fix(ui): revert node type change of metadata_accumulator -> core_metadata

The type of this node was changed due to breaking changes in f0db4d36e4.

The change to the node type means pending queue items using this node would break if the app was updated.

In the end, there were no breaking changes, so to prevent queue items being stale and erroring, we can safely revert the change.

Note: This node was hidden from the workflow editor, so nobody has used it there in any official release.

.gitattributes

@@ -2,4 +2,3 @@
 # Only affects text files and ignores other file types.
 # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
 * text=auto
-docker/** text eol=lf

.github/workflows/pyflakes.yml

@@ -0,0 +1,20 @@
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+      - development
+      - 'release-candidate-*'
+
+jobs:
+  pyflakes:
+    name: runner / pyflakes
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: pyflakes
+        uses: reviewdog/action-pyflakes@v1
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          reporter: github-pr-review

.github/workflows/style-checks.yml

@@ -6,7 +6,7 @@ on:
     branches: main
 
 jobs:
-  ruff:
+  black:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -18,7 +18,8 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install ruff
+          pip install black flake8 Flake8-pyproject isort
 
-      - run: ruff check --output-format=github .
-      - run: ruff format --check .
+      - run: isort --check-only .
+      - run: black --check .
+      - run: flake8

Makefile

@@ -1,21 +0,0 @@
-# simple Makefile with scripts that are otherwise hard to remember
-# to use, run from the repo root `make <command>`
-
-# Runs ruff, fixing any safely-fixable errors and formatting
-ruff:
-		ruff check . --fix
-		ruff format .
-
-# Runs ruff, fixing all errors it can fix and formatting
-ruff-unsafe:
-		ruff check . --fix --unsafe-fixes
-		ruff format .
-
-# Runs mypy, using the config in pyproject.toml
-mypy:
-		mypy scripts/invokeai-web.py
-
-# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
-# (many files are ignored by the config, so this is useful for checking all files)
-mypy-all:
-		mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports

README.md

@@ -161,7 +161,7 @@ the command `npm install -g yarn` if needed)
     _For Windows/Linux with an NVIDIA GPU:_
 
     ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
     ```
 
     _For Linux with an AMD GPU:_
@@ -175,7 +175,7 @@ the command `npm install -g yarn` if needed)
     pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
     ``` 
 
-    _For Macintoshes, either Intel or M1/M2/M3:_
+    _For Macintoshes, either Intel or M1/M2:_
 
     ```sh
     pip install InvokeAI --use-pep517
@@ -395,7 +395,7 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 
 ### Troubleshooting
 
-Please check out our **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
+Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
 problems and other issues. For more help, please join our [Discord][discord link]
 
 ## Contributing

docker/.env.sample

@@ -11,5 +11,5 @@ INVOKEAI_ROOT=
 # HUGGING_FACE_HUB_TOKEN=
 
 ## optional variables specific to the docker setup.
-# GPU_DRIVER=cuda # or rocm
-# CONTAINER_UID=1000
+# GPU_DRIVER=cuda
+# CONTAINER_UID=1000

docker/Dockerfile

@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.0
-ARG TORCHVISION_VERSION=0.16
+ARG TORCH_VERSION=2.0.1
+ARG TORCHVISION_VERSION=0.15.2
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\

docker/docker-compose.yml

@@ -15,10 +15,6 @@ services:
             - driver: nvidia
               count: 1
               capabilities: [gpu]
-    # For AMD support, comment out the deploy section above and uncomment the devices section below:
-    #devices:
-    #  - /dev/kfd:/dev/kfd
-    #  - /dev/dri:/dev/dri
     build:
       context: ..
       dockerfile: docker/Dockerfile

docker/run.sh

@@ -7,5 +7,5 @@ set -e
 SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
-docker compose up -d
+docker compose up --build -d
 docker compose logs -f

docs/contributing/INVOCATIONS.md

@@ -1,6 +1,6 @@
-# Nodes
+# Invocations
 
-Features in InvokeAI are added in the form of modular nodes systems called
+Features in InvokeAI are added in the form of modular node-like systems called
 **Invocations**.
 
 An Invocation is simply a single operation that takes in some inputs and gives
@@ -9,34 +9,13 @@ complex functionality.
 
 ## Invocations Directory
 
-InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
+InvokeAI Invocations can be found in the `invokeai/app/invocations` directory.
 
-New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup. 
-
-Example `nodes`  subfolder structure:
-```py
-├── __init__.py # Invoke-managed custom node loader
-│
-├── cool_node
-│   ├── __init__.py # see example below
-│   └── cool_node.py
-│
-└── my_node_pack
-    ├── __init__.py # see example below
-    ├── tasty_node.py
-    ├── bodacious_node.py
-    ├── utils.py
-    └── extra_nodes
-        └── fancy_node.py
-```
-
-Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
- See the README in the nodes folder for more examples: 
-
-```py
-from .cool_node import CoolInvocation
-```
+You can add your new functionality to one of the existing Invocations in this
+directory or create a new file in this directory as per your needs.
 
+**Note:** _All Invocations must be inside this directory for InvokeAI to
+recognize them as valid Invocations._
 
 ## Creating A New Invocation
 
@@ -65,7 +44,7 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from .baseinvocation import BaseInvocation, invocation
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -99,8 +78,8 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -124,8 +103,8 @@ image: ImageField = InputField(description="The input image")
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
-from invokeai.app.invocations.primitives import ImageField
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -160,8 +139,8 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -189,9 +168,9 @@ all the necessary info related to image outputs. So let us use that.
 We will cover how to create your own output types later in this guide.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
+from .image import ImageOutput
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -216,9 +195,9 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
 So let's do that.
 
 ```python
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
+from .baseinvocation import BaseInvocation, InputField, invocation
+from .primitives import ImageField
+from .image import ImageOutput
 
 @invocation("resize")
 class ResizeInvocation(BaseInvocation):

docs/features/CONCEPTS.md

@@ -1,3 +1,12 @@
+---
+title: Textual Inversion Embeddings and LoRAs
+---
+
+# :material-library-shelves: Textual Inversions and LoRAs
+
+With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
+
+
 ## Using Textual Inversion Files
 
 Textual inversion (TI) files are small models that customize the output of
@@ -52,4 +61,29 @@ files it finds there for compatible models. At startup you will see a message si
 >> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
 ```
 To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and 
-select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
+select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
+
+## Using LoRAs
+
+LoRA files are models that customize the output of Stable Diffusion
+image generation.  Larger than embeddings, but much smaller than full
+models, they augment SD with improved understanding of subjects and
+artistic styles.
+
+Unlike TI files, LoRAs do not introduce novel vocabulary into the
+model's known tokens. Instead, LoRAs augment the model's weights that
+are applied to generate imagery. LoRAs may be supplied with a
+"trigger" word that they have been explicitly trained on, or may
+simply apply their effect without being triggered.
+
+LoRAs are typically stored in .safetensors files, which are the most
+secure way to store and transmit these types of weights. You may
+install any number of `.safetensors` LoRA files simply by copying them
+into the `autoimport/lora` directory of the corresponding InvokeAI models
+directory (usually `invokeai` in your home directory).
+
+To use these when generating, open the LoRA menu item in the options
+panel, select the LoRAs you want to apply and ensure that they have
+the appropriate weight recommended by the model provider. Typically,
+most LoRAs perform best at a weight of .75-1.
+

docs/features/CONTROLNET.md

@@ -150,6 +150,7 @@ Start/End  - 0 represents the start of the generation, 1 represents the end. The
 
 Additionally, each  section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
 
+**Note:** T2I-Adapter models and ControlNet models cannot currently be used together.
 
 ## IP-Adapter
 

docs/features/LORAS.md

@@ -1,53 +0,0 @@
----
-title: LoRAs & LCM-LoRAs
----
-
-# :material-library-shelves: LoRAs & LCM-LoRAs
-
-With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
-
-## LoRAs
-
-Low-Rank Adaptation (LoRA) files are models that customize the output of Stable Diffusion
-image generation.  Larger than embeddings, but much smaller than full
-models, they augment SD with improved understanding of subjects and
-artistic styles.
-
-Unlike TI files, LoRAs do not introduce novel vocabulary into the
-model's known tokens. Instead, LoRAs augment the model's weights that
-are applied to generate imagery. LoRAs may be supplied with a
-"trigger" word that they have been explicitly trained on, or may
-simply apply their effect without being triggered.
-
-LoRAs are typically stored in .safetensors files, which are the most
-secure way to store and transmit these types of weights. You may
-install any number of `.safetensors` LoRA files simply by copying them
-into the `autoimport/lora` directory of the corresponding InvokeAI models
-directory (usually `invokeai` in your home directory).
-
-To use these when generating, open the LoRA menu item in the options
-panel, select the LoRAs you want to apply and ensure that they have
-the appropriate weight recommended by the model provider. Typically,
-most LoRAs perform best at a weight of .75-1.
-
-
-## LCM-LoRAs
-Latent Consistency Models (LCMs) allowed a reduced number of steps to be used to generate images with Stable Diffusion. These are created by distilling base models, creating models that only require a small number of steps to generate images. However, LCMs require that any fine-tune of a base model be distilled to be used as an LCM. 
-
-LCM-LoRAs are models that provide the benefit of LCMs but are able to be used as LoRAs and applied to any fine tune of a base model. LCM-LoRAs are created by training a small number of adapters, rather than distilling the entire fine-tuned base model. The resulting LoRA can be used the same way as a standard LoRA, but with a greatly reduced step count. This enables SDXL images to be generated up to 10x faster than without the use of LCM-LoRAs. 
-
-
-**Using LCM-LoRAs**
-
-LCM-LoRAs are natively supported in InvokeAI throughout the application. To get started, install any diffusers format LCM-LoRAs using the model manager and select it in the LoRA field.
-
-There are a number parameter differences when using LCM-LoRAs and standard generation: 
-
-- When using LCM-LoRAs, the LoRA strength should be lower than if using a standard LoRA, with 0.35 recommended as a starting point.  
-- The LCM scheduler should be used for generation
-- CFG-Scale should be reduced to ~1
-- Steps should be reduced in the range of 4-8
-
-Standard LoRAs can also be used alongside LCM-LoRAs, but will also require a lower strength, with 0.45 being recommended as a starting point. 
-
-More information can be found here: https://huggingface.co/blog/lcm_lora#fast-inference-with-sdxl-lcm-loras

docs/features/PROMPTS.md

@@ -120,7 +120,7 @@ Generate an image with a given prompt, record the seed of the image, and then
 use the `prompt2prompt` syntax to substitute words in the original prompt for
 words in a new prompt. This works for `img2img` as well.
 
-For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
   - `a cat playing with a ball in the forest`
   - `a dog playing with a ball in the forest`
 

docs/features/index.md

@@ -20,7 +20,7 @@ a single convenient digital artist-optimized user interface.
 ### * [Prompt Engineering](PROMPTS.md)
 Get the images you want with the InvokeAI  prompt engineering language.
 
-### * The [LoRA, LyCORIS, LCM-LoRA Models](CONCEPTS.md)
+### * The [LoRA, LyCORIS and Textual Inversion Models](CONCEPTS.md)
 Add custom subjects and styles using a variety of fine-tuned models.
 
 ### * [ControlNet](CONTROLNET.md)
@@ -40,7 +40,7 @@ guide also covers optimizing models to load quickly.
 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_INVERSIONS.md)
+### * [Textual Inversion](TRAINING.md)
 Personalize models by adding your own style or subjects.
 
 ## Other Features

docs/help/FAQ.md

@@ -1,43 +0,0 @@
-# FAQs
-
-**Where do I get started? How can I install Invoke?**
-
-- You can download the latest installers [here](https://github.com/invoke-ai/InvokeAI/releases) - Note that any releases marked as *pre-release* are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the **[Latest Release](https://github.com/invoke-ai/InvokeAI/releases/latest)**
-
-**How can I download models? Can I use models I already have downloaded?**
-
-- Models can be downloaded through the model manager, or through option [4] in the invoke.bat/invoke.sh launcher script. To download a model through the Model Manager, use the HuggingFace Repo ID by pressing the “Copy” button next to the repository name. Alternatively, to download a model from CivitAi, use the download link in the Model Manager.
-- Models that are already downloaded can be used by creating a symlink to the model location in the `autoimport` folder or by using the Model Manger’s “Scan for Models” function.
-
-**My images are taking a long time to generate. How can I speed up generation?** 
-
-- A common solution is to reduce the size of your RAM & VRAM cache to 0.25. This ensures your system has enough memory to generate images.
-- Additionally, check the [hardware requirements](https://invoke-ai.github.io/InvokeAI/#hardware-requirements) to ensure that your system is capable of generating images.
-- Lastly, double check your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. 
-
-**I’ve installed Python on Windows but the installer says it can’t find it?**
-
-- Then ensure that you checked  **'Add python.exe to PATH'** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, this can be done with the modify / repair feature of the installer.
-
-**I’ve installed everything successfully but I still get an error about Triton when starting Invoke?**
-
-- This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
-
-**I updated to 3.4.0 and now xFormers can’t load C++/CUDA?**
-
-- An issue occurred with your PyTorch update. Follow these steps to fix :
-    1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
-    2. Run:`pip install ".[xformers]" --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121`
-    - If you run into an error with `typing_extensions`, re-open the developer console and run:  `pip install -U typing-extensions`
-
-**It says my pip is out of date - is that why my install isn't working?**
-- An out of date won't cause an installation to fail. The cause of the error can likely be found above the message that says pip is out of date.
-- If you saw that warning but the install went well, don't worry about it (but you can update pip afterwards if you'd like).   
-
-**How can I generate the exact same that I found on the internet?**
-Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
-
-
-**Where can I get more help?** 
-
-- Create an issue on [GitHub](https://github.com/invoke-ai/InvokeAI/issues) or post in the [#help channel](https://discord.com/channels/1020123559063990373/1149510134058471514) of the InvokeAI Discord

docs/index.md

@@ -101,13 +101,16 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
 <div align="center"><img src="assets/invoke-web-server-1.png" width=640></div>
 
+!!! Note
+
+    This project is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates as it will help aid response time.
+
 ## :octicons-link-24: Quick Links
 
 <div class="button-container">
     <a href="installation/INSTALLATION"> <button class="button">Installation</button> </a>
     <a href="features/"> <button class="button">Features</button> </a>
     <a href="help/gettingStartedWithAI/"> <button class="button">Getting Started</button> </a>
-    <a href="help/FAQ/"> <button class="button">FAQ</button> </a>
     <a href="contributing/CONTRIBUTING/"> <button class="button">Contributing</button> </a>
     <a href="https://github.com/invoke-ai/InvokeAI/"> <button class="button">Code and Downloads</button> </a>
     <a href="https://github.com/invoke-ai/InvokeAI/issues"> <button class="button">Bug Reports </button> </a>
@@ -195,7 +198,6 @@ The list of schedulers has been completely revamped and brought up to date:
 | **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
 | **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
 | **unipc**      | UniPCMultistepScheduler         | CPU only                    |
-| **lcm**        | LCMScheduler                    |                             |
 
 Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -179,7 +179,7 @@ experimental versions later.
     you will have the choice of CUDA (NVidia cards), ROCm (AMD cards),
     or CPU (no graphics acceleration). On Windows, you'll have the
     choice of CUDA vs CPU, and on Macs you'll be offered CPU only. When
-    you select CPU on M1/M2/M3 Macintoshes, you will get MPS-based
+    you select CPU on M1 or M2 Macintoshes, you will get MPS-based
     graphics acceleration without installing additional drivers. If you
     are unsure what GPU you are using, you can ask the installer to
     guess.
@@ -471,7 +471,7 @@ Then type the following commands:
 
 === "NVIDIA System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu118
     pip install xformers
     ```
 

docs/installation/020_INSTALL_MANUAL.md

@@ -148,7 +148,7 @@ manager, please follow these steps:
     === "CUDA (NVidia)"
 
         ```bash
-        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
         ```
 
     === "ROCm (AMD)"
@@ -327,7 +327,7 @@ installation protocol (important!)
 
     === "CUDA (NVidia)"
         ```bash
-        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
         ```
 
     === "ROCm (AMD)"
@@ -375,7 +375,7 @@ you can do so using this unsupported recipe:
 mkdir ~/invokeai
 conda create -n invokeai python=3.10
 conda activate invokeai
-pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
 invokeai-configure --root ~/invokeai
 invokeai --root ~/invokeai --web
 ```

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -85,7 +85,7 @@ You can find which version you should download from [this link](https://docs.nvi
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/cu121` as described in the [Manual
+https://download.pytorch.org/whl/cu118` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 ## :simple-amd: ROCm

docs/installation/040_INSTALL_DOCKER.md

@@ -30,7 +30,7 @@ methodology for details on why running applications in such a stateless fashion
 The container is configured for CUDA by default, but can be built to support AMD GPUs
 by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
 
-Developers on Apple silicon (M1/M2/M3): You
+Developers on Apple silicon (M1/M2): You
 [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
 and performance is reduced compared with running it directly on macOS but for
 development purposes it's fine. Once you're done with development tasks on your
@@ -99,14 +99,3 @@ If using an AMD GPU:
 Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
 
 Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090)
-
-## Troubleshooting / FAQ
-
-- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
-- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
-    and you may have cloned this repository before the issue was fixed. To solve this, please change
-    the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
-    (`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
-    Finally, you may delete `docker-entrypoint.sh` followed by  `git pull; git checkout docker/docker-entrypoint.sh`
-    to reset the file to its most recent version.
-    For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)

docs/installation/070_INSTALL_XFORMERS.md

@@ -28,7 +28,7 @@ command line, then just be sure to activate it's virtual environment.
 Then run the following three commands:
 
 ```sh
-pip install xformers~=0.0.22
+pip install xformers~=0.0.19
 pip install triton    # WON'T WORK ON WINDOWS
 python -m xformers.info output
 ```
@@ -42,7 +42,7 @@ If all goes well, you'll see a report like the
 following:
 
 ```sh
-xFormers 0.0.22
+xFormers 0.0.20
 memory_efficient_attention.cutlassF:               available
 memory_efficient_attention.cutlassB:               available
 memory_efficient_attention.flshattF:               available
@@ -59,14 +59,14 @@ swiglu.gemm_fused_operand_sum:                     available
 swiglu.fused.p.cpp:                                available
 is_triton_available:                               True
 is_functorch_available:                            False
-pytorch.version:                                   2.1.0+cu121
+pytorch.version:                                   2.0.1+cu118
 pytorch.cuda:                                      available
 gpu.compute_capability:                            8.9
 gpu.name:                                          NVIDIA GeForce RTX 4070
 build.info:                                        available
 build.cuda_version:                                1108
 build.python_version:                              3.10.11
-build.torch_version:                               2.1.0+cu121
+build.torch_version:                               2.0.1+cu118
 build.env.TORCH_CUDA_ARCH_LIST:                    5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
 build.env.XFORMERS_BUILD_TYPE:                     Release
 build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS:        None
@@ -92,22 +92,33 @@ installed from source. These instructions were written for a system
 running Ubuntu 22.04, but other Linux distributions should be able to
 adapt this recipe.
 
-#### 1. Install CUDA Toolkit 12.1
+#### 1. Install CUDA Toolkit 11.8
 
 You will need the CUDA developer's toolkit in order to compile and
 install xFormers. **Do not try to install Ubuntu's nvidia-cuda-toolkit
 package.** It is out of date and will cause conflicts among the NVIDIA
 driver and binaries. Instead install the CUDA Toolkit package provided
-by NVIDIA itself. Go to [CUDA Toolkit 12.1
-Downloads](https://developer.nvidia.com/cuda-12-1-0-download-archive)
+by NVIDIA itself. Go to [CUDA Toolkit 11.8
+Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive)
 and use the target selection wizard to choose your platform and Linux
 distribution. Select an installer type of "runfile (local)" at the
 last step.
 
 This will provide you with a recipe for downloading and running a
-install shell script that will install the toolkit and drivers.
+install shell script that will install the toolkit and drivers. For
+example, the install script recipe for Ubuntu 22.04 running on a
+x86_64 system is:
 
-#### 2. Confirm/Install pyTorch 2.1.0 with CUDA 12.1 support
+```
+wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
+sudo sh cuda_11.8.0_520.61.05_linux.run
+```
+
+Rather than cut-and-paste this example, We recommend that you walk
+through the toolkit wizard in order to get the most up to date
+installer for your system.
+
+#### 2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support
 
 If you are using InvokeAI 3.0.2 or higher, these will already be
 installed. If not, you can check whether you have the needed libraries
@@ -122,7 +133,7 @@ Then run the command:
 python -c 'exec("import torch\nprint(torch.__version__)")'
 ```
 
-If it prints __2.1.0+cu121__ you're good. If not, you can install the
+If it prints __1.13.1+cu118__ you're good. If not, you can install the
 most up to date libraries with this command:
 
 ```sh

docs/nodes/communityNodes.md

@@ -4,11 +4,7 @@ These are nodes that have been developed by the community, for the community. If
 
 If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
 
-To use a node, add the node to the `nodes` folder found in your InvokeAI install location. 
-
-The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future. 
-
-If you'd prefer, you can also just download the whole node folder from the linked repository and add it to the `nodes` folder. 
+To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node. 
 
 To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. 
 
@@ -26,15 +22,12 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Image Picker](#image-picker)
     + [Load Video Frame](#load-video-frame)
     + [Make 3D](#make-3d)
-    + [Match Histogram](#match-histogram)
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
-    + [Remote Image](#remote-image)
     + [Retroize](#retroize)
     + [Size Stepper Nodes](#size-stepper-nodes)
     + [Text font to Image](#text-font-to-image)
     + [Thresholding](#thresholding)
-    + [Unsharp Mask](#unsharp-mask)
     + [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes)
 - [Example Node Template](#example-node-template)
 - [Disclaimer](#disclaimer)
@@ -209,23 +202,6 @@ This includes 15 Nodes:
 <img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
 <img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
 
---------------------------------
-### Match Histogram
-
-**Description:** An InvokeAI node to match a histogram from one image to another.  This is a bit like the `color correct` node in the main InvokeAI but this works in the YCbCr colourspace and can handle images of different sizes. Also does not require a mask input.
-- Option to only transfer luminance channel.
-- Option to save output as grayscale
-
-A good use case for this node is to normalize the colors of an image that has been through the tiled scaling workflow of my XYGrid Nodes. 
-
-See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/match_histogram
-
-**Output Examples** 
-
-<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
-
 --------------------------------
 ### Oobabooga
 
@@ -255,41 +231,22 @@ This node works best with SDXL models, especially as the style can be described
 --------------------------------
 ### Prompt Tools 
 
-**Description:** A set of InvokeAI nodes that add general prompt (string) manipulation tools.  Designed to accompany the `Prompts From File` node and other prompt generation nodes.
-
-1. `Prompt To File` - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
-2. `PTFields Collect` - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
-3. `PTFields Expand` - Takes Json string and converts it to individual generation parameters. This can be fed from the Prompts to file node.
-4. `Prompt Strength` - Formats prompt with strength like the weighted format of compel 
-5. `Prompt Strength Combine` - Combines weighted prompts for .and()/.blend()
-6. `CSV To Index String` - Gets a string from a CSV by index. Includes a Random index option
-
-The following Nodes are now included in v3.2 of Invoke and are nolonger in this set of tools.<br>
-- `Prompt Join` -> `String Join`
-- `Prompt Join Three` -> `String Join Three`
-- `Prompt Replace` -> `String Replace`
-- `Prompt Split Neg` -> `String Split Neg`
+**Description:** A set of InvokeAI nodes that add general prompt manipulation tools.  These were written to accompany the PromptsFromFile node and other prompt generation nodes.
 
+1. PromptJoin - Joins to prompts into one.
+2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
+3. PromptSplitNeg - splits a prompt into positive and negative using the old V2 method of [] for negative.
+4. PromptToFile - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
+5. PTFieldsCollect - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
+6. PTFieldsExpand - Takes Json string and converts it to individual generation parameters This can be fed from the Prompts to file node.
+7. PromptJoinThree -  Joins 3 prompt together.
+8. PromptStrength - This take a string and float and outputs another string in the format of (string)strength like the weighted format of compel. 
+9. PromptStrengthCombine - This takes a collection of prompt strength strings and outputs a string in the .and() or .blend() format that can be fed into a proper prompt node.
 
 See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
 
 **Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
 
-**Workflow Examples** 
-
-<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
-
---------------------------------
-### Remote Image
-
-**Description:** This is a pack of nodes to interoperate with other services, be they public websites or bespoke local servers. The pack consists of these nodes:
-
-- *Load Remote Image* - Lets you load remote images such as a realtime webcam image, an image of the day, or dynamically created images.
-- *Post Image to Remote Server* - Lets you upload an image to a remote server using an HTTP POST request, eg for storage, display or further processing.
-
-**Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
-
-
 --------------------------------
 ### Retroize
 
@@ -355,45 +312,26 @@ Highlights/Midtones/Shadows (with LUT blur enabled):
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
 
---------------------------------
-### Unsharp Mask
-
-**Description:** Applies an unsharp mask filter to an image, preserving its alpha channel in the process.
-
-**Node Link:** https://github.com/JPPhoto/unsharp-mask-node
-
 --------------------------------
 ### XY Image to Grid and Images to Grids nodes
 
-**Description:** These nodes add the following to InvokeAI:
-- Generate grids of images from multiple input images
-- Create XY grid images with labels from parameters
-- Split images into overlapping tiles for processing (for super-resolution workflows)
-- Recombine image tiles into a single output image blending the seams 
+**Description:** Image to grid nodes and supporting tools.
 
-The nodes include:
-1. `Images To Grids` - Combine multiple images into a grid of images
-2. `XYImage To Grid` - Take X & Y params and creates a labeled image grid.
-3. `XYImage Tiles` - Super-resolution (embiggen) style tiled resizing
-4. `Image Tot XYImages` - Takes an image and cuts it up into a number of columns and rows.
-5. Multiple supporting nodes - Helper nodes for data wrangling and building `XYImage` collections
+1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images.
+2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details.
 
