslint/types" "5.57.0"
-    "@typescript-eslint/visitor-keys" "5.57.0"
+    "@typescript-eslint/types" "5.52.0"
+    "@typescript-eslint/visitor-keys" "5.52.0"
 
-"@typescript-eslint/type-utils@5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-5.57.0.tgz#98e7531c4e927855d45bd362de922a619b4319f2"
-  integrity sha512-kxXoq9zOTbvqzLbdNKy1yFrxLC6GDJFE2Yuo3KqSwTmDOFjUGeWSakgoXT864WcK5/NAJkkONCiKb1ddsqhLXQ==
+"@typescript-eslint/type-utils@5.52.0":
+  version "5.52.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-5.52.0.tgz#9fd28cd02e6f21f5109e35496df41893f33167aa"
+  integrity sha512-tEKuUHfDOv852QGlpPtB3lHOoig5pyFQN/cUiZtpw99D93nEBjexRLre5sQZlkMoHry/lZr8qDAt2oAHLKA6Jw==
   dependencies:
-    "@typescript-eslint/typescript-estree" "5.57.0"
-    "@typescript-eslint/utils" "5.57.0"
+    "@typescript-eslint/typescript-estree" "5.52.0"
+    "@typescript-eslint/utils" "5.52.0"
     debug "^4.3.4"
     tsutils "^3.21.0"
 
@@ -1834,18 +1822,13 @@
   resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-5.52.0.tgz#19e9abc6afb5bd37a1a9bea877a1a836c0b3241b"
   integrity sha512-oV7XU4CHYfBhk78fS7tkum+/Dpgsfi91IIDy7fjCyq2k6KB63M6gMC0YIvy+iABzmXThCRI6xpCEyVObBdWSDQ==
 
-"@typescript-eslint/types@5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-5.57.0.tgz#727bfa2b64c73a4376264379cf1f447998eaa132"
-  integrity sha512-mxsod+aZRSyLT+jiqHw1KK6xrANm19/+VFALVFP5qa/aiJnlP38qpyaTd0fEKhWvQk6YeNZ5LGwI1pDpBRBhtQ==
-
-"@typescript-eslint/typescript-estree@5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-5.57.0.tgz#ebcd0ee3e1d6230e888d88cddf654252d41e2e40"
-  integrity sha512-LTzQ23TV82KpO8HPnWuxM2V7ieXW8O142I7hQTxWIHDcCEIjtkat6H96PFkYBQqGFLW/G/eVVOB9Z8rcvdY/Vw==
+"@typescript-eslint/typescript-estree@5.52.0", "@typescript-eslint/typescript-estree@^5.13.0":
+  version "5.52.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-5.52.0.tgz#6408cb3c2ccc01c03c278cb201cf07e73347dfca"
+  integrity sha512-WeWnjanyEwt6+fVrSR0MYgEpUAuROxuAH516WPjUblIrClzYJj0kBbjdnbQXLpgAN8qbEuGywiQsXUVDiAoEuQ==
   dependencies:
-    "@typescript-eslint/types" "5.57.0"
-    "@typescript-eslint/visitor-keys" "5.57.0"
+    "@typescript-eslint/types" "5.52.0"
+    "@typescript-eslint/visitor-keys" "5.52.0"
     debug "^4.3.4"
     globby "^11.1.0"
     is-glob "^4.0.3"
@@ -1865,31 +1848,18 @@
     semver "^7.3.5"
     tsutils "^3.21.0"
 
-"@typescript-eslint/typescript-estree@^5.13.0":
+"@typescript-eslint/utils@5.52.0":
   version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-5.52.0.tgz#6408cb3c2ccc01c03c278cb201cf07e73347dfca"
-  integrity sha512-WeWnjanyEwt6+fVrSR0MYgEpUAuROxuAH516WPjUblIrClzYJj0kBbjdnbQXLpgAN8qbEuGywiQsXUVDiAoEuQ==
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-5.52.0.tgz#b260bb5a8f6b00a0ed51db66bdba4ed5e4845a72"
+  integrity sha512-As3lChhrbwWQLNk2HC8Ree96hldKIqk98EYvypd3It8Q1f8d5zWyIoaZEp2va5667M4ZyE7X8UUR+azXrFl+NA==
   dependencies:
-    "@typescript-eslint/types" "5.52.0"
-    "@typescript-eslint/visitor-keys" "5.52.0"
-    debug "^4.3.4"
-    globby "^11.1.0"
-    is-glob "^4.0.3"
-    semver "^7.3.7"
-    tsutils "^3.21.0"
-
-"@typescript-eslint/utils@5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-5.57.0.tgz#eab8f6563a2ac31f60f3e7024b91bf75f43ecef6"
-  integrity sha512-ps/4WohXV7C+LTSgAL5CApxvxbMkl9B9AUZRtnEFonpIxZDIT7wC1xfvuJONMidrkB9scs4zhtRyIwHh4+18kw==
-  dependencies:
-    "@eslint-community/eslint-utils" "^4.2.0"
     "@types/json-schema" "^7.0.9"
     "@types/semver" "^7.3.12"
-    "@typescript-eslint/scope-manager" "5.57.0"
-    "@typescript-eslint/types" "5.57.0"
-    "@typescript-eslint/typescript-estree" "5.57.0"
+    "@typescript-eslint/scope-manager" "5.52.0"
+    "@typescript-eslint/types" "5.52.0"
+    "@typescript-eslint/typescript-estree" "5.52.0"
     eslint-scope "^5.1.1"
+    eslint-utils "^3.0.0"
     semver "^7.3.7"
 
 "@typescript-eslint/visitor-keys@4.33.0":
@@ -1908,14 +1878,6 @@
     "@typescript-eslint/types" "5.52.0"
     eslint-visitor-keys "^3.3.0"
 
-"@typescript-eslint/visitor-keys@5.57.0":
-  version "5.57.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-5.57.0.tgz#e2b2f4174aff1d15eef887ce3d019ecc2d7a8ac1"
-  integrity sha512-ery2g3k0hv5BLiKpPuwYt9KBkAp2ugT6VvyShXdLOkax895EC55sP0Tx5L0fZaQueiK3fBLvHVvEl3jFS5ia+g==
-  dependencies:
-    "@typescript-eslint/types" "5.57.0"
-    eslint-visitor-keys "^3.3.0"
-
 "@vitejs/plugin-react-swc@^3.2.0":
   version "3.2.0"
   resolved "https://registry.yarnpkg.com/@vitejs/plugin-react-swc/-/plugin-react-swc-3.2.0.tgz#7c4f6e116a296c27f680d05750f9dbf798cf7709"
@@ -1928,15 +1890,15 @@
   resolved "https://registry.yarnpkg.com/@yarnpkg/lockfile/-/lockfile-1.1.0.tgz#e77a97fbd345b76d83245edcd17d393b1b41fb31"
   integrity sha512-GpSwvyXOcOOlV70vbnzjj4fW5xW/FdUF6nQEt1ENy7m4ZCczi1+/buVUPAqmGfqznsORNFzUMjctTIp8a9tuCQ==
 
-"@zag-js/element-size@0.3.2":
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.3.2.tgz#ebb76af2a024230482406db41344598d1a9f54f4"
-  integrity sha512-bVvvigUGvAuj7PCkE5AbzvTJDTw5f3bg9nQdv+ErhVN8SfPPppLJEmmWdxqsRzrHXgx8ypJt/+Ty0kjtISVDsQ==
+"@zag-js/element-size@0.3.1":
+  version "0.3.1"
+  resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.3.1.tgz#f9f6ae98355e2250d18d0f6e2f1134a0ae4c6a2f"
+  integrity sha512-jR5j4G//bRzcxwAACWi9EfITnwjNmn10LxF4NmALrdZU7/PNWP3uUCdhCxd/0SCyeiJXUl0yvD57rWAbKPs1nw==
 
-"@zag-js/focus-visible@0.2.2":
-  version "0.2.2"
-  resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.2.2.tgz#56233480ca1275d3218fb2e10696a33d1a6b9e64"
-  integrity sha512-0j2gZq8HiZ51z4zNnSkF1iSkqlwRDvdH+son3wHdoz+7IUdMN/5Exd4TxMJ+gq2Of1DiXReYLL9qqh2PdQ4wgA==
+"@zag-js/focus-visible@0.2.1":
+  version "0.2.1"
+  resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.2.1.tgz#bf4f1009f4fd35a9728dfaa9214d8cb318fe8b1e"
+  integrity sha512-19uTjoZGP4/Ax7kSNhhay9JA83BirKzpqLkeEAilrpdI1hE5xuq6q+tzJOsrMOOqJrm7LkmZp5lbsTQzvK2pYg==
 
 accepts@~1.3.4:
   version "1.3.8"
@@ -4548,10 +4510,10 @@ react-dropzone@^14.2.3:
     file-selector "^0.6.0"
     prop-types "^15.8.1"
 
-react-fast-compare@3.2.1:
-  version "3.2.1"
-  resolved "https://registry.yarnpkg.com/react-fast-compare/-/react-fast-compare-3.2.1.tgz#53933d9e14f364281d6cba24bfed7a4afb808b5f"
-  integrity sha512-xTYf9zFim2pEif/Fw16dBiXpe0hoy5PxcD8+OwBnTtNLfIm3g6WxhKNurY+6OmdH1u6Ta/W/Vl6vjbYP1MFnDg==
+react-fast-compare@3.2.0:
+  version "3.2.0"
+  resolved "https://registry.yarnpkg.com/react-fast-compare/-/react-fast-compare-3.2.0.tgz#641a9da81b6a6320f270e89724fb45a0b39e43bb"
+  integrity sha512-rtGImPZ0YyLrscKI9xTpV8psd6I8VAtjKCzQDlzyDvqJA8XOW78TXYQwNRNd8g8JZnDu8q9Fu/1v4HPAVwVdHA==
 
 react-fast-compare@^2.0.1:
   version "2.0.4"
@@ -5349,11 +5311,6 @@ typescript@^4.0.0, typescript@^4.5.5:
   resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.9.5.tgz#095979f9bcc0d09da324d58d03ce8f8374cbe65a"
   integrity sha512-1FXk9E2Hm+QzZQ7z+McJiHL4NW1F2EzMu9Nq9i3zAaGqibafqYwCVU6WyWAuyQRRzOlxou8xZSyXLEN8oKj24g==
 
-typescript@^5.0.3:
-  version "5.0.3"
-  resolved "https://registry.yarnpkg.com/typescript/-/typescript-5.0.3.tgz#fe976f0c826a88d0a382007681cbb2da44afdedf"
-  integrity sha512-xv8mOEDnigb/tN9PSMTwSEqAnUvkoXMQlicOb0IUVDBSQCgBSaAAROUZYy2IcUy5qU6XajK5jjjO7TMWqBTKZA==
-
 unbox-primitive@^1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/unbox-primitive/-/unbox-primitive-1.0.2.tgz#29032021057d5e6cdbd08c5129c226dff8ed6f9e"

ldm/generate.py

@@ -22,7 +22,6 @@ import transformers
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.utils.import_utils import is_xformers_available
 from omegaconf import OmegaConf
-from pathlib import Path
 from PIL import Image, ImageOps
 from pytorch_lightning import logging, seed_everything
 
@@ -201,8 +200,6 @@ class Generate:
         # it wasn't actually doing anything. This logic could be reinstated.
         self.device = torch.device(choose_torch_device())
         print(f">> Using device_type {self.device.type}")
-        if self.device.type == 'cuda':
-            print(f">> CUDA device '{torch.cuda.get_device_name(torch.cuda.current_device())}' (GPU {os.environ.get('CUDA_VISIBLE_DEVICES') or 0})")
         if full_precision:
             if self.precision != "auto":
                 raise ValueError("Remove --full_precision / -F if using --precision")
@@ -529,11 +526,6 @@ class Generate:
                 log_tokens=self.log_tokenization,
             )
 
-
-            # untested code, so commented out for now
-            # if self.model.peft_manager:
-            #     self.model = self.model.peft_manager.load(self.model, self.model.unet.dtype)
-
             init_image, mask_image = self._make_images(
                 init_img,
                 init_mask,
@@ -915,8 +907,12 @@ class Generate:
         return self._load_generator(".omnibus", "Omnibus")
 
     def _load_generator(self, module, class_name):
-        mn = f"ldm.invoke.generator{module}"
-        cn = class_name
+        if self.is_legacy_model(self.model_name):
+            mn = f"ldm.invoke.ckpt_generator{module}"
+            cn = f"Ckpt{class_name}"
+        else:
+            mn = f"ldm.invoke.generator{module}"
+            cn = class_name
         module = importlib.import_module(mn)
         constructor = getattr(module, cn)
         return constructor(self.model, self.precision)
@@ -978,7 +974,7 @@ class Generate:
         self.generators = {}
 
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
-        if self.embedding_path and not model_data.get("ti_embeddings_loaded"):
+        if self.embedding_path is not None:
             print(f'>> Loading embeddings from {self.embedding_path}')
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
@@ -986,24 +982,14 @@ class Generate:
                     self.model.textual_inversion_manager.load_textual_inversion(
                         ti_path, defer_injecting_tokens=True
                     )
-            model_data["ti_embeddings_loaded"] = True
-        print(
-            f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}'
-        )
+            print(
+                f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}'
+            )
 
         self.model_name = model_name
         self._set_sampler()  # requires self.model_name to be set first
-        self._save_last_used_model(model_name)
         return self.model
 
-    def _save_last_used_model(self,model_name:str):
-        """
-        Save name of the last model used.
-        """
-        model_file_path = Path(Globals.root,'.last_model')
-        with open(model_file_path,'w') as f:
-            f.write(model_name)
-        
     def load_huggingface_concepts(self, concepts: list[str]):
         self.model.textual_inversion_manager.load_huggingface_concepts(concepts)
 
@@ -1044,8 +1030,6 @@ class Generate:
         image_callback=None,
         prefix=None,
     ):
-
-        results = []
         for r in image_list:
             image, seed = r
             try:
@@ -1099,10 +1083,6 @@ class Generate:
             else:
                 r[0] = image
 
-            results.append([image, seed])
-
-        return results
-
     def apply_textmask(
         self, image_path: str, prompt: str, callback, threshold: float = 0.5
     ):
@@ -1131,6 +1111,9 @@ class Generate:
     def sample_to_lowres_estimated_image(self, samples):
         return self._make_base().sample_to_lowres_estimated_image(samples)
 
+    def is_legacy_model(self, model_name) -> bool:
+        return self.model_manager.is_legacy(model_name)
+
     def _set_sampler(self):
         if isinstance(self.model, DiffusionPipeline):
             return self._set_scheduler()

ldm/invoke/CLI.py

@@ -4,7 +4,6 @@ import shlex
 import sys
 import traceback
 from argparse import Namespace
-from packaging import version
 from pathlib import Path
 from typing import Union
 
@@ -17,15 +16,13 @@ if sys.platform == "darwin":
 
 import pyparsing  # type: ignore
 
-print(f'DEBUG: [1] All system modules imported', file=sys.stderr)
-
 import ldm.invoke
 
 from ..generate import Generate
 from .args import (Args, dream_cmd_from_png, metadata_dumps,
                              metadata_from_png)
 from .generator.diffusers_pipeline import PipelineIntermediateState
-from .globals import Globals, global_config_dir
+from .globals import Globals
 from .image_util import make_grid
 from .log import write_log
 from .model_manager import ModelManager
@@ -33,21 +30,14 @@ from .pngwriter import PngWriter, retrieve_metadata, write_metadata
 from .readline import Completer, get_completer
 from ..util import url_attachment_name
 
-print(f'DEBUG: [2] All invokeai modules imported', file=sys.stderr)
-
 # global used in multiple functions (fix)
 infile = None
 