 See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
 
 **Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
 
-**Output Examples** 
-
-<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
-
 --------------------------------
 ### Example Node Template
 
 **Description:** This node allows you to do super cool things with InvokeAI.
 
-**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
+**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
 
-**Example Workflow:**  https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json
+**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
 
 **Output Examples** 
 

docs/nodes/contributingNodes.md

@@ -4,7 +4,7 @@ To learn about the specifics of creating a new node, please visit our [Node crea
 
 Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: 
 
-- Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily. 
+- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
 - Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
     - Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
 - A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.

docs/nodes/defaultNodes.md

@@ -1,106 +1,104 @@
 # List of Default Nodes
 
-The table below contains a list of the default nodes shipped with InvokeAI and
-their descriptions.
+The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
 
-| Node <img width=160 align="right">                            | Function                                                                                                                                             |
-| :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Add Integers                                                  | Adds two numbers                                                                                                                                     |
-| Boolean Primitive Collection                                  | A collection of boolean primitive values                                                                                                             |
-| Boolean Primitive                                             | A boolean primitive value                                                                                                                            |
-| Canny Processor                                               | Canny edge detection for ControlNet                                                                                                                  |
-| CenterPadCrop                                                 | Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image.                                          |
-| CLIP Skip                                                     | Skip layers in clip text_encoder model.                                                                                                              |
-| Collect                                                       | Collects values into a collection                                                                                                                    |
-| Color Correct                                                 | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image. |
-| Color Primitive                                               | A color primitive value                                                                                                                              |
-| Compel Prompt                                                 | Parse prompt using compel package to conditioning.                                                                                                   |
-| Conditioning Primitive Collection                             | A collection of conditioning tensor primitive values                                                                                                 |
-| Conditioning Primitive                                        | A conditioning tensor primitive value                                                                                                                |
-| Content Shuffle Processor                                     | Applies content shuffle processing to image                                                                                                          |
-| ControlNet                                                    | Collects ControlNet info to pass to other nodes                                                                                                      |
-| Denoise Latents                                               | Denoises noisy latents to decodable images                                                                                                           |
-| Divide Integers                                               | Divides two numbers                                                                                                                                  |
-| Dynamic Prompt                                                | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator                                                                      |
-| [FaceMask](./detailedNodes/faceTools.md#facemask)             | Generates masks for faces in an image to use with Inpainting                                                                                         |
-| [FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image                                                                                                              |
-| [FaceOff](./detailedNodes/faceTools.md#faceoff)               | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting                                                             |
-| Float Math                                                    | Perform basic math operations on two floats                                                                                                          |
-| Float Primitive Collection                                    | A collection of float primitive values                                                                                                               |
-| Float Primitive                                               | A float primitive value                                                                                                                              |
-| Float Range                                                   | Creates a range                                                                                                                                      |
-| HED (softedge) Processor                                      | Applies HED edge detection to image                                                                                                                  |
-| Blur Image                                                    | Blurs an image                                                                                                                                       |
-| Extract Image Channel                                         | Gets a channel from an image.                                                                                                                        |
-| Image Primitive Collection                                    | A collection of image primitive values                                                                                                               |
-| Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
-| Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
-| Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
-| Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
-| Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
-| Image Primitive                                               | An image primitive value                                                                                                                             |
-| Lerp Image                                                    | Linear interpolation of all pixels of an image                                                                                                       |
-| Offset Image Channel                                          | Add to or subtract from an image color channel by a uniform value.                                                                                   |
-| Multiply Image Channel                                        | Multiply or Invert an image color channel by a scalar value.                                                                                         |
-| Multiply Images                                               | Multiplies two images together using `PIL.ImageChops.multiply()`.                                                                                    |
-| Blur NSFW Image                                               | Add blur to NSFW-flagged images                                                                                                                      |
-| Paste Image                                                   | Pastes an image into another image.                                                                                                                  |
-| ImageProcessor                                                | Base class for invocations that preprocess images for ControlNet                                                                                     |
-| Resize Image                                                  | Resizes an image to specific dimensions                                                                                                              |
-| Round Float                                                   | Rounds a float to a specified number of decimal places                                                                                               |
-| Float to Integer                                              | Converts a float to an integer. Optionally rounds to an even multiple of a input number.                                                             |
-| Scale Image                                                   | Scales an image by a factor                                                                                                                          |
-| Image to Latents                                              | Encodes an image into latents.                                                                                                                       |
-| Add Invisible Watermark                                       | Add an invisible watermark to an image                                                                                                               |
-| Solid Color Infill                                            | Infills transparent areas of an image with a solid color                                                                                             |
-| PatchMatch Infill                                             | Infills transparent areas of an image using the PatchMatch algorithm                                                                                 |
-| Tile Infill                                                   | Infills transparent areas of an image with tiles of the image                                                                                        |
-| Integer Primitive Collection                                  | A collection of integer primitive values                                                                                                             |
-| Integer Primitive                                             | An integer primitive value                                                                                                                           |
-| Iterate                                                       | Iterates over a list of items                                                                                                                        |
-| Latents Primitive Collection                                  | A collection of latents tensor primitive values                                                                                                      |
-| Latents Primitive                                             | A latents tensor primitive value                                                                                                                     |
-| Latents to Image                                              | Generates an image from latents.                                                                                                                     |
-| Leres (Depth) Processor                                       | Applies leres processing to image                                                                                                                    |
-| Lineart Anime Processor                                       | Applies line art anime processing to image                                                                                                           |
-| Lineart Processor                                             | Applies line art processing to image                                                                                                                 |
-| LoRA Loader                                                   | Apply selected lora to unet and text_encoder.                                                                                                        |
-| Main Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
-| Combine Mask                                                  | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.                                                                    |
-| Mask Edge                                                     | Applies an edge mask to an image                                                                                                                     |
-| Mask from Alpha                                               | Extracts the alpha channel of an image as a mask.                                                                                                    |
-| Mediapipe Face Processor                                      | Applies mediapipe face processing to image                                                                                                           |
-| Midas (Depth) Processor                                       | Applies Midas depth processing to image                                                                                                              |
-| MLSD Processor                                                | Applies MLSD processing to image                                                                                                                     |
-| Multiply Integers                                             | Multiplies two numbers                                                                                                                               |
-| Noise                                                         | Generates latent noise.                                                                                                                              |
-| Normal BAE Processor                                          | Applies NormalBae processing to image                                                                                                                |
-| ONNX Latents to Image                                         | Generates an image from latents.                                                                                                                     |
-| ONNX Prompt (Raw)                                             | A node to process inputs and produce outputs. May use dependency injection in **init** to receive providers.                                         |
-| ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
-| ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
-| OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
-| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
-| PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
-| Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
-| Random Integer                                                | Outputs a single random integer.                                                                                                                     |
-| Random Range                                                  | Creates a collection of random numbers                                                                                                               |
-| Integer Range                                                 | Creates a range of numbers from start to stop with step                                                                                              |
-| Integer Range of Size                                         | Creates a range from start to start + size with step                                                                                                 |
-| Resize Latents                                                | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.                                                    |
-| SDXL Compel Prompt                                            | Parse prompt using compel package to conditioning.                                                                                                   |
-| SDXL LoRA Loader                                              | Apply selected lora to unet and text_encoder.                                                                                                        |
-| SDXL Main Model Loader                                        | Loads an sdxl base model, outputting its submodels.                                                                                                  |
-| SDXL Refiner Compel Prompt                                    | Parse prompt using compel package to conditioning.                                                                                                   |
-| SDXL Refiner Model Loader                                     | Loads an sdxl refiner model, outputting its submodels.                                                                                               |
-| Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
-| Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
-| Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
-| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
-| String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
-| String Primitive                                              | A string primitive value                                                                                                                             |
-| Subtract Integers                                             | Subtracts two numbers                                                                                                                                |
-| Tile Resample Processor                                       | Tile resampler processor                                                                                                                             |
-| Upscale (RealESRGAN)                                          | Upscales an image using RealESRGAN.                                                                                                                  |
-| VAE Loader                                                    | Loads a VAE model, outputting a VaeLoaderOutput                                                                                                      |
-| Zoe (Depth) Processor                                         | Applies Zoe depth processing to image                                                                                                                |
+| Node <img width=160 align="right"> | Function                                                                              |
+|: ---------------------------------- | :--------------------------------------------------------------------------------------|
+|Add Integers 			| Adds two numbers|
+|Boolean Primitive Collection 			| A collection of boolean primitive values|
+|Boolean Primitive 			| A boolean primitive value|
+|Canny Processor 			| Canny edge detection for ControlNet|
+|CLIP Skip 			| Skip layers in clip text_encoder model.|
+|Collect 			| Collects values into a collection|
+|Color Correct 			| Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image.|
+|Color Primitive 			| A color primitive value|
+|Compel Prompt 			| Parse prompt using compel package to conditioning.|
+|Conditioning Primitive Collection 			| A collection of conditioning tensor primitive values|
+|Conditioning Primitive 			| A conditioning tensor primitive value|
+|Content Shuffle Processor 			| Applies content shuffle processing to image|
+|ControlNet 			| Collects ControlNet info to pass to other nodes|
+|Denoise Latents 			| Denoises noisy latents to decodable images|
+|Divide Integers 			| Divides two numbers|
+|Dynamic Prompt 			| Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
+|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
+|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier)             | Identifies and labels faces in an image|
+|[FaceOff](./detailedNodes/faceTools.md#faceoff)             | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
+|Float Math             | Perform basic math operations on two floats|
+|Float Primitive Collection 			| A collection of float primitive values|
+|Float Primitive 			| A float primitive value|
+|Float Range 			| Creates a range|
+|HED (softedge) Processor 			| Applies HED edge detection to image|
+|Blur Image 			| Blurs an image|
+|Extract Image Channel 			| Gets a channel from an image.|
+|Image Primitive Collection 			| A collection of image primitive values|
+|Integer Math           | Perform basic math operations on two integers|
+|Convert Image Mode 			| Converts an image to a different mode.|
+|Crop Image 			| Crops an image to a specified box. The box can be outside of the image.|
+|Image Hue Adjustment 			| Adjusts the Hue of an image.|
+|Inverse Lerp Image 			| Inverse linear interpolation of all pixels of an image|
+|Image Primitive 			| An image primitive value|
+|Lerp Image 			| Linear interpolation of all pixels of an image|
+|Offset Image Channel 			| Add to or subtract from an image color channel by a uniform value.|
+|Multiply Image Channel 			| Multiply or Invert an image color channel by a scalar value.|
+|Multiply Images 			| Multiplies two images together using `PIL.ImageChops.multiply()`.|
+|Blur NSFW Image 			| Add blur to NSFW-flagged images|
+|Paste Image 			| Pastes an image into another image.|
+|ImageProcessor 			| Base class for invocations that preprocess images for ControlNet|
+|Resize Image 			| Resizes an image to specific dimensions|
+|Round Float            | Rounds a float to a specified number of decimal places|
+|Float to Integer       | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
+|Scale Image 			| Scales an image by a factor|
+|Image to Latents 			| Encodes an image into latents.|
+|Add Invisible Watermark 			| Add an invisible watermark to an image|
+|Solid Color Infill 			| Infills transparent areas of an image with a solid color|
+|PatchMatch Infill 			| Infills transparent areas of an image using the PatchMatch algorithm|
+|Tile Infill 			| Infills transparent areas of an image with tiles of the image|
+|Integer Primitive Collection 			| A collection of integer primitive values|
+|Integer Primitive 			| An integer primitive value|
+|Iterate 			| Iterates over a list of items|
+|Latents Primitive Collection 			| A collection of latents tensor primitive values|
+|Latents Primitive 			| A latents tensor primitive value|
+|Latents to Image 			| Generates an image from latents.|
+|Leres (Depth) Processor 			| Applies leres processing to image|
+|Lineart Anime Processor 			| Applies line art anime processing to image|
+|Lineart Processor 			| Applies line art processing to image|
+|LoRA Loader 			| Apply selected lora to unet and text_encoder.|
+|Main Model Loader 			| Loads a main model, outputting its submodels.|
+|Combine Mask 			| Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.|
+|Mask Edge 			| Applies an edge mask to an image|
+|Mask from Alpha 			| Extracts the alpha channel of an image as a mask.|
+|Mediapipe Face Processor 			| Applies mediapipe face processing to image|
+|Midas (Depth) Processor 			| Applies Midas depth processing to image|
+|MLSD Processor 			| Applies MLSD processing to image|
+|Multiply Integers 			| Multiplies two numbers|
+|Noise 			| Generates latent noise.|
+|Normal BAE Processor 			| Applies NormalBae processing to image|
+|ONNX Latents to Image 			| Generates an image from latents.|
+|ONNX Prompt (Raw) 			| A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
+|ONNX Text to Latents 			| Generates latents from conditionings.|
+|ONNX Model Loader 			| Loads a main model, outputting its submodels.|
+|OpenCV Inpaint 			| Simple inpaint using opencv.|
+|Openpose Processor 			| Applies Openpose processing to image|
+|PIDI Processor 			| Applies PIDI processing to image|
+|Prompts from File 			| Loads prompts from a text file|
+|Random Integer 			| Outputs a single random integer.|
+|Random Range 			| Creates a collection of random numbers|
+|Integer Range 			| Creates a range of numbers from start to stop with step|
+|Integer Range of Size 			| Creates a range from start to start + size with step|
+|Resize Latents 			| Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.|
+|SDXL Compel Prompt 			| Parse prompt using compel package to conditioning.|
+|SDXL LoRA Loader 			| Apply selected lora to unet and text_encoder.|
+|SDXL Main Model Loader 			| Loads an sdxl base model, outputting its submodels.|
+|SDXL Refiner Compel Prompt 			| Parse prompt using compel package to conditioning.|
+|SDXL Refiner Model Loader 			| Loads an sdxl refiner model, outputting its submodels.|
+|Scale Latents 			| Scales latents by a given factor.|
+|Segment Anything Processor 			| Applies segment anything processing to image|
+|Show Image 			| Displays a provided image, and passes it forward in the pipeline.|
+|Step Param Easing 			| Experimental per-step parameter easing for denoising steps|
+|String Primitive Collection 			| A collection of string primitive values|
+|String Primitive 			| A string primitive value|
+|Subtract Integers 			| Subtracts two numbers|
+|Tile Resample Processor 			| Tile resampler processor|
+|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
+|VAE Loader 			| Loads a VAE model, outputting a VaeLoaderOutput|
+|Zoe (Depth) Processor 			| Applies Zoe depth processing to image|

docs/nodes/exampleWorkflows.md

@@ -2,17 +2,13 @@
 
 We've curated some example workflows for you to get started with Workflows in InvokeAI
 
-To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
+To use them, right click on your desired workflow, press "Download Linked File". You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
 
 If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
 
 * [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
 * [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
-* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
-* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
-* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
-* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Prompt_from_File.json)
-* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json)
+* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json) 
+* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)
 * [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
 * [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
-* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/QR_Code_Monster.json)

docs/workflows/Multi_ControlNet_Canny_and_Depth.json

@@ -1,985 +0,0 @@
-{
-  "name": "Multi ControlNet (Canny & Depth)",
-  "author": "Millu",
-  "description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
-  "version": "0.1.0",
-  "contact": "millun@invoke.ai",
-  "tags": "ControlNet, canny, depth",
-  "notes": "",
-  "exposedFields": [
-    {
-      "nodeId": "54486974-835b-4d81-8f82-05f9f32ce9e9",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "7ce68934-3419-42d4-ac70-82cfc9397306",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "c4b23e64-7986-40c4-9cad-46327b12e204",
-      "fieldName": "image"
-    },
-    {
-      "nodeId": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
-      "fieldName": "image"
-    }
-  ],
-  "meta": {
-    "version": "1.0.0"
-  },
-  "nodes": [
-    {
-      "id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
-      "type": "invocation",
-      "data": {
-        "id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
-        "type": "image",
-        "inputs": {
-          "image": {
-            "id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": "Depth Input Image"
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 225,
-      "position": {
-        "x": 3617.163483500202,
-        "y": 40.5529847930888
-      }
-    },
-    {
-      "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
-      "type": "invocation",
-      "data": {
-        "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
-        "type": "controlnet",
-        "inputs": {
-          "image": {
-            "id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "control_model": {
-            "id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
-            "name": "control_model",
-            "type": "ControlNetModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "sd-controlnet-depth",
-              "base_model": "sd-1"
-            }
-          },
-          "control_weight": {
-            "id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
-            "name": "control_weight",
-            "type": "FloatPolymorphic",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "begin_step_percent": {
-            "id": "c258a276-352a-416c-8358-152f11005c0c",
-            "name": "begin_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 0
-          },
-          "end_step_percent": {
-            "id": "43001125-0d70-4f87-8e79-da6603ad6c33",
-            "name": "end_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "control_mode": {
-            "id": "d2f14561-9443-4374-9270-e2f05007944e",
-            "name": "control_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "balanced"
-          },
-          "resize_mode": {
-            "id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
-            "name": "resize_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "just_resize"
-          }
-        },
-        "outputs": {
-          "control": {
-            "id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
-            "name": "control",
-            "type": "ControlField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 508,
-      "position": {
-        "x": 4477.604342844504,
-        "y": -49.39005411272677
-      }
-    },
-    {
-      "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
-      "type": "invocation",
-      "data": {
-        "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
-        "type": "compel",
-        "inputs": {
-          "prompt": {
-            "id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15",
-            "name": "prompt",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "Negative Prompt",
-            "value": ""
-          },
-          "clip": {
-            "id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "conditioning": {
-            "id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 261,
-      "position": {
-        "x": 4444.706437017514,
-        "y": -924.0715320874991
-      }
-    },
-    {
-      "id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
-      "type": "invocation",
-      "data": {
-        "id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
-        "type": "main_model_loader",
-        "inputs": {
-          "model": {
-            "id": "f4a915a5-593e-4b6d-9198-c78eb5cefaed",
-            "name": "model",
-            "type": "MainModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "stable-diffusion-v1-5",
-              "base_model": "sd-1",
-              "model_type": "main"
-            }
-          }
-        },
-        "outputs": {
-          "unet": {
-            "id": "ee24fb16-da38-4c66-9fbc-e8f296ed40d2",
-            "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "output"
-          },
-          "clip": {
-            "id": "f3fb0524-8803-41c1-86db-a61a13ee6a33",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "output"
-          },
-          "vae": {
-            "id": "5c4878a8-b40f-44ab-b146-1c1f42c860b3",
-            "name": "vae",
-            "type": "VaeField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 226,
-      "position": {
-        "x": 3837.096149678291,
-        "y": -1050.015351148365
-      }
-    },
-    {
-      "id": "7ce68934-3419-42d4-ac70-82cfc9397306",
-      "type": "invocation",
-      "data": {
-        "id": "7ce68934-3419-42d4-ac70-82cfc9397306",
-        "type": "compel",
-        "inputs": {
-          "prompt": {
-            "id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15",
-            "name": "prompt",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "Positive Prompt",
-            "value": ""
-          },
-          "clip": {
-            "id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "conditioning": {
-            "id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 261,
-      "position": {
-        "x": 4449.356038911986,
-        "y": -1201.659695420063
-      }
-    },
-    {
-      "id": "d204d184-f209-4fae-a0a1-d152800844e1",
-      "type": "invocation",
-      "data": {
-        "id": "d204d184-f209-4fae-a0a1-d152800844e1",
-        "type": "controlnet",
-        "inputs": {
-          "image": {
-            "id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "control_model": {
-            "id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
-            "name": "control_model",
-            "type": "ControlNetModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "sd-controlnet-canny",
-              "base_model": "sd-1"
-            }
-          },
-          "control_weight": {
-            "id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
-            "name": "control_weight",
-            "type": "FloatPolymorphic",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "begin_step_percent": {
-            "id": "c258a276-352a-416c-8358-152f11005c0c",
-            "name": "begin_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 0
-          },
-          "end_step_percent": {
-            "id": "43001125-0d70-4f87-8e79-da6603ad6c33",
-            "name": "end_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "control_mode": {
-            "id": "d2f14561-9443-4374-9270-e2f05007944e",
-            "name": "control_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "balanced"
-          },
-          "resize_mode": {
-            "id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
-            "name": "resize_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "just_resize"
-          }
-        },
-        "outputs": {
-          "control": {
-            "id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
-            "name": "control",
-            "type": "ControlField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 508,
-      "position": {
-        "x": 4479.68542130465,
-        "y": -618.4221638099414
-      }
-    },
-    {
-      "id": "c4b23e64-7986-40c4-9cad-46327b12e204",
-      "type": "invocation",
-      "data": {
-        "id": "c4b23e64-7986-40c4-9cad-46327b12e204",
-        "type": "image",
-        "inputs": {
-          "image": {
-            "id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": "Canny Input Image"
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 225,
-      "position": {
-        "x": 3593.7474460420153,
-        "y": -538.1200472386865
-      }
-    },
-    {
-      "id": "ca4d5059-8bfb-447f-b415-da0faba5a143",
-      "type": "invocation",
-      "data": {
-        "id": "ca4d5059-8bfb-447f-b415-da0faba5a143",
-        "type": "collect",
-        "inputs": {
-          "item": {
-            "id": "b16ae602-8708-4b1b-8d4f-9e0808d429ab",
-            "name": "item",
-            "type": "CollectionItem",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "collection": {
-            "id": "d8987dd8-dec8-4d94-816a-3e356af29884",
-            "name": "collection",
-            "type": "Collection",
-            "fieldKind": "output"
-          }
-        },
-        "label": "ControlNet Collection",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 104,
-      "position": {
-        "x": 4866.191497139488,
-        "y": -299.0538619537037
-      }
-    },
-    {
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docs/workflows/Prompt_from_File.json

@@ -1,719 +0,0 @@
-{
-  "name": "Prompt from File",
-  "author": "InvokeAI",
-  "description": "Sample workflow using prompt from file capabilities of InvokeAI ",
-  "version": "0.1.0",
-  "contact": "millun@invoke.ai",
-  "tags": "text2image, prompt from file, default",
-  "notes": "",
-  "exposedFields": [
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-      "nodeId": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-      "fieldName": "file_path"
-    }
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-  "meta": {
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-  },
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-      "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-      "type": "invocation",
-      "data": {
-        "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-        "type": "compel",
-        "inputs": {
-          "prompt": {
-            "id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62",
-            "name": "prompt",
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-            "fieldKind": "input",
-            "label": "",
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-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
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-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
-          }
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-        "label": "",
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-        "embedWorkflow": false,
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-        "id": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-        "type": "prompt_from_file",
-        "inputs": {
-          "file_path": {
-            "id": "37e37684-4f30-4ec8-beae-b333e550f904",
-            "name": "file_path",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "Prompts File Path",
-            "value": ""
-          },
-          "pre_prompt": {
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-            "name": "pre_prompt",
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docs/workflows/QR_Code_Monster.json

@@ -1,758 +0,0 @@
-{
-  "name": "QR Code Monster",
-  "author": "InvokeAI",
-  "description": "Sample workflow for create images with QR code Monster ControlNet",
-  "version": "1.0.1",
-  "contact": "invoke@invoke.ai",
-  "tags": "qrcode, controlnet, default",
-  "notes": "",
-  "exposedFields": [
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-      "fieldName": "prompt"
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-          "clip": {
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-            "name": "clip",
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-      "sourceHandle": "value",
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docs/workflows/SDXL_Text_to_Image.json

@@ -26,6 +26,10 @@
     {
       "nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
       "fieldName": "style"
+    },
+    {
+      "nodeId": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "fieldName": "steps"
     }
   ],
   "meta": {
@@ -36,6 +40,7 @@
       "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
         "type": "sdxl_compel_prompt",
         "inputs": {
@@ -130,12 +135,10 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 793,
+      "height": 764,
       "position": {
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         "y": -350
@@ -145,6 +148,7 @@
       "id": "55705012-79b9-4aac-9f26-c0b10309785b",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "55705012-79b9-4aac-9f26-c0b10309785b",
         "type": "noise",
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@@ -205,9 +209,7 @@
         "isOpen": false,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
       "height": 32,
@@ -216,10 +218,83 @@
         "y": -300
       }
     },
+    {
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+      "type": "invocation",
+      "data": {
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+        "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+        "type": "l2i",
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+            "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
+            "name": "tiled",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "fp32": {
+            "id": "b146d873-ffb9-4767-986a-5360504841a2",
+            "name": "fp32",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": true
+          },
+          "latents": {
+            "id": "65441abd-7713-4b00-9d8d-3771404002e8",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "vae": {
+            "id": "a478b833-6e13-4611-9a10-842c89603c74",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "c87ae925-f858-417a-8940-8708ba9b4b53",
+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "23e41c00-a354-48e8-8f59-5875679c27ab",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": true,
+        "isIntermediate": false
+      },
+      "width": 320,
+      "height": 224,
+      "position": {
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+        "y": -250
+      }
+    },
     {
       "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
         "type": "rand_int",
         "inputs": {
@@ -252,9 +327,7 @@
         "isOpen": false,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": false,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
       "height": 32,
@@ -267,6 +340,7 @@
       "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
         "type": "sdxl_model_loader",
         "inputs": {
@@ -277,7 +351,7 @@
             "fieldKind": "input",
             "label": "",
             "value": {
-              "model_name": "stable-diffusion-xl-base-1-0",
+              "model_name": "stable-diffusion-xl-base-1.0",
               "base_model": "sdxl",
               "model_type": "main"
             }
@@ -313,12 +387,10 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 258,
+      "height": 234,
       "position": {
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         "y": 25
@@ -328,6 +400,7 @@
       "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
         "type": "sdxl_compel_prompt",
         "inputs": {
@@ -422,143 +495,48 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
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+      "height": 764,
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         "y": -350
       }
     },
     {
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       "data": {
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-            "value": false
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-          "fp32": {
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-            "name": "fp32",
-            "type": "boolean",
-            "fieldKind": "input",
-            "label": "",
-            "value": false
-          }
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-        "outputs": {
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-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
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-      "type": "invocation",
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+        "version": "1.0.0",
+        "id": "87ee6243-fb0d-4f77-ad5f-56591659339e",
         "type": "denoise_latents",
         "inputs": {
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-          },
           "noise": {
-            "id": "4ba06bb7-eb45-4fb9-9984-31001b545587",
+            "id": "4884a4b7-cc19-4fea-83c7-1f940e6edd24",
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             "fieldKind": "input",
             "label": ""
           },
           "steps": {
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             "name": "steps",
             "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "value": 10
+            "value": 36
           },
           "cfg_scale": {
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+            "id": "f8213f35-4637-4a1a-83f4-1f8cfb9ccd2c",
             "name": "cfg_scale",
-            "type": "FloatPolymorphic",
+            "type": "float",
             "fieldKind": "input",
             "label": "",
             "value": 7.5
           },
           "denoising_start": {
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             "type": "float",
             "fieldKind": "input",
@@ -566,7 +544,7 @@
             "value": 0
           },
           "denoising_end": {
-            "id": "3adfb7ae-c9f7-4a40-b6e0-4c2050bd1a99",
+            "id": "3db95479-a73b-4c75-9b44-08daec16b224",
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@@ -574,71 +552,71 @@
             "value": 1
           },
           "scheduler": {
-            "id": "14423e0d-7215-4ee0-b065-f9e95eaa8d7d",
+            "id": "db8430a9-64c3-4c54-ae38-9f597cf7b6d5",
             "name": "scheduler",
             "type": "Scheduler",
             "fieldKind": "input",
             "label": "",
             "value": "euler"
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-            "label": ""
-          },
           "control": {
-            "id": "dab351b3-0c86-4ea5-9782-4e8edbfb0607",
+            "id": "599b49e8-6435-4576-be41-a5155f3a17e3",
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-          "ip_adapter": {
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-          "t2i_adapter": {
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-            "name": "t2i_adapter",
-            "type": "T2IAdapterPolymorphic",
+            "type": "ControlField",
             "fieldKind": "input",
             "label": ""
           },
           "latents": {
-            "id": "3a922c6a-3d8c-4c9e-b3ec-2f4d81cda077",
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             "fieldKind": "input",
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           },
           "denoise_mask": {
-            "id": "cd7ce032-835f-495f-8b45-d57272f33132",
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             "name": "denoise_mask",
             "type": "DenoiseMaskField",
             "fieldKind": "input",
             "label": ""
+          },
+          "positive_conditioning": {
+            "id": "02fc400a-110d-470e-8411-f404f966a949",
+            "name": "positive_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "negative_conditioning": {
+            "id": "4bd3bdfa-fcf4-42be-8e47-1e314255798f",
+            "name": "negative_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
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+          },
+          "unet": {
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+            "name": "unet",
+            "type": "UNetField",
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         "outputs": {
           "latents": {
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+            "id": "6a6fa492-de26-4e95-b1d9-a322fe37eb13",
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@@ -648,15 +626,13 @@
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         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.4.0"
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+      "height": 558,
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+        "x": 1650,
+        "y": -250
       }
     }
   ],
@@ -710,42 +686,50 @@
     {
       "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
       "sourceHandle": "vae",
-      "target": "63e91020-83b2-4f35-b174-ad9692aabb48",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
       "targetHandle": "vae",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-63e91020-83b2-4f35-b174-ad9692aabb48vae",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
       "type": "default"
     },
     {
-      "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
-      "sourceHandle": "unet",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
-      "targetHandle": "unet",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbunet",
+      "source": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "sourceHandle": "latents",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-87ee6243-fb0d-4f77-ad5f-56591659339elatents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
       "type": "default"
     },
     {
       "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
       "sourceHandle": "conditioning",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
       "targetHandle": "positive_conditioning",
-      "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbpositive_conditioning",
+      "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-87ee6243-fb0d-4f77-ad5f-56591659339epositive_conditioning",
       "type": "default"
     },
     {
       "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
       "sourceHandle": "conditioning",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
       "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnegative_conditioning",
+      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-87ee6243-fb0d-4f77-ad5f-56591659339enegative_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
+      "sourceHandle": "unet",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-87ee6243-fb0d-4f77-ad5f-56591659339eunet",
       "type": "default"
     },
     {
       "source": "55705012-79b9-4aac-9f26-c0b10309785b",
       "sourceHandle": "noise",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
       "targetHandle": "noise",
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnoise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-87ee6243-fb0d-4f77-ad5f-56591659339enoise",
       "type": "default"
     }
   ]
-}
+}

docs/workflows/Text_to_Image.json

@@ -18,6 +18,10 @@
     {
       "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "fieldName": "prompt"
+    },
+    {
+      "nodeId": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "fieldName": "steps"
     }
   ],
   "meta": {
@@ -28,6 +32,7 @@
       "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
         "type": "compel",
         "inputs": {
@@ -59,21 +64,20 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 261,
+      "height": 235,
       "position": {
-        "x": 995.7263915923627,
-        "y": 239.67783573351227
+        "x": 1400,
+        "y": -75
       }
     },
     {
       "id": "55705012-79b9-4aac-9f26-c0b10309785b",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "55705012-79b9-4aac-9f26-c0b10309785b",
         "type": "noise",
         "inputs": {
@@ -134,21 +138,92 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 389,
+      "height": 364,
       "position": {
-        "x": 993.4442117555518,
-        "y": 605.6757415334787
+        "x": 1000,
+        "y": 350
+      }
+    },
+    {
+      "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+        "type": "l2i",
+        "inputs": {
+          "tiled": {
+            "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
+            "name": "tiled",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "fp32": {
+            "id": "b146d873-ffb9-4767-986a-5360504841a2",
+            "name": "fp32",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "latents": {
+            "id": "65441abd-7713-4b00-9d8d-3771404002e8",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "vae": {
+            "id": "a478b833-6e13-4611-9a10-842c89603c74",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "c87ae925-f858-417a-8940-8708ba9b4b53",
+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "23e41c00-a354-48e8-8f59-5875679c27ab",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": true,
+        "isIntermediate": false
+      },
+      "width": 320,
+      "height": 266,
+      "position": {
+        "x": 1800,
+        "y": 200
       }
     },
     {
       "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
         "type": "main_model_loader",
         "inputs": {
@@ -186,24 +261,23 @@
           }
         },
         "label": "",
-        "isOpen": true,
+        "isOpen": false,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 226,
+      "height": 32,
       "position": {
-        "x": 163.04436745878343,
-        "y": 254.63156870373479
+        "x": 1000,
+        "y": 200
       }
     },
     {
       "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
         "type": "compel",
         "inputs": {
@@ -235,21 +309,20 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 261,
+      "height": 235,
       "position": {
-        "x": 595.7263915923627,
-        "y": 239.67783573351227
+        "x": 1000,
+        "y": -75
       }
     },
     {
       "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
         "type": "rand_int",
         "inputs": {
@@ -279,66 +352,51 @@
           }
         },
         "label": "Random Seed",
-        "isOpen": true,
+        "isOpen": false,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": false,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 218,
+      "height": 32,
       "position": {
-        "x": 541.094822888628,
-        "y": 694.5704476446829
+        "x": 1000,
+        "y": 275
       }
     },
     {
-      "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "type": "invocation",
       "data": {
-        "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+        "version": "1.0.0",
+        "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
         "type": "denoise_latents",
         "inputs": {
-          "positive_conditioning": {
-            "id": "90b7f4f8-ada7-4028-8100-d2e54f192052",
-            "name": "positive_conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "negative_conditioning": {
-            "id": "9393779e-796c-4f64-b740-902a1177bf53",
-            "name": "negative_conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "input",
-            "label": ""
-          },
           "noise": {
-            "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
+            "id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
             "name": "noise",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "steps": {
-            "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
+            "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
             "name": "steps",
             "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "value": 10
+            "value": 36
           },
           "cfg_scale": {
-            "id": "87dd04d3-870e-49e1-98bf-af003a810109",
+            "id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
             "name": "cfg_scale",
-            "type": "FloatPolymorphic",
+            "type": "float",
             "fieldKind": "input",
             "label": "",
             "value": 7.5
           },
           "denoising_start": {
-            "id": "f369d80f-4931-4740-9bcd-9f0620719fab",
+            "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
             "name": "denoising_start",
             "type": "float",
             "fieldKind": "input",
@@ -346,7 +404,7 @@
             "value": 0
           },
           "denoising_end": {
-            "id": "747d10e5-6f02-445c-994c-0604d814de8c",
+            "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
             "name": "denoising_end",
             "type": "float",
             "fieldKind": "input",
@@ -354,71 +412,71 @@
             "value": 1
           },
           "scheduler": {
-            "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
+            "id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
             "name": "scheduler",
             "type": "Scheduler",
             "fieldKind": "input",
             "label": "",
             "value": "euler"
           },
-          "unet": {
-            "id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
-            "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "input",
-            "label": ""
-          },
           "control": {
-            "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
+            "id": "7abe57cc-469d-437e-ad72-a18efa28215f",
             "name": "control",
-            "type": "ControlPolymorphic",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "ip_adapter": {
-            "id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
-            "name": "ip_adapter",
-            "type": "IPAdapterPolymorphic",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "t2i_adapter": {
-            "id": "75e67b09-952f-4083-aaf4-6b804d690412",
-            "name": "t2i_adapter",
-            "type": "T2IAdapterPolymorphic",
+            "type": "ControlField",
             "fieldKind": "input",
             "label": ""
           },
           "latents": {
-            "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
+            "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "denoise_mask": {
-            "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
+            "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
             "name": "denoise_mask",
             "type": "DenoiseMaskField",
             "fieldKind": "input",
             "label": ""
+          },
+          "positive_conditioning": {
+            "id": "c7160303-8a23-4f15-9197-855d48802a7f",
+            "name": "positive_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "negative_conditioning": {
+            "id": "fd750efa-1dfc-4d0b-accb-828e905ba320",
+            "name": "negative_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "unet": {
+            "id": "af1f41ba-ce2a-4314-8d7f-494bb5800381",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "input",
+            "label": ""
           }
         },
         "outputs": {
           "latents": {
-            "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
+            "id": "8508d04d-f999-4a44-94d0-388ab1401d27",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "output"
           },
           "width": {
-            "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
+            "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
             "name": "width",
             "type": "integer",
             "fieldKind": "output"
           },
           "height": {
-            "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
+            "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
             "name": "height",
             "type": "integer",
             "fieldKind": "output"
@@ -428,95 +486,13 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.4.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 646,
+      "height": 558,
       "position": {
-        "x": 1476.5794704734735,
-        "y": 256.80174342731783
-      }
-    },
-    {
-      "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "invocation",
-      "data": {
-        "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-        "type": "l2i",
-        "inputs": {
-          "metadata": {
-            "id": "ab375f12-0042-4410-9182-29e30db82c85",
-            "name": "metadata",
-            "type": "MetadataField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "latents": {
-            "id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
-            "name": "latents",
-            "type": "LatentsField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "vae": {
-            "id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
-            "name": "vae",
-            "type": "VaeField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "tiled": {
-            "id": "da52059a-0cee-4668-942f-519aa794d739",
-            "name": "tiled",
-            "type": "boolean",
-            "fieldKind": "input",
-            "label": "",
-            "value": false
-          },
-          "fp32": {
-            "id": "c4841df3-b24e-4140-be3b-ccd454c2522c",
-            "name": "fp32",
-            "type": "boolean",
-            "fieldKind": "input",
-            "label": "",
-            "value": false
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "230f359c-b4ea-436c-b372-332d7dcdca85",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": false,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 267,
-      "position": {
-        "x": 2037.9648469717395,
-        "y": 426.10844427600136
+        "x": 1400,
+        "y": 200
       }
     }
   ],
@@ -546,52 +522,52 @@
       "type": "default"
     },
     {
-      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "sourceHandle": "noise",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "targetHandle": "noise",
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "vae",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "vae",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
+      "type": "default"
+    },
+    {
+      "source": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "sourceHandle": "latents",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-75899702-fa44-46d2-b2d5-3e17f234c3e7latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
       "type": "default"
     },
     {
       "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
       "sourceHandle": "conditioning",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "targetHandle": "positive_conditioning",
-      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
       "type": "default"
     },
     {
       "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "sourceHandle": "conditioning",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
       "type": "default"
     },
     {
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "sourceHandle": "unet",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "targetHandle": "unet",
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
       "type": "default"
     },
     {
-      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "sourceHandle": "latents",
-      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "targetHandle": "latents",
-      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
-      "type": "default"
-    },
-    {
-      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
-      "sourceHandle": "vae",
-      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "targetHandle": "vae",
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
+      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "sourceHandle": "noise",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "noise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-75899702-fa44-46d2-b2d5-3e17f234c3e7noise",
       "type": "default"
     }
   ]
-}
+}

installer/lib/installer.py

@@ -244,7 +244,7 @@ class InvokeAiInstance:
                 "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
                 "urllib3~=1.26.0",
                 "requests~=2.28.0",
-                "torch==2.1.0",
+                "torch~=2.0.0",
                 "torchmetrics==0.11.4",
                 "torchvision>=0.14.1",
                 "--force-reinstall",
@@ -460,10 +460,10 @@ def get_torch_source() -> (Union[str, None], str):
             url = "https://download.pytorch.org/whl/cpu"
 
     if device == "cuda":
-        url = "https://download.pytorch.org/whl/cu121"
+        url = "https://download.pytorch.org/whl/cu118"
         optional_modules = "[xformers,onnx-cuda]"
     if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu121"
+        url = "https://download.pytorch.org/whl/cu118"
         optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13

installer/lib/messages.py

@@ -137,7 +137,7 @@ def dest_path(dest=None) -> Path:
         path_completer = PathCompleter(
             only_directories=True,
             expanduser=True,
-            get_paths=lambda: [browse_start],  # noqa: B023
+            get_paths=lambda: [browse_start],
             # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
         )
 
@@ -149,7 +149,7 @@ def dest_path(dest=None) -> Path:
             completer=path_completer,
             default=str(browse_start) + os.sep,
             vi_mode=True,
-            complete_while_typing=True,
+            complete_while_typing=True
             # Test that this is not needed on Windows
             # complete_style=CompleteStyle.READLINE_LIKE,
         )

invokeai/app/api/dependencies.py

@@ -2,6 +2,7 @@
 
 from logging import Logger
 
+from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -23,13 +24,12 @@ from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
 from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
 from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
 from ..services.model_manager.model_manager_default import ModelManagerService
-from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
 from ..services.session_processor.session_processor_default import DefaultSessionProcessor
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
 from ..services.shared.default_graphs import create_system_graphs
 from ..services.shared.graph import GraphExecutionState, LibraryGraph
-from ..services.shared.sqlite.sqlite_database import SqliteDatabase
+from ..services.shared.sqlite import SqliteDatabase
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -85,7 +85,6 @@ class ApiDependencies:
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
         model_manager = ModelManagerService(config, logger)
-        model_record_service = ModelRecordServiceSQL(db=db)
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
         processor = DefaultInvocationProcessor()
@@ -93,6 +92,7 @@ class ApiDependencies:
         session_processor = DefaultSessionProcessor()
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
+        workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
 
         services = InvocationServices(
@@ -111,7 +111,6 @@ class ApiDependencies:
             latents=latents,
             logger=logger,
             model_manager=model_manager,
-            model_records=model_record_service,
             names=names,
             performance_statistics=performance_statistics,
             processor=processor,
@@ -119,12 +118,14 @@ class ApiDependencies:
             session_processor=session_processor,
             session_queue=session_queue,
             urls=urls,
+            workflow_image_records=workflow_image_records,
             workflow_records=workflow_records,
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
+
         db.clean()
 
     @staticmethod

invokeai/app/api/events.py

@@ -28,7 +28,7 @@ class FastAPIEventService(EventServiceBase):
         self.__queue.put(None)
 
     def dispatch(self, event_name: str, payload: Any) -> None:
-        self.__queue.put({"event_name": event_name, "payload": payload})
+        self.__queue.put(dict(event_name=event_name, payload=payload))
 
     async def __dispatch_from_queue(self, stop_event: threading.Event):
         """Get events on from the queue and dispatch them, from the correct thread"""

invokeai/app/api/routers/app_info.py

@@ -1,11 +1,7 @@
 import typing
 from enum import Enum
-from importlib.metadata import PackageNotFoundError, version
 from pathlib import Path
-from platform import python_version
-from typing import Optional
 
-import torch
 from fastapi import Body
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
@@ -44,24 +40,6 @@ class AppVersion(BaseModel):
     version: str = Field(description="App version")
 
 
-class AppDependencyVersions(BaseModel):
-    """App depencency Versions Response"""
-
-    accelerate: str = Field(description="accelerate version")
-    compel: str = Field(description="compel version")
-    cuda: Optional[str] = Field(description="CUDA version")
-    diffusers: str = Field(description="diffusers version")
-    numpy: str = Field(description="Numpy version")
-    opencv: str = Field(description="OpenCV version")
-    onnx: str = Field(description="ONNX version")
-    pillow: str = Field(description="Pillow (PIL) version")
-    python: str = Field(description="Python version")
-    torch: str = Field(description="PyTorch version")
-    torchvision: str = Field(description="PyTorch Vision version")
-    transformers: str = Field(description="transformers version")
-    xformers: Optional[str] = Field(description="xformers version")
-
-
 class AppConfig(BaseModel):
     """App Config Response"""
 
@@ -76,29 +54,6 @@ async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
-@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
-async def get_app_deps() -> AppDependencyVersions:
-    try:
-        xformers = version("xformers")
-    except PackageNotFoundError:
-        xformers = None
-    return AppDependencyVersions(
-        accelerate=version("accelerate"),
-        compel=version("compel"),
-        cuda=torch.version.cuda,
-        diffusers=version("diffusers"),
-        numpy=version("numpy"),
-        opencv=version("opencv-python"),
-        onnx=version("onnx"),
-        pillow=version("pillow"),
-        python=python_version(),
-        torch=torch.version.__version__,
-        torchvision=version("torchvision"),
-        transformers=version("transformers"),
-        xformers=xformers,
-    )
-
-
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
     infill_methods = ["tile", "lama", "cv2"]

invokeai/app/api/routers/images.py

@@ -8,11 +8,10 @@ from fastapi.routing import APIRouter
 from PIL import Image
 from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
+from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID, WorkflowWithoutIDValidator
 
 from ..dependencies import ApiDependencies
 
@@ -74,7 +73,7 @@ async def upload_image(
     workflow_raw = pil_image.info.get("invokeai_workflow", None)
     if workflow_raw is not None:
         try:
-            workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw)
+            workflow = WorkflowFieldValidator.validate_json(workflow_raw)
         except ValidationError:
             ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
             pass
@@ -185,18 +184,6 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
-    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=Optional[WorkflowWithoutID]
-)
-async def get_image_workflow(
-    image_name: str = Path(description="The name of image whose workflow to get"),
-) -> Optional[WorkflowWithoutID]:
-    try:
-        return ApiDependencies.invoker.services.images.get_workflow(image_name)
-    except Exception:
-        raise HTTPException(status_code=404)
-
-
 @images_router.api_route(
     "/i/{image_name}/full",
     methods=["GET", "HEAD"],

invokeai/app/api/routers/model_records.py

@@ -1,164 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein
-"""FastAPI route for model configuration records."""
-
-
-from hashlib import sha1
-from random import randbytes
-from typing import List, Optional
-
-from fastapi import Body, Path, Query, Response
-from fastapi.routing import APIRouter
-from pydantic import BaseModel, ConfigDict
-from starlette.exceptions import HTTPException
-from typing_extensions import Annotated
-
-from invokeai.app.services.model_records import (
-    DuplicateModelException,
-    InvalidModelException,
-    UnknownModelException,
-)
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    ModelType,
-)
-
-from ..dependencies import ApiDependencies
-
-model_records_router = APIRouter(prefix="/v1/model/record", tags=["models"])
-
-
-class ModelsList(BaseModel):
-    """Return list of configs."""
-
-    models: list[AnyModelConfig]
-
-    model_config = ConfigDict(use_enum_values=True)
-
-
-@model_records_router.get(
-    "/",
-    operation_id="list_model_records",
-)
-async def list_model_records(
-    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
-    model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
-) -> ModelsList:
-    """Get a list of models."""
-    record_store = ApiDependencies.invoker.services.model_records
-    found_models: list[AnyModelConfig] = []
-    if base_models:
-        for base_model in base_models:
-            found_models.extend(record_store.search_by_attr(base_model=base_model, model_type=model_type))
-    else:
-        found_models.extend(record_store.search_by_attr(model_type=model_type))
-    return ModelsList(models=found_models)
-
-
-@model_records_router.get(
-    "/i/{key}",
-    operation_id="get_model_record",
-    responses={
-        200: {"description": "Success"},
-        400: {"description": "Bad request"},
-        404: {"description": "The model could not be found"},
-    },
-)
-async def get_model_record(
-    key: str = Path(description="Key of the model record to fetch."),
-) -> AnyModelConfig:
-    """Get a model record"""
-    record_store = ApiDependencies.invoker.services.model_records
-    try:
-        return record_store.get_model(key)
-    except UnknownModelException as e:
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@model_records_router.patch(
-    "/i/{key}",
-    operation_id="update_model_record",
-    responses={
-        200: {"description": "The model was updated successfully"},
-        400: {"description": "Bad request"},
-        404: {"description": "The model could not be found"},
-        409: {"description": "There is already a model corresponding to the new name"},
-    },
-    status_code=200,
-    response_model=AnyModelConfig,
-)
-async def update_model_record(
-    key: Annotated[str, Path(description="Unique key of model")],
-    info: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
-) -> AnyModelConfig:
-    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
-    logger = ApiDependencies.invoker.services.logger
-    record_store = ApiDependencies.invoker.services.model_records
-    try:
-        model_response = record_store.update_model(key, config=info)
-        logger.info(f"Updated model: {key}")
-    except UnknownModelException as e:
-        raise HTTPException(status_code=404, detail=str(e))
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    return model_response
-
-
-@model_records_router.delete(
-    "/i/{key}",
-    operation_id="del_model_record",
-    responses={
-        204: {"description": "Model deleted successfully"},
-        404: {"description": "Model not found"},
-    },
-    status_code=204,
-)
-async def del_model_record(
-    key: str = Path(description="Unique key of model to remove from model registry."),
-) -> Response:
-    """Delete Model"""
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        record_store = ApiDependencies.invoker.services.model_records
-        record_store.del_model(key)
-        logger.info(f"Deleted model: {key}")
-        return Response(status_code=204)
-    except UnknownModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@model_records_router.post(
-    "/i/",
-    operation_id="add_model_record",
-    responses={
-        201: {"description": "The model added successfully"},
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-        415: {"description": "Unrecognized file/folder format"},
-    },
-    status_code=201,
-)
-async def add_model_record(
-    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
-) -> AnyModelConfig:
-    """
-    Add a model using the configuration information appropriate for its type.
-    """
-    logger = ApiDependencies.invoker.services.logger
-    record_store = ApiDependencies.invoker.services.model_records
-    if config.key == "<NOKEY>":
-        config.key = sha1(randbytes(100)).hexdigest()
-        logger.info(f"Created model {config.key} for {config.name}")
-    try:
-        record_store.add_model(config.key, config)
-    except DuplicateModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-
-    # now fetch it out
-    return record_store.get_model(config.key)

invokeai/app/api/routers/models.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein
 
+
 import pathlib
 from typing import Annotated, List, Literal, Optional, Union
 
@@ -54,7 +55,7 @@ async def list_models(
 ) -> ModelsList:
     """Gets a list of models"""
     if base_models and len(base_models) > 0:
-        models_raw = []
+        models_raw = list()
         for base_model in base_models:
             models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
     else:

invokeai/app/api/routers/workflows.py

@@ -1,19 +1,7 @@
-from typing import Optional
-
-from fastapi import APIRouter, Body, HTTPException, Path, Query
+from fastapi import APIRouter, Path
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.workflow_records.workflow_records_common import (
-    Workflow,
-    WorkflowCategory,
-    WorkflowNotFoundError,
-    WorkflowRecordDTO,
-    WorkflowRecordListItemDTO,
-    WorkflowRecordOrderBy,
-    WorkflowWithoutID,
-)
+from invokeai.app.invocations.baseinvocation import WorkflowField
 
 workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
 
@@ -22,76 +10,11 @@ workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
     "/i/{workflow_id}",
     operation_id="get_workflow",
     responses={
-        200: {"model": WorkflowRecordDTO},
+        200: {"model": WorkflowField},
     },
 )
 async def get_workflow(
     workflow_id: str = Path(description="The workflow to get"),
-) -> WorkflowRecordDTO:
+) -> WorkflowField:
     """Gets a workflow"""
-    try:
-        return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
-    except WorkflowNotFoundError:
-        raise HTTPException(status_code=404, detail="Workflow not found")
-
-
-@workflows_router.patch(
-    "/i/{workflow_id}",
-    operation_id="update_workflow",
-    responses={
-        200: {"model": WorkflowRecordDTO},
-    },
-)
-async def update_workflow(
-    workflow: Workflow = Body(description="The updated workflow", embed=True),
-) -> WorkflowRecordDTO:
-    """Updates a workflow"""
-    return ApiDependencies.invoker.services.workflow_records.update(workflow=workflow)
-
-
-@workflows_router.delete(
-    "/i/{workflow_id}",
-    operation_id="delete_workflow",
-)
-async def delete_workflow(
-    workflow_id: str = Path(description="The workflow to delete"),
-) -> None:
-    """Deletes a workflow"""
-    ApiDependencies.invoker.services.workflow_records.delete(workflow_id)
-
-
-@workflows_router.post(
-    "/",
-    operation_id="create_workflow",
-    responses={
-        200: {"model": WorkflowRecordDTO},
-    },
-)
-async def create_workflow(
-    workflow: WorkflowWithoutID = Body(description="The workflow to create", embed=True),
-) -> WorkflowRecordDTO:
-    """Creates a workflow"""
-    return ApiDependencies.invoker.services.workflow_records.create(workflow=workflow)
-
-
-@workflows_router.get(
-    "/",
-    operation_id="list_workflows",
-    responses={
-        200: {"model": PaginatedResults[WorkflowRecordListItemDTO]},
-    },
-)
-async def list_workflows(
-    page: int = Query(default=0, description="The page to get"),
-    per_page: int = Query(default=10, description="The number of workflows per page"),
-    order_by: WorkflowRecordOrderBy = Query(
-        default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
-    ),
-    direction: SQLiteDirection = Query(default=SQLiteDirection.Ascending, description="The direction to order by"),
-    category: WorkflowCategory = Query(default=WorkflowCategory.User, description="The category of workflow to get"),
-    query: Optional[str] = Query(default=None, description="The text to query by (matches name and description)"),
-) -> PaginatedResults[WorkflowRecordListItemDTO]:
-    """Gets a page of workflows"""
-    return ApiDependencies.invoker.services.workflow_records.get_many(
-        page=page, per_page=per_page, order_by=order_by, direction=direction, query=query, category=category
-    )
+    return ApiDependencies.invoker.services.workflow_records.get(workflow_id)

invokeai/app/api/sockets.py

@@ -34,4 +34,4 @@ class SocketIO:
 
     async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
         if "queue_id" in data:
-            await self.__sio.leave_room(sid, data["queue_id"])
+            await self.__sio.enter_room(sid, data["queue_id"])

invokeai/app/api_app.py

@@ -1,17 +1,14 @@
-# parse_args() must be called before any other imports. if it is not called first, consumers of the config
-# which are imported/used before parse_args() is called will get the default config values instead of the
-# values from the command line or config file.
-import sys
+from typing import Any
 