+
 def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
-    
-    print('DEBUG: [3] Entered main()', file=sys.stderr)
-    print('DEBUG: INVOKEAI ENVIRONMENT:')
-    print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')
-    print("\n".join([f'{x}:{os.environ[x]}' for x in os.environ.keys()]))
-    print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')
-    
+
     opt = Args()
     args = opt.parse_args()
     if not args:
@@ -74,14 +64,7 @@ def main():
     Globals.internet_available = args.internet_available and check_internet()
     Globals.disable_xformers = not args.xformers
     Globals.sequential_guidance = args.sequential_guidance
-    Globals.ckpt_convert = True  # always true as of 2.3.4 for LoRA support
-
-    print(f'DEBUG: [4] Globals initialized', file=sys.stderr)
-
-    # run any post-install patches needed
-    run_patches()
-
-    print(f'DEBUG: [5] Patches run', file=sys.stderr)
+    Globals.ckpt_convert = args.ckpt_convert
 
     print(f">> Internet connectivity is {Globals.internet_available}")
 
@@ -98,9 +81,8 @@ def main():
     # loading here to avoid long delays on startup
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
-    print(f'DEBUG: [6] Importing torch modules', file=sys.stderr)
-    
     import transformers  # type: ignore
+
     from ldm.generate import Generate
 
     transformers.logging.set_verbosity_error()
@@ -108,7 +90,6 @@ def main():
 
     diffusers.logging.set_verbosity_error()
 
-    print(f'DEBUG: [7] loading restoration models', file=sys.stderr)
     # Loading Face Restoration and ESRGAN Modules
     gfpgan, codeformer, esrgan = load_face_restoration(opt)
 
@@ -126,11 +107,7 @@ def main():
     else:
         embedding_path = None
 
-    if opt.lora_path:
-        Globals.lora_models_dir = opt.lora_path
-
     # migrate legacy models
-    print(f'DEBUG: [8] migrating  models', file=sys.stderr)
     ModelManager.migrate_models()
 
     # load the infile as a list of lines
@@ -146,14 +123,11 @@ def main():
             print(f"{e}. Aborting.")
             sys.exit(-1)
 
-    model = opt.model or retrieve_last_used_model()
-
-    print(f'DEBUG: [9] Creating generate object', file=sys.stderr)
     # creating a Generate object:
     try:
         gen = Generate(
             conf=opt.conf,
-            model=model,
+            model=opt.model,
             sampler_name=opt.sampler_name,
             embedding_path=embedding_path,
             full_precision=opt.full_precision,
@@ -175,7 +149,6 @@ def main():
         print(">> changed to seamless tiling mode")
 
     # preload the model
-    print(f'DEBUG: [10] Loading default model', file=sys.stderr)
     try:
         gen.load_model()
     except KeyError:
@@ -183,20 +156,15 @@ def main():
     except Exception as e:
         report_model_error(opt, e)
 
-    # completer is the readline object
-    completer = get_completer(opt, models=gen.model_manager.list_models())
-            
     # try to autoconvert new models
     if path := opt.autoimport:
         gen.model_manager.heuristic_import(
-            str(path),
-            commit_to_conf=opt.conf,
-            config_file_callback=lambda x: _pick_configuration_file(completer,x),
+            str(path), convert=False, commit_to_conf=opt.conf
         )
 
     if path := opt.autoconvert:
         gen.model_manager.heuristic_import(
-            str(path), commit_to_conf=opt.conf
+            str(path), convert=True, commit_to_conf=opt.conf
         )
 
     # web server loops forever
@@ -210,7 +178,7 @@ def main():
         )
 
     try:
-        main_loop(gen, opt, completer)
+        main_loop(gen, opt)
     except KeyboardInterrupt:
         print(
             f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
@@ -221,9 +189,8 @@ def main():
 
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
-def main_loop(gen, opt, completer):
+def main_loop(gen, opt):
     """prompt/read/execute loop"""
-    print(f'DEBUG: [11] In main loop', file=sys.stderr)
     global infile
     done = False
     doneAfterInFile = infile is not None
@@ -233,6 +200,7 @@ def main_loop(gen, opt, completer):
     # The readline completer reads history from the .dream_history file located in the
     # output directory specified at the time of script launch. We do not currently support
     # changing the history file midstream when the output directory is changed.
+    completer = get_completer(opt, models=gen.model_manager.list_models())
     set_default_output_dir(opt, completer)
     if gen.model:
         add_embedding_terms(gen, completer)
@@ -421,7 +389,6 @@ def main_loop(gen, opt, completer):
                         prior_variations,
                         postprocessed,
                         first_seed,
-                        gen.model_name,
                     )
                     path = file_writer.save_image_and_prompt_to_png(
                         image=image,
@@ -435,7 +402,6 @@ def main_loop(gen, opt, completer):
                                 else first_seed
                             ],
                             model_hash=gen.model_hash,
-                            model_id=gen.model_name,
                         ),
                         name=filename,
                         compress_level=opt.png_compression,
@@ -659,7 +625,7 @@ def set_default_output_dir(opt: Args, completer: Completer):
     completer.set_default_dir(opt.outdir)
 
 
-def import_model(model_path: str, gen, opt, completer):
+def import_model(model_path: str, gen, opt, completer, convert=False):
     """
     model_path can be (1) a URL to a .ckpt file; (2) a local .ckpt file path;
     (3) a huggingface repository id; or (4) a local directory containing a
@@ -690,10 +656,11 @@ def import_model(model_path: str, gen, opt, completer):
         model_path,
         model_name=model_name,
         description=model_desc,
-        config_file_callback=lambda x: _pick_configuration_file(completer,x),
+        convert=convert,
     )
+
     if not imported_name:
-        print("** Aborting import.")
+        print("** Import failed or was skipped")
         return
 
     if not _verify_load(imported_name, gen):
@@ -707,48 +674,6 @@ def import_model(model_path: str, gen, opt, completer):
     completer.update_models(gen.model_manager.list_models())
     print(f">> {imported_name} successfully installed")
 
-def _pick_configuration_file(completer, checkpoint_path: Path)->Path:
-    print(
-f"""
-Please select the type of the model at checkpoint {checkpoint_path}:
-[1] A Stable Diffusion v1.x ckpt/safetensors model
-[2] A Stable Diffusion v1.x inpainting ckpt/safetensors model
-[3] A Stable Diffusion v2.x base model (512 pixels; there should be no 'parameterization:' line in its yaml file)
-[4] A Stable Diffusion v2.x v-predictive model (768 pixels; look for a 'parameterization: "v"' line in its yaml file)
-[5] Other (you will be prompted to enter the config file path)
-[Q] I have no idea! Skip the import.
-""")
-    choices = [
-        global_config_dir() / 'stable-diffusion' / x
-        for x in [
-                'v1-inference.yaml',
-                'v1-inpainting-inference.yaml',
-                'v2-inference.yaml',
-                'v2-inference-v.yaml',
-        ]
-    ]
-
-    ok = False
-    while not ok:
-        try:
-            choice = input('select 0-5, Q > ').strip()
-            if choice.startswith(('q','Q')):
-                return
-            if choice == '5':
-                completer.complete_extensions(('.yaml'))
-                choice = Path(input('Select config file for this model> ').strip()).absolute()
-                completer.complete_extensions(None)
-                ok = choice.exists()
-            else:
-                choice = choices[int(choice)-1]
-                ok = True
-        except (ValueError, IndexError):
-            print(f'{choice} is not a valid choice')
-        except EOFError:
-            return
-    return choice
-
-
 def _verify_load(model_name: str, gen) -> bool:
     print(">> Verifying that new model loads...")
     current_model = gen.model_name
@@ -795,10 +720,14 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             original_config_file = Path(model_info["config"])
             model_name = model_name_or_path
             model_description = model_info["description"]
-            vae_path = model_info.get("vae")
+            vae = model_info["vae"]
         else:
             print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
+        if vae_repo := ldm.invoke.model_manager.VAE_TO_REPO_ID.get(Path(vae).stem):
+            vae_repo = dict(repo_id=vae_repo)
+        else:
+            vae_repo = None
         model_name = manager.convert_and_import(
             ckpt_path,
             diffusers_path=Path(
@@ -807,16 +736,16 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             model_name=model_name,
             model_description=model_description,
             original_config_file=original_config_file,
-            vae_path=vae_path,
+            vae=vae_repo,
         )
     else:
         try:
-            import_model(model_name_or_path, gen, opt, completer)
+            import_model(model_name_or_path, gen, opt, completer, convert=True)
         except KeyboardInterrupt:
             return
 
-    manager.commit(opt.conf)
-    if ckpt_path and click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
+    manager.commit(opt.conf)    
+    if click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
         ckpt_path.unlink(missing_ok=True)
         print(f"{ckpt_path} deleted")
 
@@ -853,7 +782,6 @@ def edit_model(model_name: str, gen, opt, completer):
     print(f"\n>> Editing model {model_name} from configuration file {opt.conf}")
     new_name = _get_model_name(manager.list_models(), completer, model_name)
 
-    completer.complete_extensions(('.yaml','.ckpt','.safetensors','.pt'))
     for attribute in info.keys():
         if type(info[attribute]) != str:
             continue
@@ -861,7 +789,6 @@ def edit_model(model_name: str, gen, opt, completer):
             continue
         completer.set_line(info[attribute])
         info[attribute] = input(f"{attribute}: ") or info[attribute]
-    completer.complete_extensions(None)
 
     if info["format"] == "diffusers":
         vae = info.get("vae", dict(repo_id=None, path=None, subfolder=None))
@@ -1014,14 +941,13 @@ def add_postprocessing_to_metadata(opt, original_file, new_file, tool, command):
 
 
 def prepare_image_metadata(
-        opt,
-        prefix,
-        seed,
-        operation="generate",
-        prior_variations=[],
-        postprocessed=False,
-        first_seed=None,
-        model_id='unknown',
+    opt,
+    prefix,
+    seed,
+    operation="generate",
+    prior_variations=[],
+    postprocessed=False,
+    first_seed=None,
 ):
     if postprocessed and opt.save_original:
         filename = choose_postprocess_name(opt, prefix, seed)
@@ -1029,7 +955,6 @@ def prepare_image_metadata(
         wildcards = dict(opt.__dict__)
         wildcards["prefix"] = prefix
         wildcards["seed"] = seed
-        wildcards["model_id"] = model_id
         try:
             filename = opt.fnformat.format(**wildcards)
         except KeyError as e:
@@ -1047,17 +972,18 @@ def prepare_image_metadata(
         first_seed = first_seed or seed
         this_variation = [[seed, opt.variation_amount]]
         opt.with_variations = prior_variations + this_variation
-        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,model_id=model_id)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
     elif len(prior_variations) > 0:
-        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,model_id=model_id)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
     elif operation == "postprocess":
         formatted_dream_prompt = "!fix " + opt.dream_prompt_str(
-            seed=seed, prompt=opt.input_file_path, model_id=model_id,
+            seed=seed, prompt=opt.input_file_path
         )
     else:
-        formatted_dream_prompt = opt.dream_prompt_str(seed=seed,model_id=model_id)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
     return filename, formatted_dream_prompt
 
+
 def choose_postprocess_name(opt, prefix, seed) -> str:
     match = re.search("postprocess:(\w+)", opt.last_operation)
     if match:
@@ -1308,75 +1234,6 @@ def check_internet() -> bool:
     except:
         return False
 
-
-def retrieve_last_used_model()->str:
-    """
-    Return name of the last model used.
-    """
-    model_file_path = Path(Globals.root,'.last_model')
-    if not model_file_path.exists():
-        return None
-    with open(model_file_path,'r') as f:
-        return f.readline()
-
-# This routine performs any patch-ups needed after installation
-def run_patches():
-    install_missing_config_files()
-    version_file = Path(Globals.root,'.version')
-    if version_file.exists():
-        with open(version_file,'r') as f:
-            root_version = version.parse(f.readline() or 'v2.3.2')
-    else:
-        root_version = version.parse('v2.3.2')
-    app_version = version.parse(ldm.invoke.__version__)
-    if root_version < app_version:
-        try:
-            do_version_update(root_version, ldm.invoke.__version__)
-            with open(version_file,'w') as f:
-                f.write(ldm.invoke.__version__)
-        except:
-            print("** Update failed. Will try again on next launch")
-
-def install_missing_config_files():
-    """
-    install ckpt configuration files that may have been added to the
-    distro after original root directory configuration
-    """
-    pass
-    # import invokeai.configs as conf
-    # from shutil import copyfile
-    
-    # root_configs = Path(global_config_dir(), 'stable-diffusion')
-    # repo_configs = Path(conf.__path__[0], 'stable-diffusion')
-    # for src in repo_configs.iterdir():
-    #     dest = root_configs / src.name
-    #     if not dest.exists():
-    #         copyfile(src,dest)
-    
-def do_version_update(root_version: version.Version, app_version: Union[str, version.Version]):
-    """
-    Make any updates to the launcher .sh and .bat scripts that may be needed
-    from release to release. This is not an elegant solution. Instead, the 
-    launcher should be moved into the source tree and installed using pip.
-    """
-    if root_version < version.Version('v2.3.4'):
-        dest = Path(Globals.root,'loras')
-        dest.mkdir(exist_ok=True)
-    if root_version < version.Version('v2.3.3'):
-        if sys.platform == "linux":
-            print('>> Downloading new version of launcher script and its config file')
-            from ldm.util import download_with_progress_bar
-            url_base = f'https://raw.githubusercontent.com/invoke-ai/InvokeAI/v{str(app_version)}/installer/templates/'
-
-            dest = Path(Globals.root,'invoke.sh.in')
-            assert download_with_progress_bar(url_base+'invoke.sh.in',dest)
-            dest.replace(Path(Globals.root,'invoke.sh'))
-            os.chmod(Path(Globals.root,'invoke.sh'), 0o0755)
-
-            dest = Path(Globals.root,'dialogrc')
-            assert download_with_progress_bar(url_base+'dialogrc',dest)
-            dest.replace(Path(Globals.root,'.dialogrc'))
-
 if __name__ == '__main__':
     main()
     

ldm/invoke/_version.py

@@ -1 +1 @@
-__version__='2.3.4'
+__version__='2.3.1.post2'

ldm/invoke/args.py

@@ -333,7 +333,7 @@ class Args(object):
             switches.append(f'-V {formatted_variations}')
         if 'variations' in a and len(a['variations'])>0:
             switches.append(f'-V {a["variations"]}')
-        return ' '.join(switches) + f' # model_id={kwargs.get("model_id","unknown model")}'
+        return ' '.join(switches)
 
     def __getattribute__(self,name):
         '''
@@ -522,8 +522,8 @@ class Args(object):
             '--ckpt_convert',
             action=argparse.BooleanOptionalAction,
             dest='ckpt_convert',
-            default=True,
-            help='Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.'
+            default=False,
+            help='Load legacy ckpt files as diffusers. Pass --no-ckpt-convert to inhibit this behavior',
         )
         model_group.add_argument(
             '--internet',
@@ -654,13 +654,6 @@ class Args(object):
             type=str,
             help='Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)'
         )
-        render_group.add_argument(
-            '--lora_directory',
-            dest='lora_path',
-            default='loras',
-            type=str,
-            help='Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)'
-        )
         render_group.add_argument(
             '--embeddings',
             action=argparse.BooleanOptionalAction,
@@ -798,12 +791,12 @@ class Args(object):
             *Model manipulation*
             !models                                   -- list models in configs/models.yaml
             !switch <model_name>                      -- switch to model named <model_name>
-            !import_model /path/to/weights/file       -- imports a model from a ckpt or safetensors file
+            !import_model /path/to/weights/file.ckpt  -- adds a .ckpt model to your config
             !import_model /path/to/weights/           -- interactively import models from a directory
-            !import_model http://path_to_model        -- downloads and adds a ckpt or safetensors model to your config
-            !import_model hakurei/waifu-diffusion     -- downloads and adds a diffusers model to your config using its repo_id
-            !optimize_model <model_name>              -- converts a .ckpt/.safetensors model to a diffusers model
-            !convert_model /path/to/weights/file      -- converts a .ckpt file path to a diffusers model and adds to your config
+            !import_model http://path_to_model.ckpt   -- downloads and adds a .ckpt model to your config
+            !import_model hakurei/waifu-diffusion     -- downloads and adds a diffusers model to your config
+            !optimize_model <model_name>              -- converts a .ckpt model to a diffusers model
+            !convert_model /path/to/weights/file.ckpt -- converts a .ckpt file path to a diffusers model
             !edit_model <model_name>                  -- edit a model's description
             !del_model <model_name>                   -- delete a model
             """
@@ -885,7 +878,7 @@ class Args(object):
         )
         render_group.add_argument(
             '--fnformat',
-            default=None,
+            default='{prefix}.{seed}.png',
             type=str,
             help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
         )
@@ -1162,7 +1155,6 @@ def format_metadata(**kwargs):
 def metadata_dumps(opt,
                    seeds=[],
                    model_hash=None,
-                   model_id=None,
                    postprocessing=None):
     '''
     Given an Args object, returns a dict containing the keys and
@@ -1175,7 +1167,7 @@ def metadata_dumps(opt,
     # top-level metadata minus `image` or `images`
     metadata = {
         'model'       : 'stable diffusion',
-        'model_id'    : model_id or opt.model,
+        'model_id'    : opt.model,
         'model_hash'  : model_hash,
         'app_id'      : ldm.invoke.__app_id__,
         'app_version' : ldm.invoke.__version__,
@@ -1188,7 +1180,7 @@ def metadata_dumps(opt,
     )
 