-from invokeai.version.invokeai_version import __version__
+from fastapi.responses import HTMLResponse
 
 from .services.config import InvokeAIAppConfig
 
+# parse_args() must be called before any other imports. if it is not called first, consumers of the config
+# which are imported/used before parse_args() is called will get the default config values instead of the
+# values from the command line or config file.
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
-if app_config.version:
-    print(f"InvokeAI version {__version__}")
-    sys.exit(0)
 
 if True:  # hack to make flake8 happy with imports coming after setting up the config
     import asyncio
@@ -19,7 +16,6 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     import socket
     from inspect import signature
     from pathlib import Path
-    from typing import Any
 
     import uvicorn
     from fastapi import FastAPI
@@ -27,7 +23,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     from fastapi.middleware.gzip import GZipMiddleware
     from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
     from fastapi.openapi.utils import get_openapi
-    from fastapi.responses import FileResponse, HTMLResponse
+    from fastapi.responses import FileResponse
     from fastapi.staticfiles import StaticFiles
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
@@ -38,6 +34,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
     import invokeai.frontend.web as web_dir
+    from invokeai.version.invokeai_version import __version__
 
     from ..backend.util.logging import InvokeAILogger
     from .api.dependencies import ApiDependencies
@@ -46,7 +43,6 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         board_images,
         boards,
         images,
-        model_records,
         models,
         session_queue,
         sessions,
@@ -54,12 +50,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         workflows,
     )
     from .api.sockets import SocketIO
-    from .invocations.baseinvocation import (
-        BaseInvocation,
-        InputFieldJSONSchemaExtra,
-        OutputFieldJSONSchemaExtra,
-        UIConfigBase,
-    )
+    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
 
     if is_mps_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
@@ -115,7 +106,6 @@ app.include_router(sessions.session_router, prefix="/api")
 
 app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(models.models_router, prefix="/api")
-app.include_router(model_records.model_records_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
 app.include_router(board_images.board_images_router, prefix="/api")
@@ -140,7 +130,7 @@ def custom_openapi() -> dict[str, Any]:
     # Add all outputs
     all_invocations = BaseInvocation.get_invocations()
     output_types = set()
-    output_type_titles = {}
+    output_type_titles = dict()
     for invoker in all_invocations:
         output_type = signature(invoker.invoke).return_annotation
         output_types.add(output_type)
@@ -155,11 +145,7 @@ def custom_openapi() -> dict[str, Any]:
 
     # Add Node Editor UI helper schemas
     ui_config_schemas = models_json_schema(
-        [
-            (UIConfigBase, "serialization"),
-            (InputFieldJSONSchemaExtra, "serialization"),
-            (OutputFieldJSONSchemaExtra, "serialization"),
-        ],
+        [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
         ref_template="#/components/schemas/{model}",
     )
     for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
@@ -167,7 +153,7 @@ def custom_openapi() -> dict[str, Any]:
 
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
-        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
+        invoker_name = invoker.__name__
         output_type = signature(obj=invoker.invoke).return_annotation
         output_type_title = output_type_titles[output_type.__name__]
         invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
@@ -185,12 +171,12 @@ def custom_openapi() -> dict[str, Any]:
             # print(f"Config with name {name} already defined")
             continue
 
-        openapi_schema["components"]["schemas"][name] = {
-            "title": name,
-            "description": "An enumeration.",
-            "type": "string",
-            "enum": [v.value for v in model_config_format_enum],
-        }
+        openapi_schema["components"]["schemas"][name] = dict(
+            title=name,
+            description="An enumeration.",
+            type="string",
+            enum=list(v.value for v in model_config_format_enum),
+        )
 
     app.openapi_schema = openapi_schema
     return app.openapi_schema
@@ -285,4 +271,7 @@ def invoke_api() -> None:
 
 
 if __name__ == "__main__":
-    invoke_api()
+    if app_config.version:
+        print(f"InvokeAI version {__version__}")
+    else:
+        invoke_api()

invokeai/app/invocations/__init__.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 
-custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
+custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
 custom_nodes_path.mkdir(parents=True, exist_ok=True)
 
 custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
@@ -25,4 +25,4 @@ spec.loader.exec_module(module)
 
 # add core nodes to __all__
 python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py"))
-__all__ = [f.stem for f in python_files]  # type: ignore
+__all__ = list(f.stem for f in python_files)  # type: ignore

invokeai/app/invocations/baseinvocation.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI team
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from __future__ import annotations
 
@@ -8,7 +8,7 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import signature
 from types import UnionType
-from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
 
 import semver
 from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
@@ -16,19 +16,11 @@ from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
-from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
-from invokeai.backend.util.logging import InvokeAILogger
 
 if TYPE_CHECKING:
     from ..services.invocation_services import InvocationServices
 
-logger = InvokeAILogger.get_logger()
-
-CUSTOM_NODE_PACK_SUFFIX = "__invokeai-custom-node"
-
 
 class InvalidVersionError(ValueError):
     pass
@@ -38,7 +30,71 @@ class InvalidFieldError(TypeError):
     pass
 
 
-class Input(str, Enum, metaclass=MetaEnum):
+class FieldDescriptions:
+    denoising_start = "When to start denoising, expressed a percentage of total steps"
+    denoising_end = "When to stop denoising, expressed a percentage of total steps"
+    cfg_scale = "Classifier-Free Guidance scale"
+    scheduler = "Scheduler to use during inference"
+    positive_cond = "Positive conditioning tensor"
+    negative_cond = "Negative conditioning tensor"
+    noise = "Noise tensor"
+    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
+    unet = "UNet (scheduler, LoRAs)"
+    vae = "VAE"
+    cond = "Conditioning tensor"
+    controlnet_model = "ControlNet model to load"
+    vae_model = "VAE model to load"
+    lora_model = "LoRA model to load"
+    main_model = "Main model (UNet, VAE, CLIP) to load"
+    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
+    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
+    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
+    lora_weight = "The weight at which the LoRA is applied to each model"
+    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
+    raw_prompt = "Raw prompt text (no parsing)"
+    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
+    skipped_layers = "Number of layers to skip in text encoder"
+    seed = "Seed for random number generation"
+    steps = "Number of steps to run"
+    width = "Width of output (px)"
+    height = "Height of output (px)"
+    control = "ControlNet(s) to apply"
+    ip_adapter = "IP-Adapter to apply"
+    t2i_adapter = "T2I-Adapter(s) to apply"
+    denoised_latents = "Denoised latents tensor"
+    latents = "Latents tensor"
+    strength = "Strength of denoising (proportional to steps)"
+    metadata = "Optional metadata to be saved with the image"
+    metadata_collection = "Collection of Metadata"
+    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
+    metadata_item_label = "Label for this metadata item"
+    metadata_item_value = "The value for this metadata item (may be any type)"
+    workflow = "Optional workflow to be saved with the image"
+    interp_mode = "Interpolation mode"
+    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
+    fp32 = "Whether or not to use full float32 precision"
+    precision = "Precision to use"
+    tiled = "Processing using overlapping tiles (reduce memory consumption)"
+    detect_res = "Pixel resolution for detection"
+    image_res = "Pixel resolution for output image"
+    safe_mode = "Whether or not to use safe mode"
+    scribble_mode = "Whether or not to use scribble mode"
+    scale_factor = "The factor by which to scale"
+    blend_alpha = (
+        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
+    )
+    num_1 = "The first number"
+    num_2 = "The second number"
+    mask = "The mask to use for the operation"
+    board = "The board to save the image to"
+    image = "The image to process"
+    tile_size = "Tile size"
+    inclusive_low = "The inclusive low value"
+    exclusive_high = "The exclusive high value"
+    decimal_places = "The number of decimal places to round to"
+
+
+class Input(str, Enum):
     """
     The type of input a field accepts.
     - `Input.Direct`: The field must have its value provided directly, when the invocation and field \
@@ -52,124 +108,86 @@ class Input(str, Enum, metaclass=MetaEnum):
     Any = "any"
 
 
-class FieldKind(str, Enum, metaclass=MetaEnum):
+class UIType(str, Enum):
     """
-    The kind of field.
-    - `Input`: An input field on a node.
-    - `Output`: An output field on a node.
-    - `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
-    one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
-    "metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
-    allowing "metadata" for that field.
-    - `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
-    but which are used to store information about the node. For example, the `id` and `type` fields are node
-    attributes.
-
-    The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
-    startup, and when generating the OpenAPI schema for the workflow editor.
+    Type hints for the UI.
+    If a field should be provided a data type that does not exactly match the python type of the field, \
+    use this to provide the type that should be used instead. See the node development docs for detail \
+    on adding a new field type, which involves client-side changes.
     """
 
-    Input = "input"
-    Output = "output"
-    Internal = "internal"
-    NodeAttribute = "node_attribute"
+    # region Primitives
+    Boolean = "boolean"
+    Color = "ColorField"
+    Conditioning = "ConditioningField"
+    Control = "ControlField"
+    Float = "float"
+    Image = "ImageField"
+    Integer = "integer"
+    Latents = "LatentsField"
+    String = "string"
+    # endregion
 
+    # region Collection Primitives
+    BooleanCollection = "BooleanCollection"
+    ColorCollection = "ColorCollection"
+    ConditioningCollection = "ConditioningCollection"
+    ControlCollection = "ControlCollection"
+    FloatCollection = "FloatCollection"
+    ImageCollection = "ImageCollection"
+    IntegerCollection = "IntegerCollection"
+    LatentsCollection = "LatentsCollection"
+    StringCollection = "StringCollection"
+    # endregion
 
-class UIType(str, Enum, metaclass=MetaEnum):
-    """
-    Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
+    # region Polymorphic Primitives
+    BooleanPolymorphic = "BooleanPolymorphic"
+    ColorPolymorphic = "ColorPolymorphic"
+    ConditioningPolymorphic = "ConditioningPolymorphic"
+    ControlPolymorphic = "ControlPolymorphic"
+    FloatPolymorphic = "FloatPolymorphic"
+    ImagePolymorphic = "ImagePolymorphic"
+    IntegerPolymorphic = "IntegerPolymorphic"
+    LatentsPolymorphic = "LatentsPolymorphic"
+    StringPolymorphic = "StringPolymorphic"
+    # endregion
 
-    - Model Fields
-    The most common node-author-facing use will be for model fields. Internally, there is no difference
-    between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
-    base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
-    the field is an SDXL main model field.
-
-    - Any Field
-    We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
-    indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
-
-    - Scheduler Field
-    Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
-
-    - Internal Fields
-    Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
-    handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
-    should not be used by node authors.
-
-    - DEPRECATED Fields
-    These types are deprecated and should not be used by node authors. A warning will be logged if one is
-    used, and the type will be ignored. They are included here for backwards compatibility.
-    """
-
-    # region Model Field Types
+    # region Models
+    MainModel = "MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
-    VaeModel = "VAEModelField"
+    VaeModel = "VaeModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
+    UNet = "UNetField"
+    Vae = "VaeField"
+    CLIP = "ClipField"
     # endregion
 
-    # region Misc Field Types
-    Scheduler = "SchedulerField"
-    Any = "AnyField"
+    # region Iterate/Collect
+    Collection = "Collection"
+    CollectionItem = "CollectionItem"
     # endregion
 
-    # region Internal Field Types
-    _Collection = "CollectionField"
-    _CollectionItem = "CollectionItemField"
-    # endregion
-
-    # region DEPRECATED
-    Boolean = "DEPRECATED_Boolean"
-    Color = "DEPRECATED_Color"
-    Conditioning = "DEPRECATED_Conditioning"
-    Control = "DEPRECATED_Control"
-    Float = "DEPRECATED_Float"
-    Image = "DEPRECATED_Image"
-    Integer = "DEPRECATED_Integer"
-    Latents = "DEPRECATED_Latents"
-    String = "DEPRECATED_String"
-    BooleanCollection = "DEPRECATED_BooleanCollection"
-    ColorCollection = "DEPRECATED_ColorCollection"
-    ConditioningCollection = "DEPRECATED_ConditioningCollection"
-    ControlCollection = "DEPRECATED_ControlCollection"
-    FloatCollection = "DEPRECATED_FloatCollection"
-    ImageCollection = "DEPRECATED_ImageCollection"
-    IntegerCollection = "DEPRECATED_IntegerCollection"
-    LatentsCollection = "DEPRECATED_LatentsCollection"
-    StringCollection = "DEPRECATED_StringCollection"
-    BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
-    ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
-    ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
-    ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
-    FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
-    ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
-    IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
-    LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
-    StringPolymorphic = "DEPRECATED_StringPolymorphic"
-    MainModel = "DEPRECATED_MainModel"
-    UNet = "DEPRECATED_UNet"
-    Vae = "DEPRECATED_Vae"
-    CLIP = "DEPRECATED_CLIP"
-    Collection = "DEPRECATED_Collection"
-    CollectionItem = "DEPRECATED_CollectionItem"
-    Enum = "DEPRECATED_Enum"
-    WorkflowField = "DEPRECATED_WorkflowField"
-    IsIntermediate = "DEPRECATED_IsIntermediate"
-    BoardField = "DEPRECATED_BoardField"
-    MetadataItem = "DEPRECATED_MetadataItem"
-    MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
-    MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
-    MetadataDict = "DEPRECATED_MetadataDict"
+    # region Misc
+    Enum = "enum"
+    Scheduler = "Scheduler"
+    WorkflowField = "WorkflowField"
+    IsIntermediate = "IsIntermediate"
+    BoardField = "BoardField"
+    Any = "Any"
+    MetadataItem = "MetadataItem"
+    MetadataItemCollection = "MetadataItemCollection"
+    MetadataItemPolymorphic = "MetadataItemPolymorphic"
+    MetadataDict = "MetadataDict"
     # endregion
 
 
-class UIComponent(str, Enum, metaclass=MetaEnum):
+class UIComponent(str, Enum):
     """
-    The type of UI component to use for a field, used to override the default components, which are
+    The type of UI component to use for a field, used to override the default components, which are \
     inferred from the field type.
     """
 
@@ -178,22 +196,21 @@ class UIComponent(str, Enum, metaclass=MetaEnum):
     Slider = "slider"
 
 
-class InputFieldJSONSchemaExtra(BaseModel):
+class _InputField(BaseModel):
     """
-    Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
-    and by the workflow editor during schema parsing and UI rendering.
+    *DO NOT USE*
+    This helper class is used to tell the client about our custom field attributes via OpenAPI
+    schema generation, and Typescript type generation from that schema. It serves no functional
+    purpose in the backend.
     """
 
     input: Input
-    orig_required: bool
-    field_kind: FieldKind
-    default: Optional[Any] = None
-    orig_default: Optional[Any] = None
-    ui_hidden: bool = False
-    ui_type: Optional[UIType] = None
-    ui_component: Optional[UIComponent] = None
-    ui_order: Optional[int] = None
-    ui_choice_labels: Optional[dict[str, str]] = None
+    ui_hidden: bool
+    ui_type: Optional[UIType]
+    ui_component: Optional[UIComponent]
+    ui_order: Optional[int]
+    ui_choice_labels: Optional[dict[str, str]]
+    item_default: Optional[Any]
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -201,13 +218,14 @@ class InputFieldJSONSchemaExtra(BaseModel):
     )
 
 
-class OutputFieldJSONSchemaExtra(BaseModel):
+class _OutputField(BaseModel):
     """
-    Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
-    during schema parsing and UI rendering.
+    *DO NOT USE*
+    This helper class is used to tell the client about our custom field attributes via OpenAPI
+    schema generation, and Typescript type generation from that schema. It serves no functional
+    purpose in the backend.
     """
 
-    field_kind: FieldKind
     ui_hidden: bool
     ui_type: Optional[UIType]
     ui_order: Optional[int]
@@ -218,9 +236,13 @@ class OutputFieldJSONSchemaExtra(BaseModel):
     )
 
 
+def get_type(klass: BaseModel) -> str:
+    """Helper function to get an invocation or invocation output's type. This is the default value of the `type` field."""
+    return klass.model_fields["type"].default
+
+
 def InputField(
     # copied from pydantic's Field
-    # TODO: Can we support default_factory?
     default: Any = _Unset,
     default_factory: Callable[[], Any] | None = _Unset,
     title: str | None = _Unset,
@@ -244,11 +266,12 @@ def InputField(
     ui_hidden: bool = False,
     ui_order: Optional[int] = None,
     ui_choice_labels: Optional[dict[str, str]] = None,
+    item_default: Optional[Any] = None,
 ) -> Any:
     """
     Creates an input field for an invocation.
 
-    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
+    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
     that adds a few extra parameters to support graph execution and the node editor UI.
 
     :param Input input: [Input.Any] The kind of input this field requires. \
@@ -268,102 +291,108 @@ def InputField(
       For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
       For this case, you could provide `UIComponent.Textarea`.
 
-    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
+    : param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
 
-    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
+    : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
 
-    :param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
+    : param bool item_default: [None] Specifies the default item value, if this is a collection input. \
+      Ignored for non-collection fields.
     """
 
-    json_schema_extra_ = InputFieldJSONSchemaExtra(
+    json_schema_extra_: dict[str, Any] = dict(
         input=input,
         ui_type=ui_type,
         ui_component=ui_component,
         ui_hidden=ui_hidden,
         ui_order=ui_order,
+        item_default=item_default,
         ui_choice_labels=ui_choice_labels,
-        field_kind=FieldKind.Input,
-        orig_required=True,
+        _field_kind="input",
+    )
+
+    field_args = dict(
+        default=default,
+        default_factory=default_factory,
+        title=title,
+        description=description,
+        pattern=pattern,
+        strict=strict,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        min_length=min_length,
+        max_length=max_length,
     )
 
     """
-    There is a conflict between the typing of invocation definitions and the typing of an invocation's
-    `invoke()` function.
-
-    On instantiation of a node, the invocation definition is used to create the python class. At this time,
-    any number of fields may be optional, because they may be provided by connections.
-
-    On calling of `invoke()`, however, those fields may be required.
+    Invocation definitions have their fields typed correctly for their `invoke()` functions.
+    This typing is often more specific than the actual invocation definition requires, because
+    fields may have values provided only by connections.
 
     For example, consider an ResizeImageInvocation with an `image: ImageField` field.
 
     `image` is required during the call to `invoke()`, but when the python class is instantiated,
     the field may not be present. This is fine, because that image field will be provided by a
-    connection from an ancestor node, which outputs an image.
+    an ancestor node that outputs the image.
 
-    This means we want to type the `image` field as optional for the node class definition, but required
-    for the `invoke()` function.
+    So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then
+    we need to handle a lot of extra logic in the `invoke()` function to check if the field has a
+    value or not. This is very tedious.
 
-    If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
-    `invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
-    any static type analysis tools will complain.
+    Ideally, the invocation definition would be able to specify that the field is required during
+    invocation, but optional during instantiation. So the field would be typed as `image: ImageField`,
+    but when calling the `invoke()` function, we raise an error if the field is not present.
 
-    To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
-    but secretly make them optional in `InputField()`. We also store the original required bool and/or default
-    value. When we call `invoke()`, we use this stored information to do an additional check on the class.
+    To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do
+    extra validation when calling `invoke()`.
+
+    There is some additional logic here to cleaning create the pydantic field via the wrapper.
     """
 
-    if default_factory is not _Unset and default_factory is not None:
-        default = default_factory()
-        logger.warn('"default_factory" is not supported, calling it now to set "default"')
-
-    # These are the args we may wish pass to the pydantic `Field()` function
-    field_args = {
-        "default": default,
-        "title": title,
-        "description": description,
-        "pattern": pattern,
-        "strict": strict,
-        "gt": gt,
-        "ge": ge,
-        "lt": lt,
-        "le": le,
-        "multiple_of": multiple_of,
-        "allow_inf_nan": allow_inf_nan,
-        "max_digits": max_digits,
-        "decimal_places": decimal_places,
-        "min_length": min_length,
-        "max_length": max_length,
-    }
-
-    # We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
+    # Filter out field args not provided
     provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
 
-    # Because we are manually making fields optional, we need to store the original required bool for reference later
-    json_schema_extra_.orig_required = default is PydanticUndefined
+    if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined):
+        raise ValueError("Cannot specify both default and default_factory")
 
-    # Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
-    if input is Input.Any or input is Input.Connection:
+    # because we are manually making fields optional, we need to store the original required bool for reference later
+    if default is PydanticUndefined and default_factory is PydanticUndefined:
+        json_schema_extra_.update(dict(orig_required=True))
+    else:
+        json_schema_extra_.update(dict(orig_required=False))
+
+    # make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
+    if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
         default_ = None if default is PydanticUndefined else default
-        provided_args.update({"default": default_})
+        provided_args.update(dict(default=default_))
         if default is not PydanticUndefined:
-            # Before invoking, we'll check for the original default value and set it on the field if the field has no value
-            json_schema_extra_.default = default
-            json_schema_extra_.orig_default = default
-    elif default is not PydanticUndefined:
+            # before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
+            json_schema_extra_.update(dict(default=default))
+            json_schema_extra_.update(dict(orig_default=default))
+    elif default is not PydanticUndefined and default_factory is PydanticUndefined:
         default_ = default
-        provided_args.update({"default": default_})
-        json_schema_extra_.orig_default = default_
+        provided_args.update(dict(default=default_))
+        json_schema_extra_.update(dict(orig_default=default_))
+    elif default_factory is not PydanticUndefined:
+        provided_args.update(dict(default_factory=default_factory))
+        # TODO: cannot serialize default_factory...
+        # json_schema_extra_.update(dict(orig_default_factory=default_factory))
 
     return Field(
         **provided_args,
-        json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
+        json_schema_extra=json_schema_extra_,
     )
 
 
 def OutputField(
     # copied from pydantic's Field
     default: Any = _Unset,
+    default_factory: Callable[[], Any] | None = _Unset,
     title: str | None = _Unset,
     description: str | None = _Unset,
     pattern: str | None = _Unset,
@@ -396,12 +425,13 @@ def OutputField(
       `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
       `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
 
-    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
+    : param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
 
-    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
+    : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
     """
     return Field(
         default=default,
+        default_factory=default_factory,
         title=title,
         description=description,
         pattern=pattern,
@@ -416,12 +446,12 @@ def OutputField(
         decimal_places=decimal_places,
         min_length=min_length,
         max_length=max_length,
-        json_schema_extra=OutputFieldJSONSchemaExtra(
+        json_schema_extra=dict(
             ui_type=ui_type,
             ui_hidden=ui_hidden,
             ui_order=ui_order,
-            field_kind=FieldKind.Output,
-        ).model_dump(exclude_none=True),
+            _field_kind="output",
+        ),
     )
 
 
@@ -434,10 +464,10 @@ class UIConfigBase(BaseModel):
     tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
     title: Optional[str] = Field(default=None, description="The node's display name")
     category: Optional[str] = Field(default=None, description="The node's category")
-    version: str = Field(
+    version: Optional[str] = Field(
+        default=None,
         description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
     )
-    node_pack: Optional[str] = Field(default=None, description="Whether or not this is a custom node")
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -453,7 +483,6 @@ class InvocationContext:
     queue_id: str
     queue_item_id: int
     queue_batch_id: str
-    workflow: Optional[WorkflowWithoutID]
 
     def __init__(
         self,
@@ -462,14 +491,12 @@ class InvocationContext:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        workflow: Optional[WorkflowWithoutID],
     ):
         self.services = services
         self.graph_execution_state_id = graph_execution_state_id
         self.queue_id = queue_id
         self.queue_item_id = queue_item_id
         self.queue_batch_id = queue_batch_id
-        self.workflow = workflow
 
 
 class BaseInvocationOutput(BaseModel):
@@ -483,39 +510,29 @@ class BaseInvocationOutput(BaseModel):
 
     @classmethod
     def register_output(cls, output: BaseInvocationOutput) -> None:
-        """Registers an invocation output."""
         cls._output_classes.add(output)
 
     @classmethod
     def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
-        """Gets all invocation outputs."""
         return cls._output_classes
 
     @classmethod
     def get_outputs_union(cls) -> UnionType:
-        """Gets a union of all invocation outputs."""
         outputs_union = Union[tuple(cls._output_classes)]  # type: ignore [valid-type]
         return outputs_union  # type: ignore [return-value]
 
     @classmethod
     def get_output_types(cls) -> Iterable[str]:
-        """Gets all invocation output types."""
-        return (i.get_type() for i in BaseInvocationOutput.get_outputs())
+        return map(lambda i: get_type(i), BaseInvocationOutput.get_outputs())
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
-        """Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
         # Because we use a pydantic Literal field with default value for the invocation type,
         # it will be typed as optional in the OpenAPI schema. Make it required manually.
         if "required" not in schema or not isinstance(schema["required"], list):
-            schema["required"] = []
+            schema["required"] = list()
         schema["required"].extend(["type"])
 
-    @classmethod
-    def get_type(cls) -> str:
-        """Gets the invocation output's type, as provided by the `@invocation_output` decorator."""
-        return cls.model_fields["type"].default
-
     model_config = ConfigDict(
         protected_namespaces=(),
         validate_assignment=True,
@@ -545,29 +562,21 @@ class BaseInvocation(ABC, BaseModel):
 
     _invocation_classes: ClassVar[set[BaseInvocation]] = set()
 
-    @classmethod
-    def get_type(cls) -> str:
-        """Gets the invocation's type, as provided by the `@invocation` decorator."""
-        return cls.model_fields["type"].default
-
     @classmethod
     def register_invocation(cls, invocation: BaseInvocation) -> None:
-        """Registers an invocation."""
         cls._invocation_classes.add(invocation)
 
     @classmethod
     def get_invocations_union(cls) -> UnionType:
-        """Gets a union of all invocation types."""
         invocations_union = Union[tuple(cls._invocation_classes)]  # type: ignore [valid-type]
         return invocations_union  # type: ignore [return-value]
 
     @classmethod
     def get_invocations(cls) -> Iterable[BaseInvocation]:
-        """Gets all invocations, respecting the allowlist and denylist."""
         app_config = InvokeAIAppConfig.get_config()
         allowed_invocations: set[BaseInvocation] = set()
         for sc in cls._invocation_classes:
-            invocation_type = sc.get_type()
+            invocation_type = get_type(sc)
             is_in_allowlist = (
                 invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
             )
@@ -580,35 +589,36 @@ class BaseInvocation(ABC, BaseModel):
 
     @classmethod
     def get_invocations_map(cls) -> dict[str, BaseInvocation]:
-        """Gets a map of all invocation types to their invocation classes."""
-        return {i.get_type(): i for i in BaseInvocation.get_invocations()}
+        # Get the type strings out of the literals and into a dictionary
+        return dict(
+            map(
+                lambda i: (get_type(i), i),
+                BaseInvocation.get_invocations(),
+            )
+        )
 
     @classmethod
     def get_invocation_types(cls) -> Iterable[str]:
-        """Gets all invocation types."""
-        return (i.get_type() for i in BaseInvocation.get_invocations())
+        return map(lambda i: get_type(i), BaseInvocation.get_invocations())
 
     @classmethod
-    def get_output_annotation(cls) -> BaseInvocationOutput:
-        """Gets the invocation's output annotation (i.e. the return annotation of its `invoke()` method)."""
+    def get_output_type(cls) -> BaseInvocationOutput:
         return signature(cls.invoke).return_annotation
 
     @staticmethod
-    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> None:
-        """Adds various UI-facing attributes to the invocation's OpenAPI schema."""
-        uiconfig = cast(UIConfigBase | None, getattr(model_class, "UIConfig", None))
-        if uiconfig is not None:
-            if uiconfig.title is not None:
-                schema["title"] = uiconfig.title
-            if uiconfig.tags is not None:
-                schema["tags"] = uiconfig.tags
-            if uiconfig.category is not None:
-                schema["category"] = uiconfig.category
-            if uiconfig.node_pack is not None:
-                schema["node_pack"] = uiconfig.node_pack
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
+        # Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
+        uiconfig = getattr(model_class, "UIConfig", None)
+        if uiconfig and hasattr(uiconfig, "title"):
+            schema["title"] = uiconfig.title
+        if uiconfig and hasattr(uiconfig, "tags"):
+            schema["tags"] = uiconfig.tags
+        if uiconfig and hasattr(uiconfig, "category"):
+            schema["category"] = uiconfig.category
+        if uiconfig and hasattr(uiconfig, "version"):
             schema["version"] = uiconfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
-            schema["required"] = []
+            schema["required"] = list()
         schema["required"].extend(["type", "id"])
 
     @abstractmethod
@@ -617,10 +627,6 @@ class BaseInvocation(ABC, BaseModel):
         pass
 
     def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
-        """
-        Internal invoke method, calls `invoke()` after some prep.
-        Handles optional fields that are required to call `invoke()` and invocation cache.
-        """
         for field_name, field in self.model_fields.items():
             if not field.json_schema_extra or callable(field.json_schema_extra):
                 # something has gone terribly awry, we should always have this and it should be a dict
@@ -660,20 +666,21 @@ class BaseInvocation(ABC, BaseModel):
             context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
             return self.invoke(context)
 
+    def get_type(self) -> str:
+        return self.model_fields["type"].default
+
     id: str = Field(
         default_factory=uuid_string,
         description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
-        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
+        json_schema_extra=dict(_field_kind="internal"),
     )
     is_intermediate: bool = Field(
         default=False,
         description="Whether or not this is an intermediate invocation.",
-        json_schema_extra={"ui_type": "IsIntermediate", "field_kind": FieldKind.NodeAttribute},
+        json_schema_extra=dict(ui_type=UIType.IsIntermediate, _field_kind="internal"),
     )
     use_cache: bool = Field(
-        default=True,
-        description="Whether or not to use the cache",
-        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
+        default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal")
     )
 
     UIConfig: ClassVar[Type[UIConfigBase]]
@@ -690,15 +697,12 @@ class BaseInvocation(ABC, BaseModel):
 TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
 
 
-RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
+RESERVED_INPUT_FIELD_NAMES = {
     "id",
     "is_intermediate",
     "use_cache",
     "type",
     "workflow",
-}
-
-RESERVED_INPUT_FIELD_NAMES = {
     "metadata",
 }
 
@@ -710,65 +714,48 @@ class _Model(BaseModel):
 
 
 # Get all pydantic model attrs, methods, etc
-RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in inspect.getmembers(_Model())}
+RESERVED_PYDANTIC_FIELD_NAMES = set(map(lambda m: m[0], inspect.getmembers(_Model())))
 
 
 def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
     """
     Validates the fields of an invocation or invocation output:
-    - Must not override any pydantic reserved fields
-    - Must have a type annotation
-    - Must have a json_schema_extra dict
-    - Must have field_kind in json_schema_extra
-    - Field name must not be reserved, according to its field_kind
+    - must not override any pydantic reserved fields
+    - must be created via `InputField`, `OutputField`, or be an internal field defined in this file
     """
     for name, field in model_fields.items():
         if name in RESERVED_PYDANTIC_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
 
-        if not field.annotation:
-            raise InvalidFieldError(f'Invalid field type "{name}" on "{model_type}" (missing annotation)')
-
-        if not isinstance(field.json_schema_extra, dict):
-            raise InvalidFieldError(
-                f'Invalid field definition for "{name}" on "{model_type}" (missing json_schema_extra dict)'
-            )
-
-        field_kind = field.json_schema_extra.get("field_kind", None)
+        field_kind = (
+            # _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
+            field.json_schema_extra.get("_field_kind", None)
+            if field.json_schema_extra
+            else None
+        )
 
         # must have a field_kind
-        if not isinstance(field_kind, FieldKind):
+        if field_kind is None or field_kind not in {"input", "output", "internal"}:
             raise InvalidFieldError(
                 f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
             )
 
-        if field_kind is FieldKind.Input and (
-            name in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES or name in RESERVED_INPUT_FIELD_NAMES
-        ):
+        if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
 
-        if field_kind is FieldKind.Output and name in RESERVED_OUTPUT_FIELD_NAMES:
+        if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
 
-        if (field_kind is FieldKind.Internal) and name not in RESERVED_INPUT_FIELD_NAMES:
+        # internal fields *must* be in the reserved list
+        if (
+            field_kind == "internal"
+            and name not in RESERVED_INPUT_FIELD_NAMES
+            and name not in RESERVED_OUTPUT_FIELD_NAMES
+        ):
             raise InvalidFieldError(
                 f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
             )
 
-        # node attribute fields *must* be in the reserved list
-        if (
-            field_kind is FieldKind.NodeAttribute
-            and name not in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES
-            and name not in RESERVED_OUTPUT_FIELD_NAMES
-        ):
-            raise InvalidFieldError(
-                f'Invalid field name "{name}" on "{model_type}" (node attribute field without reserved name)'
-            )
-
-        ui_type = field.json_schema_extra.get("ui_type", None)
-        if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
-            field.json_schema_extra.pop("ui_type")
     return None
 
 
@@ -803,30 +790,21 @@ def invocation(
         validate_fields(cls.model_fields, invocation_type)
 
         # Add OpenAPI schema extras
-        uiconfig_name = cls.__qualname__ + ".UIConfig"
-        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconfig_name:
-            cls.UIConfig = type(uiconfig_name, (UIConfigBase,), {})
-        cls.UIConfig.title = title
-        cls.UIConfig.tags = tags
-        cls.UIConfig.category = category
-
-        # Grab the node pack's name from the module name, if it's a custom node
-        is_custom_node = cls.__module__.rsplit(".", 1)[0] == "invokeai.app.invocations"
-        if is_custom_node:
-            cls.UIConfig.node_pack = cls.__module__.split(".")[0]
-        else:
-            cls.UIConfig.node_pack = None
-
+        uiconf_name = cls.__qualname__ + ".UIConfig"
+        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
+            cls.UIConfig = type(uiconf_name, (UIConfigBase,), dict())
+        if title is not None:
+            cls.UIConfig.title = title
+        if tags is not None:
+            cls.UIConfig.tags = tags
+        if category is not None:
+            cls.UIConfig.category = category
         if version is not None:
             try:
                 semver.Version.parse(version)
             except ValueError as e:
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             cls.UIConfig.version = version
-        else:
-            logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
-            cls.UIConfig.version = "1.0.0"
-
         if use_cache is not None:
             cls.model_fields["use_cache"].default = use_cache
 
@@ -841,7 +819,7 @@ def invocation(
 
         invocation_type_annotation = Literal[invocation_type]  # type: ignore
         invocation_type_field = Field(
-            title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
+            title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal")
         )
 
         docstring = cls.__doc__
@@ -887,9 +865,7 @@ def invocation_output(
         # Add the output type to the model.
 
         output_type_annotation = Literal[output_type]  # type: ignore
-        output_type_field = Field(
-            title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
-        )
+        output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal"))
 
         docstring = cls.__doc__
         cls = create_model(
@@ -907,6 +883,24 @@ def invocation_output(
     return wrapper
 
 
+class WorkflowField(RootModel):
+    """
+    Pydantic model for workflows with custom root of type dict[str, Any].
+    Workflows are stored without a strict schema.
+    """
+
+    root: dict[str, Any] = Field(description="The workflow")
+
+
+WorkflowFieldValidator = TypeAdapter(WorkflowField)
+
+
+class WithWorkflow(BaseModel):
+    workflow: Optional[WorkflowField] = Field(
+        default=None, description=FieldDescriptions.workflow, json_schema_extra=dict(_field_kind="internal")
+    )
+
+
 class MetadataField(RootModel):
     """
     Pydantic model for metadata with custom root of type dict[str, Any].
@@ -921,21 +915,5 @@ MetadataFieldValidator = TypeAdapter(MetadataField)
 
 class WithMetadata(BaseModel):
     metadata: Optional[MetadataField] = Field(
-        default=None,
-        description=FieldDescriptions.metadata,
-        json_schema_extra=InputFieldJSONSchemaExtra(
-            field_kind=FieldKind.Internal,
-            input=Input.Connection,
-            orig_required=False,
-        ).model_dump(exclude_none=True),
+        default=None, description=FieldDescriptions.metadata, json_schema_extra=dict(_field_kind="internal")
     )
-
-
-class WithWorkflow:
-    workflow = None
-
-    def __init_subclass__(cls) -> None:
-        logger.warn(
-            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
-        )
-        super().__init_subclass__()

invokeai/app/invocations/collections.py

@@ -5,7 +5,7 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
-from invokeai.app.util.misc import SEED_MAX
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
@@ -55,7 +55,7 @@ class RangeOfSizeInvocation(BaseInvocation):
     title="Random Range",
     tags=["range", "integer", "random", "collection"],
     category="collections",
-    version="1.0.1",
+    version="1.0.0",
     use_cache=False,
 )
 class RandomRangeInvocation(BaseInvocation):
@@ -65,10 +65,10 @@ class RandomRangeInvocation(BaseInvocation):
     high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
     size: int = InputField(default=1, description="The number of values to generate")
     seed: int = InputField(
-        default=0,
         ge=0,
         le=SEED_MAX,
         description="The seed for the RNG (omit for random)",
+        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:

invokeai/app/invocations/compel.py

@@ -7,7 +7,6 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ExtraConditioningInfo,
@@ -20,6 +19,7 @@ from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -108,15 +108,13 @@ class CompelInvocation(BaseInvocation):
                 print(f'Warn: trigger: "{trigger}" not found')
 
         with (
+            ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
             ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
-            text_encoder_info as text_encoder,
-            # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
-            ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
-            # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
+            text_encoder_info as text_encoder,
         ):
             compel = Compel(
                 tokenizer=tokenizer,
@@ -231,15 +229,13 @@ class SDXLPromptInvocationBase:
                 print(f'Warn: trigger: "{trigger}" not found')
 
         with (
+            ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
             ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
-            text_encoder_info as text_encoder,
-            # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
-            ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
-            # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
+            text_encoder_info as text_encoder,
         ):
             compel = Compel(
                 tokenizer=tokenizer,

invokeai/app/invocations/controlnet_image_processors.py

@@ -28,17 +28,18 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
     OutputField,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -95,7 +96,7 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
@@ -128,7 +129,7 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation, WithMetadata):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -152,7 +153,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         """Builds an ImageOutput and its ImageField"""
@@ -172,7 +173,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -195,7 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -224,7 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -246,7 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -269,7 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Openpose Processor",
     tags=["controlnet", "openpose", "pose"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Openpose processing to image"""
@@ -294,7 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -321,7 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -338,7 +339,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -361,7 +362,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -388,7 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -418,7 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -434,7 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -457,7 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -486,7 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -526,7 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -568,7 +569,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.0.0",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/custom_nodes/init.py

@@ -32,15 +32,13 @@ for d in Path(__file__).parent.iterdir():
     if module_name in globals():
         continue
 
-    # load the module, appending adding a suffix to identify it as a custom node pack
+    # we have a legit module to import
     spec = spec_from_file_location(module_name, init.absolute())
 
     if spec is None or spec.loader is None:
         logger.warn(f"Could not load {init}")
         continue
 
-    logger.info(f"Loading node pack {module_name}")
-
     module = module_from_spec(spec)
     sys.modules[spec.name] = module
     spec.loader.exec_module(module)
@@ -49,5 +47,5 @@ for d in Path(__file__).parent.iterdir():
 
     del init, module_name
 
-if loaded_count > 0:
-    logger.info(f"Loaded {loaded_count} node packs from {Path(__file__).parent}")
+
+logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")

invokeai/app/invocations/cv.py

@@ -8,11 +8,11 @@ from PIL import Image, ImageOps
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
-class CvInpaintInvocation(BaseInvocation, WithMetadata):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
+class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
@@ -41,7 +41,7 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/facetools.py

@@ -17,6 +17,7 @@ from invokeai.app.invocations.baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -130,7 +131,7 @@ def prepare_faces_list(
     deduped_faces: list[FaceResultData] = []
 
     if len(face_result_list) == 0:
-        return []
+        return list()
 
     for candidate in face_result_list:
         should_add = True
@@ -209,7 +210,7 @@ def generate_face_box_mask(
     # Check if any face is detected.
     if results.multi_face_landmarks:  # type: ignore # this are via protobuf and not typed
         # Search for the face_id in the detected faces.
-        for _face_id, face_landmarks in enumerate(results.multi_face_landmarks):  # type: ignore #this are via protobuf and not typed
+        for face_id, face_landmarks in enumerate(results.multi_face_landmarks):  # type: ignore #this are via protobuf and not typed
             # Get the bounding box of the face mesh.
             x_coordinates = [landmark.x for landmark in face_landmarks.landmark]
             y_coordinates = [landmark.y for landmark in face_landmarks.landmark]
@@ -437,8 +438,8 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
-class FaceOffInvocation(BaseInvocation, WithMetadata):
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
+class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
     image: ImageField = InputField(description="Image for face detection")
@@ -507,7 +508,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -531,8 +532,8 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
-class FaceMaskInvocation(BaseInvocation, WithMetadata):
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
+class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -626,7 +627,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -649,9 +650,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
 )
-class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
+class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -715,7 +716,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/image.py

@@ -8,12 +8,20 @@ import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import (
+    BaseInvocation,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    WithMetadata,
+    WithWorkflow,
+    invocation,
+)
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -36,14 +44,8 @@ class ShowImageInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "blank_image",
-    title="Blank Image",
-    tags=["image"],
-    category="image",
-    version="1.2.0",
-)
-class BlankImageInvocation(BaseInvocation, WithMetadata):
+@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
+class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Creates a blank image and forwards it to the pipeline"""
 
     width: int = InputField(default=512, description="The width of the image")
@@ -62,7 +64,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -72,14 +74,8 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_crop",
-    title="Crop Image",
-    tags=["image", "crop"],
-    category="image",
-    version="1.2.0",
-)
-class ImageCropInvocation(BaseInvocation, WithMetadata):
+@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
+class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Crops an image to a specified box. The box can be outside of the image."""
 
     image: ImageField = InputField(description="The image to crop")
@@ -102,7 +98,7 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -112,69 +108,8 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_paste",
-    title="Paste Image",
-    tags=["image", "paste"],
-    category="image",
-    version="1.2.0",
-)
-class CenterPadCropInvocation(BaseInvocation):
-    """Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
-
-    image: ImageField = InputField(description="The image to crop")
-    left: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the left (negative values crop inwards, positive values pad outwards)",
-    )
-    right: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the right (negative values crop inwards, positive values pad outwards)",
-    )
-    top: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the top (negative values crop inwards, positive values pad outwards)",
-    )
-    bottom: int = InputField(
-        default=0,
-        description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-
-        # Calculate and create new image dimensions
-        new_width = image.width + self.right + self.left
-        new_height = image.height + self.top + self.bottom
-        image_crop = Image.new(mode="RGBA", size=(new_width, new_height), color=(0, 0, 0, 0))
-
-        # Paste new image onto input
-        image_crop.paste(image, (self.left, self.top))
-
-        image_dto = context.services.images.create(
-            image=image_crop,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-@invocation(
-    invocation_type="img_pad_crop",
-    title="Center Pad or Crop Image",
-    category="image",
-    tags=["image", "pad", "crop"],
-    version="1.0.0",
-)
-class ImagePasteInvocation(BaseInvocation, WithMetadata):
+@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
+class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Pastes an image into another image."""
 
     base_image: ImageField = InputField(description="The base image")
@@ -217,7 +152,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -227,14 +162,8 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "tomask",
-    title="Mask from Alpha",
-    tags=["image", "mask"],
-    category="image",
-    version="1.2.0",
-)
-class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
+@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
+class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Extracts the alpha channel of an image as a mask."""
 
     image: ImageField = InputField(description="The image to create the mask from")
@@ -255,7 +184,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -265,14 +194,8 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_mul",
-    title="Multiply Images",
-    tags=["image", "multiply"],
-    category="image",
-    version="1.2.0",
-)
-class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
+@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
+class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
     image1: ImageField = InputField(description="The first image to multiply")
@@ -292,7 +215,7 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -305,14 +228,8 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
-@invocation(
-    "img_chan",
-    title="Extract Image Channel",
-    tags=["image", "channel"],
-    category="image",
-    version="1.2.0",
-)
-class ImageChannelInvocation(BaseInvocation, WithMetadata):
+@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
+class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Gets a channel from an image."""
 
     image: ImageField = InputField(description="The image to get the channel from")
@@ -331,7 +248,7 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -344,14 +261,8 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 
 
-@invocation(
-    "img_conv",
-    title="Convert Image Mode",
-    tags=["image", "convert"],
-    category="image",
-    version="1.2.0",
-)
-class ImageConvertInvocation(BaseInvocation, WithMetadata):
+@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
+class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Converts an image to a different mode."""
 
     image: ImageField = InputField(description="The image to convert")
@@ -370,7 +281,7 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -380,14 +291,8 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_blur",
-    title="Blur Image",
-    tags=["image", "blur"],
-    category="image",
-    version="1.2.0",
-)
-class ImageBlurInvocation(BaseInvocation, WithMetadata):
+@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
+class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Blurs an image"""
 
     image: ImageField = InputField(description="The image to blur")
@@ -411,7 +316,7 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -441,14 +346,8 @@ PIL_RESAMPLING_MAP = {
 }
 
 
-@invocation(
-    "img_resize",
-    title="Resize Image",
-    tags=["image", "resize"],
-    category="image",
-    version="1.2.0",
-)
-class ImageResizeInvocation(BaseInvocation, WithMetadata):
+@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
+class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
@@ -474,7 +373,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -484,14 +383,8 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_scale",
-    title="Scale Image",
-    tags=["image", "scale"],
-    category="image",
-    version="1.2.0",
-)
-class ImageScaleInvocation(BaseInvocation, WithMetadata):
+@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
+class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Scales an image by a factor"""
 
     image: ImageField = InputField(description="The image to scale")
@@ -522,7 +415,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -532,14 +425,8 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_lerp",
-    title="Lerp Image",
-    tags=["image", "lerp"],
-    category="image",
-    version="1.2.0",
-)
-class ImageLerpInvocation(BaseInvocation, WithMetadata):
+@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
+class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -562,7 +449,7 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -572,14 +459,8 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_ilerp",
-    title="Inverse Lerp Image",
-    tags=["image", "ilerp"],
-    category="image",
-    version="1.2.0",
-)
-class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
+@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
+class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Inverse linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -602,7 +483,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -612,14 +493,8 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_nsfw",
-    title="Blur NSFW Image",
-    tags=["image", "nsfw"],
-    category="image",
-    version="1.2.0",
-)
-class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
+@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
+class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add blur to NSFW-flagged images"""
 
     image: ImageField = InputField(description="The image to check")
@@ -644,7 +519,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -665,9 +540,9 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
     title="Add Invisible Watermark",
     tags=["image", "watermark"],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
+class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add an invisible watermark to an image"""
 
     image: ImageField = InputField(description="The image to check")
@@ -684,7 +559,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -694,14 +569,8 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "mask_edge",
-    title="Mask Edge",
-    tags=["image", "mask", "inpaint"],
-    category="image",
-    version="1.2.0",
-)
-class MaskEdgeInvocation(BaseInvocation, WithMetadata):
+@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
+class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Applies an edge mask to an image"""
 
     image: ImageField = InputField(description="The image to apply the mask to")
@@ -736,7 +605,7 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -751,9 +620,9 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
     title="Combine Masks",
     tags=["image", "mask", "multiply"],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class MaskCombineInvocation(BaseInvocation, WithMetadata):
+class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
 
     mask1: ImageField = InputField(description="The first mask to combine")
@@ -773,7 +642,7 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -783,14 +652,8 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "color_correct",
-    title="Color Correct",
-    tags=["image", "color"],
-    category="image",
-    version="1.2.0",
-)
-class ColorCorrectInvocation(BaseInvocation, WithMetadata):
+@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
+class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """
     Shifts the colors of a target image to match the reference image, optionally
     using a mask to only color-correct certain regions of the target image.
@@ -890,7 +753,7 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -900,14 +763,8 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation(
-    "img_hue_adjust",
-    title="Adjust Image Hue",
-    tags=["image", "hue"],
-    category="image",
-    version="1.2.0",
-)
-class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
+@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
+class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Adjusts the Hue of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -936,7 +793,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1009,9 +866,9 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
+class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Add or subtract a value from a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1046,7 +903,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1080,9 +937,9 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
         "value",
     ],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
+class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Scale a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1121,7 +978,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
-            workflow=context.workflow,
+            workflow=self.workflow,
             metadata=self.metadata,
         )
 
@@ -1139,10 +996,10 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.2.0",
+    version="1.0.1",
     use_cache=False,
 )
-class SaveImageInvocation(BaseInvocation, WithMetadata):
+class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
 
     image: ImageField = InputField(description=FieldDescriptions.image)
@@ -1160,7 +1017,7 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -1168,35 +1025,3 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
             width=image_dto.width,
             height=image_dto.height,
         )
-
-
-@invocation(
-    "linear_ui_output",
-    title="Linear UI Image Output",
-    tags=["primitives", "image"],
-    category="primitives",
-    version="1.0.1",
-    use_cache=False,
-)
-class LinearUIOutputInvocation(BaseInvocation, WithMetadata):
-    """Handles Linear UI Image Outputting tasks."""
-
-    image: ImageField = InputField(description=FieldDescriptions.image)
-    board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image_dto = context.services.images.get_dto(self.image.image_name)
-
-        if self.board:
-            context.services.board_images.add_image_to_board(self.board.board_id, self.image.image_name)
-
-        if image_dto.is_intermediate != self.is_intermediate:
-            context.services.images.update(
-                self.image.image_name, changes=ImageRecordChanges(is_intermediate=self.is_intermediate)
-            )
-
-        return ImageOutput(
-            image=ImageField(image_name=self.image.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/infill.py

@@ -8,12 +8,12 @@ from PIL import Image, ImageOps
 
 from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.util.misc import SEED_MAX
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
 from invokeai.backend.image_util.lama import LaMA
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -118,8 +118,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class InfillColorInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
+class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -144,7 +144,7 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -154,17 +154,17 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class InfillTileInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
+class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
     tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
     seed: int = InputField(
-        default=0,
         ge=0,
         le=SEED_MAX,
         description="The seed to use for tile generation (omit for random)",
+        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -181,7 +181,7 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -192,9 +192,9 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
+class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -235,7 +235,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -245,8 +245,8 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class LaMaInfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
+class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -264,7 +264,7 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -274,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
         )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class CV2InfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
+class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -293,7 +293,7 @@ class CV2InfillInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/ip_adapter.py

@@ -7,15 +7,16 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType, ModelType
 from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
 
@@ -66,7 +67,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
     weight: Union[float, List[float]] = InputField(
-        default=1, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
+        default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
     )
 
     begin_step_percent: float = InputField(

invokeai/app/invocations/latent.py

@@ -10,7 +10,7 @@ import torch
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.models.adapter import T2IAdapter
+from diffusers.models.adapter import FullAdapterXL, T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -34,7 +34,6 @@ from invokeai.app.invocations.primitives import (
 )
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@@ -58,12 +57,14 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
     OutputField,
     UIType,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -76,13 +77,7 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
-SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
-
-# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
-# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
-# factor is hard-coded to a literal '8' rather than using this constant.
-# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
-LATENT_SCALE_FACTOR = 8
+SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
 @invocation_output("scheduler_output")
@@ -220,7 +215,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.0",
+    version="1.4.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -278,14 +273,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=7,
     )
-    cfg_rescale_multiplier: float = InputField(
-        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
-    )
     latents: Optional[LatentsField] = InputField(
-        default=None,
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-        ui_order=4,
+        default=None, description=FieldDescriptions.latents, input=Input.Connection
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
@@ -340,7 +329,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             unconditioned_embeddings=uc,
             text_embeddings=c,
             guidance_scale=self.cfg_scale,
-            guidance_rescale_multiplier=self.cfg_rescale_multiplier,
             extra=extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
                 threshold=0.0,  # threshold,
@@ -399,9 +387,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> List[ControlNetData]:
-        # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
-        control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
-        control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
+        # assuming fixed dimensional scaling of 8:1 for image:latents
+        control_height_resize = latents_shape[2] * 8
+        control_width_resize = latents_shape[3] * 8
         if control_input is None:
             control_list = None
         elif isinstance(control_input, list) and len(control_input) == 0:
@@ -574,6 +562,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             t2i_adapter_model: T2IAdapter
             with t2i_adapter_model_info as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
+                if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
+                    # HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
+                    # this PR: https://github.com/huggingface/diffusers/pull/5134.
+                    total_downscale_factor = total_downscale_factor // 2
 
                 # Resize the T2I-Adapter input image.
                 # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
@@ -718,11 +710,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
             with (
                 ExitStack() as exit_stack,
-                ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
+                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
                 set_seamless(unet_info.context.model, self.unet.seamless_axes),
                 unet_info as unet,
-                # Apply the LoRA after unet has been moved to its target device for faster patching.
-                ModelPatcher.apply_lora_unet(unet, _lora_loader()),
             ):
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
@@ -801,9 +791,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.0",
+    version="1.0.0",
 )
-class LatentsToImageInvocation(BaseInvocation, WithMetadata):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -885,7 +875,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -914,12 +904,12 @@ class ResizeLatentsInvocation(BaseInvocation):
     )
     width: int = InputField(
         ge=64,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         description=FieldDescriptions.width,
     )
     height: int = InputField(
         ge=64,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         description=FieldDescriptions.width,
     )
     mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
@@ -933,7 +923,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
-            size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
+            size=(self.height // 8, self.width // 8),
             mode=self.mode,
             antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
@@ -1116,7 +1106,7 @@ class BlendLatentsInvocation(BaseInvocation):
         latents_b = context.services.latents.get(self.latents_b.latents_name)
 
         if latents_a.shape != latents_b.shape:
-            raise Exception("Latents to blend must be the same size.")
+            raise "Latents to blend must be the same size."
 
         # TODO:
         device = choose_torch_device()
@@ -1171,60 +1161,3 @@ class BlendLatentsInvocation(BaseInvocation):
         # context.services.latents.set(name, resized_latents)
         context.services.latents.save(name, blended_latents)
         return build_latents_output(latents_name=name, latents=blended_latents)
-
-
-# The Crop Latents node was copied from @skunkworxdark's implementation here:
-# https://github.com/skunkworxdark/XYGrid_nodes/blob/74647fa9c1fa57d317a94bd43ca689af7f0aae5e/images_to_grids.py#L1117C1-L1167C80
-@invocation(
-    "crop_latents",
-    title="Crop Latents",
-    tags=["latents", "crop"],
-    category="latents",
-    version="1.0.0",
-)
-# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
-# Currently, if the class names conflict then 'GET /openapi.json' fails.
-class CropLatentsCoreInvocation(BaseInvocation):
-    """Crops a latent-space tensor to a box specified in image-space. The box dimensions and coordinates must be
-    divisible by the latent scale factor of 8.
-    """
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    x: int = InputField(
-        ge=0,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    y: int = InputField(
-        ge=0,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    width: int = InputField(
-        ge=1,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The width (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-    height: int = InputField(
-        ge=1,
-        multiple_of=LATENT_SCALE_FACTOR,
-        description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
-    )
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        x1 = self.x // LATENT_SCALE_FACTOR
-        y1 = self.y // LATENT_SCALE_FACTOR
-        x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
-        y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
-
-        cropped_latents = latents[..., y1:y2, x1:x2]
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, cropped_latents)
-
-        return build_latents_output(latents_name=name, latents=cropped_latents)

invokeai/app/invocations/math.py

@@ -6,9 +6,8 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
-from invokeai.app.shared.fields import FieldDescriptions
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
@@ -145,17 +144,17 @@ INTEGER_OPERATIONS = Literal[
 ]
 
 
-INTEGER_OPERATIONS_LABELS = {
-    "ADD": "Add A+B",
-    "SUB": "Subtract A-B",
-    "MUL": "Multiply A*B",
-    "DIV": "Divide A/B",
-    "EXP": "Exponentiate A^B",
-    "MOD": "Modulus A%B",
-    "ABS": "Absolute Value of A",
-    "MIN": "Minimum(A,B)",
-    "MAX": "Maximum(A,B)",
-}
+INTEGER_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    MOD="Modulus A%B",
+    ABS="Absolute Value of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
 
 
 @invocation(
@@ -183,8 +182,8 @@ class IntegerMathInvocation(BaseInvocation):
     operation: INTEGER_OPERATIONS = InputField(
         default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
     )
-    a: int = InputField(default=1, description=FieldDescriptions.num_1)
-    b: int = InputField(default=1, description=FieldDescriptions.num_2)
+    a: int = InputField(default=0, description=FieldDescriptions.num_1)
+    b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
     @field_validator("b")
     def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
@@ -231,17 +230,17 @@ FLOAT_OPERATIONS = Literal[
 ]
 
 
-FLOAT_OPERATIONS_LABELS = {
-    "ADD": "Add A+B",
-    "SUB": "Subtract A-B",
-    "MUL": "Multiply A*B",
-    "DIV": "Divide A/B",
-    "EXP": "Exponentiate A^B",
-    "ABS": "Absolute Value of A",
-    "SQRT": "Square Root of A",
-    "MIN": "Minimum(A,B)",
-    "MAX": "Maximum(A,B)",
-}
+FLOAT_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    ABS="Absolute Value of A",
+    SQRT="Square Root of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
 
 
 @invocation(
@@ -257,8 +256,8 @@ class FloatMathInvocation(BaseInvocation):
     operation: FLOAT_OPERATIONS = InputField(
         default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
     )
-    a: float = InputField(default=1, description=FieldDescriptions.num_1)
-    b: float = InputField(default=1, description=FieldDescriptions.num_2)
+    a: float = InputField(default=0, description=FieldDescriptions.num_1)
+    b: float = InputField(default=0, description=FieldDescriptions.num_2)
 
     @field_validator("b")
     def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
@@ -266,7 +265,7 @@ class FloatMathInvocation(BaseInvocation):
             raise ValueError("Cannot divide by zero")
         elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
             raise ValueError("Cannot raise zero to a negative power")
-        elif info.data["operation"] == "EXP" and isinstance(info.data["a"] ** v, complex):
+        elif info.data["operation"] == "EXP" and type(info.data["a"] ** v) is complex:
             raise ValueError("Root operation resulted in a complex number")
         return v
 

invokeai/app/invocations/metadata.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     MetadataField,
@@ -18,7 +19,6 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
-from invokeai.app.shared.fields import FieldDescriptions
 
 from ...version import __version__
 
@@ -112,8 +112,10 @@ GENERATION_MODES = Literal[
 ]
 
 
-@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.1")
-class CoreMetadataInvocation(BaseInvocation):
+@invocation(
+    "metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="2.0.0"
+)
+class MetadataAccumulatorInvocation(BaseInvocation):
     """Collects core generation metadata into a MetadataField"""
 
     generation_mode: Optional[GENERATION_MODES] = InputField(
@@ -127,9 +129,6 @@ class CoreMetadataInvocation(BaseInvocation):
     seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
     rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
     cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
-    cfg_rescale_multiplier: Optional[float] = InputField(
-        default=None, description=FieldDescriptions.cfg_rescale_multiplier
-    )
     steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
     scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
     seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
@@ -163,14 +162,13 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # High resolution fix metadata.
-    hrf_enabled: Optional[bool] = InputField(
+    hrf_width: Optional[int] = InputField(
         default=None,
-        description="Whether or not high resolution fix was enabled.",
+        description="The high resolution fix height and width multipler.",
     )
-    # TODO: should this be stricter or do we just let the UI handle it?
-    hrf_method: Optional[str] = InputField(
+    hrf_height: Optional[int] = InputField(
         default=None,
-        description="The high resolution fix upscale method.",
+        description="The high resolution fix height and width multipler.",
     )
     hrf_strength: Optional[float] = InputField(
         default=None,

invokeai/app/invocations/model.py

@@ -3,17 +3,16 @@ from typing import List, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.shared.models import FreeUConfig
-
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -37,7 +36,6 @@ class UNetField(BaseModel):
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
-    freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
 
 
 class ClipField(BaseModel):
@@ -53,32 +51,13 @@ class VaeField(BaseModel):
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
-@invocation_output("unet_output")
-class UNetOutput(BaseInvocationOutput):
-    """Base class for invocations that output a UNet field"""
-
-    unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-
-
-@invocation_output("vae_output")
-class VAEOutput(BaseInvocationOutput):
-    """Base class for invocations that output a VAE field"""
-
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
-
-
-@invocation_output("clip_output")
-class CLIPOutput(BaseInvocationOutput):
-    """Base class for invocations that output a CLIP field"""
-
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-
-
 @invocation_output("model_loader_output")
-class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
+class ModelLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
-    pass
+    unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
+    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 class MainModelField(BaseModel):
@@ -387,6 +366,13 @@ class VAEModelField(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
+@invocation_output("vae_loader_output")
+class VaeLoaderOutput(BaseInvocationOutput):
+    """VAE output"""
+
+    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
 @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
 class VaeLoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
@@ -394,10 +380,11 @@ class VaeLoaderInvocation(BaseInvocation):
     vae_model: VAEModelField = InputField(
         description=FieldDescriptions.vae_model,
         input=Input.Direct,
+        ui_type=UIType.VaeModel,
         title="VAE",
     )
 
-    def invoke(self, context: InvocationContext) -> VAEOutput:
+    def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
         base_model = self.vae_model.base_model
         model_name = self.vae_model.model_name
         model_type = ModelType.Vae
@@ -408,7 +395,7 @@ class VaeLoaderInvocation(BaseInvocation):
             model_type=model_type,
         ):
             raise Exception(f"Unkown vae name: {model_name}!")
-        return VAEOutput(
+        return VaeLoaderOutput(
             vae=VaeField(
                 vae=ModelInfo(
                     model_name=model_name,
@@ -470,24 +457,3 @@ class SeamlessModeInvocation(BaseInvocation):
             vae.seamless_axes = seamless_axes_list
 
         return SeamlessModeOutput(unet=unet, vae=vae)
-
-
-@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.0")
-class FreeUInvocation(BaseInvocation):
-    """
-    Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):
-
-    SD1.5: 1.2/1.4/0.9/0.2,
-    SD2: 1.1/1.2/0.9/0.2,
-    SDXL: 1.1/1.2/0.6/0.4,
-    """
-
-    unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet")
-    b1: float = InputField(default=1.2, ge=-1, le=3, description=FieldDescriptions.freeu_b1)
-    b2: float = InputField(default=1.4, ge=-1, le=3, description=FieldDescriptions.freeu_b2)
-    s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1)
-    s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2)
-
-    def invoke(self, context: InvocationContext) -> UNetOutput:
-        self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2)
-        return UNetOutput(unet=self.unet)

invokeai/app/invocations/noise.py

@@ -5,13 +5,13 @@ import torch
 from pydantic import field_validator
 
 from invokeai.app.invocations.latent import LatentsField
-from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.util.misc import SEED_MAX
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     OutputField,
@@ -83,16 +83,16 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
     title="Noise",
     tags=["latents", "noise"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     seed: int = InputField(
-        default=0,
         ge=0,
         le=SEED_MAX,
         description=FieldDescriptions.seed,
+        default_factory=get_random_seed,
     )
     width: int = InputField(
         default=512,

invokeai/app/invocations/onnx.py

@@ -14,7 +14,6 @@ from tqdm import tqdm
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend import BaseModelType, ModelType, SubModelType
 
@@ -24,6 +23,7 @@ from ...backend.util import choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -31,6 +31,7 @@ from .baseinvocation import (
     UIComponent,
     UIType,
     WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -53,7 +54,7 @@ ORT_TO_NP_TYPE = {
     "tensor(double)": np.float64,
 }
 
-PRECISION_VALUES = Literal[tuple(ORT_TO_NP_TYPE.keys())]
+PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]
 
 
 @invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning", version="1.0.0")
@@ -251,7 +252,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
         scheduler.set_timesteps(self.steps)
         latents = latents * np.float64(scheduler.init_noise_sigma)
 
-        extra_step_kwargs = {}
+        extra_step_kwargs = dict()
         if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
             extra_step_kwargs.update(
                 eta=0.0,
@@ -325,9 +326,9 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     title="ONNX Latents to Image",
     tags=["latents", "image", "vae", "onnx"],
     category="image",
-    version="1.2.0",
+    version="1.0.0",
 )
-class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -377,7 +378,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/param_easing.py

@@ -100,7 +100,7 @@ EASING_FUNCTIONS_MAP = {
     "BounceInOut": BounceEaseInOut,
 }
 
-EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
+EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
 
 
 # actually I think for now could just use CollectionOutput (which is list[Any]
@@ -161,7 +161,7 @@ class StepParamEasingInvocation(BaseInvocation):
         easing_class = EASING_FUNCTIONS_MAP[self.easing]
         if log_diagnostics:
             context.services.logger.debug("easing class: " + str(easing_class))
-        easing_list = []
+        easing_list = list()
         if self.mirror:  # "expected" mirroring
             # if number of steps is even, squeeze duration down to (number_of_steps)/2
             # and create reverse copy of list to append
@@ -178,7 +178,7 @@ class StepParamEasingInvocation(BaseInvocation):
                 end=self.end_value,
                 duration=base_easing_duration - 1,
             )
-            base_easing_vals = []
+            base_easing_vals = list()
             for step_index in range(base_easing_duration):
                 easing_val = easing_function.ease(step_index)
                 base_easing_vals.append(easing_val)

invokeai/app/invocations/primitives.py

@@ -5,11 +5,10 @@ from typing import Optional, Tuple
 import torch
 from pydantic import BaseModel, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
-
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -62,12 +61,12 @@ class BooleanInvocation(BaseInvocation):
     title="Boolean Collection Primitive",
     tags=["primitives", "boolean", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class BooleanCollectionInvocation(BaseInvocation):
     """A collection of boolean primitive values"""
 
-    collection: list[bool] = InputField(default=[], description="The collection of boolean values")
+    collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")
 
     def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
         return BooleanCollectionOutput(collection=self.collection)
@@ -111,12 +110,12 @@ class IntegerInvocation(BaseInvocation):
     title="Integer Collection Primitive",
     tags=["primitives", "integer", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class IntegerCollectionInvocation(BaseInvocation):
     """A collection of integer primitive values"""
 
-    collection: list[int] = InputField(default=[], description="The collection of integer values")
+    collection: list[int] = InputField(default_factory=list, description="The collection of integer values")
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(collection=self.collection)
@@ -158,12 +157,12 @@ class FloatInvocation(BaseInvocation):
     title="Float Collection Primitive",
     tags=["primitives", "float", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class FloatCollectionInvocation(BaseInvocation):
     """A collection of float primitive values"""
 
-    collection: list[float] = InputField(default=[], description="The collection of float values")
+    collection: list[float] = InputField(default_factory=list, description="The collection of float values")
 
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         return FloatCollectionOutput(collection=self.collection)
@@ -205,12 +204,12 @@ class StringInvocation(BaseInvocation):
     title="String Collection Primitive",
     tags=["primitives", "string", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class StringCollectionInvocation(BaseInvocation):
     """A collection of string primitive values"""
 
-    collection: list[str] = InputField(default=[], description="The collection of string values")
+    collection: list[str] = InputField(default_factory=list, description="The collection of string values")
 
     def invoke(self, context: InvocationContext) -> StringCollectionOutput:
         return StringCollectionOutput(collection=self.collection)
@@ -294,7 +293,7 @@ class DenoiseMaskField(BaseModel):
     """An inpaint mask field"""
 
     mask_name: str = Field(description="The name of the mask image")
-    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
+    masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
 
 
 @invocation_output("denoise_mask_output")
@@ -467,13 +466,13 @@ class ConditioningInvocation(BaseInvocation):
     title="Conditioning Collection Primitive",
     tags=["primitives", "conditioning", "collection"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
 )
 class ConditioningCollectionInvocation(BaseInvocation):
     """A collection of conditioning tensor primitive values"""
 
     collection: list[ConditioningField] = InputField(
-        default=[],
+        default_factory=list,
         description="The collection of conditioning tensors",
     )
 

invokeai/app/invocations/prompt.py

@@ -44,7 +44,7 @@ class DynamicPromptInvocation(BaseInvocation):
     title="Prompts from File",
     tags=["prompt", "file"],
     category="prompt",
-    version="1.0.1",
+    version="1.0.0",
 )
 class PromptsFromFileInvocation(BaseInvocation):
     """Loads prompts from a text file"""
@@ -82,7 +82,7 @@ class PromptsFromFileInvocation(BaseInvocation):
         end_line = start_line + max_prompts
         if max_prompts <= 0:
             end_line = np.iinfo(np.int32).max
-        with open(file_path, encoding="utf-8") as f:
+        with open(file_path) as f:
             for i, line in enumerate(f):
                 if i >= start_line and i < end_line:
                     prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))

invokeai/app/invocations/sdxl.py

@@ -1,9 +1,8 @@
-from invokeai.app.shared.fields import FieldDescriptions
-
 from ...backend.model_management import ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/t2i_adapter.py

@@ -5,16 +5,17 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType
 
 
@@ -58,7 +59,7 @@ class T2IAdapterInvocation(BaseInvocation):
         ui_order=-1,
     )
     weight: Union[float, list[float]] = InputField(
-        default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
+        default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
     )
     begin_step_percent: float = InputField(
         default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"

invokeai/app/invocations/tiles.py

@@ -1,180 +0,0 @@
-import numpy as np
-from PIL import Image
-from pydantic import BaseModel
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InputField,
-    InvocationContext,
-    OutputField,
-    WithMetadata,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
-from invokeai.backend.tiles.utils import Tile
-
-
-class TileWithImage(BaseModel):
-    tile: Tile
-    image: ImageField
-
-
-@invocation_output("calculate_image_tiles_output")
-class CalculateImageTilesOutput(BaseInvocationOutput):
-    tiles: list[Tile] = OutputField(description="The tiles coordinates that cover a particular image shape.")
-
-
-@invocation("calculate_image_tiles", title="Calculate Image Tiles", tags=["tiles"], category="tiles", version="1.0.0")
-class CalculateImageTilesInvocation(BaseInvocation):
-    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
-
-    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
-    image_height: int = InputField(
-        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
-    )
-    tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
-    tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
-    overlap: int = InputField(
-        ge=0,
-        default=128,
-        description="The target overlap, in pixels, between adjacent tiles. Adjacent tiles will overlap by at least this amount",
-    )
-
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
-        tiles = calc_tiles_with_overlap(
-            image_height=self.image_height,
-            image_width=self.image_width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            overlap=self.overlap,
-        )
-        return CalculateImageTilesOutput(tiles=tiles)
-
-
-@invocation_output("tile_to_properties_output")
-class TileToPropertiesOutput(BaseInvocationOutput):
-    coords_left: int = OutputField(description="Left coordinate of the tile relative to its parent image.")
-    coords_right: int = OutputField(description="Right coordinate of the tile relative to its parent image.")
-    coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
-    coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
-
-    # HACK: The width and height fields are 'meta' fields that can easily be calculated from the other fields on this
-    # object. Including redundant fields that can cheaply/easily be re-calculated goes against conventional API design
-    # principles. These fields are included, because 1) they are often useful in tiled workflows, and 2) they are
-    # difficult to calculate in a workflow (even though it's just a couple of subtraction nodes the graph gets
-    # surprisingly complicated).
-    width: int = OutputField(description="The width of the tile. Equal to coords_right - coords_left.")
-    height: int = OutputField(description="The height of the tile. Equal to coords_bottom - coords_top.")
-
-    overlap_top: int = OutputField(description="Overlap between this tile and its top neighbor.")
-    overlap_bottom: int = OutputField(description="Overlap between this tile and its bottom neighbor.")
-    overlap_left: int = OutputField(description="Overlap between this tile and its left neighbor.")
-    overlap_right: int = OutputField(description="Overlap between this tile and its right neighbor.")
-
-
-@invocation("tile_to_properties", title="Tile to Properties", tags=["tiles"], category="tiles", version="1.0.0")
-class TileToPropertiesInvocation(BaseInvocation):
-    """Split a Tile into its individual properties."""
-
-    tile: Tile = InputField(description="The tile to split into properties.")
-
-    def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
-        return TileToPropertiesOutput(
-            coords_left=self.tile.coords.left,
-            coords_right=self.tile.coords.right,
-            coords_top=self.tile.coords.top,
-            coords_bottom=self.tile.coords.bottom,
-            width=self.tile.coords.right - self.tile.coords.left,
-            height=self.tile.coords.bottom - self.tile.coords.top,
-            overlap_top=self.tile.overlap.top,
-            overlap_bottom=self.tile.overlap.bottom,
-            overlap_left=self.tile.overlap.left,
-            overlap_right=self.tile.overlap.right,
-        )
-
-
-@invocation_output("pair_tile_image_output")
-class PairTileImageOutput(BaseInvocationOutput):
-    tile_with_image: TileWithImage = OutputField(description="A tile description with its corresponding image.")
-
-
-@invocation("pair_tile_image", title="Pair Tile with Image", tags=["tiles"], category="tiles", version="1.0.0")
-class PairTileImageInvocation(BaseInvocation):
-    """Pair an image with its tile properties."""
-
-    # TODO(ryand): The only reason that PairTileImage is needed is because the iterate/collect nodes don't preserve
-    # order. Can this be fixed?
-
-    image: ImageField = InputField(description="The tile image.")
-    tile: Tile = InputField(description="The tile properties.")
-
-    def invoke(self, context: InvocationContext) -> PairTileImageOutput:
-        return PairTileImageOutput(
-            tile_with_image=TileWithImage(
-                tile=self.tile,
-                image=self.image,
-            )
-        )
-
-
-@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.1.0")
-class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
-    """Merge multiple tile images into a single image."""
-
-    # Inputs
-    tiles_with_images: list[TileWithImage] = InputField(description="A list of tile images with tile properties.")
-    blend_amount: int = InputField(
-        ge=0,
-        description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        images = [twi.image for twi in self.tiles_with_images]
-        tiles = [twi.tile for twi in self.tiles_with_images]
-
-        # Infer the output image dimensions from the max/min tile limits.
-        height = 0
-        width = 0
-        for tile in tiles:
-            height = max(height, tile.coords.bottom)
-            width = max(width, tile.coords.right)
-
-        # Get all tile images for processing.
-        # TODO(ryand): It pains me that we spend time PNG decoding each tile from disk when they almost certainly
-        # existed in memory at an earlier point in the graph.
-        tile_np_images: list[np.ndarray] = []
-        for image in images:
-            pil_image = context.services.images.get_pil_image(image.image_name)
-            pil_image = pil_image.convert("RGB")
-            tile_np_images.append(np.array(pil_image))
-
-        # Prepare the output image buffer.
-        # Check the first tile to determine how many image channels are expected in the output.
-        channels = tile_np_images[0].shape[-1]
-        dtype = tile_np_images[0].dtype
-        np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
-
-        merge_tiles_with_linear_blending(
-            dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
-        )
-        pil_image = Image.fromarray(np_image)
-
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/upscale.py

@@ -2,19 +2,19 @@
 from pathlib import Path
 from typing import Literal
 
-import cv2
+import cv2 as cv
 import numpy as np
 import torch
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from pydantic import ConfigDict
+from realesrgan import RealESRGANer
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -29,8 +29,8 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
-class ESRGANInvocation(BaseInvocation, WithMetadata):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
+class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -92,9 +92,9 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
 
-        upscaler = RealESRGAN(
+        upsampler = RealESRGANer(
             scale=netscale,
-            model_path=models_path / esrgan_model_path,
+            model_path=str(models_path / esrgan_model_path),
             model=rrdbnet_model,
             half=False,
             tile=self.tile_size,
@@ -102,9 +102,15 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
         # TODO: This strips the alpha... is that okay?
-        cv2_image = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
-        upscaled_image = upscaler.upscale(cv2_image)
-        pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
+        cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
+
+        # We can pass an `outscale` value here, but it just resizes the image by that factor after
+        # upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
+        # upscaling, you'll need to add a resize node after this one.
+        upscaled_image, img_mode = upsampler.enhance(cv_image)
+
+        # back to PIL
+        pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
 
         torch.cuda.empty_cache()
         if choose_torch_device() == torch.device("mps"):
@@ -118,7 +124,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=context.workflow,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -4,7 +4,7 @@ from typing import Optional, cast
 
 from invokeai.app.services.image_records.image_records_common import ImageRecord, deserialize_image_record
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 from .board_image_records_base import BoardImageRecordStorageBase
 
@@ -139,7 +139,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 (board_id,),
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            images = [deserialize_image_record(dict(r)) for r in result]
+            images = list(map(lambda r: deserialize_image_record(dict(r)), result))
 
             self._cursor.execute(
                 """--sql
@@ -167,7 +167,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 (board_id,),
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = [r[0] for r in result]
+            image_names = list(map(lambda r: r[0], result))
             return image_names
         except sqlite3.Error as e:
             self._conn.rollback()

invokeai/app/services/board_records/board_records_sqlite.py

@@ -3,7 +3,7 @@ import threading
 from typing import Union, cast
 
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.app.util.misc import uuid_string
 
 from .board_records_base import BoardRecordStorageBase
@@ -199,7 +199,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
             )
 
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            boards = [deserialize_board_record(dict(r)) for r in result]
+            boards = list(map(lambda r: deserialize_board_record(dict(r)), result))
 
             # Get the total number of boards
             self._cursor.execute(
@@ -236,7 +236,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
             )
 
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            boards = [deserialize_board_record(dict(r)) for r in result]
+            boards = list(map(lambda r: deserialize_board_record(dict(r)), result))
 
             return boards
 

invokeai/app/services/config/config_base.py

@@ -15,7 +15,7 @@ import os
 import sys
 from argparse import ArgumentParser
 from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
+from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
 
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from pydantic_settings import BaseSettings, SettingsConfigDict
@@ -24,7 +24,10 @@ from invokeai.app.services.config.config_common import PagingArgumentParser, int
 
 
 class InvokeAISettings(BaseSettings):
-    """Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
+    """
+    Runtime configuration settings in which default values are
+    read from an omegaconf .yaml file.
+    """
 
     initconf: ClassVar[Optional[DictConfig]] = None
     argparse_groups: ClassVar[Dict] = {}
@@ -32,7 +35,6 @@ class InvokeAISettings(BaseSettings):
     model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
 
     def parse_args(self, argv: Optional[list] = sys.argv[1:]):
-        """Call to parse command-line arguments."""
         parser = self.get_parser()
         opt, unknown_opts = parser.parse_known_args(argv)
         if len(unknown_opts) > 0:
@@ -47,21 +49,22 @@ class InvokeAISettings(BaseSettings):
                 setattr(self, name, value)
 
     def to_yaml(self) -> str:
-        """Return a YAML string representing our settings. This can be used as the contents of `invokeai.yaml` to restore settings later."""
+        """
+        Return a YAML string representing our settings. This can be used
+        as the contents of `invokeai.yaml` to restore settings later.
+        """
         cls = self.__class__
         type = get_args(get_type_hints(cls)["type"])[0]
-        field_dict: Dict[str, Dict[str, Any]] = {type: {}}
+        field_dict = dict({type: dict()})
         for name, field in self.model_fields.items():
             if name in cls._excluded_from_yaml():
                 continue
-            assert isinstance(field.json_schema_extra, dict)
             category = (
                 field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
             )
             value = getattr(self, name)
-            assert isinstance(category, str)
             if category not in field_dict[type]:
-                field_dict[type][category] = {}
+                field_dict[type][category] = dict()
             # keep paths as strings to make it easier to read
             field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
         conf = OmegaConf.create(field_dict)
@@ -69,7 +72,6 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def add_parser_arguments(cls, parser):
-        """Dynamically create arguments for a settings parser."""
         if "type" in get_type_hints(cls):
             settings_stanza = get_args(get_type_hints(cls)["type"])[0]
         else:
@@ -87,7 +89,7 @@ class InvokeAISettings(BaseSettings):
         # create an upcase version of the environment in
         # order to achieve case-insensitive environment
         # variables (the way Windows does)
-        upcase_environ = {}
+        upcase_environ = dict()
         for key, value in os.environ.items():
             upcase_environ[key.upper()] = value
 
@@ -114,7 +116,6 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def cmd_name(cls, command_field: str = "type") -> str:
-        """Return the category of a setting."""
         hints = get_type_hints(cls)
         if command_field in hints:
             return get_args(hints[command_field])[0]
@@ -123,7 +124,6 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def get_parser(cls) -> ArgumentParser:
-        """Get the command-line parser for a setting."""
         parser = PagingArgumentParser(
             prog=cls.cmd_name(),
             description=cls.__doc__,
@@ -152,14 +152,10 @@ class InvokeAISettings(BaseSettings):
             "free_gpu_mem",
             "xformers_enabled",
             "tiled_decode",
-            "lora_dir",
-            "embedding_dir",
-            "controlnet_dir",
         ]
 
     @classmethod
     def add_field_argument(cls, command_parser, name: str, field, default_override=None):
-        """Add the argparse arguments for a setting parser."""
         field_type = get_type_hints(cls).get(name)
         default = (
             default_override

invokeai/app/services/config/config_default.py

@@ -45,7 +45,6 @@ InvokeAI:
     ram: 13.5
     vram: 0.25
     lazy_offload: true
-    log_memory_usage: false
   Device:
     device: auto
     precision: auto
@@ -177,7 +176,6 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hint
 
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, TypeAdapter
-from pydantic.config import JsonDict
 from pydantic_settings import SettingsConfigDict
 
 from .config_base import InvokeAISettings
@@ -189,24 +187,28 @@ DEFAULT_MAX_VRAM = 0.5
 
 
 class Categories(object):
-    """Category headers for configuration variable groups."""
-
-    WebServer: JsonDict = {"category": "Web Server"}
-    Features: JsonDict = {"category": "Features"}
-    Paths: JsonDict = {"category": "Paths"}
-    Logging: JsonDict = {"category": "Logging"}
-    Development: JsonDict = {"category": "Development"}
-    Other: JsonDict = {"category": "Other"}
-    ModelCache: JsonDict = {"category": "Model Cache"}
-    Device: JsonDict = {"category": "Device"}
-    Generation: JsonDict = {"category": "Generation"}
-    Queue: JsonDict = {"category": "Queue"}
-    Nodes: JsonDict = {"category": "Nodes"}
-    MemoryPerformance: JsonDict = {"category": "Memory/Performance"}
+    WebServer = dict(category="Web Server")
+    Features = dict(category="Features")
+    Paths = dict(category="Paths")
+    Logging = dict(category="Logging")
+    Development = dict(category="Development")
+    Other = dict(category="Other")
+    ModelCache = dict(category="Model Cache")
+    Device = dict(category="Device")
+    Generation = dict(category="Generation")
+    Queue = dict(category="Queue")
+    Nodes = dict(category="Nodes")
+    MemoryPerformance = dict(category="Memory/Performance")
 
 
 class InvokeAIAppConfig(InvokeAISettings):
-    """Configuration object for InvokeAI App."""
+    """
+    Generate images using Stable Diffusion. Use "invokeai" to launch
+    the command-line client (recommended for experts only), or
+    "invokeai-web" to launch the web server. Global options
+    can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
+    setting environment variables INVOKEAI_<setting>.
+    """
 
     singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
     singleton_init: ClassVar[Optional[Dict]] = None
@@ -231,12 +233,15 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # PATHS
     root                : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
-    autoimport_dir      : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
-    conf_path           : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
-    models_dir          : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
-    legacy_conf_dir     : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
-    db_dir              : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
-    outdir              : Path = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
+    autoimport_dir      : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
+    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
+    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
+    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
+    conf_path           : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
+    models_dir          : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
+    legacy_conf_dir     : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
+    db_dir              : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
+    outdir              : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
     use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
     custom_nodes_dir    : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
     from_file           : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
@@ -256,7 +261,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     ram                 : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
     vram                : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
     lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
-    log_memory_usage    : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
 
     # DEVICE
     device              : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
@@ -279,15 +283,11 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
+    free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
     max_cache_size      : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
     max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance)
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance)
     tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance)
-    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
-    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-    # this is not referred to in the source code and can be removed entirely
-    #free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
 
     # See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
     # fmt: on
@@ -301,8 +301,8 @@ class InvokeAIAppConfig(InvokeAISettings):
         clobber=False,
     ):
         """
-        Update settings with contents of init file, environment, and command-line settings.
-
+        Update settings with contents of init file, environment, and
+        command-line settings.
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
         :param clobber: ovewrite any initialization parameters passed during initialization
@@ -335,7 +335,9 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @classmethod
     def get_config(cls, **kwargs) -> InvokeAIAppConfig:
-        """Return a singleton InvokeAIAppConfig configuration object."""
+        """
+        This returns a singleton InvokeAIAppConfig configuration object.
+        """
         if (
             cls.singleton_config is None
             or type(cls.singleton_config) is not cls
@@ -347,7 +349,9 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def root_path(self) -> Path:
-        """Path to the runtime root directory."""
+        """
+        Path to the runtime root directory
+        """
         if self.root:
             root = Path(self.root).expanduser().absolute()
         else:
@@ -357,7 +361,9 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def root_dir(self) -> Path:
-        """Alias for above."""
+        """
+        Alias for above.
+        """
         return self.root_path
 
     def _resolve(self, partial_path: Path) -> Path:
@@ -365,95 +371,108 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def init_file_path(self) -> Path:
-        """Path to invokeai.yaml."""
-        resolved_path = self._resolve(INIT_FILE)
-        assert resolved_path is not None
-        return resolved_path
+        """
+        Path to invokeai.yaml
+        """
+        return self._resolve(INIT_FILE)
 
     @property
-    def output_path(self) -> Optional[Path]:
-        """Path to defaults outputs directory."""
+    def output_path(self) -> Path:
+        """
+        Path to defaults outputs directory.
+        """
         return self._resolve(self.outdir)
 
     @property
     def db_path(self) -> Path:
-        """Path to the invokeai.db file."""
-        db_dir = self._resolve(self.db_dir)
-        assert db_dir is not None
-        return db_dir / DB_FILE
+        """
+        Path to the invokeai.db file.
+        """
+        return self._resolve(self.db_dir) / DB_FILE
 
     @property
-    def model_conf_path(self) -> Optional[Path]:
-        """Path to models configuration file."""
+    def model_conf_path(self) -> Path:
+        """
+        Path to models configuration file.
+        """
         return self._resolve(self.conf_path)
 
     @property
-    def legacy_conf_path(self) -> Optional[Path]:
-        """Path to directory of legacy configuration files (e.g. v1-inference.yaml)."""
+    def legacy_conf_path(self) -> Path:
+        """
+        Path to directory of legacy configuration files (e.g. v1-inference.yaml)
+        """
         return self._resolve(self.legacy_conf_dir)
 
     @property
-    def models_path(self) -> Optional[Path]:
-        """Path to the models directory."""
+    def models_path(self) -> Path:
+        """
+        Path to the models directory
+        """
         return self._resolve(self.models_dir)
 
     @property
     def custom_nodes_path(self) -> Path:
-        """Path to the custom nodes directory."""
-        custom_nodes_path = self._resolve(self.custom_nodes_dir)
-        assert custom_nodes_path is not None
-        return custom_nodes_path
+        """
+        Path to the custom nodes directory
+        """
+        return self._resolve(self.custom_nodes_dir)
 
     # the following methods support legacy calls leftover from the Globals era
     @property
     def full_precision(self) -> bool:
-        """Return true if precision set to float32."""
+        """Return true if precision set to float32"""
         return self.precision == "float32"
 
     @property
     def try_patchmatch(self) -> bool:
-        """Return true if patchmatch true."""
+        """Return true if patchmatch true"""
         return self.patchmatch
 
     @property
     def nsfw_checker(self) -> bool:
-        """Return value for NSFW checker. The NSFW node is always active and disabled from Web UI."""
+        """NSFW node is always active and disabled from Web UIe"""
         return True
 
     @property
     def invisible_watermark(self) -> bool:
-        """Return value of invisible watermark. It is always active and disabled from Web UI."""
+        """invisible watermark node is always active and disabled from Web UIe"""
         return True
 
     @property
     def ram_cache_size(self) -> Union[Literal["auto"], float]:
-        """Return the ram cache size using the legacy or modern setting."""
         return self.max_cache_size or self.ram
 
     @property
     def vram_cache_size(self) -> Union[Literal["auto"], float]:
-        """Return the vram cache size using the legacy or modern setting."""
         return self.max_vram_cache_size or self.vram
 
     @property
     def use_cpu(self) -> bool:
-        """Return true if the device is set to CPU or the always_use_cpu flag is set."""
         return self.always_use_cpu or self.device == "cpu"
 
     @property
     def disable_xformers(self) -> bool:
-        """Return true if enable_xformers is false (reversed logic) and attention type is not set to xformers."""
+        """
+        Return true if enable_xformers is false (reversed logic)
+        and attention type is not set to xformers.
+        """
         disabled_in_config = not self.xformers_enabled
         return disabled_in_config and self.attention_type != "xformers"
 
     @staticmethod
     def find_root() -> Path:
-        """Choose the runtime root directory when not specified on command line or init file."""
+        """
+        Choose the runtime root directory when not specified on command line or
+        init file.
+        """
         return _find_root()
 
 
 def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
-    """Legacy function which returns InvokeAIAppConfig.get_config()."""
+    """
+    Legacy function which returns InvokeAIAppConfig.get_config()
+    """
     return InvokeAIAppConfig.get_config(**kwargs)
 
 
@@ -461,7 +480,7 @@ def _find_root() -> Path:
     venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
     if os.environ.get("INVOKEAI_ROOT"):
         root = Path(os.environ["INVOKEAI_ROOT"])
-    elif any((venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]):
+    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
         root = (venv.parent).resolve()
     else:
         root = Path("~/invokeai").expanduser().resolve()

invokeai/app/services/events/events_base.py

@@ -27,7 +27,7 @@ class EventServiceBase:
         payload["timestamp"] = get_timestamp()
         self.dispatch(
             event_name=EventServiceBase.queue_event,
-            payload={"event": event_name, "data": payload},
+            payload=dict(event=event_name, data=payload),
         )
 
     # Define events here for every event in the system.
@@ -48,18 +48,18 @@ class EventServiceBase:
         """Emitted when there is generation progress"""
         self.__emit_queue_event(
             event_name="generator_progress",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node.get("id"),
-                "source_node_id": source_node_id,
-                "progress_image": progress_image.model_dump() if progress_image is not None else None,
-                "step": step,
-                "order": order,
-                "total_steps": total_steps,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node_id=node.get("id"),
+                source_node_id=source_node_id,
+                progress_image=progress_image.model_dump() if progress_image is not None else None,
+                step=step,
+                order=order,
+                total_steps=total_steps,
+            ),
         )
 
     def emit_invocation_complete(
@@ -75,15 +75,15 @@ class EventServiceBase:
         """Emitted when an invocation has completed"""
         self.__emit_queue_event(
             event_name="invocation_complete",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node": node,
-                "source_node_id": source_node_id,
-                "result": result,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node=node,
+                source_node_id=source_node_id,
+                result=result,
+            ),
         )
 
     def emit_invocation_error(
@@ -100,16 +100,16 @@ class EventServiceBase:
         """Emitted when an invocation has completed"""
         self.__emit_queue_event(
             event_name="invocation_error",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node": node,
-                "source_node_id": source_node_id,
-                "error_type": error_type,
-                "error": error,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node=node,
+                source_node_id=source_node_id,
+                error_type=error_type,
+                error=error,
+            ),
         )
 
     def emit_invocation_started(
@@ -124,14 +124,14 @@ class EventServiceBase:
         """Emitted when an invocation has started"""
         self.__emit_queue_event(
             event_name="invocation_started",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node": node,
-                "source_node_id": source_node_id,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node=node,
+                source_node_id=source_node_id,
+            ),
         )
 
     def emit_graph_execution_complete(
@@ -140,12 +140,12 @@ class EventServiceBase:
         """Emitted when a session has completed all invocations"""
         self.__emit_queue_event(
             event_name="graph_execution_state_complete",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+            ),
         )
 
     def emit_model_load_started(
@@ -162,16 +162,16 @@ class EventServiceBase:
         """Emitted when a model is requested"""
         self.__emit_queue_event(
             event_name="model_load_started",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "model_name": model_name,
-                "base_model": base_model,
-                "model_type": model_type,
-                "submodel": submodel,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+            ),
         )
 
     def emit_model_load_completed(
@@ -189,19 +189,19 @@ class EventServiceBase:
         """Emitted when a model is correctly loaded (returns model info)"""
         self.__emit_queue_event(
             event_name="model_load_completed",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "model_name": model_name,
-                "base_model": base_model,
-                "model_type": model_type,
-                "submodel": submodel,
-                "hash": model_info.hash,
-                "location": str(model_info.location),
-                "precision": str(model_info.precision),
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+                hash=model_info.hash,
+                location=str(model_info.location),
+                precision=str(model_info.precision),
+            ),
         )
 
     def emit_session_retrieval_error(
@@ -216,14 +216,14 @@ class EventServiceBase:
         """Emitted when session retrieval fails"""
         self.__emit_queue_event(
             event_name="session_retrieval_error",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "error_type": error_type,
-                "error": error,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                error_type=error_type,
+                error=error,
+            ),
         )
 
     def emit_invocation_retrieval_error(
@@ -239,15 +239,15 @@ class EventServiceBase:
         """Emitted when invocation retrieval fails"""
         self.__emit_queue_event(
             event_name="invocation_retrieval_error",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node_id,
-                "error_type": error_type,
-                "error": error,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node_id=node_id,
+                error_type=error_type,
+                error=error,
+            ),
         )
 
     def emit_session_canceled(
@@ -260,12 +260,12 @@ class EventServiceBase:
         """Emitted when a session is canceled"""
         self.__emit_queue_event(
             event_name="session_canceled",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+            ),
         )
 
     def emit_queue_item_status_changed(
@@ -277,39 +277,39 @@ class EventServiceBase:
         """Emitted when a queue item's status changes"""
         self.__emit_queue_event(
             event_name="queue_item_status_changed",
-            payload={
-                "queue_id": queue_status.queue_id,
-                "queue_item": {
-                    "queue_id": session_queue_item.queue_id,
-                    "item_id": session_queue_item.item_id,
-                    "status": session_queue_item.status,
-                    "batch_id": session_queue_item.batch_id,
-                    "session_id": session_queue_item.session_id,
-                    "error": session_queue_item.error,
-                    "created_at": str(session_queue_item.created_at) if session_queue_item.created_at else None,
-                    "updated_at": str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
-                    "started_at": str(session_queue_item.started_at) if session_queue_item.started_at else None,
-                    "completed_at": str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
-                },
-                "batch_status": batch_status.model_dump(),
-                "queue_status": queue_status.model_dump(),
-            },
+            payload=dict(
+                queue_id=queue_status.queue_id,
+                queue_item=dict(
+                    queue_id=session_queue_item.queue_id,
+                    item_id=session_queue_item.item_id,
+                    status=session_queue_item.status,
+                    batch_id=session_queue_item.batch_id,
+                    session_id=session_queue_item.session_id,
+                    error=session_queue_item.error,
+                    created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
+                    updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
+                    started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
+                    completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
+                ),
+                batch_status=batch_status.model_dump(),
+                queue_status=queue_status.model_dump(),
+            ),
         )
 
     def emit_batch_enqueued(self, enqueue_result: EnqueueBatchResult) -> None:
         """Emitted when a batch is enqueued"""
         self.__emit_queue_event(
             event_name="batch_enqueued",
-            payload={
-                "queue_id": enqueue_result.queue_id,
-                "batch_id": enqueue_result.batch.batch_id,
-                "enqueued": enqueue_result.enqueued,
-            },
+            payload=dict(
+                queue_id=enqueue_result.queue_id,
+                batch_id=enqueue_result.batch.batch_id,
+                enqueued=enqueue_result.enqueued,
+            ),
         )
 
     def emit_queue_cleared(self, queue_id: str) -> None:
         """Emitted when the queue is cleared"""
         self.__emit_queue_event(
             event_name="queue_cleared",
-            payload={"queue_id": queue_id},
+            payload=dict(queue_id=queue_id),
         )

invokeai/app/services/image_files/image_files_base.py

@@ -4,8 +4,7 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 
 
 class ImageFileStorageBase(ABC):
@@ -34,7 +33,7 @@ class ImageFileStorageBase(ABC):
         image: PILImageType,
         image_name: str,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
         thumbnail_size: int = 256,
     ) -> None:
         """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@@ -44,8 +43,3 @@ class ImageFileStorageBase(ABC):
     def delete(self, image_name: str) -> None:
         """Deletes an image and its thumbnail (if one exists)."""
         pass
-
-    @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
-        """Gets the workflow of an image."""
-        pass

invokeai/app/services/image_files/image_files_disk.py

@@ -7,9 +7,8 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
-from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 from .image_files_base import ImageFileStorageBase
@@ -26,7 +25,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
     __invoker: Invoker
 
     def __init__(self, output_folder: Union[str, Path]):
-        self.__cache = {}
+        self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = 10  # TODO: get this from config
 
@@ -57,7 +56,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         image: PILImageType,
         image_name: str,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
@@ -65,19 +64,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
             image_path = self.get_path(image_name)
 
             pnginfo = PngImagePlugin.PngInfo()
-            info_dict = {}
 
             if metadata is not None:
-                metadata_json = metadata.model_dump_json()
-                info_dict["invokeai_metadata"] = metadata_json
-                pnginfo.add_text("invokeai_metadata", metadata_json)
+                pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
             if workflow is not None:
-                workflow_json = workflow.model_dump_json()
-                info_dict["invokeai_workflow"] = workflow_json
-                pnginfo.add_text("invokeai_workflow", workflow_json)
+                pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
 
-            # When saving the image, the image object's info field is not populated. We need to set it
-            image.info = info_dict
             image.save(
                 image_path,
                 "PNG",
@@ -129,13 +121,6 @@ class DiskImageFileStorage(ImageFileStorageBase):
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
 
-    def get_workflow(self, image_name: str) -> WorkflowWithoutID | None:
-        image = self.get(image_name)
-        workflow = image.info.get("invokeai_workflow", None)
-        if workflow is not None:
-            return WorkflowWithoutID.model_validate_json(workflow)
-        return None
-
     def __validate_storage_folders(self) -> None:
         """Checks if the required output folders exist and create them if they don't"""
         folders: list[Path] = [self.__output_folder, self.__thumbnails_folder]

invokeai/app/services/image_records/image_records_base.py

@@ -75,7 +75,6 @@ class ImageRecordStorageBase(ABC):
         image_category: ImageCategory,
         width: int,
         height: int,
-        has_workflow: bool,
         is_intermediate: Optional[bool] = False,
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,

invokeai/app/services/image_records/image_records_common.py

@@ -90,24 +90,25 @@ class ImageRecordDeleteException(Exception):
 
 
 IMAGE_DTO_COLS = ", ".join(
-    [
-        "images." + c
-        for c in [
-            "image_name",
-            "image_origin",
-            "image_category",
-            "width",
-            "height",
-            "session_id",
-            "node_id",
-            "has_workflow",
-            "is_intermediate",
-            "created_at",
-            "updated_at",
-            "deleted_at",
-            "starred",
-        ]
-    ]
+    list(
+        map(
+            lambda c: "images." + c,
+            [
+                "image_name",
+                "image_origin",
+                "image_category",
+                "width",
+                "height",
+                "session_id",
+                "node_id",
+                "is_intermediate",
+                "created_at",
+                "updated_at",
+                "deleted_at",
+                "starred",
+            ],
+        )
+    )
 )
 
 
@@ -146,7 +147,6 @@ class ImageRecord(BaseModelExcludeNull):
     """The node ID that generated this image, if it is a generated image."""
     starred: bool = Field(description="Whether this image is starred.")
     """Whether this image is starred."""
-    has_workflow: bool = Field(description="Whether this image has a workflow.")
 
 
 class ImageRecordChanges(BaseModelExcludeNull, extra="allow"):
@@ -190,7 +190,6 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
     deleted_at = image_dict.get("deleted_at", get_iso_timestamp())
     is_intermediate = image_dict.get("is_intermediate", False)
     starred = image_dict.get("starred", False)
-    has_workflow = image_dict.get("has_workflow", False)
 
     return ImageRecord(
         image_name=image_name,
@@ -205,5 +204,4 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
         deleted_at=deleted_at,
         is_intermediate=is_intermediate,
         starred=starred,
-        has_workflow=has_workflow,
     )

invokeai/app/services/image_records/image_records_sqlite.py

@@ -5,7 +5,7 @@ from typing import Optional, Union, cast
 
 from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 from .image_records_base import ImageRecordStorageBase
 from .image_records_common import (
@@ -117,16 +117,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             """
         )
 
-        self._cursor.execute("PRAGMA table_info(images)")
-        columns = [column[1] for column in self._cursor.fetchall()]
-        if "has_workflow" not in columns:
-            self._cursor.execute(
-                """--sql
-                ALTER TABLE images
-                ADD COLUMN has_workflow BOOLEAN DEFAULT FALSE;
-                """
-            )
-
     def get(self, image_name: str) -> ImageRecord:
         try:
             self._lock.acquire()
@@ -273,7 +263,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
             if categories is not None:
                 # Convert the enum values to unique list of strings
-                category_strings = [c.value for c in set(categories)]
+                category_strings = list(map(lambda c: c.value, set(categories)))
                 # Create the correct length of placeholders
                 placeholders = ",".join("?" * len(category_strings))
 
@@ -317,7 +307,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             # Build the list of images, deserializing each row
             self._cursor.execute(images_query, images_params)
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            images = [deserialize_image_record(dict(r)) for r in result]
+            images = list(map(lambda r: deserialize_image_record(dict(r)), result))
 
             # Set up and execute the count query, without pagination
             count_query += query_conditions + ";"
@@ -396,7 +386,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 """
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = [r[0] for r in result]
+            image_names = list(map(lambda r: r[0], result))
             self._cursor.execute(
                 """--sql
                 DELETE FROM images
@@ -418,7 +408,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         image_category: ImageCategory,
         width: int,
         height: int,
-        has_workflow: bool,
         is_intermediate: Optional[bool] = False,
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
@@ -440,10 +429,9 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     session_id,
                     metadata,
                     is_intermediate,
-                    starred,
-                    has_workflow
+                    starred
                     )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
                 """,
                 (
                     image_name,
@@ -456,7 +444,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     metadata_json,
                     is_intermediate,
                     starred,
-                    has_workflow,
                 ),
             )
             self._conn.commit()

invokeai/app/services/images/images_base.py

@@ -3,7 +3,7 @@ from typing import Callable, Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
     ImageRecord,
@@ -12,7 +12,6 @@ from invokeai.app.services.image_records.image_records_common import (
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 
 class ImageServiceABC(ABC):
@@ -22,8 +21,8 @@ class ImageServiceABC(ABC):
     _on_deleted_callbacks: list[Callable[[str], None]]
 
     def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
 
     def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
         """Register a callback for when an image is changed"""
@@ -52,7 +51,7 @@ class ImageServiceABC(ABC):
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -86,11 +85,6 @@ class ImageServiceABC(ABC):
         """Gets an image's metadata."""
         pass
 
-    @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
-        """Gets an image's workflow."""
-        pass
-
     @abstractmethod
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         """Gets an image's path."""

invokeai/app/services/images/images_common.py

@@ -24,6 +24,11 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
         default=None, description="The id of the board the image belongs to, if one exists."
     )
     """The id of the board the image belongs to, if one exists."""
+    workflow_id: Optional[str] = Field(
+        default=None,
+        description="The workflow that generated this image.",
+    )
+    """The workflow that generated this image."""
 
 
 def image_record_to_dto(
@@ -31,6 +36,7 @@ def image_record_to_dto(
     image_url: str,
     thumbnail_url: str,
     board_id: Optional[str],
+    workflow_id: Optional[str],
 ) -> ImageDTO:
     """Converts an image record to an image DTO."""
     return ImageDTO(
@@ -38,4 +44,5 @@ def image_record_to_dto(
         image_url=image_url,
         thumbnail_url=thumbnail_url,
         board_id=board_id,
+        workflow_id=workflow_id,
     )

invokeai/app/services/images/images_default.py

@@ -2,10 +2,9 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 from ..image_files.image_files_common import (
     ImageFileDeleteException,
@@ -43,7 +42,7 @@ class ImageService(ImageServiceABC):
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        workflow: Optional[WorkflowField] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
             raise InvalidOriginException
@@ -56,6 +55,12 @@ class ImageService(ImageServiceABC):
         (width, height) = image.size
 
         try:
+            if workflow is not None:
+                created_workflow = self.__invoker.services.workflow_records.create(workflow)
+                workflow_id = created_workflow.model_dump()["id"]
+            else:
+                workflow_id = None
+
             # TODO: Consider using a transaction here to ensure consistency between storage and database
             self.__invoker.services.image_records.save(
                 # Non-nullable fields
@@ -64,7 +69,6 @@ class ImageService(ImageServiceABC):
                 image_category=image_category,
                 width=width,
                 height=height,
-                has_workflow=workflow is not None,
                 # Meta fields
                 is_intermediate=is_intermediate,
                 # Nullable fields
@@ -74,6 +78,8 @@ class ImageService(ImageServiceABC):
             )
             if board_id is not None:
                 self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
+            if workflow_id is not None:
+                self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name)
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow
             )
@@ -137,6 +143,7 @@ class ImageService(ImageServiceABC):
                 image_url=self.__invoker.services.urls.get_image_url(image_name),
                 thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
                 board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
+                workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name),
             )
 
             return image_dto
@@ -157,15 +164,18 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image DTO")
             raise e
 
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
         try:
-            return self.__invoker.services.image_files.get_workflow(image_name)
-        except ImageFileNotFoundException:
-            self.__invoker.services.logger.error("Image file not found")
-            raise
-        except Exception:
-            self.__invoker.services.logger.error("Problem getting image workflow")
+            workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
+            if workflow_id is None:
+                return None
+            return self.__invoker.services.workflow_records.get(workflow_id)
+        except ImageRecordNotFoundException:
+            self.__invoker.services.logger.error("Image record not found")
             raise
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem getting image DTO")
+            raise e
 
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         try:
@@ -207,15 +217,18 @@ class ImageService(ImageServiceABC):
                 board_id,
             )
 
-            image_dtos = [
-                image_record_to_dto(
-                    image_record=r,
-                    image_url=self.__invoker.services.urls.get_image_url(r.image_name),
-                    thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
-                    board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
+            image_dtos = list(
+                map(
+                    lambda r: image_record_to_dto(
+                        image_record=r,
+                        image_url=self.__invoker.services.urls.get_image_url(r.image_name),
+                        thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
+                        board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
+                        workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
+                    ),
+                    results.items,
                 )
-                for r in results.items
-            ]
+            )
 
             return OffsetPaginatedResults[ImageDTO](
                 items=image_dtos,

invokeai/app/services/invocation_processor/invocation_processor_base.py

@@ -1,5 +1,5 @@
 from abc import ABC
 
 
-class InvocationProcessorABC(ABC):  # noqa: B024
+class InvocationProcessorABC(ABC):
     pass

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -26,7 +26,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         self.__invoker_thread = Thread(
             name="invoker_processor",
             target=self.__process,
-            kwargs={"stop_event": self.__stop_event},
+            kwargs=dict(stop_event=self.__stop_event),
         )
         self.__invoker_thread.daemon = True  # TODO: make async and do not use threads
         self.__invoker_thread.start()
@@ -108,7 +108,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                                 queue_item_id=queue_item.session_queue_item_id,
                                 queue_id=queue_item.session_queue_id,
                                 queue_batch_id=queue_item.session_queue_batch_id,
-                                workflow=queue_item.workflow,
                             )
                         )
 
@@ -179,7 +178,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             session_queue_item_id=queue_item.session_queue_item_id,
                             session_queue_id=queue_item.session_queue_id,
                             graph_execution_state=graph_execution_state,
-                            workflow=queue_item.workflow,
                             invoke_all=True,
                         )
                     except Exception as e:

invokeai/app/services/invocation_queue/invocation_queue_common.py

@@ -1,12 +1,9 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import time
-from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
-
 
 class InvocationQueueItem(BaseModel):
     graph_execution_state_id: str = Field(description="The ID of the graph execution state")
@@ -18,6 +15,5 @@ class InvocationQueueItem(BaseModel):
     session_queue_batch_id: str = Field(
         description="The ID of the session batch from which this invocation queue item came"
     )
-    workflow: Optional[WorkflowWithoutID] = Field(description="The workflow associated with this queue item")
     invoke_all: bool = Field(default=False)
     timestamp: float = Field(default_factory=time.time)

invokeai/app/services/invocation_queue/invocation_queue_memory.py

@@ -14,7 +14,7 @@ class MemoryInvocationQueue(InvocationQueueABC):
 
     def __init__(self):
         self.__queue = Queue()
-        self.__cancellations = {}
+        self.__cancellations = dict()
 
     def get(self) -> InvocationQueueItem:
         item = self.__queue.get()

invokeai/app/services/invocation_services.py

@@ -22,12 +22,12 @@ if TYPE_CHECKING:
     from .item_storage.item_storage_base import ItemStorageABC
     from .latents_storage.latents_storage_base import LatentsStorageBase
     from .model_manager.model_manager_base import ModelManagerServiceBase
-    from .model_records import ModelRecordServiceBase
     from .names.names_base import NameServiceBase
     from .session_processor.session_processor_base import SessionProcessorBase
     from .session_queue.session_queue_base import SessionQueueBase
     from .shared.graph import GraphExecutionState, LibraryGraph
     from .urls.urls_base import UrlServiceBase
+    from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
 
@@ -49,7 +49,6 @@ class InvocationServices:
     latents: "LatentsStorageBase"
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
-    model_records: "ModelRecordServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
@@ -58,6 +57,7 @@ class InvocationServices:
     invocation_cache: "InvocationCacheBase"
     names: "NameServiceBase"
     urls: "UrlServiceBase"
+    workflow_image_records: "WorkflowImageRecordsStorageBase"
     workflow_records: "WorkflowRecordsStorageBase"
 
     def __init__(
@@ -76,7 +76,6 @@ class InvocationServices:
         latents: "LatentsStorageBase",
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
-        model_records: "ModelRecordServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
@@ -85,6 +84,7 @@ class InvocationServices:
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
         urls: "UrlServiceBase",
+        workflow_image_records: "WorkflowImageRecordsStorageBase",
         workflow_records: "WorkflowRecordsStorageBase",
     ):
         self.board_images = board_images
@@ -101,7 +101,6 @@ class InvocationServices:
         self.latents = latents
         self.logger = logger
         self.model_manager = model_manager
-        self.model_records = model_records
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue
@@ -110,4 +109,5 @@ class InvocationServices:
         self.invocation_cache = invocation_cache
         self.names = names
         self.urls = urls
+        self.workflow_image_records = workflow_image_records
         self.workflow_records = workflow_records

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -122,7 +122,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
     def log_stats(self):
         completed = set()
         errored = set()
-        for graph_id, _node_log in self._stats.items():
+        for graph_id, node_log in self._stats.items():
             try:
                 current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
             except Exception:
@@ -142,7 +142,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
             cache_stats = self._cache_stats[graph_id]
             hwm = cache_stats.high_watermark / GIG
             tot = cache_stats.cache_size / GIG
-            loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GIG
+            loaded = sum([v for v in cache_stats.loaded_model_sizes.values()]) / GIG
 
             logger.info(f"TOTAL GRAPH EXECUTION TIME:  {total_time:7.3f}s")
             logger.info("RAM used by InvokeAI process: " + "%4.2fG" % self.ram_used + f" ({self.ram_changed:+5.3f}G)")

invokeai/app/services/invoker.py

@@ -2,8 +2,6 @@
 
 from typing import Optional
 
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
-
 from .invocation_queue.invocation_queue_common import InvocationQueueItem
 from .invocation_services import InvocationServices
 from .shared.graph import Graph, GraphExecutionState
@@ -24,7 +22,6 @@ class Invoker:
         session_queue_item_id: int,
         session_queue_batch_id: str,
         graph_execution_state: GraphExecutionState,
-        workflow: Optional[WorkflowWithoutID] = None,
         invoke_all: bool = False,
     ) -> Optional[str]:
         """Determines the next node to invoke and enqueues it, preparing if needed.
@@ -46,7 +43,6 @@ class Invoker:
                 session_queue_batch_id=session_queue_batch_id,
                 graph_execution_state_id=graph_execution_state.id,
                 invocation_id=invocation.id,
-                workflow=workflow,
                 invoke_all=invoke_all,
             )
         )

invokeai/app/services/item_storage/item_storage_base.py

@@ -15,8 +15,8 @@ class ItemStorageABC(ABC, Generic[T]):
     _on_deleted_callbacks: list[Callable[[str], None]]
 
     def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
 
     """Base item storage class"""
 

invokeai/app/services/item_storage/item_storage_sqlite.py

@@ -5,7 +5,7 @@ from typing import Generic, Optional, TypeVar, get_args
 from pydantic import BaseModel, TypeAdapter
 
 from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
 from .item_storage_base import ItemStorageABC
 
@@ -112,7 +112,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
             )
             result = self._cursor.fetchall()
 
-            items = [self._parse_item(r[0]) for r in result]
+            items = list(map(lambda r: self._parse_item(r[0]), result))
 
             self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
             count = self._cursor.fetchone()[0]
@@ -132,7 +132,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
             )
             result = self._cursor.fetchall()
 
-            items = [self._parse_item(r[0]) for r in result]
+            items = list(map(lambda r: self._parse_item(r[0]), result))
 
             self._cursor.execute(
                 f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",

invokeai/app/services/latents_storage/latents_storage_base.py

@@ -13,8 +13,8 @@ class LatentsStorageBase(ABC):
     _on_deleted_callbacks: list[Callable[[str], None]]
 
     def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
 
     @abstractmethod
     def get(self, name: str) -> torch.Tensor:

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -5,8 +5,6 @@ from typing import Union
 
 import torch
 
-from invokeai.app.services.invoker import Invoker
-
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -19,10 +17,6 @@ class DiskLatentsStorage(LatentsStorageBase):
         self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__output_folder.mkdir(parents=True, exist_ok=True)
 
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        self._delete_all_latents()
-
     def get(self, name: str) -> torch.Tensor:
         latent_path = self.get_path(name)
         return torch.load(latent_path)
@@ -38,21 +32,3 @@ class DiskLatentsStorage(LatentsStorageBase):
 
     def get_path(self, name: str) -> Path:
         return self.__output_folder / name
-
-    def _delete_all_latents(self) -> None:
-        """
-        Deletes all latents from disk.
-        Must be called after we have access to `self._invoker` (e.g. in `start()`).
-        """
-        deleted_latents_count = 0
-        freed_space = 0
-        for latents_file in Path(self.__output_folder).glob("*"):
-            if latents_file.is_file():
-                freed_space += latents_file.stat().st_size
-                deleted_latents_count += 1
-                latents_file.unlink()
-        if deleted_latents_count > 0:
-            freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
-            self._invoker.services.logger.info(
-                f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
-            )

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -5,8 +5,6 @@ from typing import Dict, Optional
 
 import torch
 
-from invokeai.app.services.invoker import Invoker
-
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -21,22 +19,10 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
     def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
         super().__init__()
         self.__underlying_storage = underlying_storage
-        self.__cache = {}
+        self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = max_cache_size
 
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        start_op = getattr(self.__underlying_storage, "start", None)
-        if callable(start_op):
-            start_op(invoker)
-
-    def stop(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        stop_op = getattr(self.__underlying_storage, "stop", None)
-        if callable(stop_op):
-            stop_op(invoker)
-
     def get(self, name: str) -> torch.Tensor:
         cache_item = self.__get_cache(name)
         if cache_item is not None:

invokeai/app/services/model_records/__init__.py

@@ -1,8 +0,0 @@
-"""Init file for model record services."""
-from .model_records_base import (  # noqa F401
-    DuplicateModelException,
-    InvalidModelException,
-    ModelRecordServiceBase,
-    UnknownModelException,
-)
-from .model_records_sql import ModelRecordServiceSQL  # noqa F401

invokeai/app/services/model_records/model_records_base.py

@@ -1,169 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
-"""
-Abstract base class for storing and retrieving model configuration records.
-"""
-
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import List, Optional, Union
-
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType
-
-# should match the InvokeAI version when this is first released.
-CONFIG_FILE_VERSION = "3.2.0"
-
-
-class DuplicateModelException(Exception):
-    """Raised on an attempt to add a model with the same key twice."""
-
-
-class InvalidModelException(Exception):
-    """Raised when an invalid model is detected."""
-
-
-class UnknownModelException(Exception):
-    """Raised on an attempt to fetch or delete a model with a nonexistent key."""
-
-
-class ConfigFileVersionMismatchException(Exception):
-    """Raised on an attempt to open a config with an incompatible version."""
-
-
-class ModelRecordServiceBase(ABC):
-    """Abstract base class for storage and retrieval of model configs."""
-
-    @property
-    @abstractmethod
-    def version(self) -> str:
-        """Return the config file/database schema version."""
-        pass
-
-    @abstractmethod
-    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
-        """
-        Add a model to the database.
-
-        :param key: Unique key for the model
-        :param config: Model configuration record, either a dict with the
-         required fields or a ModelConfigBase instance.
-
-        Can raise DuplicateModelException and InvalidModelConfigException exceptions.
-        """
-        pass
-
-    @abstractmethod
-    def del_model(self, key: str) -> None:
-        """
-        Delete a model.
-
-        :param key: Unique key for the model to be deleted
-
-        Can raise an UnknownModelException
-        """
-        pass
-
-    @abstractmethod
-    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
-        """
-        Update the model, returning the updated version.
-
-        :param key: Unique key for the model to be updated
-        :param config: Model configuration record. Either a dict with the
-         required fields, or a ModelConfigBase instance.
-        """
-        pass
-
-    @abstractmethod
-    def get_model(self, key: str) -> AnyModelConfig:
-        """
-        Retrieve the configuration for the indicated model.
-
-        :param key: Key of model config to be fetched.
-
-        Exceptions: UnknownModelException
-        """
-        pass
-
-    @abstractmethod
-    def exists(self, key: str) -> bool:
-        """
-        Return True if a model with the indicated key exists in the databse.
-
-        :param key: Unique key for the model to be deleted
-        """
-        pass
-
-    @abstractmethod
-    def search_by_path(
-        self,
-        path: Union[str, Path],
-    ) -> List[AnyModelConfig]:
-        """Return the model(s) having the indicated path."""
-        pass
-
-    @abstractmethod
-    def search_by_hash(
-        self,
-        hash: str,
-    ) -> List[AnyModelConfig]:
-        """Return the model(s) having the indicated original hash."""
-        pass
-
-    @abstractmethod
-    def search_by_attr(
-        self,
-        model_name: Optional[str] = None,
-        base_model: Optional[BaseModelType] = None,
-        model_type: Optional[ModelType] = None,
-    ) -> List[AnyModelConfig]:
-        """
-        Return models matching name, base and/or type.
-
-        :param model_name: Filter by name of model (optional)
-        :param base_model: Filter by base model (optional)
-        :param model_type: Filter by type of model (optional)
-
-        If none of the optional filters are passed, will return all
-        models in the database.
-        """
-        pass
-
-    def all_models(self) -> List[AnyModelConfig]:
-        """Return all the model configs in the database."""
-        return self.search_by_attr()
-
-    def model_info_by_name(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> AnyModelConfig:
-        """
-        Return information about a single model using its name, base type and model type.
-
-        If there are more than one model that match, raises a DuplicateModelException.
-        If no model matches, raises an UnknownModelException
-        """
-        model_configs = self.search_by_attr(model_name=model_name, base_model=base_model, model_type=model_type)
-        if len(model_configs) > 1:
-            raise DuplicateModelException(
-                f"More than one model matched the search criteria: base_model='{base_model}', model_type='{model_type}', model_name='{model_name}'."
-            )
-        if len(model_configs) == 0:
-            raise UnknownModelException(
-                f"More than one model matched the search criteria: base_model='{base_model}', model_type='{model_type}', model_name='{model_name}'."
-            )
-        return model_configs[0]
-
-    def rename_model(
-        self,
-        key: str,
-        new_name: str,
-    ) -> AnyModelConfig:
-        """
-        Rename the indicated model. Just a special case of update_model().
-
-        In some implementations, renaming the model may involve changing where
-        it is stored on the filesystem. So this is broken out.
-
-        :param key: Model key
-        :param new_name: New name for model
-        """
-        config = self.get_model(key)
-        config.name = new_name
-        return self.update_model(key, config)

invokeai/app/services/model_records/model_records_sql.py

@@ -1,396 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
-"""
-SQL Implementation of the ModelRecordServiceBase API
-
-Typical usage:
-
-  from invokeai.backend.model_manager import ModelConfigStoreSQL
-  store = ModelConfigStoreSQL(sqlite_db)
-  config = dict(
-        path='/tmp/pokemon.bin',
-        name='old name',
-        base_model='sd-1',
-        type='embedding',
-        format='embedding_file',
-     )
-
-   # adding - the key becomes the model's "key" field
-   store.add_model('key1', config)
-
-   # updating
-   config.name='new name'
-   store.update_model('key1', config)
-
-   # checking for existence
-   if store.exists('key1'):
-      print("yes")
-
-   # fetching config
-   new_config = store.get_model('key1')
-   print(new_config.name, new_config.base)
-   assert new_config.key == 'key1'
-
-  # deleting
-  store.del_model('key1')
-
-  # searching
-  configs = store.search_by_path(path='/tmp/pokemon.bin')
-  configs = store.search_by_hash('750a499f35e43b7e1b4d15c207aa2f01')
-  configs = store.search_by_attr(base_model='sd-2', model_type='main')
-"""
-
-
-import json
-import sqlite3
-from pathlib import Path
-from typing import List, Optional, Union
-
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    ModelConfigFactory,
-    ModelType,
-)
-
-from ..shared.sqlite.sqlite_database import SqliteDatabase
-from .model_records_base import (
-    CONFIG_FILE_VERSION,
-    DuplicateModelException,
-    ModelRecordServiceBase,
-    UnknownModelException,
-)
-
-
-class ModelRecordServiceSQL(ModelRecordServiceBase):
-    """Implementation of the ModelConfigStore ABC using a SQL database."""
-
-    _db: SqliteDatabase
-    _cursor: sqlite3.Cursor
-
-    def __init__(self, db: SqliteDatabase):
-        """
-        Initialize a new object from preexisting sqlite3 connection and threading lock objects.
-
-        :param conn: sqlite3 connection object
-        :param lock: threading Lock object
-        """
-        super().__init__()
-        self._db = db
-        self._cursor = self._db.conn.cursor()
-
-        with self._db.lock:
-            # Enable foreign keys
-            self._db.conn.execute("PRAGMA foreign_keys = ON;")
-            self._create_tables()
-            self._db.conn.commit()
-        assert (
-            str(self.version) == CONFIG_FILE_VERSION
-        ), f"Model config version {self.version} does not match expected version {CONFIG_FILE_VERSION}"
-
-    def _create_tables(self) -> None:
-        """Create sqlite3 tables."""
-        #  model_config table breaks out the fields that are common to all config objects
-        # and puts class-specific ones in a serialized json object
-        self._cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_config (
-                id TEXT NOT NULL PRIMARY KEY,
-                -- The next 3 fields are enums in python, unrestricted string here
-                base TEXT NOT NULL,
-                type TEXT NOT NULL,
-                name TEXT NOT NULL,
-                path TEXT NOT NULL,
-                original_hash TEXT, -- could be null
-                -- Serialized JSON representation of the whole config object,
-                -- which will contain additional fields from subclasses
-                config TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- unique constraint on combo of name, base and type
-                UNIQUE(name, base, type)
-            );
-            """
-        )
-
-        #  metadata table
-        self._cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_manager_metadata (
-                metadata_key TEXT NOT NULL PRIMARY KEY,
-                metadata_value TEXT NOT NULL
-            );
-            """
-        )
-
-        # Add trigger for `updated_at`.
-        self._cursor.execute(
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS model_config_updated_at
-            AFTER UPDATE
-            ON model_config FOR EACH ROW
-            BEGIN
-                UPDATE model_config SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE id = old.id;
-            END;
-            """
-        )
-
-        # Add indexes for searchable fields
-        for stmt in [
-            "CREATE INDEX IF NOT EXISTS base_index ON model_config(base);",
-            "CREATE INDEX IF NOT EXISTS type_index ON model_config(type);",
-            "CREATE INDEX IF NOT EXISTS name_index ON model_config(name);",
-            "CREATE UNIQUE INDEX IF NOT EXISTS path_index ON model_config(path);",
-        ]:
-            self._cursor.execute(stmt)
-
-        # Add our version to the metadata table
-        self._cursor.execute(
-            """--sql
-            INSERT OR IGNORE into model_manager_metadata (
-               metadata_key,
-               metadata_value
-            )
-            VALUES (?,?);
-            """,
-            ("version", CONFIG_FILE_VERSION),
-        )
-
-    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
-        """
-        Add a model to the database.
-
-        :param key: Unique key for the model
-        :param config: Model configuration record, either a dict with the
-         required fields or a ModelConfigBase instance.
-
-        Can raise DuplicateModelException and InvalidModelConfigException exceptions.
-        """
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect.
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    INSERT INTO model_config (
-                       id,
-                       base,
-                       type,
-                       name,
-                       path,
-                       original_hash,
-                       config
-                      )
-                    VALUES (?,?,?,?,?,?,?);
-                    """,
-                    (
-                        key,
-                        record.base,
-                        record.type,
-                        record.name,
-                        record.path,
-                        record.original_hash,
-                        json_serialized,
-                    ),
-                )
-                self._db.conn.commit()
-
-            except sqlite3.IntegrityError as e:
-                self._db.conn.rollback()
-                if "UNIQUE constraint failed" in str(e):
-                    if "model_config.path" in str(e):
-                        msg = f"A model with path '{record.path}' is already installed"
-                    elif "model_config.name" in str(e):
-                        msg = f"A model with name='{record.name}', type='{record.type}', base='{record.base}' is already installed"
-                    else:
-                        msg = f"A model with key '{key}' is already installed"
-                    raise DuplicateModelException(msg) from e
-                else:
-                    raise e
-            except sqlite3.Error as e:
-                self._db.conn.rollback()
-                raise e
-
-        return self.get_model(key)
-
-    @property
-    def version(self) -> str:
-        """Return the version of the database schema."""
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT metadata_value FROM model_manager_metadata
-                WHERE metadata_key=?;
-                """,
-                ("version",),
-            )
-            rows = self._cursor.fetchone()
-            if not rows:
-                raise KeyError("Models database does not have metadata key 'version'")
-            return rows[0]
-
-    def del_model(self, key: str) -> None:
-        """
-        Delete a model.
-
-        :param key: Unique key for the model to be deleted
-
-        Can raise an UnknownModelException
-        """
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    DELETE FROM model_config
-                    WHERE id=?;
-                    """,
-                    (key,),
-                )
-                if self._cursor.rowcount == 0:
-                    raise UnknownModelException("model not found")
-                self._db.conn.commit()
-            except sqlite3.Error as e:
-                self._db.conn.rollback()
-                raise e
-
-    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
-        """
-        Update the model, returning the updated version.
-
-        :param key: Unique key for the model to be updated
-        :param config: Model configuration record. Either a dict with the
-         required fields, or a ModelConfigBase instance.
-        """
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    UPDATE model_config
-                    SET base=?,
-                        type=?,
-                        name=?,
-                        path=?,
-                        config=?
-                    WHERE id=?;
-                    """,
-                    (record.base, record.type, record.name, record.path, json_serialized, key),
-                )
-                if self._cursor.rowcount == 0:
-                    raise UnknownModelException("model not found")
-                self._db.conn.commit()
-            except sqlite3.Error as e:
-                self._db.conn.rollback()
-                raise e
-
-        return self.get_model(key)
-
-    def get_model(self, key: str) -> AnyModelConfig:
-        """
-        Retrieve the ModelConfigBase instance for the indicated model.
-
-        :param key: Key of model config to be fetched.
-
-        Exceptions: UnknownModelException
-        """
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT config FROM model_config
-                WHERE id=?;
-                """,
-                (key,),
-            )
-            rows = self._cursor.fetchone()
-            if not rows:
-                raise UnknownModelException("model not found")
-            model = ModelConfigFactory.make_config(json.loads(rows[0]))
-        return model
-
-    def exists(self, key: str) -> bool:
-        """
-        Return True if a model with the indicated key exists in the databse.
-
-        :param key: Unique key for the model to be deleted
-        """
-        count = 0
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                select count(*) FROM model_config
-                WHERE id=?;
-                """,
-                (key,),
-            )
-            count = self._cursor.fetchone()[0]
-        return count > 0
-
-    def search_by_attr(
-        self,
-        model_name: Optional[str] = None,
-        base_model: Optional[BaseModelType] = None,
-        model_type: Optional[ModelType] = None,
-    ) -> List[AnyModelConfig]:
-        """
-        Return models matching name, base and/or type.
-
-        :param model_name: Filter by name of model (optional)
-        :param base_model: Filter by base model (optional)
-        :param model_type: Filter by type of model (optional)
-
-        If none of the optional filters are passed, will return all
-        models in the database.
-        """
-        results = []
-        where_clause = []
-        bindings = []
-        if model_name:
-            where_clause.append("name=?")
-            bindings.append(model_name)
-        if base_model:
-            where_clause.append("base=?")
-            bindings.append(base_model)
-        if model_type:
-            where_clause.append("type=?")
-            bindings.append(model_type)
-        where = f"WHERE {' AND '.join(where_clause)}" if where_clause else ""
-        with self._db.lock:
-            self._cursor.execute(
-                f"""--sql
-                select config FROM model_config
-                {where};
-                """,
-                tuple(bindings),
-            )
-            results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
-        return results
-
-    def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
-        """Return models with the indicated path."""
-        results = []
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT config FROM model_config
-                WHERE model_path=?;
-                """,
-                (str(path),),
-            )
-            results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
-        return results
-
-    def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
-        """Return models with the indicated original_hash."""
-        results = []
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT config FROM model_config
-                WHERE original_hash=?;
-                """,
-                (hash,),
-            )
-            results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
-        return results