     # remove any image keys not mentioned in RFC #266
-    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps','hires_fix',
+    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps',
                          'cfg_scale','threshold','perlin','step_number','width','height','extra','strength','seamless'
                          'init_img','init_mask','facetool','facetool_strength','upscale','h_symmetry_time_pct',
                          'v_symmetry_time_pct']

ldm/invoke/ckpt_generator/__init__.py

@@ -0,0 +1,4 @@
+'''
+Initialization file for the ldm.invoke.generator package
+'''
+from .base import CkptGenerator

ldm/invoke/ckpt_generator/base.py

@@ -0,0 +1,335 @@
+'''
+Base class for ldm.invoke.ckpt_generator.*
+including img2img, txt2img, and inpaint
+
+THESE MODULES ARE TRANSITIONAL AND WILL BE REMOVED AT A FUTURE DATE
+WHEN LEGACY CKPT MODEL SUPPORT IS DISCONTINUED.
+'''
+import torch
+import numpy as  np
+import random
+import os
+import os.path as osp
+import traceback
+from tqdm import tqdm, trange
+from PIL import Image, ImageFilter, ImageChops
+import cv2 as cv
+from einops import rearrange, repeat
+from pathlib import Path
+from pytorch_lightning import seed_everything
+import invokeai.assets.web as web_assets
+from ldm.invoke.devices import choose_autocast
+from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
+from ldm.util import rand_perlin_2d
+
+downsampling = 8
+CAUTION_IMG = 'caution.png'
+
+class CkptGenerator():
+    def __init__(self, model, precision):
+        self.model = model
+        self.precision = precision
+        self.seed = None
+        self.latent_channels = model.channels
+        self.downsampling_factor = downsampling   # BUG: should come from model or config
+        self.safety_checker = None
+        self.perlin = 0.0
+        self.threshold = 0
+        self.variation_amount = 0
+        self.with_variations = []
+        self.use_mps_noise = False
+        self.free_gpu_mem = None
+        self.caution_img = None
+
+    # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
+    def get_make_image(self,prompt,**kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        """
+        raise NotImplementedError("image_iterator() must be implemented in a descendent class")
+
+    def set_variation(self, seed, variation_amount, with_variations):
+        self.seed             = seed
+        self.variation_amount = variation_amount
+        self.with_variations  = with_variations
+
+    def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
+                 image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
+                 safety_checker:dict=None,
+                 attention_maps_callback = None,
+                 free_gpu_mem: bool=False,
+                 **kwargs):
+        scope = choose_autocast(self.precision)
+        self.safety_checker = safety_checker
+        self.free_gpu_mem = free_gpu_mem
+        attention_maps_images = []
+        attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
+        make_image = self.get_make_image(
+            prompt,
+            sampler = sampler,
+            init_image    = init_image,
+            width         = width,
+            height        = height,
+            step_callback = step_callback,
+            threshold     = threshold,
+            perlin        = perlin,
+            attention_maps_callback = attention_maps_callback,
+            **kwargs
+        )
+        results             = []
+        seed                = seed if seed is not None and seed >= 0 else self.new_seed()
+        first_seed          = seed
+        seed, initial_noise = self.generate_initial_noise(seed, width, height)
+
+        # There used to be an additional self.model.ema_scope() here, but it breaks
+        # the inpaint-1.5 model. Not sure what it did.... ?
+        with scope(self.model.device.type):
+            for n in trange(iterations, desc='Generating'):
+                x_T = None
+                if self.variation_amount > 0:
+                    seed_everything(seed)
+                    target_noise = self.get_noise(width,height)
+                    x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
+                elif initial_noise is not None:
+                    # i.e. we specified particular variations
+                    x_T = initial_noise
+                else:
+                    seed_everything(seed)
+                    try:
+                        x_T = self.get_noise(width,height)
+                    except:
+                        print('** An error occurred while getting initial noise **')
+                        print(traceback.format_exc())
+
+                image = make_image(x_T)
+
+                if self.safety_checker is not None:
+                    image = self.safety_check(image)
+
+                results.append([image, seed])
+
+                if image_callback is not None:
+                    attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1]
+                    image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image)
+
+                seed = self.new_seed()
+
+        return results
+
+    def sample_to_image(self,samples)->Image.Image:
+        """
+        Given samples returned from a sampler, converts
+        it into a PIL Image
+        """
+        x_samples = self.model.decode_first_stage(samples)
+        x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+        if len(x_samples) != 1:
+            raise Exception(
+                f'>> expected to get a single image, but got {len(x_samples)}')
+        x_sample = 255.0 * rearrange(
+            x_samples[0].cpu().numpy(), 'c h w -> h w c'
+        )
+        return Image.fromarray(x_sample.astype(np.uint8))
+
+        # write an approximate RGB image from latent samples for a single step to PNG
+
+    def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image, init_mask: Image.Image, mask_blur_radius: int = 8) -> Image.Image:
+        if init_image is None or init_mask is None:
+            return result
+
+        # Get the original alpha channel of the mask if there is one.
+        # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
+        pil_init_mask = init_mask.getchannel('A') if init_mask.mode == 'RGBA' else init_mask.convert('L')
+        pil_init_image = init_image.convert('RGBA') # Add an alpha channel if one doesn't exist
+
+        # Build an image with only visible pixels from source to use as reference for color-matching.
+        init_rgb_pixels = np.asarray(init_image.convert('RGB'), dtype=np.uint8)
+        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8)
+        init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
+
+        # Get numpy version of result
+        np_image = np.asarray(result, dtype=np.uint8)
+
+        # Mask and calculate mean and standard deviation
+        mask_pixels = init_a_pixels * init_mask_pixels > 0
+        np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
+        np_image_masked = np_image[mask_pixels, :]
+
+        if np_init_rgb_pixels_masked.size > 0:
+            init_means = np_init_rgb_pixels_masked.mean(axis=0)
+            init_std = np_init_rgb_pixels_masked.std(axis=0)
+            gen_means = np_image_masked.mean(axis=0)
+            gen_std = np_image_masked.std(axis=0)
+
+            # Color correct
+            np_matched_result = np_image.copy()
+            np_matched_result[:,:,:] = (((np_matched_result[:,:,:].astype(np.float32) - gen_means[None,None,:]) / gen_std[None,None,:]) * init_std[None,None,:] + init_means[None,None,:]).clip(0, 255).astype(np.uint8)
+            matched_result = Image.fromarray(np_matched_result, mode='RGB')
+        else:
+            matched_result = Image.fromarray(np_image, mode='RGB')
+
+        # Blur the mask out (into init image) by specified amount
+        if mask_blur_radius > 0:
+            nm = np.asarray(pil_init_mask, dtype=np.uint8)
+            nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
+            pmd = Image.fromarray(nmd, mode='L')
+            blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
+        else:
+            blurred_init_mask = pil_init_mask
+
+        multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
+
+        # Paste original on color-corrected generation (using blurred mask)
+        matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
+        return matched_result
+
+
+
+    def sample_to_lowres_estimated_image(self,samples):
+        # origingally adapted from code by @erucipe and @keturn here:
+        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
+
+        # these updated numbers for v1.5 are from @torridgristle
+        v1_5_latent_rgb_factors = torch.tensor([
+            #    R        G        B
+            [ 0.3444,  0.1385,  0.0670], # L1
+            [ 0.1247,  0.4027,  0.1494], # L2
+            [-0.3192,  0.2513,  0.2103], # L3
+            [-0.1307, -0.1874, -0.7445]  # L4
+        ], dtype=samples.dtype, device=samples.device)
+
+        latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
+        latents_ubyte = (((latent_image + 1) / 2)
+                         .clamp(0, 1)  # change scale from -1..1 to 0..1
+                         .mul(0xFF)  # to 0..255
+                         .byte()).cpu()
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+    def generate_initial_noise(self, seed, width, height):
+        initial_noise = None
+        if self.variation_amount > 0 or len(self.with_variations) > 0:
+            # use fixed initial noise plus random noise per iteration
+            seed_everything(seed)
+            initial_noise = self.get_noise(width,height)
+            for v_seed, v_weight in self.with_variations:
+                seed = v_seed
+                seed_everything(seed)
+                next_noise = self.get_noise(width,height)
+                initial_noise = self.slerp(v_weight, initial_noise, next_noise)
+            if self.variation_amount > 0:
+                random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
+                seed = random.randrange(0,np.iinfo(np.uint32).max)
+            return (seed, initial_noise)
+        else:
+            return (seed, None)
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height):
+        """
+        Returns a tensor filled with random numbers, either form a normal distribution
+        (txt2img) or from the latent image (img2img, inpaint)
+        """
+        raise NotImplementedError("get_noise() must be implemented in a descendent class")
+
+    def get_perlin_noise(self,width,height):
+        fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
+        return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
+
+    def new_seed(self):
+        self.seed = random.randrange(0, np.iinfo(np.uint32).max)
+        return self.seed
+
+    def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
+        '''
+        Spherical linear interpolation
+        Args:
+            t (float/np.ndarray): Float value between 0.0 and 1.0
+            v0 (np.ndarray): Starting vector
+            v1 (np.ndarray): Final vector
+            DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                                colineal. Not recommended to alter this.
+        Returns:
+            v2 (np.ndarray): Interpolation vector between v0 and v1
+        '''
+        inputs_are_torch = False
+        if not isinstance(v0, np.ndarray):
+            inputs_are_torch = True
+            v0 = v0.detach().cpu().numpy()
+        if not isinstance(v1, np.ndarray):
+            inputs_are_torch = True
+            v1 = v1.detach().cpu().numpy()
+
+        dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+        if np.abs(dot) > DOT_THRESHOLD:
+            v2 = (1 - t) * v0 + t * v1
+        else:
+            theta_0 = np.arccos(dot)
+            sin_theta_0 = np.sin(theta_0)
+            theta_t = theta_0 * t
+            sin_theta_t = np.sin(theta_t)
+            s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+            s1 = sin_theta_t / sin_theta_0
+            v2 = s0 * v0 + s1 * v1
+
+        if inputs_are_torch:
+            v2 = torch.from_numpy(v2).to(self.model.device)
+
+        return v2
+
+    def safety_check(self,image:Image.Image):
+        '''
+        If the CompViz safety checker flags an NSFW image, we
+        blur it out.
+        '''
+        import diffusers
+
+        checker = self.safety_checker['checker']
+        extractor = self.safety_checker['extractor']
+        features = extractor([image], return_tensors="pt")
+        features.to(self.model.device)
+
+        # unfortunately checker requires the numpy version, so we have to convert back
+        x_image = np.array(image).astype(np.float32) / 255.0
+        x_image = x_image[None].transpose(0, 3, 1, 2)
+
+        diffusers.logging.set_verbosity_error()
+        checked_image, has_nsfw_concept = checker(images=x_image, clip_input=features.pixel_values)
+        if has_nsfw_concept[0]:
+            print('** An image with potential non-safe content has been detected. A blurred image will be returned. **')
+            return self.blur(image)
+        else:
+            return image
+
+    def blur(self,input):
+        blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
+        try:
+            caution = self.get_caution_img()
+            if caution:
+                blurry.paste(caution,(0,0),caution)
+        except FileNotFoundError:
+            pass
+        return blurry
+
+    def get_caution_img(self):
+        path = None
+        if self.caution_img:
+            return self.caution_img
+        path = Path(web_assets.__path__[0]) / CAUTION_IMG
+        caution = Image.open(path)
+        self.caution_img = caution.resize((caution.width // 2, caution.height //2))
+        return self.caution_img
+
+    # this is a handy routine for debugging use. Given a generated sample,
+    # convert it into a PNG image and store it at the indicated path
+    def save_sample(self, sample, filepath):
+        image = self.sample_to_image(sample)
+        dirname = os.path.dirname(filepath) or '.'
+        if not os.path.exists(dirname):
+            print(f'** creating directory {dirname}')
+            os.makedirs(dirname, exist_ok=True)
+        image.save(filepath,'PNG')
+
+    def torch_dtype(self)->torch.dtype:
+        return torch.float16 if self.precision == 'float16' else torch.float32

ldm/invoke/ckpt_generator/embiggen.py

@@ -0,0 +1,501 @@
+'''
+ldm.invoke.ckpt_generator.embiggen descends from ldm.invoke.ckpt_generator
+and generates with ldm.invoke.ckpt_generator.img2img
+'''
+
+import torch
+import numpy as  np
+from tqdm import trange
+from PIL               import Image
+from ldm.invoke.ckpt_generator.base      import CkptGenerator
+from ldm.invoke.ckpt_generator.img2img   import CkptImg2Img
+from ldm.invoke.devices import choose_autocast
+from ldm.models.diffusion.ddim     import DDIMSampler
+
+class CkptEmbiggen(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent         = None
+
+    # Replace generate because Embiggen doesn't need/use most of what it does normallly
+    def generate(self,prompt,iterations=1,seed=None,
+                 image_callback=None, step_callback=None,
+                 **kwargs):
+
+        scope      = choose_autocast(self.precision)
+        make_image = self.get_make_image(
+            prompt,
+            step_callback = step_callback,
+            **kwargs
+        )
+        results             = []
+        seed                = seed if seed else self.new_seed()
+
+        # Noise will be generated by the Img2Img generator when called
+        with scope(self.model.device.type), self.model.ema_scope():
+            for n in trange(iterations, desc='Generating'):
+                # make_image will call Img2Img which will do the equivalent of get_noise itself
+                image = make_image()
+                results.append([image, seed])
+                if image_callback is not None:
+                    image_callback(image, seed, prompt_in=prompt)
+                seed = self.new_seed()
+        return results
+
+    @torch.no_grad()
+    def get_make_image(
+        self,
+        prompt,
+        sampler,
+        steps,
+        cfg_scale,
+        ddim_eta,
+        conditioning,
+        init_img,
+        strength,
+        width,
+        height,
+        embiggen,
+        embiggen_tiles,
+        step_callback=None,
+        **kwargs
+    ):
+        """
+        Returns a function returning an image derived from the prompt and multi-stage twice-baked potato layering over the img2img on the initial image
+        Return value depends on the seed at the time you call it
+        """
+        assert not sampler.uses_inpainting_model(), "--embiggen is not supported by inpainting models"
+
+        # Construct embiggen arg array, and sanity check arguments
+        if embiggen == None:  # embiggen can also be called with just embiggen_tiles
+            embiggen = [1.0]  # If not specified, assume no scaling
+        elif embiggen[0] < 0:
+            embiggen[0] = 1.0
+            print(
+                '>> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !')
+        if len(embiggen) < 2:
+            embiggen.append(0.75)
+        elif embiggen[1] > 1.0 or embiggen[1] < 0:
+            embiggen[1] = 0.75
+            print('>> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !')
+        if len(embiggen) < 3:
+            embiggen.append(0.25)
+        elif embiggen[2] < 0:
+            embiggen[2] = 0.25
+            print('>> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !')
+
+        # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
+        # and then sort them, because... people.
+        if embiggen_tiles:
+            embiggen_tiles = list(map(lambda n: n-1, embiggen_tiles))
+            embiggen_tiles.sort()
+
+        if strength >= 0.5:
+            print(f'* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45.')
+
+        # Prep img2img generator, since we wrap over it
+        gen_img2img = CkptImg2Img(self.model,self.precision)
+
+        # Open original init image (not a tensor) to manipulate
+        initsuperimage = Image.open(init_img)
+
+        with Image.open(init_img) as img:
+            initsuperimage = img.convert('RGB')
+
+        # Size of the target super init image in pixels
+        initsuperwidth, initsuperheight = initsuperimage.size
+
+        # Increase by scaling factor if not already resized, using ESRGAN as able
+        if embiggen[0] != 1.0:
+            initsuperwidth = round(initsuperwidth*embiggen[0])
+            initsuperheight = round(initsuperheight*embiggen[0])
+            if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
+                from ldm.invoke.restoration.realesrgan import ESRGAN
+                esrgan = ESRGAN()
+                print(
+                    f'>> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}')
+                if embiggen[0] > 2:
+                    initsuperimage = esrgan.process(
+                        initsuperimage,
+                        embiggen[1],  # upscale strength
+                        self.seed,
+                        4,  # upscale scale
+                    )
+                else:
+                    initsuperimage = esrgan.process(
+                        initsuperimage,
+                        embiggen[1],  # upscale strength
+                        self.seed,
+                        2,  # upscale scale
+                    )
+            # We could keep recursively re-running ESRGAN for a requested embiggen[0] larger than 4x
+            #   but from personal experiance it doesn't greatly improve anything after 4x
+            # Resize to target scaling factor resolution
+            initsuperimage = initsuperimage.resize(
+                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS)
+
+        # Use width and height as tile widths and height
+        # Determine buffer size in pixels
+        if embiggen[2] < 1:
+            if embiggen[2] < 0:
+                embiggen[2] = 0
+            overlap_size_x = round(embiggen[2] * width)
+            overlap_size_y = round(embiggen[2] * height)
+        else:
+            overlap_size_x = round(embiggen[2])
+            overlap_size_y = round(embiggen[2])
+
+        # With overall image width and height known, determine how many tiles we need
+        def ceildiv(a, b):
+            return -1 * (-a // b)
+
+        # X and Y needs to be determined independantly (we may have savings on one based on the buffer pixel count)
+        # (initsuperwidth - width) is the area remaining to the right that we need to layers tiles to fill
+        # (width - overlap_size_x) is how much new we can fill with a single tile
+        emb_tiles_x = 1
+        emb_tiles_y = 1
+        if (initsuperwidth - width) > 0:
+            emb_tiles_x = ceildiv(initsuperwidth - width,
+                                  width - overlap_size_x) + 1
+        if (initsuperheight - height) > 0:
+            emb_tiles_y = ceildiv(initsuperheight - height,
+                                  height - overlap_size_y) + 1
+        # Sanity
+        assert emb_tiles_x > 1 or emb_tiles_y > 1, f'ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don\'t need to Embiggen! Check your arguments.'
+
+        # Prep alpha layers --------------
+        # https://stackoverflow.com/questions/69321734/how-to-create-different-transparency-like-gradient-with-python-pil
+        # agradientL is Left-side transparent
+        agradientL = Image.linear_gradient('L').rotate(
+            90).resize((overlap_size_x, height))
+        # agradientT is Top-side transparent
+        agradientT = Image.linear_gradient('L').resize((width, overlap_size_y))
+        # radial corner is the left-top corner, made full circle then cut to just the left-top quadrant
+        agradientC = Image.new('L', (256, 256))
+        for y in range(256):
+            for x in range(256):
+                # Find distance to lower right corner (numpy takes arrays)
+                distanceToLR = np.sqrt([(255 - x) ** 2 + (255 - y) ** 2])[0]
+                # Clamp values to max 255
+                if distanceToLR > 255:
+                    distanceToLR = 255
+                #Place the pixel as invert of distance
+                agradientC.putpixel((x, y), round(255 - distanceToLR))
+
+        # Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
+        # Fits for a left-fading gradient on the bottom side and full opacity on the right side.
+        agradientAsymC = Image.new('L', (256, 256))
+        for y in range(256):
+            for x in range(256):
+                value = round(max(0, x-(255-y)) * (255 / max(1,y)))
+                #Clamp values
+                value = max(0, value)
+                value = min(255, value)
+                agradientAsymC.putpixel((x, y), value)
+
+        # Create alpha layers default fully white
+        alphaLayerL = Image.new("L", (width, height), 255)
+        alphaLayerT = Image.new("L", (width, height), 255)
+        alphaLayerLTC = Image.new("L", (width, height), 255)
+        # Paste gradients into alpha layers
+        alphaLayerL.paste(agradientL, (0, 0))
+        alphaLayerT.paste(agradientT, (0, 0))
+        alphaLayerLTC.paste(agradientL, (0, 0))
+        alphaLayerLTC.paste(agradientT, (0, 0))
+        alphaLayerLTC.paste(agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0))
+        # make masks with an asymmetric upper-right corner so when the curved transparent corner of the next tile
+        # to its right is placed it doesn't reveal a hard trailing semi-transparent edge in the overlapping space
+        alphaLayerTaC = alphaLayerT.copy()
+        alphaLayerTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+        alphaLayerLTaC = alphaLayerLTC.copy()
+        alphaLayerLTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+
+        if embiggen_tiles:
+            # Individual unconnected sides
+            alphaLayerR = Image.new("L", (width, height), 255)
+            alphaLayerR.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerB = Image.new("L", (width, height), 255)
+            alphaLayerB.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerTB = Image.new("L", (width, height), 255)
+            alphaLayerTB.paste(agradientT, (0, 0))
+            alphaLayerTB.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerLR = Image.new("L", (width, height), 255)
+            alphaLayerLR.paste(agradientL, (0, 0))
+            alphaLayerLR.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+
+            # Sides and corner Layers
+            alphaLayerRBC = Image.new("L", (width, height), 255)
+            alphaLayerRBC.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerRBC.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerRBC.paste(agradientC.rotate(180).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerLBC = Image.new("L", (width, height), 255)
+            alphaLayerLBC.paste(agradientL, (0, 0))
+            alphaLayerLBC.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerLBC.paste(agradientC.rotate(90).resize(
+                (overlap_size_x, overlap_size_y)), (0, height - overlap_size_y))
+            alphaLayerRTC = Image.new("L", (width, height), 255)
+            alphaLayerRTC.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerRTC.paste(agradientT, (0, 0))
+            alphaLayerRTC.paste(agradientC.rotate(270).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+
+            # All but X layers
+            alphaLayerABT = Image.new("L", (width, height), 255)
+            alphaLayerABT.paste(alphaLayerLBC, (0, 0))
+            alphaLayerABT.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerABT.paste(agradientC.rotate(180).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerABL = Image.new("L", (width, height), 255)
+            alphaLayerABL.paste(alphaLayerRTC, (0, 0))
+            alphaLayerABL.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerABL.paste(agradientC.rotate(180).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerABR = Image.new("L", (width, height), 255)
+            alphaLayerABR.paste(alphaLayerLBC, (0, 0))
+            alphaLayerABR.paste(agradientT, (0, 0))
+            alphaLayerABR.paste(agradientC.resize(
+                (overlap_size_x, overlap_size_y)), (0, 0))
+            alphaLayerABB = Image.new("L", (width, height), 255)
+            alphaLayerABB.paste(alphaLayerRTC, (0, 0))
+            alphaLayerABB.paste(agradientL, (0, 0))
+            alphaLayerABB.paste(agradientC.resize(
+                (overlap_size_x, overlap_size_y)), (0, 0))
+
+            # All-around layer
+            alphaLayerAA = Image.new("L", (width, height), 255)
+            alphaLayerAA.paste(alphaLayerABT, (0, 0))
+            alphaLayerAA.paste(agradientT, (0, 0))
+            alphaLayerAA.paste(agradientC.resize(
+                (overlap_size_x, overlap_size_y)), (0, 0))
+            alphaLayerAA.paste(agradientC.rotate(270).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+
+        # Clean up temporary gradients
+        del agradientL
+        del agradientT
+        del agradientC
+
+        def make_image():
+            # Make main tiles -------------------------------------------------
+            if embiggen_tiles:
+                print(f'>> Making {len(embiggen_tiles)} Embiggen tiles...')
+            else:
+                print(
+                    f'>> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})...')
+
+            emb_tile_store = []
+            # Although we could use the same seed for every tile for determinism, at higher strengths this may
+            # produce duplicated structures for each tile and make the tiling effect more obvious
+            # instead track and iterate a local seed we pass to Img2Img
+            seed = self.seed
+            seedintlimit = np.iinfo(np.uint32).max - 1 # only retreive this one from numpy
+
+            for tile in range(emb_tiles_x * emb_tiles_y):
+                # Don't iterate on first tile
+                if tile != 0:
+                    if seed < seedintlimit:
+                        seed += 1
+                    else:
+                        seed = 0
+
+                # Determine if this is a re-run and replace
+                if embiggen_tiles and not tile in embiggen_tiles:
+                    continue
+                # Get row and column entries
+                emb_row_i = tile // emb_tiles_x
+                emb_column_i = tile % emb_tiles_x
+                # Determine bounds to cut up the init image
+                # Determine upper-left point
+                if emb_column_i + 1 == emb_tiles_x:
+                    left = initsuperwidth - width
+                else:
+                    left = round(emb_column_i * (width - overlap_size_x))
+                if emb_row_i + 1 == emb_tiles_y:
+                    top = initsuperheight - height
+                else:
+                    top = round(emb_row_i * (height - overlap_size_y))
+                right = left + width
+                bottom = top + height
+
+                # Cropped image of above dimension (does not modify the original)
+                newinitimage = initsuperimage.crop((left, top, right, bottom))
+                # DEBUG:
+                # newinitimagepath = init_img[0:-4] + f'_emb_Ti{tile}.png'
+                # newinitimage.save(newinitimagepath)
+
+                if embiggen_tiles:
+                    print(
+                        f'Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)')
+                else:
+                    print(
+                        f'Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles')
+
+                # create a torch tensor from an Image
+                newinitimage = np.array(
+                    newinitimage).astype(np.float32) / 255.0
+                newinitimage = newinitimage[None].transpose(0, 3, 1, 2)
+                newinitimage = torch.from_numpy(newinitimage)
+                newinitimage = 2.0 * newinitimage - 1.0
+                newinitimage = newinitimage.to(self.model.device)
+
+                tile_results = gen_img2img.generate(
+                    prompt,
+                    iterations     = 1,
+                    seed           = seed,
+                    sampler        = DDIMSampler(self.model, device=self.model.device),
+                    steps          = steps,
+                    cfg_scale      = cfg_scale,
+                    conditioning   = conditioning,
+                    ddim_eta       = ddim_eta,
+                    image_callback = None,  # called only after the final image is generated
+                    step_callback  = step_callback,   # called after each intermediate image is generated
+                    width          = width,
+                    height         = height,
+                    init_image     = newinitimage,    # notice that init_image is different from init_img
+                    mask_image     = None,
+                    strength       = strength,
+                )
+
+                emb_tile_store.append(tile_results[0][0])
+                # DEBUG (but, also has other uses), worth saving if you want tiles without a transparency overlap to manually composite
+                # emb_tile_store[-1].save(init_img[0:-4] + f'_emb_To{tile}.png')
+                del newinitimage
+
+            # Sanity check we have them all
+            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)):
+                outputsuperimage = Image.new(
+                    "RGBA", (initsuperwidth, initsuperheight))
+                if embiggen_tiles:
+                    outputsuperimage.alpha_composite(
+                        initsuperimage.convert('RGBA'), (0, 0))
+                for tile in range(emb_tiles_x * emb_tiles_y):
+                    if embiggen_tiles:
+                        if tile in embiggen_tiles:
+                            intileimage = emb_tile_store.pop(0)
+                        else:
+                            continue
+                    else:
+                        intileimage = emb_tile_store[tile]
+                    intileimage = intileimage.convert('RGBA')
+                    # Get row and column entries
+                    emb_row_i = tile // emb_tiles_x
+                    emb_column_i = tile % emb_tiles_x
+                    if emb_row_i == 0 and emb_column_i == 0 and not embiggen_tiles:
+                        left = 0
+                        top = 0
+                    else:
+                        # Determine upper-left point
+                        if emb_column_i + 1 == emb_tiles_x:
+                            left = initsuperwidth - width
+                        else:
+                            left = round(emb_column_i *
+                                         (width - overlap_size_x))
+                        if emb_row_i + 1 == emb_tiles_y:
+                            top = initsuperheight - height
+                        else:
+                            top = round(emb_row_i * (height - overlap_size_y))
+                        # Handle gradients for various conditions
+                        # Handle emb_rerun case
+                        if embiggen_tiles:
+                            # top of image
+                            if emb_row_i == 0:
+                                if emb_column_i == 0:
+                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
+                                        if (tile+emb_tiles_x) not in embiggen_tiles:  # Look-ahead down
+                                            intileimage.putalpha(alphaLayerB)
+                                        # Otherwise do nothing on this tile
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerR)
+                                    else:
+                                        intileimage.putalpha(alphaLayerRBC)
+                                elif emb_column_i == emb_tiles_x - 1:
+                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                        intileimage.putalpha(alphaLayerL)
+                                    else:
+                                        intileimage.putalpha(alphaLayerLBC)
+                                else:
+                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
+                                        if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                            intileimage.putalpha(alphaLayerL)
+                                        else:
+                                            intileimage.putalpha(alphaLayerLBC)
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerLR)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABT)
+                            # bottom of image
+                            elif emb_row_i == emb_tiles_y - 1:
+                                if emb_column_i == 0:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        intileimage.putalpha(alphaLayerTaC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerRTC)
+                                elif emb_column_i == emb_tiles_x - 1:
+                                    # No tiles to look ahead to
+                                    intileimage.putalpha(alphaLayerLTC)
+                                else:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        intileimage.putalpha(alphaLayerLTaC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABB)
+                            # vertical middle of image
+                            else:
+                                if emb_column_i == 0:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
+                                            intileimage.putalpha(alphaLayerTaC)
+                                        else:
+                                            intileimage.putalpha(alphaLayerTB)
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerRTC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABL)
+                                elif emb_column_i == emb_tiles_x - 1:
+                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                        intileimage.putalpha(alphaLayerLTC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABR)
+                                else:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
+                                            intileimage.putalpha(alphaLayerLTaC)
+                                        else:
+                                            intileimage.putalpha(alphaLayerABR)
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerABB)
+                                    else:
+                                        intileimage.putalpha(alphaLayerAA)
+                        # Handle normal tiling case (much simpler - since we tile left to right, top to bottom)
+                        else:
+                            if emb_row_i == 0 and emb_column_i >= 1:
+                                intileimage.putalpha(alphaLayerL)
+                            elif emb_row_i >= 1 and emb_column_i == 0:
+                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
+                                    intileimage.putalpha(alphaLayerT)
+                                else:
+                                    intileimage.putalpha(alphaLayerTaC)
+                            else:
+                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
+                                    intileimage.putalpha(alphaLayerLTC)
+                                else:
+                                    intileimage.putalpha(alphaLayerLTaC)
+                    # Layer tile onto final image
+                    outputsuperimage.alpha_composite(intileimage, (left, top))
+            else:
+                print(f'Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
+
+            # after internal loops and patching up return Embiggen image
+            return outputsuperimage
+        # end of function declaration
+        return make_image

ldm/invoke/ckpt_generator/img2img.py

@@ -0,0 +1,97 @@
+'''
+ldm.invoke.ckpt_generator.img2img descends from ldm.invoke.generator
+'''
+
+import torch
+import numpy as  np
+import PIL
+from torch import Tensor
+from PIL import Image
+from ldm.invoke.devices import choose_autocast
+from ldm.invoke.ckpt_generator.base import CkptGenerator
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+
+class CkptImg2Img(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent = None    # by get_noise()
+
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it.
+        """
+        self.perlin = perlin
+
+        sampler.make_schedule(
+            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+        )
+
+        if isinstance(init_image, PIL.Image.Image):
+            init_image = self._image_to_tensor(init_image.convert('RGB'))
+
+        scope = choose_autocast(self.precision)
+        with scope(self.model.device.type):
+            self.init_latent = self.model.get_first_stage_encoding(
+                self.model.encode_first_stage(init_image)
+            ) # move to latent space
+
+        t_enc = int(strength * steps)
+        uc, c, extra_conditioning_info   = conditioning
+
+        def make_image(x_T):
+            # encode (scaled latent)
+            z_enc = sampler.stochastic_encode(
+                self.init_latent,
+                torch.tensor([t_enc - 1]).to(self.model.device),
+                noise=x_T
+            )
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            # decode it
+            samples = sampler.decode(
+                z_enc,
+                c,
+                t_enc,
+                img_callback = step_callback,
+                unconditional_guidance_scale=cfg_scale,
+                unconditional_conditioning=uc,
+                init_latent = self.init_latent, # changes how noising is performed in ksampler
+                extra_conditioning_info = extra_conditioning_info,
+                all_timesteps_count = steps
+            )
+
+            if self.free_gpu_mem:
+                self.model.model.to("cpu")
+
+            return self.sample_to_image(samples)
+
+        return make_image
+
+    def get_noise(self,width,height):
+        device      = self.model.device
+        init_latent = self.init_latent
+        assert init_latent is not None,'call to get_noise() when init_latent not set'
+        if device.type == 'mps':
+            x = torch.randn_like(init_latent, device='cpu').to(device)
+        else:
+            x = torch.randn_like(init_latent, device=device)
+        if self.perlin > 0.0:
+            shape = init_latent.shape
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
+        return x
+
+    def _image_to_tensor(self, image:Image, normalize:bool=True)->Tensor:
+        image = np.array(image).astype(np.float32) / 255.0
+        if len(image.shape) == 2:  # 'L' image, as in a mask
+            image = image[None,None]
+        else:                      # 'RGB' image
+            image = image[None].transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image)
+        if normalize:
+            image = 2.0 * image - 1.0
+        return image.to(self.model.device)

ldm/invoke/ckpt_generator/inpaint.py

@@ -0,0 +1,358 @@
+'''
+ldm.invoke.ckpt_generator.inpaint descends from ldm.invoke.ckpt_generator
+'''
+
+import math
+import torch
+import torchvision.transforms as T
+import numpy as  np
+import cv2 as cv
+import PIL
+from PIL import Image, ImageFilter, ImageOps, ImageChops
+from skimage.exposure.histogram_matching import match_histograms
+from einops import rearrange, repeat
+from ldm.invoke.devices             import choose_autocast
+from ldm.invoke.ckpt_generator.img2img   import CkptImg2Img
+from ldm.models.diffusion.ddim     import DDIMSampler
+from ldm.models.diffusion.ksampler import KSampler
+from ldm.invoke.generator.base import downsampling
+from ldm.util import debug_image
+from ldm.invoke.patchmatch import PatchMatch 
+from ldm.invoke.globals import Globals
+
+def infill_methods()->list[str]:
+    methods = list()
+    if PatchMatch.patchmatch_available():
+        methods.append('patchmatch')
+    methods.append('tile')
+    return methods
+
+class CkptInpaint(CkptImg2Img):
+    def __init__(self, model, precision):
+        self.init_latent = None
+        self.pil_image = None
+        self.pil_mask = None
+        self.mask_blur_radius = 0
+        self.infill_method = None
+        super().__init__(model, precision)
+
+    # Outpaint support code
+    def get_tile_images(self, image: np.ndarray, width=8, height=8):
+        _nrows, _ncols, depth = image.shape
+        _strides = image.strides
+
+        nrows, _m = divmod(_nrows, height)
+        ncols, _n = divmod(_ncols, width)
+        if _m != 0 or _n != 0:
+            return None
+
+        return np.lib.stride_tricks.as_strided(
+            np.ravel(image),
+            shape=(nrows, ncols, height, width, depth),
+            strides=(height * _strides[0], width * _strides[1], *_strides),
+            writeable=False
+        )
+
+    def infill_patchmatch(self, im: Image.Image) -> Image:
+        if im.mode != 'RGBA':
+            return im
+
+        # Skip patchmatch if patchmatch isn't available
+        if not PatchMatch.patchmatch_available():
+            return im
+
+        # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
+        im_patched_np = PatchMatch.inpaint(im.convert('RGB'), ImageOps.invert(im.split()[-1]), patch_size = 3)
+        im_patched = Image.fromarray(im_patched_np, mode = 'RGB')
+        return im_patched
+
+    def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: int = None) -> Image:
+        # Only fill if there's an alpha layer
+        if im.mode != 'RGBA':
+            return im
+
+        a = np.asarray(im, dtype=np.uint8)
+
+        tile_size = (tile_size, tile_size)
+
+        # Get the image as tiles of a specified size
+        tiles = self.get_tile_images(a,*tile_size).copy()
+
+        # Get the mask as tiles
+        tiles_mask = tiles[:,:,:,:,3]
+
+        # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
+        tmask_shape = tiles_mask.shape
+        tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
+        n,ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
+        tiles_mask = (tiles_mask > 0)
+        tiles_mask = tiles_mask.reshape((n,ny)).all(axis = 1)
+
+        # Get RGB tiles in single array and filter by the mask
+        tshape = tiles.shape
+        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), * tiles.shape[2:]))
+        filtered_tiles = tiles_all[tiles_mask]
+
+        if len(filtered_tiles) == 0:
+            return im
+
+        # Find all invalid tiles and replace with a random valid tile
+        replace_count = (tiles_mask == False).sum()
+        rng = np.random.default_rng(seed = seed)
+        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count),:,:,:]
+
+        # Convert back to an image
+        tiles_all = tiles_all.reshape(tshape)
+        tiles_all = tiles_all.swapaxes(1,2)
+        st = tiles_all.reshape((math.prod(tiles_all.shape[0:2]), math.prod(tiles_all.shape[2:4]), tiles_all.shape[4]))
+        si = Image.fromarray(st, mode='RGBA')
+
+        return si
+
+
+    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
+        npimg = np.asarray(mask, dtype=np.uint8)
+
+        # Detect any partially transparent regions
+        npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
+
+        # Detect hard edges
+        npedge = cv.Canny(npimg, threshold1=100, threshold2=200)
+
+        # Combine
+        npmask = npgradient + npedge
+
+        # Expand
+        npmask = cv.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
+
+        new_mask = Image.fromarray(npmask)
+
+        if edge_blur > 0:
+            new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
+
+        return ImageOps.invert(new_mask)
+
+
+    def seam_paint(self,
+        im: Image.Image,
+        seam_size: int,
+        seam_blur: int,
+        prompt,sampler,steps,cfg_scale,ddim_eta,
+        conditioning,strength,
+        noise,
+        step_callback
+    ) -> Image.Image:
+        hard_mask = self.pil_image.split()[-1].copy()
+        mask = self.mask_edge(hard_mask, seam_size, seam_blur)
+
+        make_image = self.get_make_image(
+            prompt,
+            sampler,
+            steps,
+            cfg_scale,
+            ddim_eta,
+            conditioning,
+            init_image = im.copy().convert('RGBA'),
+            mask_image = mask.convert('RGB'), # Code currently requires an RGB mask
+            strength = strength,
+            mask_blur_radius = 0,
+            seam_size = 0,
+            step_callback = step_callback,
+            inpaint_width = im.width,
+            inpaint_height = im.height
+        )
+
+        seam_noise = self.get_noise(im.width, im.height)
+
+        result = make_image(seam_noise)
+
+        return result
+
+
+    @torch.no_grad()
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,init_image,mask_image,strength,
+                       mask_blur_radius: int = 8,
+                       # Seam settings - when 0, doesn't fill seam
+                       seam_size: int = 0,
+                       seam_blur: int = 0,
+                       seam_strength: float = 0.7,
+                       seam_steps: int = 10,
+                       tile_size: int = 32,
+                       step_callback=None,
+                       inpaint_replace=False, enable_image_debugging=False,
+                       infill_method = None,
+                       inpaint_width=None,
+                       inpaint_height=None,
+                       **kwargs):
+        """
+        Returns a function returning an image derived from the prompt and
+        the initial image + mask.  Return value depends on the seed at
+        the time you call it.  kwargs are 'init_latent' and 'strength'
+        """
+
+        self.enable_image_debugging = enable_image_debugging
+        self.infill_method = infill_method or infill_methods()[0], # The infill method to use
+        
+        self.inpaint_width = inpaint_width
+        self.inpaint_height = inpaint_height
+
+        if isinstance(init_image, PIL.Image.Image):
+            self.pil_image = init_image.copy()
+
+            # Do infill
+            if infill_method == 'patchmatch' and PatchMatch.patchmatch_available():
+                init_filled = self.infill_patchmatch(self.pil_image.copy())
+            else: # if infill_method == 'tile': # Only two methods right now, so always use 'tile' if not patchmatch
+                init_filled = self.tile_fill_missing(
+                    self.pil_image.copy(),
+                    seed = self.seed,
+                    tile_size = tile_size
+                )
+            init_filled.paste(init_image, (0,0), init_image.split()[-1])
+
+            # Resize if requested for inpainting
+            if inpaint_width and inpaint_height:
+                init_filled = init_filled.resize((inpaint_width, inpaint_height))
+
+            debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
+
+            # Create init tensor
+            init_image = self._image_to_tensor(init_filled.convert('RGB'))
+
+        if isinstance(mask_image, PIL.Image.Image):
+            self.pil_mask = mask_image.copy()
+            debug_image(mask_image, "mask_image BEFORE multiply with pil_image", debug_status=self.enable_image_debugging)
+
+            mask_image = ImageChops.multiply(mask_image, self.pil_image.split()[-1].convert('RGB'))
+            self.pil_mask = mask_image
+
+            # Resize if requested for inpainting
+            if inpaint_width and inpaint_height:
+                mask_image = mask_image.resize((inpaint_width, inpaint_height))
+
+            debug_image(mask_image, "mask_image AFTER multiply with pil_image", debug_status=self.enable_image_debugging)
+            mask_image = mask_image.resize(
+                (
+                    mask_image.width // downsampling,
+                    mask_image.height // downsampling
+                ),
+                resample=Image.Resampling.NEAREST
+            )
+            mask_image = self._image_to_tensor(mask_image,normalize=False)
+
+        self.mask_blur_radius = mask_blur_radius
+
+        # klms samplers not supported yet, so ignore previous sampler
+        if isinstance(sampler,KSampler):
+            print(
+                f">> Using recommended DDIM sampler for inpainting."
+            )
+            sampler = DDIMSampler(self.model, device=self.model.device)
+
+        sampler.make_schedule(
+            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+        )
+
+        mask_image = mask_image[0][0].unsqueeze(0).repeat(4,1,1).unsqueeze(0)
+        mask_image = repeat(mask_image, '1 ... -> b ...', b=1)
+
+        scope = choose_autocast(self.precision)
+        with scope(self.model.device.type):
+            self.init_latent = self.model.get_first_stage_encoding(
+                self.model.encode_first_stage(init_image)
+            ) # move to latent space
+
+        t_enc   = int(strength * steps)
+        # todo: support cross-attention control
+        uc, c, _ = conditioning
+
+        print(f">> target t_enc is {t_enc} steps")
+
+        @torch.no_grad()
+        def make_image(x_T):
+            # encode (scaled latent)
+            z_enc = sampler.stochastic_encode(
+                self.init_latent,
+                torch.tensor([t_enc - 1]).to(self.model.device),
+                noise=x_T
+            )
+
+            # to replace masked area with latent noise, weighted by inpaint_replace strength
+            if inpaint_replace > 0.0:
+                print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
+                l_noise = self.get_noise(kwargs['width'],kwargs['height'])
+                inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
+                masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
+                z_enc   = z_enc * mask_image + masked_region
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            # decode it
+            samples = sampler.decode(
+                z_enc,
+                c,
+                t_enc,
+                img_callback                 = step_callback,
+                unconditional_guidance_scale = cfg_scale,
+                unconditional_conditioning = uc,
+                mask                       = mask_image,
+                init_latent                = self.init_latent
+            )
+
+            result = self.sample_to_image(samples)
+
+            # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
+            if seam_size > 0:
+                old_image = self.pil_image or init_image
+                old_mask = self.pil_mask or mask_image
+
+                result = self.seam_paint(
+                    result,
+                    seam_size,
+                    seam_blur,
+                    prompt,
+                    sampler,
+                    seam_steps,
+                    cfg_scale,
+                    ddim_eta,
+                    conditioning,
+                    seam_strength,
+                    x_T,
+                    step_callback)
+
+                # Restore original settings
+                self.get_make_image(prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,
+                       old_image,
+                       old_mask,
+                       strength,
+                       mask_blur_radius, seam_size, seam_blur, seam_strength,
+                       seam_steps, tile_size, step_callback,
+                       inpaint_replace, enable_image_debugging,
+                       inpaint_width = inpaint_width,
+                       inpaint_height = inpaint_height,
+                       infill_method = infill_method,
+                       **kwargs)
+
+            return result
+
+        return make_image
+
+
+    def sample_to_image(self, samples)->Image.Image:
+        gen_result = super().sample_to_image(samples).convert('RGB')
+        debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
+
+        # Resize if necessary
+        if self.inpaint_width and self.inpaint_height:
+            gen_result = gen_result.resize(self.pil_image.size)
+
+        if self.pil_image is None or self.pil_mask is None:
+            return gen_result
+
+        corrected_result = super().repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
+        debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
+
+        return corrected_result

ldm/invoke/ckpt_generator/omnibus.py

@@ -0,0 +1,175 @@
+"""omnibus module to be used with the runwayml 9-channel custom inpainting model"""
+
+import torch
+import numpy as  np
+from einops import repeat
+from PIL import Image, ImageOps, ImageChops
+from ldm.invoke.devices import choose_autocast
+from ldm.invoke.ckpt_generator.base import downsampling
+from ldm.invoke.ckpt_generator.img2img import CkptImg2Img
+from ldm.invoke.ckpt_generator.txt2img import CkptTxt2Img
+
+class CkptOmnibus(CkptImg2Img,CkptTxt2Img):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.pil_mask = None
+        self.pil_image = None
+
+    def get_make_image(
+            self,
+            prompt,
+            sampler,
+            steps,
+            cfg_scale,
+            ddim_eta,
+            conditioning,
+            width,
+            height,
+            init_image = None,
+            mask_image = None,
+            strength = None,
+            step_callback=None,
+            threshold=0.0,
+            perlin=0.0,
+            mask_blur_radius: int = 8,
+            **kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it.
+        """
+        self.perlin = perlin
+        num_samples = 1
+
+        sampler.make_schedule(
+            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+        )
+
+        if isinstance(init_image, Image.Image):
+            self.pil_image = init_image
+            if init_image.mode != 'RGB':
+                init_image = init_image.convert('RGB')
+            init_image = self._image_to_tensor(init_image)
+
+        if isinstance(mask_image, Image.Image):
+            self.pil_mask = mask_image
+
+            mask_image = ImageChops.multiply(mask_image.convert('L'), self.pil_image.split()[-1])
+            mask_image = self._image_to_tensor(ImageOps.invert(mask_image), normalize=False)
+
+        self.mask_blur_radius = mask_blur_radius
+
+        t_enc = steps
+
+        if init_image is not None and mask_image is not None: # inpainting
+            masked_image = init_image * (1 - mask_image)  # masked image is the image masked by mask - masked regions zero
+
+        elif init_image is not None: # img2img
+            scope = choose_autocast(self.precision)
+
+            with scope(self.model.device.type):
+                self.init_latent = self.model.get_first_stage_encoding(
+                    self.model.encode_first_stage(init_image)
+                ) # move to latent space
+
+            # create a completely black mask  (1s)
+            mask_image = torch.ones(1, 1, init_image.shape[2], init_image.shape[3], device=self.model.device)
+            # and the masked image is just a copy of the original
+            masked_image = init_image
+
+        else: # txt2img
+            init_image = torch.zeros(1, 3, height, width, device=self.model.device)
+            mask_image = torch.ones(1, 1, height, width, device=self.model.device)
+            masked_image = init_image
+
+        self.init_latent = init_image
+        height = init_image.shape[2]
+        width = init_image.shape[3]
+        model = self.model
+
+        def make_image(x_T):
+            with torch.no_grad():
+                scope = choose_autocast(self.precision)
+                with scope(self.model.device.type):
+
+                    batch = self.make_batch_sd(
+                        init_image,
+                        mask_image,
+                        masked_image,
+                        prompt=prompt,
+                        device=model.device,
+                        num_samples=num_samples,
+                    )
+
+                    c = model.cond_stage_model.encode(batch["txt"])
+                    c_cat = list()
+                    for ck in model.concat_keys:
+                        cc = batch[ck].float()
+                        if ck != model.masked_image_key:
+                            bchw = [num_samples, 4, height//8, width//8]
+                            cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+                        else:
+                            cc = model.get_first_stage_encoding(model.encode_first_stage(cc))
+                        c_cat.append(cc)
+                    c_cat = torch.cat(c_cat, dim=1)
+
+                    # cond
+                    cond={"c_concat": [c_cat], "c_crossattn": [c]}
+
+                    # uncond cond
+                    uc_cross = model.get_unconditional_conditioning(num_samples, "")
+                    uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
+                    shape = [model.channels, height//8, width//8]
+
+                    samples, _ = sampler.sample(
+                        batch_size = 1,
+                        S = steps,
+                        x_T = x_T,
+                        conditioning = cond,
+                        shape = shape,
+                        verbose = False,
+                        unconditional_guidance_scale = cfg_scale,
+                        unconditional_conditioning = uc_full,
+                        eta = 1.0,
+                        img_callback = step_callback,
+                        threshold = threshold,
+                    )
+                    if self.free_gpu_mem:
+                        self.model.model.to("cpu")
+            return self.sample_to_image(samples)
+
+        return make_image
+
+    def make_batch_sd(
+            self,
+            image,
+            mask,
+            masked_image,
+            prompt,
+            device,
+            num_samples=1):
+        batch = {
+                "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
+                "txt": num_samples * [prompt],
+                "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
+                "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
+                }
+        return batch
+
+    def get_noise(self, width:int, height:int):
+        if self.init_latent is not None:
+            height = self.init_latent.shape[2]
+            width = self.init_latent.shape[3]
+        return CkptTxt2Img.get_noise(self,width,height)
+
+
+    def sample_to_image(self, samples)->Image.Image:
+        gen_result = super().sample_to_image(samples).convert('RGB')
+
+        if self.pil_image is None or self.pil_mask is None:
+            return gen_result
+        if self.pil_image.size != self.pil_mask.size:
+            return gen_result
+
+        corrected_result = super(CkptImg2Img, self).repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
+
+        return corrected_result

ldm/invoke/ckpt_generator/txt2img.py

@@ -0,0 +1,90 @@
+'''
+ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
+'''
+
+import torch
+import numpy as  np
+from ldm.invoke.ckpt_generator.base import CkptGenerator
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+import gc
+
+
+class CkptTxt2Img(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+
+    @torch.no_grad()
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
+                       attention_maps_callback=None,
+                       **kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        kwargs are 'width' and 'height'
+        """
+        self.perlin = perlin
+        uc, c, extra_conditioning_info   = conditioning
+
+        @torch.no_grad()
+        def make_image(x_T):
+            shape = [
+                self.latent_channels,
+                height // self.downsampling_factor,
+                width  // self.downsampling_factor,
+            ]
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
+
+            samples, _ = sampler.sample(
+                batch_size                   = 1,
+                S                            = steps,
+                x_T                          = x_T,
+                conditioning                 = c,
+                shape                        = shape,
+                verbose                      = False,
+                unconditional_guidance_scale = cfg_scale,
+                unconditional_conditioning   = uc,
+                extra_conditioning_info      = extra_conditioning_info,
+                eta                          = ddim_eta,
+                img_callback                 = step_callback,
+                threshold                    = threshold,
+                attention_maps_callback      = attention_maps_callback,
+            )
+
+            if self.free_gpu_mem:
+                self.model.model.to('cpu')
+                self.model.cond_stage_model.device = 'cpu'
+                self.model.cond_stage_model.to('cpu')
+                gc.collect()
+                torch.cuda.empty_cache()
+
+            return self.sample_to_image(samples)
+
+        return make_image
+
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height):
+        device         = self.model.device
+        if self.use_mps_noise or device.type == 'mps':
+            x = torch.randn([1,
+                             self.latent_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device='cpu').to(device)
+        else:
+            x = torch.randn([1,
+                             self.latent_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device=device)
+        if self.perlin > 0.0:
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
+        return x
+

ldm/invoke/ckpt_generator/txt2img2img.py

@@ -0,0 +1,182 @@
+'''
+ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
+'''
+
+import torch
+import numpy as  np
+import math
+import gc
+from ldm.invoke.ckpt_generator.base import CkptGenerator
+from ldm.invoke.ckpt_generator.omnibus import CkptOmnibus
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+from PIL import Image
+
+class CkptTxt2Img2Img(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent = None    # for get_noise()
+
+    @torch.no_grad()
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,width,height,strength,step_callback=None,**kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        kwargs are 'width' and 'height'
+        """
+        uc, c, extra_conditioning_info = conditioning
+        scale_dim = min(width, height)
+        scale = 512 / scale_dim
+
+        init_width = math.ceil(scale * width / 64) * 64
+        init_height = math.ceil(scale * height / 64) * 64
+
+        @torch.no_grad()
+        def make_image(x_T):
+
+            shape = [
+                self.latent_channels,
+                init_height // self.downsampling_factor,
+                init_width // self.downsampling_factor,
+            ]
+
+            sampler.make_schedule(
+                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+            )
+
+            #x = self.get_noise(init_width, init_height)
+            x = x_T
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            samples, _ = sampler.sample(
+                batch_size                   = 1,
+                S                            = steps,
+                x_T                          = x,
+                conditioning                 = c,
+                shape                        = shape,
+                verbose                      = False,
+                unconditional_guidance_scale = cfg_scale,
+                unconditional_conditioning   = uc,
+                eta                          = ddim_eta,
+                img_callback                 = step_callback,
+                extra_conditioning_info      = extra_conditioning_info
+            )
+
+            print(
+                  f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+                 )
+
+            # resizing
+            samples = torch.nn.functional.interpolate(
+                samples,
+                size=(height // self.downsampling_factor, width // self.downsampling_factor),
+                mode="bilinear"
+            )
+
+            t_enc = int(strength * steps)
+            ddim_sampler = DDIMSampler(self.model, device=self.model.device)
+            ddim_sampler.make_schedule(
+                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+            )
+
+            z_enc = ddim_sampler.stochastic_encode(
+                samples,
+                torch.tensor([t_enc-1]).to(self.model.device),
+                noise=self.get_noise(width,height,False)
+            )
+
+            # decode it
+            samples = ddim_sampler.decode(
+                z_enc,
+                c,
+                t_enc,
+                img_callback = step_callback,
+                unconditional_guidance_scale=cfg_scale,
+                unconditional_conditioning=uc,
+                extra_conditioning_info=extra_conditioning_info,
+                all_timesteps_count=steps
+            )
+
+            if self.free_gpu_mem:
+                self.model.model.to('cpu')
+                self.model.cond_stage_model.device = 'cpu'
+                self.model.cond_stage_model.to('cpu')
+                gc.collect()
+                torch.cuda.empty_cache()
+
+            return self.sample_to_image(samples)
+
+        # in the case of the inpainting model being loaded, the trick of
+        # providing an interpolated latent doesn't work, so we transiently
+        # create a 512x512 PIL image, upscale it, and run the inpainting
+        # over it in img2img mode. Because the inpaing model is so conservative
+        # it doesn't change the image (much)
+        def inpaint_make_image(x_T):
+            omnibus = CkptOmnibus(self.model,self.precision)
+            result = omnibus.generate(
+                prompt,
+                sampler=sampler,
+                width=init_width,
+                height=init_height,
+                step_callback=step_callback,
+                steps = steps,
+                cfg_scale = cfg_scale,
+                ddim_eta = ddim_eta,
+                conditioning = conditioning,
+                **kwargs
+            )
+            assert result is not None and len(result)>0,'** txt2img failed **'
+            image = result[0][0]
+            interpolated_image = image.resize((width,height),resample=Image.Resampling.LANCZOS)
+            print(kwargs.pop('init_image',None))
+            result = omnibus.generate(
+                prompt,
+                sampler=sampler,
+                init_image=interpolated_image,
+                width=width,
+                height=height,
+                seed=result[0][1],
+                step_callback=step_callback,
+                steps = steps,
+                cfg_scale = cfg_scale,
+                ddim_eta = ddim_eta,
+                conditioning = conditioning,
+                **kwargs
+                )
+            return result[0][0]
+
+        if sampler.uses_inpainting_model():
+            return inpaint_make_image
+        else:
+            return make_image
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height,scale = True):
+        # print(f"Get noise: {width}x{height}")
+        if scale:
+            trained_square = 512 * 512
+            actual_square = width * height
+            scale = math.sqrt(trained_square / actual_square)
+            scaled_width = math.ceil(scale * width / 64) * 64
+            scaled_height = math.ceil(scale * height / 64) * 64
+        else:
+            scaled_width = width
+            scaled_height = height
+
+        device      = self.model.device
+        if self.use_mps_noise or device.type == 'mps':
+            return torch.randn([1,
+                                self.latent_channels,
+                                scaled_height // self.downsampling_factor,
+                                scaled_width  // self.downsampling_factor],
+                                device='cpu').to(device)
+        else:
+            return torch.randn([1,
+                                self.latent_channels,
+                                scaled_height // self.downsampling_factor,
+                                scaled_width  // self.downsampling_factor],
+                                device=device)
+

ldm/invoke/concepts_lib.py

@@ -24,15 +24,13 @@ class HuggingFaceConceptsLibrary(object):
         self.concepts_loaded = dict()
         self.triggers = dict()            # concept name to trigger phrase
         self.concept_names = dict()       # trigger phrase to concept name
-        self.match_trigger = re.compile('(<[a-zA-Z0-9_\- >]+>)') # trigger is slightly less restrictive than HF concept name
-        self.match_concept = re.compile('<([a-zA-Z0-9_\-]+)>') # HF concept name can only contain A-Za-z0-9_-
+        self.match_trigger = re.compile('(<[\w\- >]+>)') # trigger is slightly less restrictive than HF concept name
+        self.match_concept = re.compile('<([\w\-]+)>') # HF concept name can only contain A-Za-z0-9_-
 
-    def list_concepts(self, minimum_likes: int=0)->list:
+    def list_concepts(self)->list:
         '''
         Return a list of all the concepts by name, without the 'sd-concepts-library' part.
         Also adds local concepts in invokeai/embeddings folder.
-        If minimum_likes is provided, then only concepts that have received at least that
-        many "likes" will be returned.
         '''
         local_concepts_now = self.get_local_concepts(os.path.join(self.root, 'embeddings'))
         local_concepts_to_add = set(local_concepts_now).difference(set(self.local_concepts))
@@ -46,7 +44,7 @@ class HuggingFaceConceptsLibrary(object):
         else:
             try:
                 models = self.hf_api.list_models(filter=ModelFilter(model_name='sd-concepts-library/'))
-                self.concept_list = [a.id.split('/')[1] for a in models if a.likes>=minimum_likes]
+                self.concept_list = [a.id.split('/')[1] for a in models]
                 # when init, add all in dir. when not init, add only concepts added between init and now
                 self.concept_list.extend(list(local_concepts_to_add))
             except Exception as e:
@@ -183,7 +181,7 @@ class HuggingFaceConceptsLibrary(object):
 
         print(f'>> Downloading {repo_id}...',end='')
         try:
-            for file in ('learned_embeds.bin','token_identifier.txt'):
+            for file in ('README.md','learned_embeds.bin','token_identifier.txt','type_of_concept.txt'):
                 url = hf_hub_url(repo_id, file)
                 request.urlretrieve(url, os.path.join(dest,file),reporthook=tally_download_size)
         except ul_error.HTTPError as e:

ldm/invoke/conditioning.py

@@ -9,11 +9,10 @@ get_uc_and_c_and_ec()           get the conditioned and unconditioned latent, an
 import re
 from typing import Union, Optional, Any
 
-from transformers import CLIPTokenizer
+from transformers import CLIPTokenizer, CLIPTextModel
 
 from compel import Compel
-from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser, \
-    Conjunction
+from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser
 from .devices import torch_dtype
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ldm.invoke.globals import Globals
@@ -53,30 +52,14 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
                     textual_inversion_manager=model.textual_inversion_manager,
                     dtype_for_device_getter=torch_dtype)
 
-    # get rid of any newline characters
-    prompt_string = prompt_string.replace("\n", " ")
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
-
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_conjunction: Conjunction
+    positive_prompt: FlattenedPrompt|Blend
     if legacy_blend is not None:
-        positive_conjunction = legacy_blend
+        positive_prompt = legacy_blend
     else:
-        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    positive_prompt = positive_conjunction.prompts[0]
-
-    should_use_lora_manager = True
-    lora_weights = positive_conjunction.lora_weights
-    lora_conditions = None
-    if model.peft_manager:
-        should_use_lora_manager = model.peft_manager.should_use(lora_weights)
-        if not should_use_lora_manager:
-            model.peft_manager.set_loras(lora_weights)
-    if model.lora_manager and should_use_lora_manager:
-        lora_conditions = model.lora_manager.set_loras_conditions(lora_weights)
-
-    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
+        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
+    negative_prompt: FlattenedPrompt|Blend = Compel.parse_prompt_string(negative_prompt_string)
 
     if log_tokens or getattr(Globals, "log_tokenization", False):
         log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
@@ -88,8 +71,7 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
 
     ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
                                                          cross_attention_control_args=options.get(
-                                                             'cross_attention_control', None),
-                                                         lora_conditions=lora_conditions)
+                                                             'cross_attention_control', None))
     return uc, c, ec
 
 
@@ -97,14 +79,12 @@ def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = Fals
     Union[FlattenedPrompt, Blend], FlattenedPrompt):
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_conjunction: Conjunction
+    positive_prompt: FlattenedPrompt|Blend
     if legacy_blend is not None:
-        positive_conjunction = legacy_blend
+        positive_prompt = legacy_blend
     else:
-        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    positive_prompt = positive_conjunction.prompts[0]
-    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
+        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
+    negative_prompt: FlattenedPrompt|Blend = Compel.parse_prompt_string(negative_prompt_string)
 
     return positive_prompt, negative_prompt
 
@@ -133,7 +113,7 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
     return tokens
 
 
-def split_prompt_to_positive_and_negative(prompt_string_uncleaned: str):
+def split_prompt_to_positive_and_negative(prompt_string_uncleaned):
     unconditioned_words = ''
     unconditional_regex = r'\[(.*?)\]'
     unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
@@ -221,26 +201,18 @@ def log_tokenization_for_text(text, tokenizer, display_label=None):
         print(f'{discarded}\x1b[0m')
 
 
-def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Conjunction]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Blend]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
+    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = []
-    weights = []
-    loras = []
-    for i, x in enumerate(parsed_conjunctions):
-        if len(x.prompts)>0:
-            flattened_prompts.append(x.prompts[0])
-            weights.append(weighted_subprompts[i][1])
-        if len(x.lora_weights)>0:
-            loras.extend(x.lora_weights)
+    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
 
-    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)],
-                       lora_weights = loras)
+    return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)
 
 
 def split_weighted_subprompts(text, skip_normalize=False)->list:

ldm/invoke/config/invokeai_configure.py

@@ -290,7 +290,7 @@ def download_vaes():
         # first the diffusers version
         repo_id = "stabilityai/sd-vae-ft-mse"
         args = dict(
-            cache_dir=global_cache_dir("hub"),
+            cache_dir=global_cache_dir("diffusers"),
         )
         if not AutoencoderKL.from_pretrained(repo_id, **args):
             raise Exception(f"download of {repo_id} failed")
@@ -655,7 +655,6 @@ def initialize_rootdir(root: str, yes_to_all: bool = False):
         "models",
         "configs",
         "embeddings",
-        "loras",
         "text-inversion-output",
         "text-inversion-training-data",
     ):

ldm/invoke/config/invokeai_update.py

@@ -4,19 +4,18 @@ pip install <path_to_git_source>.
 '''
 import os
 import platform
-import psutil
 import requests
 from rich import box, print
-from rich.console import Console, group
+from rich.console import Console, Group, group
 from rich.panel import Panel
 from rich.prompt import Prompt
 from rich.style import Style
+from rich.syntax import Syntax
+from rich.text import Text
 
 from ldm.invoke import __version__
 
 INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
-INVOKE_AI_TAG="https://github.com/invoke-ai/InvokeAI/archive/refs/tags"
-INVOKE_AI_BRANCH="https://github.com/invoke-ai/InvokeAI/archive/refs/heads"
 INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"
 
 OS = platform.uname().system
@@ -31,19 +30,6 @@ else:
 def get_versions()->dict:
     return requests.get(url=INVOKE_AI_REL).json()
 
-def invokeai_is_running()->bool:
-    for p in psutil.process_iter():
-        try:
-            cmdline = p.cmdline()
-            matches = [x for x in cmdline if x.endswith(('invokeai','invokeai.exe'))]
-            if matches:
-                print(f':exclamation: [bold red]An InvokeAI instance appears to be running as process {p.pid}[/red bold]')
-                return True
-        except psutil.AccessDenied:
-            continue
-    return False
-        
-    
 def welcome(versions: dict):
     
     @group()
@@ -55,8 +41,7 @@ def welcome(versions: dict):
         yield '[bold yellow]Options:'
         yield f'''[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
 [2] Update to the bleeding-edge development version ([italic]main[/italic])
-[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
-[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to'''        
+[3] Manually enter the tag or branch name you wish to update'''
 
     console.rule()
     print(
@@ -74,33 +59,20 @@ def welcome(versions: dict):
 
 def main():
     versions = get_versions()
-    if invokeai_is_running():
-        print(f':exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]')
-        return
-
     welcome(versions)
 
     tag = None
-    branch = None
-    release = None
-    choice = Prompt.ask('Choice:',choices=['1','2','3','4'],default='1')
+    choice = Prompt.ask('Choice:',choices=['1','2','3'],default='1')
     
     if choice=='1':
-        release = versions[0]['tag_name']
+        tag = versions[0]['tag_name']
     elif choice=='2':
-        release = 'main'
+        tag = 'main'
     elif choice=='3':
-        tag = Prompt.ask('Enter an InvokeAI tag name')
-    elif choice=='4':
-        branch = Prompt.ask('Enter an InvokeAI branch name')
+        tag = Prompt.ask('Enter an InvokeAI tag or branch name')
 
-    print(f':crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]')
-    if release:
-        cmd = f'pip install {INVOKE_AI_SRC}/{release}.zip --use-pep517 --upgrade'
-    elif tag:
-        cmd = f'pip install {INVOKE_AI_TAG}/{tag}.zip --use-pep517 --upgrade'
-    else:
-        cmd = f'pip install {INVOKE_AI_BRANCH}/{branch}.zip --use-pep517 --upgrade'
+    print(f':crossed_fingers: Upgrading to [yellow]{tag}[/yellow]')
+    cmd = f'pip install {INVOKE_AI_SRC}/{tag}.zip --use-pep517'
     print('')
     print('')
     if os.system(cmd)==0:

ldm/invoke/config/model_install_backend.py

@@ -29,13 +29,7 @@ Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 
 # the initial "configs" dir is now bundled in the `invokeai.configs` package
-Dataset_path = None
-for path in configs.__path__:
-    file =Path(path, "INITIAL_MODELS.yaml")
-    if file.exists():
-        Dataset_path = file
-        break
-assert Dataset_path,f"Could not find the file INITIAL_MODELS.yaml in {configs.__path__}"
+Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
 
 # initial models omegaconf
 Datasets = None
@@ -115,7 +109,6 @@ def install_requested_models(
                 model_manager.heuristic_import(
                     path_url_or_repo,
                     convert=convert_to_diffusers,
-                    config_file_callback=_pick_configuration_file,
                     commit_to_conf=config_file_path
                 )
             except KeyboardInterrupt:
@@ -145,45 +138,6 @@ def yes_or_no(prompt: str, default_yes=True):
     else:
         return response[0] in ("y", "Y")
 
-# -------------------------------------
-def _pick_configuration_file(checkpoint_path: Path)->Path:
-    print(
-"""
-Please select the type of this model:
-[1] A Stable Diffusion v1.x ckpt/safetensors model
-[2] A Stable Diffusion v1.x inpainting ckpt/safetensors model
-[3] A Stable Diffusion v2.x base model (512 pixels; no 'parameterization:' in its yaml file)
-[4] A Stable Diffusion v2.x v-predictive model (768 pixels; look for 'parameterization: "v"' in its yaml file)
-[5] Other (you will be prompted to enter the config file path)
-[Q] I have no idea! Skip the import.
-""")
-    choices = [
-        global_config_dir() / 'stable-diffusion' / x
-        for x in [
-                'v1-inference.yaml',
-                'v1-inpainting-inference.yaml',
-                'v2-inference.yaml',
-                'v2-inference-v.yaml',
-        ]
-    ]
-
-    ok = False
-    while not ok:
-        try:
-            choice = input('select 0-5, Q > ').strip()
-            if choice.startswith(('q','Q')):
-                return
-            if choice == '5':
-                choice = Path(input('Select config file for this model> ').strip()).absolute()
-                ok = choice.exists()
-            else:
-                choice = choices[int(choice)-1]
-                ok = True
-        except (ValueError, IndexError):
-            print(f'{choice} is not a valid choice')
-        except EOFError:
-            return
-    return choice
 
 # -------------------------------------
 def get_root(root: str = None) -> str:
@@ -308,6 +262,7 @@ def _download_diffusion_weights(
             path = download_from_hf(
                 model_class,
                 repo_id,
+                cache_subdir="diffusers",
                 safety_checker=None,
                 **extra_args,
             )

ldm/invoke/dynamic_prompts.py

@@ -1,569 +0,0 @@
-#!/usr/bin/env python
-
-"""
-Simple script to generate a file of InvokeAI prompts and settings
-that scan across steps and other parameters.
-"""
-
-import argparse
-import io
-import json
-import os
-import pydoc
-import re
-import shutil
-import sys
-import time
-from contextlib import redirect_stderr
-from io import TextIOBase
-from itertools import product
-from multiprocessing import Process
-from multiprocessing.connection import Connection, Pipe
-from pathlib import Path
-from tempfile import gettempdir
-from typing import Callable, Iterable, List
-
-import numpy as np
-import yaml
-from omegaconf import OmegaConf, dictconfig, listconfig
-
-
-def expand_prompts(
-    template_file: Path,
-    run_invoke: bool = False,
-    invoke_model: str = None,
-    invoke_outdir: str = None,
-    invoke_root: str = None,
-    processes_per_gpu: int = 1,
-):
-    """
-    :param template_file: A YAML file containing templated prompts and args
-    :param run_invoke: A boolean which if True will pass expanded prompts to invokeai CLI
-    :param invoke_model: Name of the model to load when run_invoke is true; otherwise uses default
-    :param invoke_outdir: Directory for outputs when run_invoke is true; otherwise uses default
-    """
-    if template_file.name.endswith(".json"):
-        with open(template_file, "r") as file:
-            with io.StringIO(yaml.dump(json.load(file))) as fh:
-                conf = OmegaConf.load(fh)
-    else:
-        conf = OmegaConf.load(template_file)
-
-    # loading here to avoid long wait for help message
-    import torch
-
-    torch.multiprocessing.set_start_method("spawn")
-    gpu_count = torch.cuda.device_count() if torch.cuda.is_available() else 1
-    commands = expanded_invokeai_commands(conf, run_invoke)
-    children = list()
-
-    try:
-        if run_invoke:
-            invokeai_args = [shutil.which("invokeai"), "--from_file", "-"]
-            if invoke_model:
-                invokeai_args.extend(("--model", invoke_model))
-            if invoke_root:
-                invokeai_args.extend(("--root", invoke_root))
-            if invoke_outdir:
-                outdir = os.path.expanduser(invoke_outdir)
-                invokeai_args.extend(("--outdir", outdir))
-            else:
-                outdir = gettempdir()
-            logdir = Path(outdir, "invokeai-batch-logs")
-
-            processes_to_launch = gpu_count * processes_per_gpu
-            print(
-                f">> Spawning {processes_to_launch} invokeai processes across {gpu_count} CUDA gpus",
-                file=sys.stderr,
-            )
-            print(
-                f'>> Outputs will be written into {invoke_outdir or "default InvokeAI outputs directory"}, and error logs will be written to {logdir}',
-                file=sys.stderr,
-            )
-            import ldm.invoke.CLI
-
-            print(f'DEBUG: BATCH PARENT ENVIRONMENT:')
-            print('<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<')
-            print("\n".join([f'{x}:{os.environ[x]}' for x in os.environ.keys()]))
-            print('<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<')
-            
-            parent_conn, child_conn = Pipe()
-            children = set()
-            for i in range(processes_to_launch):
-                p = Process(
-                    target=_run_invoke,
-                    kwargs=dict(
-                        entry_point=ldm.invoke.CLI.main,
-                        conn_in=child_conn,
-                        conn_out=parent_conn,
-                        args=invokeai_args,
-                        gpu=i % gpu_count,
-                        logdir=logdir,
-                    ),
-                )
-                p.start()
-                children.add(p)
-            child_conn.close()
-            sequence = 0
-            for command in commands:
-                sequence += 1
-                format = _get_fn_format(outdir, sequence)
-                parent_conn.send_bytes(
-                    (command + f' --fnformat="{format}"').encode('utf-8')
-                )
-            parent_conn.close()
-        else:
-            for command in commands:
-                print(command)
-    except KeyboardInterrupt:
-        for p in children:
-            p.terminate()
-
-def _dummy_cli_main():
-    counter = 0
-    while line := sys.stdin.readline():
-        print(f'[{counter}] {os.getpid()} got command {line.rstrip()}\n')
-        counter += 1
-        time.sleep(1)
-
-def _get_fn_format(directory:str, sequence:int)->str:
-    """
-    Get a filename that doesn't exceed filename length restrictions
-    on the current platform.
-    """
-    try:
-        max_length = os.pathconf(directory,'PC_NAME_MAX')
-    except:
-        max_length = 255
-    prefix = f'dp.{sequence:04}.'
-    suffix = '.png'
-    max_length -= len(prefix)+len(suffix)
-    return f'{prefix}{{prompt:0.{max_length}}}{suffix}'
-            
-class MessageToStdin(object):
-    def __init__(self, connection: Connection):
-        self.connection = connection
-        self.linebuffer = list()
-
-    def readline(self) -> str:
-        try:
-            if len(self.linebuffer) == 0:
-                message = self.connection.recv_bytes().decode('utf-8')
-                self.linebuffer = message.split("\n")
-            result = self.linebuffer.pop(0)
-            return result
-        except EOFError:
-            return None
-
-
-class FilterStream(object):
-    def __init__(
-        self, stream: TextIOBase, include: re.Pattern = None, exclude: re.Pattern = None
-    ):
-        self.stream = stream
-        self.include = include
-        self.exclude = exclude
-
-    def write(self, data: str):
-        if self.include and self.include.match(data):
-            self.stream.write(data)
-            self.stream.flush()
-        elif self.exclude and not self.exclude.match(data):
-            self.stream.write(data)
-            self.stream.flush()
-
-    def flush(self):
-        self.stream.flush()
-
-
-def _run_invoke(
-    entry_point: Callable,
-    conn_in: Connection,
-    conn_out: Connection,
-    args: List[str],
-    logdir: Path,
-    gpu: int = 0,
-):
-    pid = os.getpid()
-    logdir.mkdir(parents=True, exist_ok=True)
-    logfile = Path(logdir, f'{time.strftime("%Y-%m-%d_%H-%M-%S")}-pid={pid}.txt')
-    print(
-        f">> Process {pid} running on GPU {gpu}; logging to {logfile}", file=sys.stderr
-    )
-    conn_out.close()
-    os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
-    sys.argv = args
-    sys.stdin = MessageToStdin(conn_in)
-#    sys.stdout = FilterStream(sys.stdout, include=re.compile("^\[\d+\]"))
-#    with open(logfile, "w") as stderr, redirect_stderr(stderr):
-    entry_point()
-
-
-def _filter_output(stream: TextIOBase):
-    while line := stream.readline():
-        if re.match("^\[\d+\]", line):
-            print(line)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=HELP,
-    )
-    parser.add_argument(
-        "template_file",
-        type=Path,
-        nargs="?",
-        help="path to a template file, use --example to generate an example file",
-    )
-    parser.add_argument(
-        "--example",
-        action="store_true",
-        default=False,
-        help=f'Print an example template file in YAML format. Use "{sys.argv[0]} --example > example.yaml" to save output to a file',
-    )
-    parser.add_argument(
-        "--json-example",
-        action="store_true",
-        default=False,
-        help=f'Print an example template file in json format. Use "{sys.argv[0]} --json-example > example.json" to save output to a file',
-    )
-    parser.add_argument(
-        "--instructions",
-        "-i",
-        dest="instructions",
-        action="store_true",
-        default=False,
-        help="Print verbose instructions.",
-    )
-    parser.add_argument(
-        "--invoke",
-        action="store_true",
-        help="Execute invokeai using specified optional --model, --processes_per_gpu and --outdir",
-    )
-    parser.add_argument(
-        "--model",
-        help="Feed the generated prompts to the invokeai CLI using the indicated model. Will be overriden by a model: section in template file.",
-    )
-    parser.add_argument(
-        "--outdir", type=Path, help="Write images and log into indicated directory"
-    )
-    parser.add_argument(
-        "--processes_per_gpu",
-        type=int,
-        default=1,
-        help="When executing invokeai, how many parallel processes to execute per CUDA GPU.",
-    )
-    parser.add_argument(
-        '--root_dir',
-        default=None,
-        help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai'    )
-    opt = parser.parse_args()
-
-    if opt.example:
-        print(EXAMPLE_TEMPLATE_FILE)
-        sys.exit(0)
-
-    if opt.json_example:
-        print(_yaml_to_json(EXAMPLE_TEMPLATE_FILE))
-        sys.exit(0)
-
-    if opt.instructions:
-        pydoc.pager(INSTRUCTIONS)
-        sys.exit(0)
-
-    if not opt.template_file:
-        parser.print_help()
-        sys.exit(-1)
-
-    expand_prompts(
-        template_file=opt.template_file,
-        run_invoke=opt.invoke,
-        invoke_model=opt.model,
-        invoke_outdir=opt.outdir,
-        invoke_root=opt.root,
-        processes_per_gpu=opt.processes_per_gpu,
-    )
-
-
-def expanded_invokeai_commands(
-    conf: OmegaConf, always_switch_models: bool = False
-) -> List[List[str]]:
-    models = expand_values(conf.get("model"))
-    steps = expand_values(conf.get("steps")) or [30]
-    cfgs = expand_values(conf.get("cfg")) or [7.5]
-    samplers = expand_values(conf.get("sampler")) or ["ddim"]
-    seeds = expand_values(conf.get("seed")) or [0]
-    dimensions = expand_values(conf.get("dimensions")) or ["512x512"]
-    init_img = expand_values(conf.get("init_img")) or [""]
-    perlin = expand_values(conf.get("perlin")) or [0]
-    threshold = expand_values(conf.get("threshold")) or [0]
-    strength = expand_values(conf.get("strength")) or [0.75]
-    prompts = expand_prompt(conf.get("prompt")) or ["banana sushi"]
-
-    cross_product = product(
-        *[
-            models,
-            seeds,
-            prompts,
-            samplers,
-            cfgs,
-            steps,
-            perlin,
-            threshold,
-            init_img,
-            strength,
-            dimensions,
-        ]
-    )
-    previous_model = None
-
-    result = list()
-    for p in cross_product:
-        (
-            model,
-            seed,
-            prompt,
-            sampler,
-            cfg,
-            step,
-            perlin,
-            threshold,
-            init_img,
-            strength,
-            dimensions,
-        ) = tuple(p)
-        (width, height) = dimensions.split("x")
-        switch_args = (
-            f"!switch {model}\n"
-            if always_switch_models or previous_model != model
-            else ""
-        )
-        image_args = f"-I{init_img} -f{strength}" if init_img else ""
-        command = f"{switch_args}{prompt} -S{seed} -A{sampler} -C{cfg} -s{step} {image_args} --perlin={perlin} --threshold={threshold} -W{width} -H{height}"
-        result.append(command)
-        previous_model = model
-    return result
-
-
-def expand_prompt(
-    stanza: str | dict | listconfig.ListConfig | dictconfig.DictConfig,
-) -> list | range:
-    if not stanza:
-        return None
-    if isinstance(stanza, listconfig.ListConfig):
-        return stanza
-    if isinstance(stanza, str):
-        return [stanza]
-    if not isinstance(stanza, dictconfig.DictConfig):
-        raise ValueError(f"Unrecognized template: {stanza}")
-
-    if not (template := stanza.get("template")):
-        raise KeyError('"prompt" section must contain a "template" definition')
-
-    fragment_labels = re.findall("{([^{}]+?)}", template)
-    if len(fragment_labels) == 0:
-        return [template]
-    fragments = [[{x: y} for y in stanza.get(x)] for x in fragment_labels]
-    dicts = merge(product(*fragments))
-    return [template.format(**x) for x in dicts]
-
-
-def merge(dicts: Iterable) -> List[dict]:
-    result = list()
-    for x in dicts:
-        to_merge = dict()
-        for item in x:
-            to_merge = to_merge | item
-        result.append(to_merge)
-    return result
-
-
-def expand_values(stanza: str | dict | listconfig.ListConfig) -> list | range:
-    if not stanza:
-        return None
-    if isinstance(stanza, listconfig.ListConfig):
-        return stanza
-    elif match := re.match("^(-?\d+);(-?\d+)(;(\d+))?", str(stanza)):
-        (start, stop, step) = (
-            int(match.group(1)),
-            int(match.group(2)),
-            int(match.group(4)) or 1,
-        )
-        return range(start, stop + step, step)
-    elif match := re.match("^(-?[\d.]+);(-?[\d.]+)(;([\d.]+))?", str(stanza)):
-        (start, stop, step) = (
-            float(match.group(1)),
-            float(match.group(2)),
-            float(match.group(4)) or 1.0,
-        )
-        return np.arange(start, stop + step, step).tolist()
-    else:
-        return [stanza]
-
-
-def _yaml_to_json(yaml_input: str) -> str:
-    """
-    Converts a yaml string into a json string. Used internally
-    to generate the example template file.
-    """
-    with io.StringIO(yaml_input) as yaml_in:
-        data = yaml.safe_load(yaml_in)
-    return json.dumps(data, indent=2)
-
-
-HELP = """
-This script takes a prompt template file that contains multiple
-alternative values for the prompt and its generation arguments (such
-as steps). It then expands out the prompts using all combinations of
-arguments and either prints them to the terminal's standard output, or
-feeds the prompts directly to the invokeai command-line interface.
-
-Call this script again with --instructions (-i) for verbose instructions.
-"""
-
-INSTRUCTIONS = f"""
-== INTRODUCTION ==
-This script takes a prompt template file that contains multiple
-alternative values for the prompt and its generation arguments (such
-as steps). It then expands out the prompts using all combinations of
-arguments and either prints them to the terminal's standard output, or
-feeds the prompts directly to the invokeai command-line interface.
-
-If the optional --invoke argument is provided, then the generated
-prompts will be fed directly to invokeai for image generation. You
-will likely want to add the --outdir option in order to save the image
-files to their own folder.
-
-   {sys.argv[0]} --invoke --outdir=/tmp/outputs my_template.yaml
-
-If --invoke isn't specified, the expanded prompts will be printed to
-output. You can capture them to a file for inspection and editing this
-way:
-
-   {sys.argv[0]} my_template.yaml > prompts.txt
-
-And then feed them to invokeai this way:
-
-  invokeai --outdir=/tmp/outputs < prompts.txt
-
-Note that after invokeai finishes processing the list of prompts, the
-output directory will contain a markdown file named `log.md`
-containing annotated images. You can open this file using an e-book
-reader such as the cross-platform Calibre eBook reader
-(https://calibre-ebook.com/).
-
-== FORMAT OF THE TEMPLATES FILE ==
-
-This will generate an example template file that you can get
-started with:
-
-  {sys.argv[0]} --example > example.yaml
-
-An excerpt from the top of this file looks like this:
-
- model:
-   - stable-diffusion-1.5
-   - stable-diffusion-2.1-base
- steps: 30;50;1  # start steps at 30 and go up to 50, incrementing by 1 each time
- seed: 50        # fixed constant, seed=50
- cfg:            # list of CFG values to try
-   - 7
-   - 8
-   - 12
- prompt: a walk in the park   # constant value
-
-In more detail, the template file can one or more of the
-following sections:
- - model:
- - steps:
- - seed:
- - cfg:
- - sampler:
- - prompt:
- - init_img:
- - perlin:
- - threshold:
- - strength
-
-- Each section can have a constant value such as this:
-     steps: 50
-- Or a range of numeric values in the format:
-     steps: <start>;<stop>;<step>      (note semicolon, not colon!)
-- Or a list of values in the format:
-     - value1
-     - value2
-     - value3
-
-The "prompt:" section is special. It can accept a constant value:
-
-   prompt: a walk in the woods in the style of donatello
-
-Or it can accept a list of prompts:
-
-   prompt:
-      - a walk in the woods
-      - a walk on the beach
-
-Or it can accept a templated list of prompts. These allow you to
-define a series of phrases, each of which is a list. You then combine
-them together into a prompt template in this way:
-
-  prompt:
-    style:
-         - oil painting
-         - watercolor
-         - comic book
-         - studio photography
-    subject:
-         - sunny meadow in the mountains
-         - gathering storm in the mountains
-    template: a {{subject}} in the style of {{style}}
-
-In the example above, the phrase names "style" and "subject" are
-examples only. You can use whatever you like. However, the "template:"
-field is required. The output will be:
-
-  "a sunny meadow in the mountains in the style of an oil painting"
-  "a sunny meadow in the mountains in the style of watercolor masterpiece"
-  ...
-  "a gathering storm in the mountains in the style of an ink sketch"
-
-== SUPPORT FOR JSON FORMAT ==
-
-For those who prefer the JSON format, this script will accept JSON
-template files as well. Please run "{sys.argv[0]} --json-example"
-to print out a version of the example template file in json format.
-You may save it to disk and use it as a starting point for your own
-template this way:
-
-   {sys.argv[0]} --json-example > template.json 
-"""
-
-EXAMPLE_TEMPLATE_FILE = """
-model: stable-diffusion-1.5
-steps: 30;50;10
-seed: 50
-dimensions: 512x512
-perlin: 0.0
-threshold: 0
-cfg:
-  - 7
-  - 12
-sampler:
-  - k_euler_a
-  - k_lms
-prompt:
-  style:
-       - oil painting
-       - watercolor
-  location:
-       - the mountains
-       - a desert
-  object:
-       - luxurious dwelling
-       - crude tent
-  template: a {object} in {location}, in the style of {style}
-"""
-
-if __name__ == "__main__":
-    main()

ldm/invoke/generator/diffusers_pipeline.py

@@ -29,8 +29,6 @@ from typing_extensions import ParamSpec
 from ldm.invoke.globals import Globals
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings
 from ldm.modules.textual_inversion_manager import TextualInversionManager
-from ldm.modules.lora_manager import LoraManager
-from ldm.modules.peft_manager import PeftManager
 from ..devices import normalize_device, CPU_DEVICE
 from ..offloading import LazilyLoadedModelGroup, FullyLoadedModelGroup, ModelGroup
 from ...models.diffusion.cross_attention_map_saving import AttentionMapSaver
@@ -291,14 +289,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
         )
-        self.lora_manager = LoraManager(self)
-        self.peft_manager = PeftManager()
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward, is_running_diffusers=True)
         use_full_precision = (precision == 'float32' or precision == 'autocast')
         self.textual_inversion_manager = TextualInversionManager(tokenizer=self.tokenizer,
                                                                  text_encoder=self.text_encoder,
                                                                  full_precision=use_full_precision)
-
         # InvokeAI's interface for text embeddings and whatnot
         self.embeddings_provider = EmbeddingsProvider(
             tokenizer=self.tokenizer,

ldm/invoke/generator/inpaint.py

@@ -255,8 +255,8 @@ class Inpaint(Img2Img):
         pipeline.scheduler = sampler
 
         # todo: support cross-attention control
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
+        uc, c, _ = conditioning
+        conditioning_data = (ConditioningData(uc, c, cfg_scale)
                              .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
 

ldm/invoke/globals.py

@@ -23,7 +23,6 @@ Globals = Namespace()
 Globals.initfile = 'invokeai.init'
 Globals.models_file = 'models.yaml'
 Globals.models_dir = 'models'
-Globals.lora_models_dir = 'loras'
 Globals.config_dir = 'configs'
 Globals.autoscan_dir = 'weights'
 Globals.converted_ckpts_dir = 'converted_ckpts'
@@ -62,7 +61,7 @@ Globals.sequential_guidance = False
 Globals.full_precision = False
 
 # whether we should convert ckpt files into diffusers models on the fly
-Globals.ckpt_convert = True
+Globals.ckpt_convert = False
 
 # logging tokenization everywhere
 Globals.log_tokenization = False
@@ -76,11 +75,6 @@ def global_config_dir()->Path:
 def global_models_dir()->Path:
     return Path(Globals.root, Globals.models_dir)
 
-def global_lora_models_dir()->Path:
-    return Path(Globals.lora_models_dir) \
-        if Path(Globals.lora_models_dir).is_absolute() \
-           else Path(Globals.root, Globals.lora_models_dir)
-
 def global_autoscan_dir()->Path:
     return Path(Globals.root, Globals.autoscan_dir)
 
@@ -94,13 +88,16 @@ def global_cache_dir(subdir:Union[str,Path]='')->Path:
     '''
     Returns Path to the model cache directory. If a subdirectory
     is provided, it will be appended to the end of the path, allowing
-    for Hugging Face-style conventions. Currently, Hugging Face has
-    moved all models into the "hub" subfolder, so for any pretrained
-    HF model, use:
+    for huggingface-style conventions:
+         global_cache_dir('diffusers')
          global_cache_dir('hub')
-
-    The legacy location for transformers used to be global_cache_dir('transformers')
-    and global_cache_dir('diffusers') for diffusers.
+    Current HuggingFace documentation (mid-Jan 2023) indicates that
+    transformers models will be cached into a "transformers" subdirectory,
+    but in practice they seem to go into "hub". But if needed:
+         global_cache_dir('transformers')
+    One other caveat is that HuggingFace is moving some diffusers models
+    into the "hub" subdirectory as well, so this will need to be revisited
+    from time to time.
     '''
     home: str = os.getenv('HF_HOME')
 

ldm/invoke/invokeai_metadata.py

@@ -1,29 +0,0 @@
-#!/usr/bin/env python
-
-import sys
-import json
-from ldm.invoke.pngwriter import retrieve_metadata
-
-def main():
-    if len(sys.argv) < 2:
-        print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
-        print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out their metadata.")
-        exit(-1)
-
-    filenames = sys.argv[1:]
-    for f in filenames:
-        try:
-            metadata = retrieve_metadata(f)
-            print(f'{f}:\n',json.dumps(metadata['sd-metadata'], indent=4))
-        except FileNotFoundError:
-            sys.stderr.write(f'{f} not found\n')
-            continue
-        except PermissionError:
-            sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
-            continue
-
-if __name__ == "__main__":
-    try:
-        main()
-    except KeyboardInterrupt:
-        pass

ldm/invoke/merge_diffusers.py

@@ -437,10 +437,10 @@ def main():
     args = _parse_args()
     global_set_root(args.root_dir)
 
-    cache_dir = str(global_cache_dir("hub"))
+    cache_dir = str(global_cache_dir("diffusers"))
     os.environ[
         "HF_HOME"
-    ] = str(global_cache_dir())  # because not clear the merge pipeline is honoring cache_dir
+    ] = cache_dir  # because not clear the merge pipeline is honoring cache_dir
     args.cache_dir = cache_dir
 
     try: