Merge remote-tracking branch 'origin/main' into maryhipp/indexdb-per-project

bumped version to 1.0.1

.gitattributes

@@ -2,3 +2,4 @@
 # 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

@@ -1,20 +0,0 @@
-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:
-  black:
+  ruff:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -18,8 +18,7 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install black flake8 Flake8-pyproject isort
+          pip install ruff
 
-      - run: isort --check-only .
-      - run: black --check .
-      - run: flake8
+      - run: ruff check --output-format=github .
+      - run: ruff format --check .

.gitignore

@@ -1,8 +1,5 @@
 .idea/
 
-# ignore the Anaconda/Miniconda installer used while building Docker image
-anaconda.sh
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -136,12 +133,10 @@ celerybeat.pid
 
 # Environments
 .env
-.venv
+.venv*
 env/
 venv/
 ENV/
-env.bak/
-venv.bak/
 
 # Spyder project settings
 .spyderproject
@@ -186,11 +181,6 @@ cython_debug/
 .scratch/
 .vscode/
 
-# ignore environment.yml and requirements.txt
-# these are links to the real files in environments-and-requirements
-environment.yml
-requirements.txt
-
 # source installer files
 installer/*zip
 installer/install.bat

Makefile

@@ -0,0 +1,21 @@
+# 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

@@ -123,7 +123,7 @@ and go to http://localhost:9090.
 
 ### Command-Line Installation (for developers and users familiar with Terminals)
 
-You must have Python 3.9 through 3.11 installed on your machine. Earlier or
+You must have Python 3.10 through 3.11 installed on your machine. Earlier or
 later versions are not supported.
 Node.js also needs to be installed along with yarn (can be installed with
 the command `npm install -g yarn` if needed)
@@ -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/cu118
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
     ```
 
     _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:_
+    _For Macintoshes, either Intel or M1/M2/M3:_
 
     ```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 **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
+Please check out our **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
 problems and other issues. For more help, please join our [Discord][discord link]
 
 ## Contributing

docker/.env.sample

@@ -1,13 +1,15 @@
 ## Make a copy of this file named `.env` and fill in the values below.
-## Any environment variables supported by InvokeAI can be specified here.
+## Any environment variables supported by InvokeAI can be specified here,
+## in addition to the examples below.
 
 # INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
 # Outputs will also be stored here by default.
 # This **must** be an absolute path.
 INVOKEAI_ROOT=
 
-HUGGINGFACE_TOKEN=
+# Get this value from your HuggingFace account settings page.
+# HUGGING_FACE_HUB_TOKEN=
 
-## optional variables specific to the docker setup
-# GPU_DRIVER=cuda
-# CONTAINER_UID=1000
+## optional variables specific to the docker setup.
+# GPU_DRIVER=cuda # or rocm
+# CONTAINER_UID=1000

docker/Dockerfile

@@ -2,7 +2,7 @@
 
 ## Builder stage
 
-FROM library/ubuntu:22.04 AS builder
+FROM library/ubuntu:23.04 AS builder
 
 ARG DEBIAN_FRONTEND=noninteractive
 RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
@@ -10,7 +10,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt,sharing=locked \
     apt update && apt-get install -y \
         git \
-        python3.10-venv \
+        python3-venv \
         python3-pip \
         build-essential
 
@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.0.1
-ARG TORCHVISION_VERSION=0.15.2
+ARG TORCH_VERSION=2.1.0
+ARG TORCHVISION_VERSION=0.16
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,9 +35,9 @@ 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="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
+        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
     pip install $extra_index_url_arg \
         torch==$TORCH_VERSION \
@@ -70,7 +70,7 @@ RUN --mount=type=cache,target=/usr/lib/node_modules \
 
 #### Runtime stage ---------------------------------------
 
-FROM library/ubuntu:22.04 AS runtime
+FROM library/ubuntu:23.04 AS runtime
 
 ARG DEBIAN_FRONTEND=noninteractive
 ENV PYTHONUNBUFFERED=1
@@ -85,6 +85,7 @@ RUN apt update && apt install -y --no-install-recommends \
         iotop \
         bzip2 \
         gosu \
+        magic-wormhole \
         libglib2.0-0 \
         libgl1-mesa-glx \
         python3-venv \
@@ -94,10 +95,6 @@ RUN apt update && apt install -y --no-install-recommends \
         libstdc++-10-dev &&\
     apt-get clean && apt-get autoclean
 
-# globally add magic-wormhole
-# for ease of transferring data to and from the container
-# when running in sandboxed cloud environments; e.g. Runpod etc.
-RUN pip install magic-wormhole
 
 ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
@@ -120,9 +117,7 @@ WORKDIR ${INVOKEAI_SRC}
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
 RUN python3 -c "from patchmatch import patch_match"
 
-# Create unprivileged user and make the local dir
-RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
+RUN mkdir -p ${INVOKEAI_ROOT} && chown -R 1000:1000 ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]

docker/README.md

@@ -5,7 +5,7 @@ All commands are to be run from the `docker` directory: `cd docker`
 #### Linux
 
 1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
-2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
+2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
     - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
 3. Ensure docker daemon is able to access the GPU.
     - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
@@ -20,7 +20,6 @@ This is done via Docker Desktop preferences
 
 ## Quickstart
 
-
 1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
     a. the desired location of the InvokeAI runtime directory, or
     b. an existing, v3.0.0 compatible runtime directory.
@@ -42,20 +41,22 @@ The Docker daemon on the system must be already set up to use the GPU. In case o
 
 Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
 
-You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
+You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
 
-Example (most values are optional):
+Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
 
-```
+```bash
 INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
 HUGGINGFACE_TOKEN=the_actual_token
 CONTAINER_UID=1000
 GPU_DRIVER=cuda
 ```
 
+Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
+
 ## Even Moar Customizing!
 
-See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
+See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
 
 ### Reconfigure the runtime directory
 
@@ -63,7 +64,7 @@ Can be used to download additional models from the supported model list
 
 In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
 
-```
+```yaml
 command:
   - invokeai-configure
   - --yes
@@ -71,7 +72,7 @@ command:
 
 Or install models:
 
-```
+```yaml
 command:
   - invokeai-model-install
-```
+```

docker/build.sh

@@ -5,7 +5,7 @@ build_args=""
 
 [[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
 
-echo "docker-compose build args:"
+echo "docker compose build args:"
 echo $build_args
 
-docker-compose build $build_args
+docker compose build $build_args

docker/docker-compose.yml

@@ -15,6 +15,10 @@ 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/docker-entrypoint.sh

@@ -19,7 +19,7 @@ set -e -o pipefail
 # Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
 
 USER_ID=${CONTAINER_UID:-1000}
-USER=invoke
+USER=ubuntu
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
 configure() {

docker/run.sh

@@ -1,8 +1,11 @@
 #!/usr/bin/env bash
 set -e
 
+# This script is provided for backwards compatibility with the old docker setup.
+# it doesn't do much aside from wrapping the usual docker compose CLI.
+
 SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
-docker-compose up --build -d
-docker-compose logs -f
+docker compose up -d
+docker compose logs -f

docs/CHANGELOG.md

@@ -488,7 +488,7 @@ sections describe what's new for InvokeAI.
 
 - A choice of installer scripts that automate installation and configuration.
   See
-  [Installation](installation/index.md).
+  [Installation](installation/INSTALLATION.md).
 - A streamlined manual installation process that works for both Conda and
   PIP-only installs. See
   [Manual Installation](installation/020_INSTALL_MANUAL.md).
@@ -657,7 +657,7 @@ sections describe what's new for InvokeAI.
 
 ## v1.13 <small>(3 September 2022)</small>
 
-- Support image variations (see [VARIATIONS](features/VARIATIONS.md)
+- Support image variations (see [VARIATIONS](deprecated/VARIATIONS.md)
   ([Kevin Gibbons](https://github.com/bakkot) and many contributors and
   reviewers)
 - Supports a Google Colab notebook for a standalone server running on Google

docs/contributing/INVOCATIONS.md

@@ -1,6 +1,6 @@
-# Invocations
+# Nodes
 
-Features in InvokeAI are added in the form of modular node-like systems called
+Features in InvokeAI are added in the form of modular nodes systems called
 **Invocations**.
 
 An Invocation is simply a single operation that takes in some inputs and gives
@@ -9,13 +9,34 @@ complex functionality.
 
 ## Invocations Directory
 
-InvokeAI Invocations can be found in the `invokeai/app/invocations` directory.
+InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
 
-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.
+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
+```
 
-**Note:** _All Invocations must be inside this directory for InvokeAI to
-recognize them as valid Invocations._
 
 ## Creating A New Invocation
 
@@ -44,7 +65,7 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from .baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -78,8 +99,8 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -103,8 +124,8 @@ image: ImageField = InputField(description="The input image")
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -139,8 +160,8 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -168,9 +189,9 @@ 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 .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
-from .image import ImageOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.app.invocations.image import ImageOutput
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -195,9 +216,9 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
 So let's do that.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
-from .image import ImageOutput
+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
 
 @invocation("resize")
 class ResizeInvocation(BaseInvocation):

docs/contributing/contribution_guides/development.md

@@ -45,5 +45,5 @@ For backend related work, please reach out to **@blessedcoolant**, **@lstein**,
 
 ## **What does the Code of Conduct mean for me?**
 
-Our [Code of Conduct](CODE_OF_CONDUCT.md)  means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code.
+Our [Code of Conduct](../../CODE_OF_CONDUCT.md)  means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code.
 

docs/deprecated/CLI.md

@@ -211,8 +211,8 @@ Here are the invoke> command that apply to txt2img:
 | `--facetool <name>`                       | `-ft <name>`                              | `-ft gfpgan`                             | Select face restoration algorithm to use: gfpgan, codeformer                                                                                                                                                                                     |
 | `--codeformer_fidelity`                   | `-cf <float>`                             | `0.75`                                   | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality                                                                                                     |
 | `--save_original`                         | `-save_orig`                              | `False`                                  | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced.                                                                                                                                             |
-| `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](../features/VARIATIONS.md). |
-| `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](../features/VARIATIONS.md) for now to use this.                                                                                                                                                           |
+| `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](VARIATIONS.md). |
+| `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](VARIATIONS.md) for now to use this.                                                                                                                                                           |
 | `--save_intermediates <n>`                |                                           | `None`                                   | Save the image from every nth step into an "intermediates" folder inside the output directory                                                                                                                                                    |
 | `--h_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
 | `--v_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |

docs/deprecated/VARIATIONS.md

@@ -126,6 +126,6 @@ amounts of image-to-image variation even when the seed is fixed and the
 `-v` argument is very low. Others are more deterministic. Feel free to
 experiment until you find the combination that you like.
 
-Also be aware of the [Perlin Noise](OTHER.md#thresholding-and-perlin-noise-initialization-options)
+Also be aware of the [Perlin Noise](../features/OTHER.md#thresholding-and-perlin-noise-initialization-options)
 feature, which provides another way of introducing variability into your
 image generation requests.

docs/features/CONFIGURATION.md

@@ -82,7 +82,7 @@ format of YAML files can be found
 [here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
 
 You can fix a broken `invokeai.yaml` by deleting it and running the
-configuration script again -- option [7] in the launcher, "Re-run the
+configuration script again -- option [6] in the launcher, "Re-run the
 configure script".
 
 #### Reading Environment Variables

docs/features/CONTROLNET.md

@@ -17,9 +17,6 @@ image generation, providing you with a way to direct the network
 towards generating images that better fit your desired style or
 outcome.
 
-
-#### How it works
-
 ControlNet works by analyzing an input image, pre-processing that
 image to identify relevant information that can be interpreted by each
 specific ControlNet model, and then inserting that control information
@@ -27,35 +24,21 @@ into the generation process. This can be used to adjust the style,
 composition, or other aspects of the image to better achieve a
 specific result.
 
-
-#### Models
+#### Installation
 
 InvokeAI provides access to a series of ControlNet models that provide
-different effects or styles in your generated images.  Currently
-InvokeAI only supports "diffuser" style ControlNet models. These are
-folders that contain the files `config.json` and/or
-`diffusion_pytorch_model.safetensors` and
-`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
-the name of the model.
+different effects or styles in your generated images.
 
-***InvokeAI does not currently support checkpoint-format
-ControlNets. These come in the form of a single file with the
-extension `.safetensors`.***
+To install ControlNet Models:
 
-Diffuser-style ControlNet models are available at HuggingFace
-(http://huggingface.co) and accessed via their repo IDs (identifiers
-in the format "author/modelname"). The easiest way to install them is
+1. The easiest way to install them is
 to use the InvokeAI model installer application. Use the
-`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
+`invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate
 to the CONTROLNETS section. Select the models you wish to install and
 press "APPLY CHANGES". You may also enter additional HuggingFace
-repo_ids in the "Additional models" textbox:
+repo_ids in the "Additional models" textbox. 
+2. Using the "Add Model" function of the  model manager, enter the HuggingFace Repo ID of the ControlNet. The ID is in the format "author/repoName"
 
-![Model Installer -
-Controlnetl](../assets/installing-models/model-installer-controlnet.png){:width="640px"}
-
-Command-line users can launch the model installer using the command
-`invokeai-model-install`.
 
 _Be aware that some ControlNet models require additional code
 functionality in order to work properly, so just installing a
@@ -63,6 +46,17 @@ third-party ControlNet model may not have the desired effect._ Please
 read and follow the documentation for installing a third party model
 not currently included among InvokeAI's default list.
 
+Currently InvokeAI **only** supports 🤗 Diffusers-format ControlNet models. These are
+folders that contain the files `config.json` and/or
+`diffusion_pytorch_model.safetensors` and
+`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
+the name of the model.
+
+🤗 Diffusers-format ControlNet models are available at HuggingFace
+(http://huggingface.co) and accessed via their repo IDs (identifiers
+in the format "author/modelname").
+
+#### ControlNet Models
 The models currently supported include:
 
 **Canny**:
@@ -133,6 +127,29 @@ Start/End  - 0 represents the start of the generation, 1 represents the end. The
 
 Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.
 
+## T2I-Adapter
+[T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) is a tool similar to ControlNet that allows for control over the generation process by providing control information during the generation process. T2I-Adapter models tend to be smaller and more efficient than ControlNets. 
+
+##### Installation
+To install T2I-Adapter Models:
+
+1. The easiest way to install models is
+to use the InvokeAI model installer application. Use the
+`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
+to the T2I-Adapters section. Select the models you wish to install and
+press "APPLY CHANGES". You may also enter additional HuggingFace
+repo_ids in the "Additional models" textbox. 
+2. Using the "Add Model" function of the  model manager, enter the HuggingFace Repo ID of the T2I-Adapter. The ID is in the format "author/repoName"
+
+#### Usage
+Each T2I Adapter has two settings that are applied.
+
+Weight - Strength of the model applied to the generation for the section, defined by start/end.
+
+Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
+
+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.
+
 
 ## IP-Adapter
 
@@ -140,13 +157,13 @@ Additionally, each ControlNet section can be expanded in order to manipulate set
 
 ![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg)
 
-![IP-Adapter + IMG2IMG](https://github.com/tencent-ailab/IP-Adapter/blob/main/assets/demo/image-to-image.jpg)
+![IP-Adapter + IMG2IMG](https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/demo/image-to-image.jpg)
 
 #### Installation
 There are several ways to install IP-Adapter models with an existing InvokeAI installation:
 
-1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [5] to download models.
-2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
+1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
+2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
 3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 
 
 #### Using IP-Adapter

docs/features/LORAS.md

@@ -0,0 +1,53 @@
+---
+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/MODEL_MERGING.md

@@ -16,9 +16,10 @@ Model Merging can be be done by navigating to the Model Manager and clicking the
   display all the diffusers-style models that InvokeAI knows about.
   If you do not see the model you are looking for, then it is probably
   a legacy checkpoint model and needs to be converted using the
-  `invoke` command-line client and its `!optimize` command. You
-  must select at least two models to merge. The third can be left at
-  "None" if you desire.
+  "Convert" option in the Web-based Model Manager tab.
+  
+  You must select at least two models to merge. The third can be left
+  at "None" if you desire.
 
 * Alpha: This is the ratio to use when combining models. It ranges
   from 0 to 1. The higher the value, the more weight is given to the

docs/features/TEXTUAL_INVERSIONS.md

@@ -1,12 +1,3 @@
----
-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
@@ -28,8 +19,9 @@ by placing them in the designated directory for the compatible model type
 
 ### An Example
 
-Here are a few examples to illustrate how it works. All these images were
-generated using the command-line client and the Stable Diffusion 1.5 model:
+Here are a few examples to illustrate how it works. All these images
+were generated using the legacy command-line client and the Stable
+Diffusion 1.5 model:
 
 |         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
 | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
@@ -60,29 +52,4 @@ 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.
-
-## 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.
-
+select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.

docs/features/UTILITIES.md

@@ -8,7 +8,7 @@ title: Command-line Utilities
 
 InvokeAI comes with several scripts that are accessible via the
 command line. To access these commands, start the "developer's
-console" from the launcher (`invoke.bat` menu item [8]). Users who are
+console" from the launcher (`invoke.bat` menu item [7]). Users who are
 familiar with Python can alternatively activate InvokeAI's virtual
 environment (typically, but not necessarily `invokeai/.venv`).
 
@@ -34,7 +34,7 @@ invokeai-web --ram 7
 
 ## **invokeai-merge**
 
-This is the model merge script, the same as launcher option [4]. Call
+This is the model merge script, the same as launcher option [3]. Call
 it with the `--gui` command-line argument to start the interactive
 console-based GUI. Alternatively, you can run it non-interactively
 using command-line arguments as illustrated in the example below which
@@ -48,7 +48,7 @@ invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffus
 ## **invokeai-ti**
 
 This is the textual inversion training script that is run by launcher
-option [3]. Call it with `--gui` to run the interactive console-based
+option [2]. Call it with `--gui` to run the interactive console-based
 front end. It can also be run non-interactively. It has about a
 zillion arguments, but a typical training session can be launched
 with:
@@ -68,7 +68,7 @@ in Windows).
 ## **invokeai-install**
 
 This is the console-based model install script that is run by launcher
-option [5]. If called without arguments, it will launch the
+option [4]. If called without arguments, it will launch the
 interactive console-based interface. It can also be used
 non-interactively to list, add and remove models as shown by these
 examples:
@@ -148,7 +148,7 @@ launch the web server against it with `invokeai-web --root InvokeAI-New`.
 ## **invokeai-update**
 
 This is the interactive console-based script that is run by launcher
-menu item [9] to update to a new version of InvokeAI. It takes no
+menu item [8] to update to a new version of InvokeAI. It takes no
 command-line arguments.
 
 ## **invokeai-metadata**

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 and Textual Inversion Models](CONCEPTS.md)
+### * The [LoRA, LyCORIS, LCM-LoRA Models](CONCEPTS.md)
 Add custom subjects and styles using a variety of fine-tuned models.
 
 ### * [ControlNet](CONTROLNET.md)
@@ -28,7 +28,7 @@ Learn how to install and use ControlNet models for fine control over
 image output.
 
 ### * [Image-to-Image Guide](IMG2IMG.md)
-Use a seed image to build new creations in the CLI.
+Use a seed image to build new creations.
 
 ## Model Management
 
@@ -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](TRAINING.md)
+### * [Textual Inversion](TEXTUAL_INVERSIONS.md)
 Personalize models by adding your own style or subjects.
 
 ## Other Features

docs/help/FAQ.md

@@ -0,0 +1,43 @@
+# 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/help/gettingStartedWithAI.md

@@ -57,7 +57,9 @@ Prompts provide the models directions on what to generate. As a general rule of
 
 Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!)
 
-Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at ****. Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
+Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at https://models.invoke.ai 
+
+Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
 
 - *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas*
 

docs/index.md

@@ -101,16 +101,13 @@ 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>
@@ -143,7 +140,6 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 <!-- seperator -->
 ### Prompt Engineering
 - [Prompt Syntax](features/PROMPTS.md)
-- [Generating Variations](features/VARIATIONS.md)
 
 ### InvokeAI Configuration
 - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
@@ -166,10 +162,8 @@ still a work in progress, but coming soon.
 
 ### Command-Line Interface Retired
 
-The original "invokeai" command-line interface has been retired. The
-`invokeai` command will now launch a new command-line client that can
-be used by developers to create and test nodes. It is not intended to
-be used for routine image generation or manipulation.
+All "invokeai" command-line interfaces have been retired as of version
+3.4.
 
 To launch the Web GUI from the command-line, use the command
 `invokeai-web` rather than the traditional `invokeai --web`.
@@ -201,6 +195,7 @@ 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

@@ -40,7 +40,7 @@ experimental versions later.
     this, open up a command-line window ("Terminal" on Linux and
     Macintosh, "Command" or "Powershell" on Windows) and type `python
     --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
+    number. If it is version `3.10.*` or `3.11.*` you meet
     requirements.
 
     !!! warning "What to do if you have an unsupported version"
@@ -48,7 +48,7 @@ experimental versions later.
         Go to [Python Downloads](https://www.python.org/downloads/)
         and download the appropriate installer package for your
         platform. We recommend [Version
-        3.10.9](https://www.python.org/downloads/release/python-3109/),
+        3.10.12](https://www.python.org/downloads/release/python-3109/),
         which has been extensively tested with InvokeAI.
 
     _Please select your platform in the section below for platform-specific
@@ -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 or M2 Macintoshes, you will get MPS-based
+    you select CPU on M1/M2/M3 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/cu118
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
     pip install xformers
     ```
 

docs/installation/020_INSTALL_MANUAL.md

@@ -32,7 +32,7 @@ gaming):
 
 * **Python**
 
-    version 3.9 through 3.11
+    version 3.10 through 3.11
 
 * **CUDA Tools**
 
@@ -65,7 +65,7 @@ gaming):
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Please make sure you are using Python 3.9 through 3.11. The rest of the install
+1.  Please make sure you are using Python 3.10 through 3.11. The rest of the install
     procedure depends on this and will not work with other versions:
 
     ```bash
@@ -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/cu118
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
         ```
 
     === "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/cu118
+        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
         ```
 
     === "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/cu118
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
 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/cu118` as described in the [Manual
+https://download.pytorch.org/whl/cu121` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 ## :simple-amd: ROCm

docs/installation/040_INSTALL_DOCKER.md

@@ -4,38 +4,49 @@ title: Installing with Docker
 
 # :fontawesome-brands-docker: Docker
 
-!!! warning "For most users"
+!!! warning "macOS and AMD GPU Users"
 
-    We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md)
+    We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md),
+    because Docker containers can not access the GPU on macOS.
 
-!!! tip "For developers"
+!!! warning "AMD GPU Users"
 
-    For container-related development tasks or for enabling easy
-    deployment to other environments (on-premises or cloud), follow these
-    instructions.
+    Container support for AMD GPUs has been reported to work by the community, but has not received
+    extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
+    use the `build.sh` script to build the image for this to take effect at build time.
 
-    For general use, install locally to leverage your machine's GPU.
+!!! tip "Linux and Windows Users"
+
+    For optimal performance, configure your Docker daemon to access your machine's GPU.
+    Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/).
+    Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
 
 ## Why containers?
 
-They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
-use a Docker volume to store the largest model files and image outputs as a
-first step in decoupling storage and compute. Future enhancements can do this
-for other assets. See [Processes](https://12factor.net/processes) under the
-Twelve-Factor App methodology for details on why running applications in such a
-stateless fashion is important.
+They provide a flexible, reliable way to build and deploy InvokeAI.
+See [Processes](https://12factor.net/processes) under the Twelve-Factor App
+methodology for details on why running applications in such a stateless fashion is important.
 
-You can specify the target platform when building the image and running the
-container. You'll also need to specify the InvokeAI requirements file that
-matches the container's OS and the architecture it will run on.
+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): You
+Developers on Apple silicon (M1/M2/M3): 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
 laptop you can build for the target platform and architecture and deploy to
 another environment with NVIDIA GPUs on-premises or in the cloud.
 
+## TL;DR
+
+This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.
+
+    ```bash
+    # docker compose commands should be run from the `docker` directory
+    cd docker
+    docker compose up
+    ```
+
 ## Installation in a Linux container (desktop)
 
 ### Prerequisites
@@ -58,222 +69,44 @@ a token and copy it, since you will need in for the next step.
 
 ### Setup
 
-Set the fork you want to use and other variables.
+Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
 
-!!! tip
+Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
 
-    I preffer to save my env vars
-    in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
-    them when I come back.
-
-The build- and run- scripts contain default values for almost everything,
-besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
-created in the last step.
-
-Some Suggestions of variables you may want to change besides the Token:
+At a minimum, you might want to set the `INVOKEAI_ROOT` environment variable
+to point to the location where you wish to store your InvokeAI models, configuration, and outputs.
 
 <figure markdown>
 
 | Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description                                                                                                                                                                                       |
 | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `HUGGING_FACE_HUB_TOKEN`                              | No default, but **required**!                  | This is the only **required** variable, without it you can't download the huggingface models                                                                                                      |
-| `REPOSITORY_NAME`                                     | The Basename of the Repo folder                | This name will used as the container repository/image name                                                                                                                                        |
-| `VOLUMENAME`                                          | `${REPOSITORY_NAME,,}_data`                    | Name of the Docker Volume where model files will be stored                                                                                                                                        |
-| `ARCH`                                                | arch of the build machine                      | Can be changed if you want to build the image for another arch                                                                                                                                    |
-| `CONTAINER_REGISTRY`                                  | ghcr.io                                        | Name of the Container Registry to use for the full tag                                                                                                                                            |
-| `CONTAINER_REPOSITORY`                                | `$(whoami)/${REPOSITORY_NAME}`                 | Name of the Container Repository                                                                                                                                                                  |
-| `CONTAINER_FLAVOR`                                    | `cuda`                                         | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. |
-| `CONTAINER_TAG`                                       | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}`   | The Container Repository / Tag which will be used                                                                                                                                                 |
-| `INVOKE_DOCKERFILE`                                   | `Dockerfile`                                   | The Dockerfile which should be built, handy for development                                                                                                                                       |
-| `PIP_EXTRA_INDEX_URL`                                 |                                                | If you want to use a custom pip-extra-index-url                                                                                                                                                   |
+| `INVOKEAI_ROOT`                                       | `~/invokeai`                                   | **Required** - the location of your InvokeAI root directory. It will be created if it does not exist.
+| `HUGGING_FACE_HUB_TOKEN`                              |                                                | InvokeAI will work without it, but some of the integrations with HuggingFace (like downloading from models from private repositories) may not work|
+| `GPU_DRIVER`                                          | `cuda`                                         | Optionally change this to `rocm` to build the image for AMD GPUs. NOTE: Use the `build.sh` script to build the image for this to take effect.
 
 </figure>
 
 #### Build the Image
 
-I provided a build script, which is located next to the Dockerfile in
-`docker/build.sh`. It can be executed from repository root like this:
+Use the standard `docker compose build` command from within the `docker` directory.
 
-```bash
-./docker/build.sh
-```
-
-The build Script not only builds the container, but also creates the docker
-volume if not existing yet.
+If using an AMD GPU:
+    a: set the `GPU_DRIVER=rocm` environment variable in `docker-compose.yml` and continue using `docker compose build` as usual, or
+    b: set `GPU_DRIVER=rocm` in the `.env` file and use the `build.sh` script, provided for convenience
 
 #### Run the Container
 
-After the build process is done, you can run the container via the provided
-`docker/run.sh` script
+Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
 
-```bash
-./docker/run.sh
-```
+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)
 
-When used without arguments, the container will start the webserver and provide
-you the link to open it. But if you want to use some other parameters you can
-also do so.
+## Troubleshooting / FAQ
 
-!!! example "run script example"
-
-    ```bash
-    ./docker/run.sh "banana sushi" -Ak_lms -S42 -s10
-    ```
-
-    This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
-
-    Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments)
-
----
-
-## Running the container on your GPU
-
-If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
-the container with an extra environment variable to enable GPU usage and have
-the process run much faster:
-
-```bash
-GPU_FLAGS=all ./docker/run.sh
-```
-
-This passes the `--gpus all` to docker and uses the GPU.
-
-If you don't have a GPU (or your host is not yet setup to use it) you will see a
-message like this:
-
-`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
-
-You can use the full set of GPU combinations documented here:
-
-https://docs.docker.com/config/containers/resource_constraints/#gpu
-
-For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
-choose a specific device identified by a UUID.
-
----
-
-!!! warning "Deprecated"
-
-    From here on you will find the the previous Docker-Docs, which will still
-    provide some usefull informations.
-
-## Usage (time to have fun)
-
-### Startup
-
-If you're on a **Linux container** the `invoke` script is **automatically
-started** and the output dir set to the Docker volume you created earlier.
-
-If you're **directly on macOS follow these startup instructions**. With the
-Conda environment activated (`conda activate ldm`), run the interactive
-interface that combines the functionality of the original scripts `txt2img` and
-`img2img`: Use the more accurate but VRAM-intensive full precision math because
-half-precision requires autocast and won't work. By default the images are saved
-in `outputs/img-samples/`.
-
-```Shell
-python3 scripts/invoke.py --full_precision
-```
-
-You'll get the script's prompt. You can see available options or quit.
-
-```Shell
-invoke> -h
-invoke> q
-```
-
-### Text to Image
-
-For quick (but bad) image results test with 5 steps (default 50) and 1 sample
-image. This will let you know that everything is set up correctly. Then increase
-steps to 100 or more for good (but slower) results. The prompt can be in quotes
-or not.
-
-```Shell
-invoke> The hulk fighting with sheldon cooper -s5 -n1
-invoke> "woman closeup highly detailed"  -s 150
-# Reuse previous seed and apply face restoration
-invoke> "woman closeup highly detailed"  --steps 150 --seed -1 -G 0.75
-```
-
-You'll need to experiment to see if face restoration is making it better or
-worse for your specific prompt.
-
-If you're on a container the output is set to the Docker volume. You can copy it
-wherever you want. You can download it from the Docker Desktop app, Volumes,
-my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
-`docker cp` can't expand `*.png` so you'll need to specify the image file name.
-
-On your host Mac (you can use the name of any container that mounted the
-volume):
-
-```Shell
-docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
-```
-
-### Image to Image
-
-You can also do text-guided image-to-image translation. For example, turning a
-sketch into a detailed drawing.
-
-`strength` is a value between 0.0 and 1.0 that controls the amount of noise that
-is added to the input image. Values that approach 1.0 allow for lots of
-variations but will also produce images that are not semantically consistent
-with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
-
-Make sure your input image size dimensions are multiples of 64 e.g. 512x512.
-Otherwise you'll get `Error: product of dimension sizes > 2**31'`. If you still
-get the error
-[try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.)
-like 512x256.
-
-If you're on a Docker container, copy your input image into the Docker volume
-
-```Shell
-docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
-```
-
-Try it out generating an image (or more). The `invoke` script needs absolute
-paths to find the image so don't use `~`.
-
-If you're on your Mac
-
-```Shell
-invoke> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75  --steps 100 -n4
-```
-
-If you're on a Linux container on your Mac
-
-```Shell
-invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75  --steps 50 -n1
-```
-
-### Web Interface
-
-You can use the `invoke` script with a graphical web interface. Start the web
-server with:
-
-```Shell
-python3 scripts/invoke.py --full_precision --web
-```
-
-If it's running on your Mac point your Mac web browser to
-<http://127.0.0.1:9090>
-
-Press Control-C at the command line to stop the web server.
-
-### Notes
-
-Some text you can add at the end of the prompt to make it very pretty:
-
-```Shell
-cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed, painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida
-```
-
-The original scripts should work as well.
-
-```Shell
-python3 scripts/orig_scripts/txt2img.py --help
-python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1  --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray"
-python3 scripts/orig_scripts/txt2img.py --ddim_steps 5   --n_iter 1 --n_samples 1  --plms --prompt "ocean" # or --klms
-```
+- 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/050_INSTALLING_MODELS.md

@@ -84,7 +84,7 @@ InvokeAI root directory's `autoimport` folder.
 
 ### Installation via `invokeai-model-install`
 
-From the `invoke` launcher, choose option [5] "Download and install
+From the `invoke` launcher, choose option [4] "Download and install
 models." This will launch the same script that prompted you to select
 models at install time. You can use this to add models that you
 skipped the first time around. It is all right to specify a model that

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -59,8 +59,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
    `from patchmatch import patch_match`: It should look like the following:
 
     ```py
-    Python 3.9.5 (default, Nov 23 2021, 15:27:38)
-    [GCC 9.3.0] on linux
+    Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux
     Type "help", "copyright", "credits" or "license" for more information.
     >>> from patchmatch import patch_match
     Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch".

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~=0.0.19
+pip install xformers~=0.0.22
 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 0.0.20
+xFormers 0.0.22
 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:                                   2.0.1+cu118
+pytorch.version:                                   2.1.0+cu121
 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:                               2.0.1+cu118
+build.torch_version:                               2.1.0+cu121
 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
@@ -92,33 +92,22 @@ 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 11.8
+#### 1. Install CUDA Toolkit 12.1
 
 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 11.8
-Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive)
+by NVIDIA itself. Go to [CUDA Toolkit 12.1
+Downloads](https://developer.nvidia.com/cuda-12-1-0-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. For
-example, the install script recipe for Ubuntu 22.04 running on a
-x86_64 system is:
+install shell script that will install the toolkit and drivers.
 
-```
-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
+#### 2. Confirm/Install pyTorch 2.1.0 with CUDA 12.1 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
@@ -133,7 +122,7 @@ Then run the command:
 python -c 'exec("import torch\nprint(torch.__version__)")'
 ```
 
-If it prints __1.13.1+cu118__ you're good. If not, you can install the
+If it prints __2.1.0+cu121__ you're good. If not, you can install the
 most up to date libraries with this command:
 
 ```sh

docs/installation/deprecated_documentation/INSTALL_LINUX.md

@@ -79,7 +79,7 @@ title: Manual Installation, Linux
         and obtaining an access token for downloading. It will then download and
         install the weights files for you.
 
-        Please look [here](../INSTALL_MANUAL.md) for a manual process for doing
+        Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing
         the same thing.
 
 7.  Start generating images!
@@ -112,7 +112,7 @@ title: Manual Installation, Linux
         To use an alternative model you may invoke the `!switch` command in
         the CLI, or pass `--model <model_name>` during `invoke.py` launch for
         either the CLI or the Web UI. See [Command Line
-        Client](../../features/CLI.md#model-selection-and-importation). The
+        Client](../../deprecated/CLI.md#model-selection-and-importation). The
         model names are defined in `configs/models.yaml`.
 
 8. Subsequently, to relaunch the script, be sure to run "conda activate

docs/installation/deprecated_documentation/INSTALL_MAC.md

@@ -150,7 +150,7 @@ will do our best to help.
     To use an alternative model you may invoke the `!switch` command in
     the CLI, or pass `--model <model_name>` during `invoke.py` launch for
     either the CLI or the Web UI. See [Command Line
-    Client](../../features/CLI.md#model-selection-and-importation). The
+    Client](../../deprecated/CLI.md#model-selection-and-importation). The
     model names are defined in `configs/models.yaml`.
 
 ---

docs/installation/deprecated_documentation/INSTALL_SOURCE.md

@@ -128,7 +128,7 @@ python scripts/invoke.py --web --max_load_models=3 \
 ```
 
 These options are described in detail in the
-[Command-Line Interface](../../features/CLI.md) documentation.
+[Command-Line Interface](../../deprecated/CLI.md) documentation.
 
 ## Troubleshooting
 

docs/installation/deprecated_documentation/INSTALL_WINDOWS.md

@@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
           obtaining an access token for downloading. It will then download and install the
           weights files for you.
 
-          Please look [here](../INSTALL_MANUAL.md) for a manual process for doing the
+          Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing the
           same thing.
 
 8. Start generating images!
@@ -108,7 +108,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
         To use an alternative model you may invoke the `!switch` command in
         the CLI, or pass `--model <model_name>` during `invoke.py` launch for
         either the CLI or the Web UI. See [Command Line
-        Client](../../features/CLI.md#model-selection-and-importation). The
+        Client](../../deprecated/CLI.md#model-selection-and-importation). The
         model names are defined in `configs/models.yaml`.
 
 9. Subsequently, to relaunch the script, first activate the Anaconda

docs/nodes/communityNodes.md

@@ -4,11 +4,16 @@ 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 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 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 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. 
 
 - Community Nodes
+    + [Average Images](#average-images)
     + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
     + [Film Grain](#film-grain)
     + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
@@ -21,18 +26,28 @@ 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)
 - [Help](#help)
 
 
+--------------------------------
+### Average Images
+
+**Description:** This node takes in a collection of images of the same size and averages them as output. It converts everything to RGB mode first.
+
+**Node Link:** https://github.com/JPPhoto/average-images-node
+
 --------------------------------
 ### Depth Map from Wavefront OBJ
 
@@ -177,12 +192,8 @@ This includes 15 Nodes:
 
 **Node Link:** https://github.com/helix4u/load_video_frame
 
-**Example Node Graph:**  https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
-
 **Output Example:** 
-
-<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
-[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
+<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
 
 --------------------------------
 ### Make 3D
@@ -198,6 +209,23 @@ 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
 
@@ -227,22 +255,41 @@ 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 manipulation tools.  These were written to accompany the PromptsFromFile node and other prompt generation nodes.
+**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`
 
-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
 
@@ -308,26 +355,45 @@ 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:** Image to grid nodes and supporting tools.
+**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 
 
-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.
+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
 
 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/fake_node.py
+**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
 
-**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
+**Example Workflow:**  https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.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. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
+- 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. 
 - 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/exampleWorkflows.md

@@ -2,13 +2,17 @@
 
 We've curated some example workflows for you to get started with Workflows in InvokeAI
 
-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!
+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!
 
 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 (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)
+* [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)
 * [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

@@ -0,0 +1,985 @@
+{
+  "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": "",
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+}

docs/workflows/Prompt_from_File.json

@@ -0,0 +1,719 @@
+{
+  "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": [
+    {
+      "nodeId": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
+      "fieldName": "model"
+    },
+    {
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+      "fieldName": "file_path"
+    }
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+  "meta": {
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+  },
+  "nodes": [
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+      "type": "invocation",
+      "data": {
+        "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
+        "type": "compel",
+        "inputs": {
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+            "id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62",
+            "name": "prompt",
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+            "label": "",
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+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
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+}

docs/workflows/QR_Code_Monster.json

@@ -0,0 +1,758 @@
+{
+  "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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+            "value": ""
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+            "name": "clip",
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+            "name": "clip",
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+        "outputs": {
+          "conditioning": {
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+            "name": "conditioning",
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+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "input",
+            "label": "QR Code / Hidden Image"
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "54335653-0e17-42da-b9e8-83c5fb5af670",
+            "name": "image",
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+}

docs/workflows/SDXL_Text_to_Image.json

@@ -26,10 +26,6 @@
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@@ -135,10 +130,12 @@
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@@ -209,7 +205,9 @@
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@@ -218,83 +216,10 @@
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@@ -327,7 +252,9 @@
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@@ -340,7 +267,6 @@
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@@ -351,7 +277,7 @@
             "fieldKind": "input",
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             "value": {
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+              "model_name": "stable-diffusion-xl-base-1-0",
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               "model_type": "main"
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@@ -387,10 +313,12 @@
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@@ -400,7 +328,6 @@
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@@ -495,128 +422,77 @@
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-          "control": {
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@@ -626,13 +502,161 @@
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+          },
+          "scheduler": {
+            "id": "14423e0d-7215-4ee0-b065-f9e95eaa8d7d",
+            "name": "scheduler",
+            "type": "Scheduler",
+            "fieldKind": "input",
+            "label": "",
+            "value": "euler"
+          },
+          "unet": {
+            "id": "e73bbf98-6489-492b-b83c-faed215febac",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "control": {
+            "id": "dab351b3-0c86-4ea5-9782-4e8edbfb0607",
+            "name": "control",
+            "type": "ControlPolymorphic",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "ip_adapter": {
+            "id": "192daea0-a90a-43cc-a2ee-0114a8e90318",
+            "name": "ip_adapter",
+            "type": "IPAdapterPolymorphic",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "t2i_adapter": {
+            "id": "ee386a55-d4c7-48c1-ac57-7bc4e3aada7a",
+            "name": "t2i_adapter",
+            "type": "T2IAdapterPolymorphic",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "latents": {
+            "id": "3a922c6a-3d8c-4c9e-b3ec-2f4d81cda077",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "denoise_mask": {
+            "id": "cd7ce032-835f-495f-8b45-d57272f33132",
+            "name": "denoise_mask",
+            "type": "DenoiseMaskField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "latents": {
+            "id": "6260b84f-8361-470a-98d8-5b22a45c2d8c",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "aede0ecf-25b6-46be-aa30-b77f79715deb",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "519abf62-d475-48ef-ab8f-66136bc0e499",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.4.0"
+      },
+      "width": 320,
+      "height": 646,
+      "position": {
+        "x": 1642.955772577545,
+        "y": -230.2485847594651
       }
     }
   ],
@@ -686,50 +710,42 @@
     {
       "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
       "sourceHandle": "vae",
-      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "target": "63e91020-83b2-4f35-b174-ad9692aabb48",
       "targetHandle": "vae",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
-      "type": "default"
-    },
-    {
-      "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": "87ee6243-fb0d-4f77-ad5f-56591659339e",
-      "targetHandle": "positive_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": "87ee6243-fb0d-4f77-ad5f-56591659339e",
-      "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-87ee6243-fb0d-4f77-ad5f-56591659339enegative_conditioning",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-63e91020-83b2-4f35-b174-ad9692aabb48vae",
       "type": "default"
     },
     {
       "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
       "sourceHandle": "unet",
-      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
       "targetHandle": "unet",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-87ee6243-fb0d-4f77-ad5f-56591659339eunet",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbunet",
+      "type": "default"
+    },
+    {
+      "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "sourceHandle": "conditioning",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbpositive_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "sourceHandle": "conditioning",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnegative_conditioning",
       "type": "default"
     },
     {
       "source": "55705012-79b9-4aac-9f26-c0b10309785b",
       "sourceHandle": "noise",
-      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
       "targetHandle": "noise",
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-87ee6243-fb0d-4f77-ad5f-56591659339enoise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnoise",
       "type": "default"
     }
   ]
-}
+}

docs/workflows/Text_to_Image.json

@@ -18,10 +18,6 @@
     {
       "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "fieldName": "prompt"
-    },
-    {
-      "nodeId": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
-      "fieldName": "steps"
     }
   ],
   "meta": {
@@ -32,7 +28,6 @@
       "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "type": "invocation",
       "data": {
-        "version": "1.0.0",
         "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
         "type": "compel",
         "inputs": {
@@ -64,20 +59,21 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 235,
+      "height": 261,
       "position": {
-        "x": 1400,
-        "y": -75
+        "x": 995.7263915923627,
+        "y": 239.67783573351227
       }
     },
     {
       "id": "55705012-79b9-4aac-9f26-c0b10309785b",
       "type": "invocation",
       "data": {
-        "version": "1.0.0",
         "id": "55705012-79b9-4aac-9f26-c0b10309785b",
         "type": "noise",
         "inputs": {
@@ -138,92 +134,21 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 364,
+      "height": 389,
       "position": {
-        "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
+        "x": 993.4442117555518,
+        "y": 605.6757415334787
       }
     },
     {
       "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "type": "invocation",
       "data": {
-        "version": "1.0.0",
         "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
         "type": "main_model_loader",
         "inputs": {
@@ -261,23 +186,24 @@
           }
         },
         "label": "",
-        "isOpen": false,
+        "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 32,
+      "height": 226,
       "position": {
-        "x": 1000,
-        "y": 200
+        "x": 163.04436745878343,
+        "y": 254.63156870373479
       }
     },
     {
       "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
       "type": "invocation",
       "data": {
-        "version": "1.0.0",
         "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
         "type": "compel",
         "inputs": {
@@ -309,20 +235,21 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 235,
+      "height": 261,
       "position": {
-        "x": 1000,
-        "y": -75
+        "x": 595.7263915923627,
+        "y": 239.67783573351227
       }
     },
     {
       "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
       "type": "invocation",
       "data": {
-        "version": "1.0.0",
         "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
         "type": "rand_int",
         "inputs": {
@@ -352,51 +279,66 @@
           }
         },
         "label": "Random Seed",
-        "isOpen": false,
+        "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": false,
+        "version": "1.0.0"
       },
       "width": 320,
-      "height": 32,
+      "height": 218,
       "position": {
-        "x": 1000,
-        "y": 275
+        "x": 541.094822888628,
+        "y": 694.5704476446829
       }
     },
     {
-      "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
       "type": "invocation",
       "data": {
-        "version": "1.0.0",
-        "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+        "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
         "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": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
+            "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
             "name": "noise",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "steps": {
-            "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
+            "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
             "name": "steps",
             "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "value": 36
+            "value": 10
           },
           "cfg_scale": {
-            "id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
+            "id": "87dd04d3-870e-49e1-98bf-af003a810109",
             "name": "cfg_scale",
-            "type": "float",
+            "type": "FloatPolymorphic",
             "fieldKind": "input",
             "label": "",
             "value": 7.5
           },
           "denoising_start": {
-            "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
+            "id": "f369d80f-4931-4740-9bcd-9f0620719fab",
             "name": "denoising_start",
             "type": "float",
             "fieldKind": "input",
@@ -404,7 +346,7 @@
             "value": 0
           },
           "denoising_end": {
-            "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
+            "id": "747d10e5-6f02-445c-994c-0604d814de8c",
             "name": "denoising_end",
             "type": "float",
             "fieldKind": "input",
@@ -412,71 +354,71 @@
             "value": 1
           },
           "scheduler": {
-            "id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
+            "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
             "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": "7abe57cc-469d-437e-ad72-a18efa28215f",
+            "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
             "name": "control",
-            "type": "ControlField",
+            "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",
             "fieldKind": "input",
             "label": ""
           },
           "latents": {
-            "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
+            "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "denoise_mask": {
-            "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
+            "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
             "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": "8508d04d-f999-4a44-94d0-388ab1401d27",
+            "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "output"
           },
           "width": {
-            "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
+            "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
             "name": "width",
             "type": "integer",
             "fieldKind": "output"
           },
           "height": {
-            "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
+            "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
             "name": "height",
             "type": "integer",
             "fieldKind": "output"
@@ -486,13 +428,95 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.4.0"
       },
       "width": 320,
-      "height": 558,
+      "height": 646,
       "position": {
-        "x": 1400,
-        "y": 200
+        "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
       }
     }
   ],
@@ -522,52 +546,52 @@
       "type": "default"
     },
     {
-      "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",
+      "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",
       "type": "default"
     },
     {
       "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
       "sourceHandle": "conditioning",
-      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
       "targetHandle": "positive_conditioning",
-      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
       "type": "default"
     },
     {
       "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "sourceHandle": "conditioning",
-      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
       "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
       "type": "default"
     },
     {
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "sourceHandle": "unet",
-      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
       "targetHandle": "unet",
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
       "type": "default"
     },
     {
-      "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",
+      "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",
       "type": "default"
     }
   ]
-}
+}

installer/install.bat.in

@@ -1,7 +1,7 @@
 @echo off
 setlocal EnableExtensions EnableDelayedExpansion
 
-@rem This script requires the user to install Python 3.9 or higher. All other
+@rem This script requires the user to install Python 3.10 or higher. All other
 @rem requirements are downloaded as needed.
 
 @rem change to the script's directory
@@ -19,7 +19,7 @@ set INVOKEAI_VERSION=latest
 set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
 set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
 set PYTHON_URL=https://www.python.org/downloads/windows/
-set MINIMUM_PYTHON_VERSION=3.9.0
+set MINIMUM_PYTHON_VERSION=3.10.0
 set PYTHON_URL=https://www.python.org/downloads/release/python-3109/
 
 set err_msg=An error has occurred and the script could not continue.
@@ -28,8 +28,7 @@ set err_msg=An error has occurred and the script could not continue.
 echo This script will install InvokeAI and its dependencies.
 echo.
 echo BEFORE YOU START PLEASE MAKE SURE TO DO THE FOLLOWING
-echo 1. Install python 3.9 or 3.10. Python version 3.11 and above are
-echo    not supported at the moment.
+echo 1. Install python 3.10 or 3.11. Python version 3.9 is no longer supported.
 echo 2. Double-click on the file WinLongPathsEnabled.reg in order to
 echo    enable long path support on your system.
 echo 3. Install the Visual C++ core libraries.
@@ -46,19 +45,19 @@ echo ***** Checking and Updating Python *****
 
 call python --version >.tmp1 2>.tmp2
 if %errorlevel% == 1 (
-   set err_msg=Please install Python 3.10. See %INSTRUCTIONS% for details.
+   set err_msg=Please install Python 3.10-11. See %INSTRUCTIONS% for details.
    goto err_exit
 )
 
 for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i
 if "%python_version%" == "" (
-   set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.9 from %PYTHON_URL%
+   set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.12 from %PYTHON_URL%
    goto err_exit
 )
 
 call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version%
 if %errorlevel% == 1 (
-   set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.9 from %PYTHON_URL%
+   set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.12 from %PYTHON_URL%
    goto err_exit
 )
 

installer/install.sh.in

@@ -8,10 +8,10 @@ cd $scriptdir
 
 function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
 
-MINIMUM_PYTHON_VERSION=3.9.0
+MINIMUM_PYTHON_VERSION=3.10.0
 MAXIMUM_PYTHON_VERSION=3.11.100
 PYTHON=""
-for candidate in python3.11 python3.10 python3.9 python3 python ; do
+for candidate in python3.11 python3.10 python3 python ; do
     if ppath=`which $candidate`; then
         # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
         # we check that this found executable can actually run

installer/lib/installer.py

@@ -13,7 +13,7 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Union
 
-SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100"
+SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
 INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
 BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
 
@@ -67,7 +67,6 @@ class Installer:
         # Cleaning up temporary directories on Windows results in a race condition
         # and a stack trace.
         # `ignore_cleanup_errors` was only added in Python 3.10
-        # users of Python 3.9 will see a gnarly stack trace on installer exit
         if OS == "Windows" and int(platform.python_version_tuple()[1]) >= 10:
             venv_dir = TemporaryDirectory(prefix=BOOTSTRAP_VENV_PREFIX, ignore_cleanup_errors=True)
         else:
@@ -139,13 +138,6 @@ class Installer:
         except shutil.SameFileError:
             venv.create(venv_dir, with_pip=True, symlinks=True)
 
-        # upgrade pip in Python 3.9 environments
-        if int(platform.python_version_tuple()[1]) == 9:
-            from plumbum import FG, local
-
-            pip = local[get_pip_from_venv(venv_dir)]
-            pip["install", "--upgrade", "pip"] & FG
-
         return venv_dir
 
     def install(
@@ -252,7 +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~=2.0.0",
+                "torch==2.1.0",
                 "torchmetrics==0.11.4",
                 "torchvision>=0.14.1",
                 "--force-reinstall",
@@ -468,10 +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/cu118"
+        url = "https://download.pytorch.org/whl/cu121"
         optional_modules = "[xformers,onnx-cuda]"
     if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu118"
+        url = "https://download.pytorch.org/whl/cu121"
         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],
+            get_paths=lambda: [browse_start],  # noqa: B023
             # 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,
         )

installer/readme.txt

@@ -4,7 +4,7 @@ Project homepage: https://github.com/invoke-ai/InvokeAI
 
 Preparations:
 
-  You will need to install Python 3.9 or higher for this installer
+  You will need to install Python 3.10 or higher for this installer
   to work. Instructions are given here:
   https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
 
@@ -14,15 +14,15 @@ Preparations:
      python --version
 
   If all is well, it will print "Python 3.X.X", where the version number
-  is at least 3.9.*, and not higher than 3.11.*.
+  is at least 3.10.*, and not higher than 3.11.*.
 
   If this works, check the version of the Python package manager, pip:
 
      pip --version
 
   You should get a message that indicates that the pip package
-  installer was derived from Python 3.9 or 3.10. For example:
-  "pip 22.3.1 from /usr/bin/pip (python 3.9)"
+  installer was derived from Python 3.10 or 3.11. For example:
+  "pip 22.0.1 from /usr/bin/pip (python 3.10)"
 
 Long Paths on Windows:
 

installer/templates/invoke.bat.in

@@ -9,41 +9,37 @@ set INVOKEAI_ROOT=.
 :start
 echo Desired action:
 echo 1. Generate images with the browser-based interface
-echo 2. Explore InvokeAI nodes using a command-line interface
-echo 3. Run textual inversion training
-echo 4. Merge models (diffusers type only)
-echo 5. Download and install models
-echo 6. Change InvokeAI startup options
-echo 7. Re-run the configure script to fix a broken install or to complete a major upgrade
-echo 8. Open the developer console
-echo 9. Update InvokeAI
-echo 10. Run the InvokeAI image database maintenance script
-echo 11. Command-line help
+echo 2. Run textual inversion training
+echo 3. Merge models (diffusers type only)
+echo 4. Download and install models
+echo 5. Change InvokeAI startup options
+echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
+echo 7. Open the developer console
+echo 8. Update InvokeAI
+echo 9. Run the InvokeAI image database maintenance script
+echo 10. Command-line help
 echo Q - Quit
-set /P choice="Please enter 1-11, Q: [1] "
+set /P choice="Please enter 1-10, Q: [1] "
 if not defined choice set choice=1
 IF /I "%choice%" == "1" (
     echo Starting the InvokeAI browser-based UI..
     python .venv\Scripts\invokeai-web.exe %*
 ) ELSE IF /I "%choice%" == "2" (
-    echo Starting the InvokeAI command-line..
-    python .venv\Scripts\invokeai.exe %*
-) ELSE IF /I "%choice%" == "3" (
     echo Starting textual inversion training..
     python .venv\Scripts\invokeai-ti.exe --gui
-) ELSE IF /I "%choice%" == "4" (
+) ELSE IF /I "%choice%" == "3" (
     echo Starting model merging script..
     python .venv\Scripts\invokeai-merge.exe --gui
-) ELSE IF /I "%choice%" == "5" (
+) ELSE IF /I "%choice%" == "4" (
     echo Running invokeai-model-install...
     python .venv\Scripts\invokeai-model-install.exe
-) ELSE IF /I "%choice%" == "6" (
+) ELSE IF /I "%choice%" == "5" (
     echo Running invokeai-configure...
     python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
-) ELSE IF /I "%choice%" == "7" (
+) ELSE IF /I "%choice%" == "6" (
     echo Running invokeai-configure...
     python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
-) ELSE IF /I "%choice%" == "8" (
+) ELSE IF /I "%choice%" == "7" (
     echo Developer Console
     echo Python command is:
     where python
@@ -55,13 +51,13 @@ IF /I "%choice%" == "1" (
     echo *************************
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
-) ELSE IF /I "%choice%" == "9" (
+) ELSE IF /I "%choice%" == "8" (
    echo Running invokeai-update...
    python -m invokeai.frontend.install.invokeai_update
-) ELSE IF /I "%choice%" == "10" (
+) ELSE IF /I "%choice%" == "9" (
    echo Running the db maintenance script...
    python .venv\Scripts\invokeai-db-maintenance.exe
-) ELSE IF /I "%choice%" == "11" (
+) ELSE IF /I "%choice%" == "10" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai-web.exe --help %*
     pause

installer/templates/invoke.sh.in

@@ -58,52 +58,47 @@ do_choice() {
         invokeai-web $PARAMS
         ;;
     2)
-        clear
-        printf "Explore InvokeAI nodes using a command-line interface\n"
-        invokeai $PARAMS
-        ;;
-    3)
         clear
         printf "Textual inversion training\n"
         invokeai-ti --gui $PARAMS
         ;;
-    4)
+    3)
         clear
         printf "Merge models (diffusers type only)\n"
         invokeai-merge --gui $PARAMS
         ;;
-    5)
+    4)
         clear
         printf "Download and install models\n"
         invokeai-model-install --root ${INVOKEAI_ROOT}
         ;;
-    6)
+    5)
         clear
         printf "Change InvokeAI startup options\n"
         invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
         ;;
-    7)
+    6)
         clear
         printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
         invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
         ;;
-    8)
+    7)
         clear
         printf "Open the developer console\n"
         file_name=$(basename "${BASH_SOURCE[0]}")
         bash --init-file "$file_name"
         ;;
-    9)
+    8)
         clear
         printf "Update InvokeAI\n"
         python -m invokeai.frontend.install.invokeai_update
         ;;
-    10)
+    9)
         clear
         printf "Running the db maintenance script\n"
         invokeai-db-maintenance --root ${INVOKEAI_ROOT}
         ;;
-    11)
+    10)
         clear
         printf "Command-line help\n"
         invokeai-web --help
@@ -121,16 +116,15 @@ do_choice() {
 do_dialog() {
     options=(
         1 "Generate images with a browser-based interface"
-        2 "Explore InvokeAI nodes using a command-line interface"
-        3 "Textual inversion training"
-        4 "Merge models (diffusers type only)"
-        5 "Download and install models"
-        6 "Change InvokeAI startup options"
-        7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
-        8 "Open the developer console"
-        9 "Update InvokeAI"
-	10 "Run the InvokeAI image database maintenance script"
-	11 "Command-line help"
+        2 "Textual inversion training"
+        3 "Merge models (diffusers type only)"
+        4 "Download and install models"
+        5 "Change InvokeAI startup options"
+        6 "Re-run the configure script to fix a broken install or to complete a major upgrade"
+        7 "Open the developer console"
+        8 "Update InvokeAI"
+	9 "Run the InvokeAI image database maintenance script"
+	10 "Command-line help"
     )
 
     choice=$(dialog --clear \
@@ -155,18 +149,17 @@ do_line_input() {
     printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
     printf "What would you like to do?\n"
     printf "1: Generate images using the browser-based interface\n"
-    printf "2: Explore InvokeAI nodes using the command-line interface\n"
-    printf "3: Run textual inversion training\n"
-    printf "4: Merge models (diffusers type only)\n"
-    printf "5: Download and install models\n"
-    printf "6: Change InvokeAI startup options\n"
-    printf "7: Re-run the configure script to fix a broken install\n"
-    printf "8: Open the developer console\n"
-    printf "9: Update InvokeAI\n"
-    printf "10: Run the InvokeAI image database maintenance script\n"
-    printf "11: Command-line help\n"
+    printf "2: Run textual inversion training\n"
+    printf "3: Merge models (diffusers type only)\n"
+    printf "4: Download and install models\n"
+    printf "5: Change InvokeAI startup options\n"
+    printf "6: Re-run the configure script to fix a broken install\n"
+    printf "7: Open the developer console\n"
+    printf "8: Update InvokeAI\n"
+    printf "9: Run the InvokeAI image database maintenance script\n"
+    printf "10: Command-line help\n"
     printf "Q: Quit\n\n"
-    read -p "Please enter 1-11, Q: [1] " yn
+    read -p "Please enter 1-10, Q: [1] " yn
     choice=${yn:='1'}
     do_choice $choice
     clear

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__
 
@@ -23,6 +24,7 @@ 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
@@ -30,6 +32,7 @@ from ..services.shared.default_graphs import create_system_graphs
 from ..services.shared.graph import GraphExecutionState, LibraryGraph
 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
 
 
@@ -83,6 +86,7 @@ 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()
@@ -90,6 +94,8 @@ 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(
             board_image_records=board_image_records,
@@ -107,6 +113,7 @@ class ApiDependencies:
             latents=latents,
             logger=logger,
             model_manager=model_manager,
+            model_records=model_record_service,
             names=names,
             performance_statistics=performance_statistics,
             processor=processor,
@@ -114,6 +121,8 @@ 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)

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(dict(event_name=event_name, payload=payload))
+        self.__queue.put({"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/images.py

@@ -1,13 +1,14 @@
 import io
+import traceback
 from typing import Optional
 
 from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.metadata import ImageMetadata
+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
@@ -45,17 +46,38 @@ async def upload_image(
     if not file.content_type or not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
-    contents = await file.read()
+    metadata = None
+    workflow = None
 
+    contents = await file.read()
     try:
         pil_image = Image.open(io.BytesIO(contents))
         if crop_visible:
             bbox = pil_image.getbbox()
             pil_image = pil_image.crop(bbox)
     except Exception:
-        # Error opening the image
+        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
         raise HTTPException(status_code=415, detail="Failed to read image")
 
+    # TODO: retain non-invokeai metadata on upload?
+    # attempt to parse metadata from image
+    metadata_raw = pil_image.info.get("invokeai_metadata", None)
+    if metadata_raw:
+        try:
+            metadata = MetadataFieldValidator.validate_json(metadata_raw)
+        except ValidationError:
+            ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
+            pass
+
+    # attempt to parse workflow from image
+    workflow_raw = pil_image.info.get("invokeai_workflow", None)
+    if workflow_raw is not None:
+        try:
+            workflow = WorkflowFieldValidator.validate_json(workflow_raw)
+        except ValidationError:
+            ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
+            pass
+
     try:
         image_dto = ApiDependencies.invoker.services.images.create(
             image=pil_image,
@@ -63,6 +85,8 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
+            metadata=metadata,
+            workflow=workflow,
             is_intermediate=is_intermediate,
         )
 
@@ -71,6 +95,7 @@ async def upload_image(
 
         return image_dto
     except Exception:
+        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
         raise HTTPException(status_code=500, detail="Failed to create image")
 
 
@@ -87,7 +112,7 @@ async def delete_image(
         pass
 
 
-@images_router.post("/clear-intermediates", operation_id="clear_intermediates")
+@images_router.delete("/intermediates", operation_id="clear_intermediates")
 async def clear_intermediates() -> int:
     """Clears all intermediates"""
 
@@ -99,6 +124,17 @@ async def clear_intermediates() -> int:
         pass
 
 
+@images_router.get("/intermediates", operation_id="get_intermediates_count")
+async def get_intermediates_count() -> int:
+    """Gets the count of intermediate images"""
+
+    try:
+        return ApiDependencies.invoker.services.images.get_intermediates_count()
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to get intermediates")
+        pass
+
+
 @images_router.patch(
     "/i/{image_name}",
     operation_id="update_image",
@@ -135,11 +171,11 @@ async def get_image_dto(
 @images_router.get(
     "/i/{image_name}/metadata",
     operation_id="get_image_metadata",
-    response_model=ImageMetadata,
+    response_model=Optional[MetadataField],
 )
 async def get_image_metadata(
     image_name: str = Path(description="The name of image to get"),
-) -> ImageMetadata:
+) -> Optional[MetadataField]:
     """Gets an image's metadata"""
 
     try:

invokeai/app/api/routers/model_records.py

@@ -0,0 +1,164 @@
+# 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

@@ -1,6 +1,5 @@
 # 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
 
@@ -23,13 +22,13 @@ from ..dependencies import ApiDependencies
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
 
 UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
-update_models_response_adapter = TypeAdapter(UpdateModelResponse)
+UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
 
 ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
-import_models_response_adapter = TypeAdapter(ImportModelResponse)
+ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
 
 ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
-convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
+ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
 
 MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
@@ -41,7 +40,7 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
-models_list_adapter = TypeAdapter(ModelsList)
+ModelsListValidator = TypeAdapter(ModelsList)
 
 
 @models_router.get(
@@ -55,12 +54,12 @@ async def list_models(
 ) -> ModelsList:
     """Gets a list of models"""
     if base_models and len(base_models) > 0:
-        models_raw = list()
+        models_raw = []
         for base_model in base_models:
             models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
     else:
         models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
-    models = models_list_adapter.validate_python({"models": models_raw})
+    models = ModelsListValidator.validate_python({"models": models_raw})
     return models
 
 
@@ -131,7 +130,7 @@ async def update_model(
             base_model=base_model,
             model_type=model_type,
         )
-        model_response = update_models_response_adapter.validate_python(model_raw)
+        model_response = UpdateModelResponseValidator.validate_python(model_raw)
     except ModelNotFoundException as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
@@ -186,7 +185,7 @@ async def import_model(
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
             model_name=info.name, base_model=info.base_model, model_type=info.model_type
         )
-        return import_models_response_adapter.validate_python(model_raw)
+        return ImportModelResponseValidator.validate_python(model_raw)
 
     except ModelNotFoundException as e:
         logger.error(str(e))
@@ -231,7 +230,7 @@ async def add_model(
             base_model=info.base_model,
             model_type=info.model_type,
         )
-        return import_models_response_adapter.validate_python(model_raw)
+        return ImportModelResponseValidator.validate_python(model_raw)
     except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
@@ -302,7 +301,7 @@ async def convert_model(
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
             model_name, base_model=base_model, model_type=model_type
         )
-        response = convert_models_response_adapter.validate_python(model_raw)
+        response = ConvertModelResponseValidator.validate_python(model_raw)
     except ModelNotFoundException as e:
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
     except ValueError as e:
@@ -417,7 +416,7 @@ async def merge_models(
             base_model=base_model,
             model_type=ModelType.Main,
         )
-        response = convert_models_response_adapter.validate_python(model_raw)
+        response = ConvertModelResponseValidator.validate_python(model_raw)
     except ModelNotFoundException:
         raise HTTPException(
             status_code=404,

invokeai/app/api/routers/session_queue.py

@@ -12,13 +12,11 @@ from invokeai.app.services.session_queue.session_queue_common import (
     CancelByBatchIDsResult,
     ClearResult,
     EnqueueBatchResult,
-    EnqueueGraphResult,
     PruneResult,
     SessionQueueItem,
     SessionQueueItemDTO,
     SessionQueueStatus,
 )
-from invokeai.app.services.shared.graph import Graph
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
 from ..dependencies import ApiDependencies
@@ -33,23 +31,6 @@ class SessionQueueAndProcessorStatus(BaseModel):
     processor: SessionProcessorStatus
 
 
-@session_queue_router.post(
-    "/{queue_id}/enqueue_graph",
-    operation_id="enqueue_graph",
-    responses={
-        201: {"model": EnqueueGraphResult},
-    },
-)
-async def enqueue_graph(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    graph: Graph = Body(description="The graph to enqueue"),
-    prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
-) -> EnqueueGraphResult:
-    """Enqueues a graph for single execution."""
-
-    return ApiDependencies.invoker.services.session_queue.enqueue_graph(queue_id=queue_id, graph=graph, prepend=prepend)
-
-
 @session_queue_router.post(
     "/{queue_id}/enqueue_batch",
     operation_id="enqueue_batch",

invokeai/app/api/routers/sessions.py

@@ -1,57 +1,50 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Annotated, Optional, Union
 
-from fastapi import Body, HTTPException, Path, Query, Response
+from fastapi import HTTPException, Path
 from fastapi.routing import APIRouter
-from pydantic.fields import Field
 
-from invokeai.app.services.shared.pagination import PaginatedResults
-
-# Importing * is bad karma but needed here for node detection
-from ...invocations import *  # noqa: F401 F403
-from ...invocations.baseinvocation import BaseInvocation
-from ...services.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
+from ...services.shared.graph import GraphExecutionState
 from ..dependencies import ApiDependencies
 
 session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
 
 
-@session_router.post(
-    "/",
-    operation_id="create_session",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid json"},
-    },
-    deprecated=True,
-)
-async def create_session(
-    queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
-    graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"),
-) -> GraphExecutionState:
-    """Creates a new session, optionally initializing it with an invocation graph"""
-    session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
-    return session
+# @session_router.post(
+#     "/",
+#     operation_id="create_session",
+#     responses={
+#         200: {"model": GraphExecutionState},
+#         400: {"description": "Invalid json"},
+#     },
+#     deprecated=True,
+# )
+# async def create_session(
+#     queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
+#     graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"),
+# ) -> GraphExecutionState:
+#     """Creates a new session, optionally initializing it with an invocation graph"""
+#     session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
+#     return session
 
 
-@session_router.get(
-    "/",
-    operation_id="list_sessions",
-    responses={200: {"model": PaginatedResults[GraphExecutionState]}},
-    deprecated=True,
-)
-async def list_sessions(
-    page: int = Query(default=0, description="The page of results to get"),
-    per_page: int = Query(default=10, description="The number of results per page"),
-    query: str = Query(default="", description="The query string to search for"),
-) -> PaginatedResults[GraphExecutionState]:
-    """Gets a list of sessions, optionally searching"""
-    if query == "":
-        result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
-    else:
-        result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
-    return result
+# @session_router.get(
+#     "/",
+#     operation_id="list_sessions",
+#     responses={200: {"model": PaginatedResults[GraphExecutionState]}},
+#     deprecated=True,
+# )
+# async def list_sessions(
+#     page: int = Query(default=0, description="The page of results to get"),
+#     per_page: int = Query(default=10, description="The number of results per page"),
+#     query: str = Query(default="", description="The query string to search for"),
+# ) -> PaginatedResults[GraphExecutionState]:
+#     """Gets a list of sessions, optionally searching"""
+#     if query == "":
+#         result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
+#     else:
+#         result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
+#     return result
 
 
 @session_router.get(
@@ -61,7 +54,6 @@ async def list_sessions(
         200: {"model": GraphExecutionState},
         404: {"description": "Session not found"},
     },
-    deprecated=True,
 )
 async def get_session(
     session_id: str = Path(description="The id of the session to get"),
@@ -74,211 +66,211 @@ async def get_session(
         return session
 
 
-@session_router.post(
-    "/{session_id}/nodes",
-    operation_id="add_node",
-    responses={
-        200: {"model": str},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-    deprecated=True,
-)
-async def add_node(
-    session_id: str = Path(description="The id of the session"),
-    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
-        description="The node to add"
-    ),
-) -> str:
-    """Adds a node to the graph"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        raise HTTPException(status_code=404)
+# @session_router.post(
+#     "/{session_id}/nodes",
+#     operation_id="add_node",
+#     responses={
+#         200: {"model": str},
+#         400: {"description": "Invalid node or link"},
+#         404: {"description": "Session not found"},
+#     },
+#     deprecated=True,
+# )
+# async def add_node(
+#     session_id: str = Path(description="The id of the session"),
+#     node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
+#         description="The node to add"
+#     ),
+# ) -> str:
+#     """Adds a node to the graph"""
+#     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+#     if session is None:
+#         raise HTTPException(status_code=404)
 
-    try:
-        session.add_node(node)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session.id
-    except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
-    except IndexError:
-        raise HTTPException(status_code=400)
+#     try:
+#         session.add_node(node)
+#         ApiDependencies.invoker.services.graph_execution_manager.set(
+#             session
+#         )  # TODO: can this be done automatically, or add node through an API?
+#         return session.id
+#     except NodeAlreadyExecutedError:
+#         raise HTTPException(status_code=400)
+#     except IndexError:
+#         raise HTTPException(status_code=400)
 
 
-@session_router.put(
-    "/{session_id}/nodes/{node_path}",
-    operation_id="update_node",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-    deprecated=True,
-)
-async def update_node(
-    session_id: str = Path(description="The id of the session"),
-    node_path: str = Path(description="The path to the node in the graph"),
-    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
-        description="The new node"
-    ),
-) -> GraphExecutionState:
-    """Updates a node in the graph and removes all linked edges"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        raise HTTPException(status_code=404)
+# @session_router.put(
+#     "/{session_id}/nodes/{node_path}",
+#     operation_id="update_node",
+#     responses={
+#         200: {"model": GraphExecutionState},
+#         400: {"description": "Invalid node or link"},
+#         404: {"description": "Session not found"},
+#     },
+#     deprecated=True,
+# )
+# async def update_node(
+#     session_id: str = Path(description="The id of the session"),
+#     node_path: str = Path(description="The path to the node in the graph"),
+#     node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
+#         description="The new node"
+#     ),
+# ) -> GraphExecutionState:
+#     """Updates a node in the graph and removes all linked edges"""
+#     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+#     if session is None:
+#         raise HTTPException(status_code=404)
 
-    try:
-        session.update_node(node_path, node)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
-    except IndexError:
-        raise HTTPException(status_code=400)
+#     try:
+#         session.update_node(node_path, node)
+#         ApiDependencies.invoker.services.graph_execution_manager.set(
+#             session
+#         )  # TODO: can this be done automatically, or add node through an API?
+#         return session
+#     except NodeAlreadyExecutedError:
+#         raise HTTPException(status_code=400)
+#     except IndexError:
+#         raise HTTPException(status_code=400)
 
 
-@session_router.delete(
-    "/{session_id}/nodes/{node_path}",
-    operation_id="delete_node",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-    deprecated=True,
-)
-async def delete_node(
-    session_id: str = Path(description="The id of the session"),
-    node_path: str = Path(description="The path to the node to delete"),
-) -> GraphExecutionState:
-    """Deletes a node in the graph and removes all linked edges"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        raise HTTPException(status_code=404)
+# @session_router.delete(
+#     "/{session_id}/nodes/{node_path}",
+#     operation_id="delete_node",
+#     responses={
+#         200: {"model": GraphExecutionState},
+#         400: {"description": "Invalid node or link"},
+#         404: {"description": "Session not found"},
+#     },
+#     deprecated=True,
+# )
+# async def delete_node(
+#     session_id: str = Path(description="The id of the session"),
+#     node_path: str = Path(description="The path to the node to delete"),
+# ) -> GraphExecutionState:
+#     """Deletes a node in the graph and removes all linked edges"""
+#     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+#     if session is None:
+#         raise HTTPException(status_code=404)
 
-    try:
-        session.delete_node(node_path)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
-    except IndexError:
-        raise HTTPException(status_code=400)
+#     try:
+#         session.delete_node(node_path)
+#         ApiDependencies.invoker.services.graph_execution_manager.set(
+#             session
+#         )  # TODO: can this be done automatically, or add node through an API?
+#         return session
+#     except NodeAlreadyExecutedError:
+#         raise HTTPException(status_code=400)
+#     except IndexError:
+#         raise HTTPException(status_code=400)
 
 
-@session_router.post(
-    "/{session_id}/edges",
-    operation_id="add_edge",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-    deprecated=True,
-)
-async def add_edge(
-    session_id: str = Path(description="The id of the session"),
-    edge: Edge = Body(description="The edge to add"),
-) -> GraphExecutionState:
-    """Adds an edge to the graph"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        raise HTTPException(status_code=404)
+# @session_router.post(
+#     "/{session_id}/edges",
+#     operation_id="add_edge",
+#     responses={
+#         200: {"model": GraphExecutionState},
+#         400: {"description": "Invalid node or link"},
+#         404: {"description": "Session not found"},
+#     },
+#     deprecated=True,
+# )
+# async def add_edge(
+#     session_id: str = Path(description="The id of the session"),
+#     edge: Edge = Body(description="The edge to add"),
+# ) -> GraphExecutionState:
+#     """Adds an edge to the graph"""
+#     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+#     if session is None:
+#         raise HTTPException(status_code=404)
 
-    try:
-        session.add_edge(edge)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
-    except IndexError:
-        raise HTTPException(status_code=400)
+#     try:
+#         session.add_edge(edge)
+#         ApiDependencies.invoker.services.graph_execution_manager.set(
+#             session
+#         )  # TODO: can this be done automatically, or add node through an API?
+#         return session
+#     except NodeAlreadyExecutedError:
+#         raise HTTPException(status_code=400)
+#     except IndexError:
+#         raise HTTPException(status_code=400)
 
 
-# TODO: the edge being in the path here is really ugly, find a better solution
-@session_router.delete(
-    "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
-    operation_id="delete_edge",
-    responses={
-        200: {"model": GraphExecutionState},
-        400: {"description": "Invalid node or link"},
-        404: {"description": "Session not found"},
-    },
-    deprecated=True,
-)
-async def delete_edge(
-    session_id: str = Path(description="The id of the session"),
-    from_node_id: str = Path(description="The id of the node the edge is coming from"),
-    from_field: str = Path(description="The field of the node the edge is coming from"),
-    to_node_id: str = Path(description="The id of the node the edge is going to"),
-    to_field: str = Path(description="The field of the node the edge is going to"),
-) -> GraphExecutionState:
-    """Deletes an edge from the graph"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        raise HTTPException(status_code=404)
+# # TODO: the edge being in the path here is really ugly, find a better solution
+# @session_router.delete(
+#     "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
+#     operation_id="delete_edge",
+#     responses={
+#         200: {"model": GraphExecutionState},
+#         400: {"description": "Invalid node or link"},
+#         404: {"description": "Session not found"},
+#     },
+#     deprecated=True,
+# )
+# async def delete_edge(
+#     session_id: str = Path(description="The id of the session"),
+#     from_node_id: str = Path(description="The id of the node the edge is coming from"),
+#     from_field: str = Path(description="The field of the node the edge is coming from"),
+#     to_node_id: str = Path(description="The id of the node the edge is going to"),
+#     to_field: str = Path(description="The field of the node the edge is going to"),
+# ) -> GraphExecutionState:
+#     """Deletes an edge from the graph"""
+#     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+#     if session is None:
+#         raise HTTPException(status_code=404)
 
-    try:
-        edge = Edge(
-            source=EdgeConnection(node_id=from_node_id, field=from_field),
-            destination=EdgeConnection(node_id=to_node_id, field=to_field),
-        )
-        session.delete_edge(edge)
-        ApiDependencies.invoker.services.graph_execution_manager.set(
-            session
-        )  # TODO: can this be done automatically, or add node through an API?
-        return session
-    except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
-    except IndexError:
-        raise HTTPException(status_code=400)
+#     try:
+#         edge = Edge(
+#             source=EdgeConnection(node_id=from_node_id, field=from_field),
+#             destination=EdgeConnection(node_id=to_node_id, field=to_field),
+#         )
+#         session.delete_edge(edge)
+#         ApiDependencies.invoker.services.graph_execution_manager.set(
+#             session
+#         )  # TODO: can this be done automatically, or add node through an API?
+#         return session
+#     except NodeAlreadyExecutedError:
+#         raise HTTPException(status_code=400)
+#     except IndexError:
+#         raise HTTPException(status_code=400)
 
 
-@session_router.put(
-    "/{session_id}/invoke",
-    operation_id="invoke_session",
-    responses={
-        200: {"model": None},
-        202: {"description": "The invocation is queued"},
-        400: {"description": "The session has no invocations ready to invoke"},
-        404: {"description": "Session not found"},
-    },
-    deprecated=True,
-)
-async def invoke_session(
-    queue_id: str = Query(description="The id of the queue to associate the session with"),
-    session_id: str = Path(description="The id of the session to invoke"),
-    all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
-) -> Response:
-    """Invokes a session"""
-    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
-    if session is None:
-        raise HTTPException(status_code=404)
+# @session_router.put(
+#     "/{session_id}/invoke",
+#     operation_id="invoke_session",
+#     responses={
+#         200: {"model": None},
+#         202: {"description": "The invocation is queued"},
+#         400: {"description": "The session has no invocations ready to invoke"},
+#         404: {"description": "Session not found"},
+#     },
+#     deprecated=True,
+# )
+# async def invoke_session(
+#     queue_id: str = Query(description="The id of the queue to associate the session with"),
+#     session_id: str = Path(description="The id of the session to invoke"),
+#     all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
+# ) -> Response:
+#     """Invokes a session"""
+#     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+#     if session is None:
+#         raise HTTPException(status_code=404)
 
-    if session.is_complete():
-        raise HTTPException(status_code=400)
+#     if session.is_complete():
+#         raise HTTPException(status_code=400)
 
-    ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
-    return Response(status_code=202)
+#     ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
+#     return Response(status_code=202)
 
 
-@session_router.delete(
-    "/{session_id}/invoke",
-    operation_id="cancel_session_invoke",
-    responses={202: {"description": "The invocation is canceled"}},
-    deprecated=True,
-)
-async def cancel_session_invoke(
-    session_id: str = Path(description="The id of the session to cancel"),
-) -> Response:
-    """Invokes a session"""
-    ApiDependencies.invoker.cancel(session_id)
-    return Response(status_code=202)
+# @session_router.delete(
+#     "/{session_id}/invoke",
+#     operation_id="cancel_session_invoke",
+#     responses={202: {"description": "The invocation is canceled"}},
+#     deprecated=True,
+# )
+# async def cancel_session_invoke(
+#     session_id: str = Path(description="The id of the session to cancel"),
+# ) -> Response:
+#     """Invokes a session"""
+#     ApiDependencies.invoker.cancel(session_id)
+#     return Response(status_code=202)

invokeai/app/api/routers/workflows.py

@@ -0,0 +1,20 @@
+from fastapi import APIRouter, Path
+
+from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.app.invocations.baseinvocation import WorkflowField
+
+workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
+
+
+@workflows_router.get(
+    "/i/{workflow_id}",
+    operation_id="get_workflow",
+    responses={
+        200: {"model": WorkflowField},
+    },
+)
+async def get_workflow(
+    workflow_id: str = Path(description="The workflow to get"),
+) -> WorkflowField:
+    """Gets a workflow"""
+    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.enter_room(sid, data["queue_id"])
+            await self.__sio.leave_room(sid, data["queue_id"])

invokeai/app/api_app.py

@@ -1,10 +1,17 @@
-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.
+import sys
+
+from invokeai.version.invokeai_version import __version__
+
+from .services.config import InvokeAIAppConfig
+
 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
@@ -12,38 +19,55 @@ 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 torch
     import uvicorn
     from fastapi import FastAPI
     from fastapi.middleware.cors import CORSMiddleware
+    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.staticfiles import StaticFiles
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
     from pydantic.json_schema import models_json_schema
+    from torch.backends.mps import is_available as is_mps_available
 
+    # for PyCharm:
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
     import invokeai.frontend.web as web_dir
-    from invokeai.version.invokeai_version import __version__
 
     from ..backend.util.logging import InvokeAILogger
     from .api.dependencies import ApiDependencies
-    from .api.routers import app_info, board_images, boards, images, models, session_queue, utilities
+    from .api.routers import (
+        app_info,
+        board_images,
+        boards,
+        images,
+        model_records,
+        models,
+        session_queue,
+        sessions,
+        utilities,
+        workflows,
+    )
     from .api.sockets import SocketIO
-    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
+    from .invocations.baseinvocation import (
+        BaseInvocation,
+        InputFieldJSONSchemaExtra,
+        OutputFieldJSONSchemaExtra,
+        UIConfigBase,
+    )
 
-    if torch.backends.mps.is_available():
-        # noinspection PyUnresolvedReferences
+    if is_mps_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
 
 
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
 logger = InvokeAILogger.get_logger(config=app_config)
-
 # fix for windows mimetypes registry entries being borked
 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
 mimetypes.add_type("application/javascript", ".js")
@@ -71,40 +95,38 @@ app.add_middleware(
     allow_headers=app_config.allow_headers,
 )
 
+app.add_middleware(GZipMiddleware, minimum_size=1000)
+
 
 # Add startup event to load dependencies
 @app.on_event("startup")
-async def startup_event():
+async def startup_event() -> None:
     ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
 
 
 # Shut down threads
 @app.on_event("shutdown")
-async def shutdown_event():
+async def shutdown_event() -> None:
     ApiDependencies.shutdown()
 
 
 # Include all routers
-# app.include_router(sessions.session_router, prefix="/api")
+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")
-
 app.include_router(app_info.app_router, prefix="/api")
-
 app.include_router(session_queue.session_queue_router, prefix="/api")
+app.include_router(workflows.workflows_router, prefix="/api")
 
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
-def custom_openapi():
+def custom_openapi() -> dict[str, Any]:
     if app.openapi_schema:
         return app.openapi_schema
     openapi_schema = get_openapi(
@@ -118,7 +140,7 @@ def custom_openapi():
     # Add all outputs
     all_invocations = BaseInvocation.get_invocations()
     output_types = set()
-    output_type_titles = dict()
+    output_type_titles = {}
     for invoker in all_invocations:
         output_type = signature(invoker.invoke).return_annotation
         output_types.add(output_type)
@@ -133,7 +155,11 @@ def custom_openapi():
 
     # Add Node Editor UI helper schemas
     ui_config_schemas = models_json_schema(
-        [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
+        [
+            (UIConfigBase, "serialization"),
+            (InputFieldJSONSchemaExtra, "serialization"),
+            (OutputFieldJSONSchemaExtra, "serialization"),
+        ],
         ref_template="#/components/schemas/{model}",
     )
     for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
@@ -141,7 +167,7 @@ def custom_openapi():
 
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
-        invoker_name = invoker.__name__
+        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
         output_type = signature(obj=invoker.invoke).return_annotation
         output_type_title = output_type_titles[output_type.__name__]
         invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
@@ -159,13 +185,12 @@ def custom_openapi():
             # print(f"Config with name {name} already defined")
             continue
 
-        # "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"}
-        openapi_schema["components"]["schemas"][name] = dict(
-            title=name,
-            description="An enumeration.",
-            type="string",
-            enum=list(v.value for v in model_config_format_enum),
-        )
+        openapi_schema["components"]["schemas"][name] = {
+            "title": name,
+            "description": "An enumeration.",
+            "type": "string",
+            "enum": [v.value for v in model_config_format_enum],
+        }
 
     app.openapi_schema = openapi_schema
     return app.openapi_schema
@@ -173,34 +198,43 @@ def custom_openapi():
 
 app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
 
-# Override API doc favicons
-app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
-
 
 @app.get("/docs", include_in_schema=False)
-def overridden_swagger():
+def overridden_swagger() -> HTMLResponse:
     return get_swagger_ui_html(
-        openapi_url=app.openapi_url,
+        openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
         title=app.title,
-        swagger_favicon_url="/static/favicon.ico",
+        swagger_favicon_url="/static/docs/favicon.ico",
     )
 
 
 @app.get("/redoc", include_in_schema=False)
-def overridden_redoc():
+def overridden_redoc() -> HTMLResponse:
     return get_redoc_html(
-        openapi_url=app.openapi_url,
+        openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
         title=app.title,
-        redoc_favicon_url="/static/favicon.ico",
+        redoc_favicon_url="/static/docs/favicon.ico",
     )
 
 
-# Must mount *after* the other routes else it borks em
-app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
+web_root_path = Path(list(web_dir.__path__)[0])
 
 
-def invoke_api():
-    def find_port(port: int):
+# Cannot add headers to StaticFiles, so we must serve index.html with a custom route
+# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
+@app.get("/", include_in_schema=False, name="ui_root")
+def get_index() -> FileResponse:
+    return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
+
+
+# # Must mount *after* the other routes else it borks em
+app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static")  # docs favicon is in here
+app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
+app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
+
+
+def invoke_api() -> None:
+    def find_port(port: int) -> int:
         """Find a port not in use starting at given port"""
         # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
         # https://github.com/WaylonWalker
@@ -235,7 +269,7 @@ def invoke_api():
         app=app,
         host=app_config.host,
         port=port,
-        loop=loop,
+        loop="asyncio",
         log_level=app_config.log_level,
     )
     server = uvicorn.Server(config)
@@ -251,7 +285,4 @@ def invoke_api():
 
 
 if __name__ == "__main__":
-    if app_config.version:
-        print(f"InvokeAI version {__version__}")
-    else:
-        invoke_api()
+    invoke_api()

invokeai/app/cli/commands.py

@@ -1,312 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-import argparse
-from abc import ABC, abstractmethod
-from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
-
-import matplotlib.pyplot as plt
-import networkx as nx
-from pydantic import BaseModel, Field
-
-import invokeai.backend.util.logging as logger
-
-from ..invocations.baseinvocation import BaseInvocation
-from ..invocations.image import ImageField
-from ..services.graph import Edge, GraphExecutionState, LibraryGraph
-from ..services.invoker import Invoker
-
-
-def add_field_argument(command_parser, name: str, field, default_override=None):
-    default = (
-        default_override
-        if default_override is not None
-        else field.default
-        if field.default_factory is None
-        else field.default_factory()
-    )
-    if get_origin(field.annotation) == Literal:
-        allowed_values = get_args(field.annotation)
-        allowed_types = set()
-        for val in allowed_values:
-            allowed_types.add(type(val))
-        allowed_types_list = list(allowed_types)
-        field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
-
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field_type,
-            default=default,
-            choices=allowed_values,
-            help=field.description,
-        )
-    else:
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field.annotation,
-            default=default,
-            help=field.description,
-        )
-
-
-def add_parsers(
-    subparsers,
-    commands: list[type],
-    command_field: str = "type",
-    exclude_fields: list[str] = ["id", "type"],
-    add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None,
-):
-    """Adds parsers for each command to the subparsers"""
-
-    # Create subparsers for each command
-    for command in commands:
-        hints = get_type_hints(command)
-        cmd_name = get_args(hints[command_field])[0]
-        command_parser = subparsers.add_parser(cmd_name, help=command.__doc__)
-
-        if add_arguments is not None:
-            add_arguments(command_parser)
-
-        # Convert all fields to arguments
-        fields = command.__fields__  # type: ignore
-        for name, field in fields.items():
-            if name in exclude_fields:
-                continue
-
-            add_field_argument(command_parser, name, field)
-
-
-def add_graph_parsers(
-    subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
-):
-    for graph in graphs:
-        command_parser = subparsers.add_parser(graph.name, help=graph.description)
-
-        if add_arguments is not None:
-            add_arguments(command_parser)
-
-        # Add arguments for inputs
-        for exposed_input in graph.exposed_inputs:
-            node = graph.graph.get_node(exposed_input.node_path)
-            field = node.__fields__[exposed_input.field]
-            default_override = getattr(node, exposed_input.field)
-            add_field_argument(command_parser, exposed_input.alias, field, default_override)
-
-
-class CliContext:
-    invoker: Invoker
-    session: GraphExecutionState
-    parser: argparse.ArgumentParser
-    defaults: dict[str, Any]
-    graph_nodes: dict[str, str]
-    nodes_added: list[str]
-
-    def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser):
-        self.invoker = invoker
-        self.session = session
-        self.parser = parser
-        self.defaults = dict()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
-
-    def get_session(self):
-        self.session = self.invoker.services.graph_execution_manager.get(self.session.id)
-        return self.session
-
-    def reset(self):
-        self.session = self.invoker.create_execution_state()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
-        # Leave defaults unchanged
-
-    def add_node(self, node: BaseInvocation):
-        self.get_session()
-        self.session.graph.add_node(node)
-        self.nodes_added.append(node.id)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
-    def add_edge(self, edge: Edge):
-        self.get_session()
-        self.session.add_edge(edge)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
-
-class ExitCli(Exception):
-    """Exception to exit the CLI"""
-
-    pass
-
-
-class BaseCommand(ABC, BaseModel):
-    """A CLI command"""
-
-    # All commands must include a type name like this:
-
-    @classmethod
-    def get_all_subclasses(cls):
-        subclasses = []
-        toprocess = [cls]
-        while len(toprocess) > 0:
-            next = toprocess.pop(0)
-            next_subclasses = next.__subclasses__()
-            subclasses.extend(next_subclasses)
-            toprocess.extend(next_subclasses)
-        return subclasses
-
-    @classmethod
-    def get_commands(cls):
-        return tuple(BaseCommand.get_all_subclasses())
-
-    @classmethod
-    def get_commands_map(cls):
-        # Get the type strings out of the literals and into a dictionary
-        return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses()))
-
-    @abstractmethod
-    def run(self, context: CliContext) -> None:
-        """Run the command. Raise ExitCli to exit."""
-        pass
-
-
-class ExitCommand(BaseCommand):
-    """Exits the CLI"""
-
-    type: Literal["exit"] = "exit"
-
-    def run(self, context: CliContext) -> None:
-        raise ExitCli()
-
-
-class HelpCommand(BaseCommand):
-    """Shows help"""
-
-    type: Literal["help"] = "help"
-
-    def run(self, context: CliContext) -> None:
-        context.parser.print_help()
-
-
-def get_graph_execution_history(
-    graph_execution_state: GraphExecutionState,
-) -> Iterable[str]:
-    """Gets the history of fully-executed invocations for a graph execution"""
-    return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
-
-
-def get_invocation_command(invocation) -> str:
-    fields = invocation.__fields__.items()
-    type_hints = get_type_hints(type(invocation))
-    command = [invocation.type]
-    for name, field in fields:
-        if name in ["id", "type"]:
-            continue
-
-        # TODO: add links
-
-        # Skip image fields when serializing command
-        type_hint = type_hints.get(name) or None
-        if type_hint is ImageField or ImageField in get_args(type_hint):
-            continue
-
-        field_value = getattr(invocation, name)
-        field_default = field.default
-        if field_value != field_default:
-            if type_hint is str or str in get_args(type_hint):
-                command.append(f'--{name} "{field_value}"')
-            else:
-                command.append(f"--{name} {field_value}")
-
-    return " ".join(command)
-
-
-class HistoryCommand(BaseCommand):
-    """Shows the invocation history"""
-
-    type: Literal["history"] = "history"
-
-    # Inputs
-    # fmt: off
-    count: int = Field(default=5, gt=0, description="The number of history entries to show")
-    # fmt: on
-
-    def run(self, context: CliContext) -> None:
-        history = list(get_graph_execution_history(context.get_session()))
-        for i in range(min(self.count, len(history))):
-            entry_id = history[-1 - i]
-            entry = context.get_session().graph.get_node(entry_id)
-            logger.info(f"{entry_id}: {get_invocation_command(entry)}")
-
-
-class SetDefaultCommand(BaseCommand):
-    """Sets a default value for a field"""
-
-    type: Literal["default"] = "default"
-
-    # Inputs
-    # fmt: off
-    field: str = Field(description="The field to set the default for")
-    value: str = Field(description="The value to set the default to, or None to clear the default")
-    # fmt: on
-
-    def run(self, context: CliContext) -> None:
-        if self.value is None:
-            if self.field in context.defaults:
-                del context.defaults[self.field]
-        else:
-            context.defaults[self.field] = self.value
-
-
-class DrawGraphCommand(BaseCommand):
-    """Debugs a graph"""
-
-    type: Literal["draw_graph"] = "draw_graph"
-
-    def run(self, context: CliContext) -> None:
-        session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
-        nxgraph = session.graph.nx_graph_flat()
-
-        # Draw the networkx graph
-        plt.figure(figsize=(20, 20))
-        pos = nx.spectral_layout(nxgraph)
-        nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
-        nx.draw_networkx_edges(nxgraph, pos, width=2)
-        nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
-        plt.axis("off")
-        plt.show()
-
-
-class DrawExecutionGraphCommand(BaseCommand):
-    """Debugs an execution graph"""
-
-    type: Literal["draw_xgraph"] = "draw_xgraph"
-
-    def run(self, context: CliContext) -> None:
-        session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
-        nxgraph = session.execution_graph.nx_graph_flat()
-
-        # Draw the networkx graph
-        plt.figure(figsize=(20, 20))
-        pos = nx.spectral_layout(nxgraph)
-        nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
-        nx.draw_networkx_edges(nxgraph, pos, width=2)
-        nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
-        plt.axis("off")
-        plt.show()
-
-
-class SortedHelpFormatter(argparse.HelpFormatter):
-    def _iter_indented_subactions(self, action):
-        try:
-            get_subactions = action._get_subactions
-        except AttributeError:
-            pass
-        else:
-            self._indent()
-            if isinstance(action, argparse._SubParsersAction):
-                for subaction in sorted(get_subactions(), key=lambda x: x.dest):
-                    yield subaction
-            else:
-                for subaction in get_subactions():
-                    yield subaction
-                self._dedent()

invokeai/app/cli/completer.py

@@ -1,171 +0,0 @@
-"""
-Readline helper functions for cli_app.py
-You may import the global singleton `completer` to get access to the
-completer object.
-"""
-import atexit
-import readline
-import shlex
-from pathlib import Path
-from typing import Dict, List, Literal, get_args, get_origin, get_type_hints
-
-import invokeai.backend.util.logging as logger
-
-from ...backend import ModelManager
-from ..invocations.baseinvocation import BaseInvocation
-from ..services.invocation_services import InvocationServices
-from .commands import BaseCommand
-
-# singleton object, class variable
-completer = None
-
-
-class Completer(object):
-    def __init__(self, model_manager: ModelManager):
-        self.commands = self.get_commands()
-        self.matches = None
-        self.linebuffer = None
-        self.manager = model_manager
-        return
-
-    def complete(self, text, state):
-        """
-        Complete commands and switches fromm the node CLI command line.
-        Switches are determined in a context-specific manner.
-        """
-
-        buffer = readline.get_line_buffer()
-        if state == 0:
-            options = None
-            try:
-                current_command, current_switch = self.get_current_command(buffer)
-                options = self.get_command_options(current_command, current_switch)
-            except IndexError:
-                pass
-            options = options or list(self.parse_commands().keys())
-
-            if not text:  # first time
-                self.matches = options
-            else:
-                self.matches = [s for s in options if s and s.startswith(text)]
-
-        try:
-            match = self.matches[state]
-        except IndexError:
-            match = None
-        return match
-
-    @classmethod
-    def get_commands(self) -> List[object]:
-        """
-        Return a list of all the client commands and invocations.
-        """
-        return BaseCommand.get_commands() + BaseInvocation.get_invocations()
-
-    def get_current_command(self, buffer: str) -> tuple[str, str]:
-        """
-        Parse the readline buffer to find the most recent command and its switch.
-        """
-        if len(buffer) == 0:
-            return None, None
-        tokens = shlex.split(buffer)
-        command = None
-        switch = None
-        for t in tokens:
-            if t[0].isalpha():
-                if switch is None:
-                    command = t
-            else:
-                switch = t
-        # don't try to autocomplete switches that are already complete
-        if switch and buffer.endswith(" "):
-            switch = None
-        return command or "", switch or ""
-
-    def parse_commands(self) -> Dict[str, List[str]]:
-        """
-        Return a dict in which the keys are the command name
-        and the values are the parameters the command takes.
-        """
-        result = dict()
-        for command in self.commands:
-            hints = get_type_hints(command)
-            name = get_args(hints["type"])[0]
-            result.update({name: hints})
-        return result
-
-    def get_command_options(self, command: str, switch: str) -> List[str]:
-        """
-        Return all the parameters that can be passed to the command as
-        command-line switches. Returns None if the command is unrecognized.
-        """
-        parsed_commands = self.parse_commands()
-        if command not in parsed_commands:
-            return None
-
-        # handle switches in the format "-foo=bar"
-        argument = None
-        if switch and "=" in switch:
-            switch, argument = switch.split("=")
-
-        parameter = switch.strip("-")
-        if parameter in parsed_commands[command]:
-            if argument is None:
-                return self.get_parameter_options(parameter, parsed_commands[command][parameter])
-            else:
-                return [
-                    f"--{parameter}={x}"
-                    for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])
-                ]
-        else:
-            return [f"--{x}" for x in parsed_commands[command].keys()]
-
-    def get_parameter_options(self, parameter: str, typehint) -> List[str]:
-        """
-        Given a parameter type (such as Literal), offers autocompletions.
-        """
-        if get_origin(typehint) == Literal:
-            return get_args(typehint)
-        if parameter == "model":
-            return self.manager.model_names()
-
-    def _pre_input_hook(self):
-        if self.linebuffer:
-            readline.insert_text(self.linebuffer)
-            readline.redisplay()
-            self.linebuffer = None
-
-
-def set_autocompleter(services: InvocationServices) -> Completer:
-    global completer
-
-    if completer:
-        return completer
-
-    completer = Completer(services.model_manager)
-
-    readline.set_completer(completer.complete)
-    try:
-        readline.set_auto_history(True)
-    except AttributeError:
-        # pyreadline3 does not have a set_auto_history() method
-        pass
-    readline.set_pre_input_hook(completer._pre_input_hook)
-    readline.set_completer_delims(" ")
-    readline.parse_and_bind("tab: complete")
-    readline.parse_and_bind("set print-completions-horizontally off")
-    readline.parse_and_bind("set page-completions on")
-    readline.parse_and_bind("set skip-completed-text on")
-    readline.parse_and_bind("set show-all-if-ambiguous on")
-
-    histfile = Path(services.configuration.root_dir / ".invoke_history")
-    try:
-        readline.read_history_file(histfile)
-        readline.set_history_length(1000)
-    except FileNotFoundError:
-        pass
-    except OSError:  # file likely corrupted
-        newname = f"{histfile}.old"
-        logger.error(f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}")
-        histfile.replace(Path(newname))
-    atexit.register(readline.write_history_file, histfile)

invokeai/app/cli_app.py

@@ -1,484 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-
-from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
-
-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.
-
-if True:  # hack to make flake8 happy with imports coming after setting up the config
-    import argparse
-    import re
-    import shlex
-    import sqlite3
-    import sys
-    import time
-    from typing import Optional, Union, get_type_hints
-
-    import torch
-    from pydantic import BaseModel, ValidationError
-    from pydantic.fields import Field
-
-    import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-    from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
-    from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
-    from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
-    from invokeai.app.services.boards import BoardService, BoardServiceDependencies
-    from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-    from invokeai.app.services.images import ImageService, ImageServiceDependencies
-    from invokeai.app.services.invocation_stats import InvocationStatsService
-    from invokeai.app.services.resource_name import SimpleNameService
-    from invokeai.app.services.urls import LocalUrlService
-    from invokeai.backend.util.logging import InvokeAILogger
-    from invokeai.version.invokeai_version import __version__
-
-    from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
-    from .cli.completer import set_autocompleter
-    from .invocations.baseinvocation import BaseInvocation
-    from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id
-    from .services.events import EventServiceBase
-    from .services.graph import (
-        Edge,
-        EdgeConnection,
-        GraphExecutionState,
-        GraphInvocation,
-        LibraryGraph,
-        are_connection_types_compatible,
-    )
-    from .services.image_file_storage import DiskImageFileStorage
-    from .services.invocation_queue import MemoryInvocationQueue
-    from .services.invocation_services import InvocationServices
-    from .services.invoker import Invoker
-    from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-    from .services.model_manager_service import ModelManagerService
-    from .services.processor import DefaultInvocationProcessor
-    from .services.sqlite import SqliteItemStorage
-
-    if torch.backends.mps.is_available():
-        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
-
-config = InvokeAIAppConfig.get_config()
-config.parse_args()
-logger = InvokeAILogger().get_logger(config=config)
-
-
-class CliCommand(BaseModel):
-    command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type")  # type: ignore
-
-
-class InvalidArgs(Exception):
-    pass
-
-
-def add_invocation_args(command_parser):
-    # Add linking capability
-    command_parser.add_argument(
-        "--link",
-        "-l",
-        action="append",
-        nargs=3,
-        help="A link in the format 'source_node source_field dest_field'. source_node can be relative to history (e.g. -1)",
-    )
-
-    command_parser.add_argument(
-        "--link_node",
-        "-ln",
-        action="append",
-        help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)",
-    )
-
-
-def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
-    # Create invocation parser
-    parser = argparse.ArgumentParser(formatter_class=SortedHelpFormatter)
-
-    def exit(*args, **kwargs):
-        raise InvalidArgs
-
-    parser.exit = exit
-    subparsers = parser.add_subparsers(dest="type")
-
-    # Create subparsers for each invocation
-    invocations = BaseInvocation.get_all_subclasses()
-    add_parsers(subparsers, invocations, add_arguments=add_invocation_args)
-
-    # Create subparsers for each command
-    commands = BaseCommand.get_all_subclasses()
-    add_parsers(subparsers, commands, exclude_fields=["type"])
-
-    # Create subparsers for exposed CLI graphs
-    # TODO: add a way to identify these graphs
-    text_to_image = services.graph_library.get(default_text_to_image_graph_id)
-    add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args)
-
-    return parser
-
-
-class NodeField:
-    alias: str
-    node_path: str
-    field: str
-    field_type: type
-
-    def __init__(self, alias: str, node_path: str, field: str, field_type: type):
-        self.alias = alias
-        self.node_path = node_path
-        self.field = field
-        self.field_type = field_type
-
-
-def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str, NodeField]:
-    return {k: NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
-
-
-def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_input.node_path))
-    return NodeField(
-        alias=exposed_input.alias,
-        node_path=f"{node_id}.{exposed_input.node_path}",
-        field=exposed_input.field,
-        field_type=get_type_hints(node_type)[exposed_input.field],
-    )
-
-
-def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_output.node_path))
-    node_output_type = node_type.get_output_type()
-    return NodeField(
-        alias=exposed_output.alias,
-        node_path=f"{node_id}.{exposed_output.node_path}",
-        field=exposed_output.field,
-        field_type=get_type_hints(node_output_type)[exposed_output.field],
-    )
-
-
-def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the inputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs}
-
-
-def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the outputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
-
-
-def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliContext) -> list[Edge]:
-    """Generates all possible edges between two invocations"""
-    afields = get_node_outputs(a, context)
-    bfields = get_node_inputs(b, context)
-
-    matching_fields = set(afields.keys()).intersection(bfields.keys())
-
-    # Remove invalid fields
-    invalid_fields = set(["type", "id"])
-    matching_fields = matching_fields.difference(invalid_fields)
-
-    # Validate types
-    matching_fields = [
-        f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)
-    ]
-
-    edges = [
-        Edge(
-            source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
-            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field),
-        )
-        for alias in matching_fields
-    ]
-    return edges
-
-
-class SessionError(Exception):
-    """Raised when a session error has occurred"""
-
-    pass
-
-
-def invoke_all(context: CliContext):
-    """Runs all invocations in the specified session"""
-    context.invoker.invoke(context.session, invoke_all=True)
-    while not context.get_session().is_complete():
-        # Wait some time
-        time.sleep(0.1)
-
-    # Print any errors
-    if context.session.has_error():
-        for n in context.session.errors:
-            context.invoker.services.logger.error(
-                f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
-            )
-
-        raise SessionError()
-
-
-def invoke_cli():
-    logger.info(f"InvokeAI version {__version__}")
-    # get the optional list of invocations to execute on the command line
-    parser = config.get_parser()
-    parser.add_argument("commands", nargs="*")
-    invocation_commands = parser.parse_args().commands
-
-    # get the optional file to read commands from.
-    # Simplest is to use it for STDIN
-    if infile := config.from_file:
-        sys.stdin = open(infile, "r")
-
-    model_manager = ModelManagerService(config, logger)
-
-    events = EventServiceBase()
-    output_folder = config.output_path
-
-    # TODO: build a file/path manager?
-    if config.use_memory_db:
-        db_location = ":memory:"
-    else:
-        db_location = config.db_path
-        db_location.parent.mkdir(parents=True, exist_ok=True)
-
-    db_conn = sqlite3.connect(db_location, check_same_thread=False)  # TODO: figure out a better threading solution
-    logger.info(f'InvokeAI database location is "{db_location}"')
-
-    graph_execution_manager = SqliteItemStorage[GraphExecutionState](conn=db_conn, table_name="graph_executions")
-
-    urls = LocalUrlService()
-    image_record_storage = SqliteImageRecordStorage(conn=db_conn)
-    image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-    names = SimpleNameService()
-
-    board_record_storage = SqliteBoardRecordStorage(conn=db_conn)
-    board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn)
-
-    boards = BoardService(
-        services=BoardServiceDependencies(
-            board_image_record_storage=board_image_record_storage,
-            board_record_storage=board_record_storage,
-            image_record_storage=image_record_storage,
-            url=urls,
-            logger=logger,
-        )
-    )
-
-    board_images = BoardImagesService(
-        services=BoardImagesServiceDependencies(
-            board_image_record_storage=board_image_record_storage,
-            board_record_storage=board_record_storage,
-            image_record_storage=image_record_storage,
-            url=urls,
-            logger=logger,
-        )
-    )
-
-    images = ImageService(
-        services=ImageServiceDependencies(
-            board_image_record_storage=board_image_record_storage,
-            image_record_storage=image_record_storage,
-            image_file_storage=image_file_storage,
-            url=urls,
-            logger=logger,
-            names=names,
-            graph_execution_manager=graph_execution_manager,
-        )
-    )
-
-    services = InvocationServices(
-        model_manager=model_manager,
-        events=events,
-        latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")),
-        images=images,
-        boards=boards,
-        board_images=board_images,
-        queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, table_name="graphs"),
-        graph_execution_manager=graph_execution_manager,
-        processor=DefaultInvocationProcessor(),
-        performance_statistics=InvocationStatsService(graph_execution_manager),
-        logger=logger,
-        configuration=config,
-        invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
-    )
-
-    system_graphs = create_system_graphs(services.graph_library)
-    system_graph_names = set([g.name for g in system_graphs])
-    set_autocompleter(services)
-
-    invoker = Invoker(services)
-    session: GraphExecutionState = invoker.create_execution_state()
-    parser = get_command_parser(services)
-
-    re_negid = re.compile("^-[0-9]+$")
-
-    # Uncomment to print out previous sessions at startup
-    # print(services.session_manager.list())
-
-    context = CliContext(invoker, session, parser)
-    set_autocompleter(services)
-
-    command_line_args_exist = len(invocation_commands) > 0
-    done = False
-
-    while not done:
-        try:
-            if command_line_args_exist:
-                cmd_input = invocation_commands.pop(0)
-                done = len(invocation_commands) == 0
-            else:
-                cmd_input = input("invoke> ")
-        except (KeyboardInterrupt, EOFError):
-            # Ctrl-c exits
-            break
-
-        try:
-            # Refresh the state of the session
-            # history = list(get_graph_execution_history(context.session))
-            history = list(reversed(context.nodes_added))
-
-            # Split the command for piping
-            cmds = cmd_input.split("|")
-            start_id = len(context.nodes_added)
-            current_id = start_id
-            new_invocations = list()
-            for cmd in cmds:
-                if cmd is None or cmd.strip() == "":
-                    raise InvalidArgs("Empty command")
-
-                # Parse args to create invocation
-                args = vars(context.parser.parse_args(shlex.split(cmd.strip())))
-
-                # Override defaults
-                for field_name, field_default in context.defaults.items():
-                    if field_name in args:
-                        args[field_name] = field_default
-
-                # Parse invocation
-                command: CliCommand = None  # type:ignore
-                system_graph: Optional[LibraryGraph] = None
-                if args["type"] in system_graph_names:
-                    system_graph = next(filter(lambda g: g.name == args["type"], system_graphs))
-                    invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
-                    for exposed_input in system_graph.exposed_inputs:
-                        if exposed_input.alias in args:
-                            node = invocation.graph.get_node(exposed_input.node_path)
-                            field = exposed_input.field
-                            setattr(node, field, args[exposed_input.alias])
-                    command = CliCommand(command=invocation)
-                    context.graph_nodes[invocation.id] = system_graph.id
-                else:
-                    args["id"] = current_id
-                    command = CliCommand(command=args)
-
-                if command is None:
-                    continue
-
-                # Run any CLI commands immediately
-                if isinstance(command.command, BaseCommand):
-                    # Invoke all current nodes to preserve operation order
-                    invoke_all(context)
-
-                    # Run the command
-                    command.command.run(context)
-                    continue
-
-                # TODO: handle linking with library graphs
-                # Pipe previous command output (if there was a previous command)
-                edges: list[Edge] = list()
-                if len(history) > 0 or current_id != start_id:
-                    from_id = history[0] if current_id == start_id else str(current_id - 1)
-                    from_node = (
-                        next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
-                        if current_id != start_id
-                        else context.session.graph.get_node(from_id)
-                    )
-                    matching_edges = generate_matching_edges(from_node, command.command, context)
-                    edges.extend(matching_edges)
-
-                # Parse provided links
-                if "link_node" in args and args["link_node"]:
-                    for link in args["link_node"]:
-                        node_id = link
-                        if re_negid.match(node_id):
-                            node_id = str(current_id + int(node_id))
-
-                        link_node = context.session.graph.get_node(node_id)
-                        matching_edges = generate_matching_edges(link_node, command.command, context)
-                        matching_destinations = [e.destination for e in matching_edges]
-                        edges = [e for e in edges if e.destination not in matching_destinations]
-                        edges.extend(matching_edges)
-
-                if "link" in args and args["link"]:
-                    for link in args["link"]:
-                        edges = [
-                            e
-                            for e in edges
-                            if e.destination.node_id != command.command.id or e.destination.field != link[2]
-                        ]
-
-                        node_id = link[0]
-                        if re_negid.match(node_id):
-                            node_id = str(current_id + int(node_id))
-
-                        # TODO: handle missing input/output
-                        node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]]
-                        node_input = get_node_inputs(command.command, context)[link[2]]
-
-                        edges.append(
-                            Edge(
-                                source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
-                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field),
-                            )
-                        )
-
-                new_invocations.append((command.command, edges))
-
-                current_id = current_id + 1
-
-                # Add the node to the session
-                context.add_node(command.command)
-                for edge in edges:
-                    print(edge)
-                    context.add_edge(edge)
-
-            # Execute all remaining nodes
-            invoke_all(context)
-
-        except InvalidArgs:
-            invoker.services.logger.warning('Invalid command, use "help" to list commands')
-            continue
-
-        except ValidationError:
-            invoker.services.logger.warning('Invalid command arguments, run "<command> --help" for summary')
-
-        except SessionError:
-            # Start a new session
-            invoker.services.logger.warning("Session error: creating a new session")
-            context.reset()
-
-        except ExitCli:
-            break
-
-        except SystemExit:
-            continue
-
-    invoker.stop()
-
-
-if __name__ == "__main__":
-    if config.version:
-        print(f"InvokeAI version {__version__}")
-    else:
-        invoke_cli()

invokeai/app/invocations/__init__.py

@@ -1,8 +1,28 @@
-import os
+import shutil
+import sys
+from importlib.util import module_from_spec, spec_from_file_location
+from pathlib import Path
 
-__all__ = []
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
 
-dirname = os.path.dirname(os.path.abspath(__file__))
-for f in os.listdir(dirname):
-    if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
-        __all__.append(f[:-3])
+custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
+custom_nodes_path.mkdir(parents=True, exist_ok=True)
+
+custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
+custom_nodes_readme_path = str(custom_nodes_path / "README.md")
+
+# copy our custom nodes __init__.py to the custom nodes directory
+shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
+shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
+
+# Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
+spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
+if spec is None or spec.loader is None:
+    raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}")
+module = module_from_spec(spec)
+sys.modules[spec.name] = module
+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

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, get_random_seed
+from invokeai.app.util.misc import SEED_MAX
 
 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="1.0.0",
+    version="1.0.1",
     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

@@ -7,6 +7,7 @@ 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,
@@ -19,7 +20,6 @@ from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -66,21 +66,25 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.get_model(
+        tokenizer_info = context.services.model_manager.get_model(
             **self.clip.tokenizer.model_dump(),
+            context=context,
         )
-        text_encoder_info = context.get_model(
+        text_encoder_info = context.services.model_manager.get_model(
             **self.clip.text_encoder.model_dump(),
+            context=context,
         )
 
         def _lora_loader():
             for lora in self.clip.loras:
-                lora_info = context.get_model(**lora.model_dump(exclude={"weight"}))
+                lora_info = context.services.model_manager.get_model(
+                    **lora.model_dump(exclude={"weight"}), context=context
+                )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
@@ -89,10 +93,11 @@ class CompelInvocation(BaseInvocation):
                 ti_list.append(
                     (
                         name,
-                        context.get_model(
+                        context.services.model_manager.get_model(
                             model_name=name,
                             base_model=self.clip.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
+                            context=context,
                         ).context.model,
                     )
                 )
@@ -103,13 +108,15 @@ 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,
             ),
-            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
             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),
         ):
             compel = Compel(
                 tokenizer=tokenizer,
@@ -121,7 +128,7 @@ class CompelInvocation(BaseInvocation):
 
             conjunction = Compel.parse_prompt_string(self.prompt)
 
-            if context.config.log_tokenization:
+            if context.services.configuration.log_tokenization:
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@@ -142,7 +149,8 @@ class CompelInvocation(BaseInvocation):
             ]
         )
 
-        conditioning_name = context.save_conditioning(conditioning_data)
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
 
         return ConditioningOutput(
             conditioning=ConditioningField(
@@ -223,13 +231,15 @@ 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,
             ),
-            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
             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),
         ):
             compel = Compel(
                 tokenizer=tokenizer,

invokeai/app/invocations/controlnet_image_processors.py

@@ -28,16 +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,
 )
@@ -94,7 +96,7 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
@@ -127,12 +129,12 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         # superclass just passes through image without processing
         return image
 
@@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
             session_id=context.graph_execution_state_id,
             node_id=self.id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -170,7 +173,7 @@ class ImageProcessorInvocation(BaseInvocation):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -193,7 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -222,7 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -244,7 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -267,7 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Openpose Processor",
     tags=["controlnet", "openpose", "pose"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Openpose processing to image"""
@@ -292,7 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -319,7 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -336,7 +339,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.1.0"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -359,7 +362,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.1.0"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -386,7 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -416,7 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -432,7 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -455,7 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -484,7 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -524,7 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -566,7 +569,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.0.0",
+    version="1.1.0",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/custom_nodes/README.md

@@ -0,0 +1,51 @@
+# Custom Nodes / Node Packs
+
+Copy your node packs to this directory.
+
+When nodes are added or changed, you must restart the app to see the changes.
+
+## Directory Structure
+
+For a node pack to be loaded, it must be placed in a directory alongside this
+file. Here's an example 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
+```
+
+## Node Pack `__init__.py`
+
+Each node pack must have an `__init__.py` file that imports its nodes.
+
+The structure of each node or node pack is otherwise not important.
+
+Here are examples, based on the example directory structure.
+
+### `cool_node/__init__.py`
+
+```py
+from .cool_node import CoolInvocation
+```
+
+### `my_node_pack/__init__.py`
+
+```py
+from .tasty_node import TastyInvocation
+from .bodacious_node import BodaciousInvocation
+from .extra_nodes.fancy_node import FancyInvocation
+```
+
+Only nodes imported in the `__init__.py` file are loaded.

invokeai/app/invocations/custom_nodes/init.py

@@ -0,0 +1,54 @@
+"""
+Invoke-managed custom node loader. See README.md for more information.
+"""
+
+import sys
+from importlib.util import module_from_spec, spec_from_file_location
+from pathlib import Path
+
+from invokeai.app.invocations.baseinvocation import CUSTOM_NODE_PACK_SUFFIX
+from invokeai.backend.util.logging import InvokeAILogger
+
+logger = InvokeAILogger.get_logger()
+loaded_count = 0
+
+
+for d in Path(__file__).parent.iterdir():
+    # skip files
+    if not d.is_dir():
+        continue
+
+    # skip hidden directories
+    if d.name.startswith("_") or d.name.startswith("."):
+        continue
+
+    # skip directories without an `__init__.py`
+    init = d / "__init__.py"
+    if not init.exists():
+        continue
+
+    module_name = init.parent.stem
+
+    # skip if already imported
+    if module_name in globals():
+        continue
+
+    # load the module, appending adding a suffix to identify it as a custom node pack
+    spec = spec_from_file_location(f"{module_name}{CUSTOM_NODE_PACK_SUFFIX}", 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)
+
+    loaded_count += 1
+
+    del init, module_name
+
+if loaded_count > 0:
+    logger.info(f"Loaded {loaded_count} node packs 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, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
-class CvInpaintInvocation(BaseInvocation):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.1.0")
+class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")

invokeai/app/invocations/facetools.py

@@ -16,6 +16,8 @@ from invokeai.app.invocations.baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
+    WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -129,7 +131,7 @@ def prepare_faces_list(
     deduped_faces: list[FaceResultData] = []
 
     if len(face_result_list) == 0:
-        return list()
+        return []
 
     for candidate in face_result_list:
         should_add = True
@@ -208,7 +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]
@@ -436,8 +438,8 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
-class FaceOffInvocation(BaseInvocation):
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.1.0")
+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")
@@ -530,8 +532,8 @@ class FaceOffInvocation(BaseInvocation):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
-class FaceMaskInvocation(BaseInvocation):
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.1.0")
+class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -648,9 +650,9 @@ class FaceMaskInvocation(BaseInvocation):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.1.0"
 )
-class FaceIdentifierInvocation(BaseInvocation):
+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")

invokeai/app/invocations/image.py

@@ -7,13 +7,13 @@ import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -36,14 +36,8 @@ class ShowImageInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "blank_image",
-    title="Blank Image",
-    tags=["image"],
-    category="image",
-    version="1.0.0",
-)
-class BlankImageInvocation(BaseInvocation):
+@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.1.0")
+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")
@@ -61,6 +55,7 @@ class BlankImageInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -71,14 +66,8 @@ class BlankImageInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_crop",
-    title="Crop Image",
-    tags=["image", "crop"],
-    category="image",
-    version="1.0.0",
-)
-class ImageCropInvocation(BaseInvocation):
+@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.1.0")
+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")
@@ -100,6 +89,7 @@ class ImageCropInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -110,14 +100,8 @@ class ImageCropInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_paste",
-    title="Paste Image",
-    tags=["image", "paste"],
-    category="image",
-    version="1.0.1",
-)
-class ImagePasteInvocation(BaseInvocation):
+@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.1.0")
+class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Pastes an image into another image."""
 
     base_image: ImageField = InputField(description="The base image")
@@ -159,6 +143,7 @@ class ImagePasteInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -169,14 +154,8 @@ class ImagePasteInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "tomask",
-    title="Mask from Alpha",
-    tags=["image", "mask"],
-    category="image",
-    version="1.0.0",
-)
-class MaskFromAlphaInvocation(BaseInvocation):
+@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.1.0")
+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")
@@ -196,6 +175,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -206,14 +186,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_mul",
-    title="Multiply Images",
-    tags=["image", "multiply"],
-    category="image",
-    version="1.0.0",
-)
-class ImageMultiplyInvocation(BaseInvocation):
+@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.1.0")
+class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
     image1: ImageField = InputField(description="The first image to multiply")
@@ -232,6 +206,7 @@ class ImageMultiplyInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -245,14 +220,8 @@ class ImageMultiplyInvocation(BaseInvocation):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
-@invocation(
-    "img_chan",
-    title="Extract Image Channel",
-    tags=["image", "channel"],
-    category="image",
-    version="1.0.0",
-)
-class ImageChannelInvocation(BaseInvocation):
+@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.1.0")
+class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Gets a channel from an image."""
 
     image: ImageField = InputField(description="The image to get the channel from")
@@ -270,6 +239,7 @@ class ImageChannelInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -283,14 +253,8 @@ class ImageChannelInvocation(BaseInvocation):
 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="1.0.0",
-)
-class ImageConvertInvocation(BaseInvocation):
+@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.1.0")
+class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Converts an image to a different mode."""
 
     image: ImageField = InputField(description="The image to convert")
@@ -308,6 +272,7 @@ class ImageConvertInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -318,14 +283,8 @@ class ImageConvertInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_blur",
-    title="Blur Image",
-    tags=["image", "blur"],
-    category="image",
-    version="1.0.0",
-)
-class ImageBlurInvocation(BaseInvocation):
+@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.1.0")
+class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Blurs an image"""
 
     image: ImageField = InputField(description="The image to blur")
@@ -348,6 +307,7 @@ class ImageBlurInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -378,26 +338,17 @@ PIL_RESAMPLING_MAP = {
 }
 
 
-@invocation(
-    "img_resize",
-    title="Resize Image",
-    tags=["image", "resize"],
-    category="image",
-    version="1.0.0",
-)
-class ImageResizeInvocation(BaseInvocation):
+@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.1.0")
+class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
     width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
     height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.get_image(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
         resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
 
@@ -406,23 +357,26 @@ class ImageResizeInvocation(BaseInvocation):
             resample=resample_mode,
         )
 
-        image_name = context.save_image(image=resize_image)
+        image_dto = context.services.images.create(
+            image=resize_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=self.workflow,
+        )
 
         return ImageOutput(
-            image=ImageField(image_name=image_name),
-            width=resize_image.width,
-            height=resize_image.height,
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
         )
 
 
-@invocation(
-    "img_scale",
-    title="Scale Image",
-    tags=["image", "scale"],
-    category="image",
-    version="1.0.0",
-)
-class ImageScaleInvocation(BaseInvocation):
+@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.1.0")
+class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Scales an image by a factor"""
 
     image: ImageField = InputField(description="The image to scale")
@@ -452,6 +406,7 @@ class ImageScaleInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -462,14 +417,8 @@ class ImageScaleInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_lerp",
-    title="Lerp Image",
-    tags=["image", "lerp"],
-    category="image",
-    version="1.0.0",
-)
-class ImageLerpInvocation(BaseInvocation):
+@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.1.0")
+class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -491,6 +440,7 @@ class ImageLerpInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -501,14 +451,8 @@ class ImageLerpInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_ilerp",
-    title="Inverse Lerp Image",
-    tags=["image", "ilerp"],
-    category="image",
-    version="1.0.0",
-)
-class ImageInverseLerpInvocation(BaseInvocation):
+@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.1.0")
+class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Inverse linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -530,6 +474,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -540,20 +485,11 @@ class ImageInverseLerpInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_nsfw",
-    title="Blur NSFW Image",
-    tags=["image", "nsfw"],
-    category="image",
-    version="1.0.0",
-)
-class ImageNSFWBlurInvocation(BaseInvocation):
+@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.1.0")
+class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add blur to NSFW-flagged images"""
 
     image: ImageField = InputField(description="The image to check")
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -574,7 +510,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.model_dump() if self.metadata else None,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -596,16 +532,13 @@ class ImageNSFWBlurInvocation(BaseInvocation):
     title="Add Invisible Watermark",
     tags=["image", "watermark"],
     category="image",
-    version="1.0.0",
+    version="1.1.0",
 )
-class ImageWatermarkInvocation(BaseInvocation):
+class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add an invisible watermark to an image"""
 
     image: ImageField = InputField(description="The image to check")
     text: str = InputField(default="InvokeAI", description="Watermark text")
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -617,7 +550,7 @@ class ImageWatermarkInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.model_dump() if self.metadata else None,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -628,14 +561,8 @@ class ImageWatermarkInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "mask_edge",
-    title="Mask Edge",
-    tags=["image", "mask", "inpaint"],
-    category="image",
-    version="1.0.0",
-)
-class MaskEdgeInvocation(BaseInvocation):
+@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.1.0")
+class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Applies an edge mask to an image"""
 
     image: ImageField = InputField(description="The image to apply the mask to")
@@ -669,6 +596,7 @@ class MaskEdgeInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -684,9 +612,9 @@ class MaskEdgeInvocation(BaseInvocation):
     title="Combine Masks",
     tags=["image", "mask", "multiply"],
     category="image",
-    version="1.0.0",
+    version="1.1.0",
 )
-class MaskCombineInvocation(BaseInvocation):
+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")
@@ -705,6 +633,7 @@ class MaskCombineInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -715,14 +644,8 @@ class MaskCombineInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "color_correct",
-    title="Color Correct",
-    tags=["image", "color"],
-    category="image",
-    version="1.0.0",
-)
-class ColorCorrectInvocation(BaseInvocation):
+@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.1.0")
+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.
@@ -821,6 +744,7 @@ class ColorCorrectInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -831,14 +755,8 @@ class ColorCorrectInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "img_hue_adjust",
-    title="Adjust Image Hue",
-    tags=["image", "hue"],
-    category="image",
-    version="1.0.0",
-)
-class ImageHueAdjustmentInvocation(BaseInvocation):
+@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.1.0")
+class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Adjusts the Hue of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -866,6 +784,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -939,9 +858,9 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.0.0",
+    version="1.1.0",
 )
-class ImageChannelOffsetInvocation(BaseInvocation):
+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")
@@ -975,6 +894,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -1009,9 +929,9 @@ class ImageChannelOffsetInvocation(BaseInvocation):
         "value",
     ],
     category="image",
-    version="1.0.0",
+    version="1.1.0",
 )
-class ImageChannelMultiplyInvocation(BaseInvocation):
+class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Scale a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -1051,6 +971,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             workflow=self.workflow,
+            metadata=self.metadata,
         )
 
         return ImageOutput(
@@ -1067,19 +988,14 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.0.1",
+    version="1.1.0",
     use_cache=False,
 )
-class SaveImageInvocation(BaseInvocation):
+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)
-    board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None,
-        description=FieldDescriptions.core_metadata,
-        ui_hidden=True,
-    )
+    board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -1092,7 +1008,7 @@ class SaveImageInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.model_dump() if self.metadata else None,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -1101,3 +1017,35 @@ class SaveImageInvocation(BaseInvocation):
             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, WithWorkflow, 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, get_random_seed
+from invokeai.app.util.misc import SEED_MAX
 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, 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="1.0.0")
-class InfillColorInvocation(BaseInvocation):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
+class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -153,17 +154,17 @@ class InfillColorInvocation(BaseInvocation):
         )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
-class InfillTileInvocation(BaseInvocation):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.1")
+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:
@@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -190,9 +192,9 @@ class InfillTileInvocation(BaseInvocation):
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation):
+class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 
@@ -242,8 +245,8 @@ class InfillPatchMatchInvocation(BaseInvocation):
         )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
-class LaMaInfillInvocation(BaseInvocation):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
+class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation):
         )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
-class CV2InfillInvocation(BaseInvocation):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
+class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/ip_adapter.py

@@ -7,16 +7,15 @@ 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
 
@@ -36,7 +35,7 @@ class CLIPVisionModelField(BaseModel):
 
 
 class IPAdapterField(BaseModel):
-    image: ImageField = Field(description="The IP-Adapter image prompt.")
+    image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
     ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
     image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
@@ -55,19 +54,19 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
     # Inputs
-    image: ImageField = InputField(description="The IP-Adapter image prompt.")
+    image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
     ip_adapter_model: IPAdapterModelField = InputField(
         description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
     )
 
     # 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=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
+        default=1, ge=-1, description="The weight given to the IP-Adapter", 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 FullAdapterXL, T2IAdapter
+from diffusers.models.adapter import T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -23,7 +23,6 @@ from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.invocations.primitives import (
     DenoiseMaskField,
     DenoiseMaskOutput,
@@ -35,6 +34,7 @@ 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 +58,13 @@ 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,
 )
@@ -76,7 +77,7 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
-SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
+SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
 
 
 @invocation_output("scheduler_output")
@@ -182,8 +183,9 @@ def get_scheduler(
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.get_model(
+    orig_scheduler_info = context.services.model_manager.get_model(
         **scheduler_info.model_dump(),
+        context=context,
     )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
@@ -213,7 +215,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.3.0",
+    version="1.4.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -272,7 +274,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
         ui_order=7,
     )
     latents: Optional[LatentsField] = InputField(
-        default=None, description=FieldDescriptions.latents, input=Input.Connection
+        default=None,
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+        ui_order=4,
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
@@ -297,12 +302,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def dispatch_progress(
         self,
         context: InvocationContext,
+        source_node_id: str,
         intermediate_state: PipelineIntermediateState,
         base_model: BaseModelType,
     ) -> None:
         stable_diffusion_step_callback(
             context=context,
             intermediate_state=intermediate_state,
+            node=self.model_dump(),
+            source_node_id=source_node_id,
             base_model=base_model,
         )
 
@@ -313,11 +321,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
         unet,
         seed,
     ) -> ConditioningData:
-        positive_cond_data = context.get_conditioning(self.positive_conditioning.conditioning_name)
+        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
         c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
         extra_conditioning_info = c.extra_conditioning
 
-        negative_cond_data = context.get_conditioning(self.negative_conditioning.conditioning_name)
+        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
         uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
         conditioning_data = ConditioningData(
@@ -404,16 +412,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
         controlnet_data = []
         for control_info in control_list:
             control_model = exit_stack.enter_context(
-                context.get_model(
+                context.services.model_manager.get_model(
                     model_name=control_info.control_model.model_name,
                     model_type=ModelType.ControlNet,
                     base_model=control_info.control_model.base_model,
+                    context=context,
                 )
             )
 
             # control_models.append(control_model)
             control_image_field = control_info.image
-            input_image = context.get_image(control_image_field.image_name)
+            input_image = context.services.images.get_pil_image(control_image_field.image_name)
             # self.image.image_type, self.image.image_name
             # FIXME: still need to test with different widths, heights, devices, dtypes
             #        and add in batch_size, num_images_per_prompt?
@@ -471,29 +480,36 @@ class DenoiseLatentsInvocation(BaseInvocation):
         conditioning_data.ip_adapter_conditioning = []
         for single_ip_adapter in ip_adapter:
             ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-                context.get_model(
+                context.services.model_manager.get_model(
                     model_name=single_ip_adapter.ip_adapter_model.model_name,
                     model_type=ModelType.IPAdapter,
                     base_model=single_ip_adapter.ip_adapter_model.base_model,
+                    context=context,
                 )
             )
 
-            image_encoder_model_info = context.get_model(
+            image_encoder_model_info = context.services.model_manager.get_model(
                 model_name=single_ip_adapter.image_encoder_model.model_name,
                 model_type=ModelType.CLIPVision,
                 base_model=single_ip_adapter.image_encoder_model.base_model,
+                context=context,
             )
 
-            input_image = context.get_image(single_ip_adapter.image.image_name)
+            # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
+            single_ipa_images = single_ip_adapter.image
+            if not isinstance(single_ipa_images, list):
+                single_ipa_images = [single_ipa_images]
+
+            single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
 
             # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
             # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
             with image_encoder_model_info as image_encoder_model:
                 # Get image embeddings from CLIP and ImageProjModel.
-                (
-                    image_prompt_embeds,
-                    uncond_image_prompt_embeds,
-                ) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
+                image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
+                    single_ipa_images, image_encoder_model
+                )
+
                 conditioning_data.ip_adapter_conditioning.append(
                     IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
                 )
@@ -528,12 +544,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            t2i_adapter_model_info = context.get_model(
+            t2i_adapter_model_info = context.services.model_manager.get_model(
                 model_name=t2i_adapter_field.t2i_adapter_model.model_name,
                 model_type=ModelType.T2IAdapter,
                 base_model=t2i_adapter_field.t2i_adapter_model.base_model,
+                context=context,
             )
-            image = context.get_image(t2i_adapter_field.image.image_name)
+            image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
             if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
@@ -548,10 +565,6 @@ 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
@@ -643,11 +656,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
             seed = None
             noise = None
             if self.noise is not None:
-                noise = context.get_latents(self.noise.latents_name)
+                noise = context.services.latents.get(self.noise.latents_name)
                 seed = self.noise.seed
 
             if self.latents is not None:
-                latents = context.get_latents(self.latents.latents_name)
+                latents = context.services.latents.get(self.latents.latents_name)
                 if seed is None:
                     seed = self.latents.seed
 
@@ -673,26 +686,34 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
+            # Get the source node id (we are invoking the prepared node)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+
             def step_callback(state: PipelineIntermediateState):
-                self.dispatch_progress(context, state, self.unet.unet.base_model)
+                self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
 
             def _lora_loader():
                 for lora in self.unet.loras:
-                    lora_info = context.get_model(
+                    lora_info = context.services.model_manager.get_model(
                         **lora.model_dump(exclude={"weight"}),
+                        context=context,
                     )
                     yield (lora_info.context.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.get_model(
+            unet_info = context.services.model_manager.get_model(
                 **self.unet.unet.model_dump(),
+                context=context,
             )
             with (
                 ExitStack() as exit_stack,
-                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
+                ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
                 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:
@@ -761,8 +782,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if choose_torch_device() == torch.device("mps"):
                 mps.empty_cache()
 
-            latents_name = context.save_latents(result_latents)
-        return build_latents_output(latents_name=latents_name, latents=result_latents, seed=seed)
+            name = f"{context.graph_execution_state_id}__{self.id}"
+            context.services.latents.save(name, result_latents)
+        return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
 
 
 @invocation(
@@ -770,9 +792,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.0.0",
+    version="1.1.0",
 )
-class LatentsToImageInvocation(BaseInvocation):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -785,18 +807,14 @@ class LatentsToImageInvocation(BaseInvocation):
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
     fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None,
-        description=FieldDescriptions.core_metadata,
-        ui_hidden=True,
-    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.get_latents(self.latents.latents_name)
+        latents = context.services.latents.get(self.latents.latents_name)
 
-        vae_info = context.get_model(
+        vae_info = context.services.model_manager.get_model(
             **self.vae.vae.model_dump(),
+            context=context,
         )
 
         with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
@@ -826,7 +844,7 @@ class LatentsToImageInvocation(BaseInvocation):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.config.tiled_decode:
+            if self.tiled or context.services.configuration.tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -850,12 +868,21 @@ class LatentsToImageInvocation(BaseInvocation):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_name = context.save_image(image, category=context.categories.GENERAL)
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=self.workflow,
+        )
 
         return ImageOutput(
-            image=ImageField(image_name=image_name),
-            width=image.width,
-            height=image.height,
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
         )
 
 
@@ -1080,7 +1107,7 @@ class BlendLatentsInvocation(BaseInvocation):
         latents_b = context.services.latents.get(self.latents_b.latents_name)
 
         if latents_a.shape != latents_b.shape:
-            raise "Latents to blend must be the same size."
+            raise Exception("Latents to blend must be the same size.")
 
         # TODO:
         device = choose_torch_device()

invokeai/app/invocations/math.py

@@ -3,11 +3,12 @@
 from typing import Literal
 
 import numpy as np
-from pydantic import field_validator
+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, FieldDescriptions, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
@@ -144,17 +145,17 @@ INTEGER_OPERATIONS = Literal[
 ]
 
 
-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)",
-)
+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)",
+}
 
 
 @invocation(
@@ -182,16 +183,16 @@ class IntegerMathInvocation(BaseInvocation):
     operation: INTEGER_OPERATIONS = InputField(
         default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
     )
-    a: int = InputField(default=0, description=FieldDescriptions.num_1)
-    b: int = InputField(default=0, description=FieldDescriptions.num_2)
+    a: int = InputField(default=1, description=FieldDescriptions.num_1)
+    b: int = InputField(default=1, description=FieldDescriptions.num_2)
 
     @field_validator("b")
-    def no_unrepresentable_results(cls, v, values):
-        if values["operation"] == "DIV" and v == 0:
+    def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
+        if info.data["operation"] == "DIV" and v == 0:
             raise ValueError("Cannot divide by zero")
-        elif values["operation"] == "MOD" and v == 0:
+        elif info.data["operation"] == "MOD" and v == 0:
             raise ValueError("Cannot divide by zero")
-        elif values["operation"] == "EXP" and v < 0:
+        elif info.data["operation"] == "EXP" and v < 0:
             raise ValueError("Result of exponentiation is not an integer")
         return v
 
@@ -230,17 +231,17 @@ FLOAT_OPERATIONS = Literal[
 ]
 
 
-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)",
-)
+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)",
+}
 
 
 @invocation(
@@ -256,16 +257,16 @@ class FloatMathInvocation(BaseInvocation):
     operation: FLOAT_OPERATIONS = InputField(
         default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
     )
-    a: float = InputField(default=0, description=FieldDescriptions.num_1)
-    b: float = InputField(default=0, description=FieldDescriptions.num_2)
+    a: float = InputField(default=1, description=FieldDescriptions.num_1)
+    b: float = InputField(default=1, description=FieldDescriptions.num_2)
 
     @field_validator("b")
-    def no_unrepresentable_results(cls, v, values):
-        if values["operation"] == "DIV" and v == 0:
+    def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
+        if info.data["operation"] == "DIV" and v == 0:
             raise ValueError("Cannot divide by zero")
-        elif values["operation"] == "EXP" and values["a"] == 0 and v < 0:
+        elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
             raise ValueError("Cannot raise zero to a negative power")
-        elif values["operation"] == "EXP" and type(values["a"] ** v) is complex:
+        elif info.data["operation"] == "EXP" and isinstance(info.data["a"] ** v, complex):
             raise ValueError("Root operation resulted in a complex number")
         return v
 

invokeai/app/invocations/metadata.py

@@ -1,13 +1,15 @@
-from typing import Optional
+from typing import Any, Literal, Optional, Union
 
-from pydantic import Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     InputField,
     InvocationContext,
+    MetadataField,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -16,116 +18,105 @@ 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.util.model_exclude_null import BaseModelExcludeNull
+from invokeai.app.shared.fields import FieldDescriptions
 
 from ...version import __version__
 
 
-class LoRAMetadataField(BaseModelExcludeNull):
-    """LoRA metadata for an image generated in InvokeAI."""
-
-    lora: LoRAModelField = Field(description="The LoRA model")
-    weight: float = Field(description="The weight of the LoRA model")
+class MetadataItemField(BaseModel):
+    label: str = Field(description=FieldDescriptions.metadata_item_label)
+    value: Any = Field(description=FieldDescriptions.metadata_item_value)
 
 
-class IPAdapterMetadataField(BaseModelExcludeNull):
+class LoRAMetadataField(BaseModel):
+    """LoRA Metadata Field"""
+
+    lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
+    weight: float = Field(description=FieldDescriptions.lora_weight)
+
+
+class IPAdapterMetadataField(BaseModel):
+    """IP Adapter Field, minus the CLIP Vision Encoder model"""
+
     image: ImageField = Field(description="The IP-Adapter image prompt.")
-    ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
-    weight: float = Field(description="The weight of the IP-Adapter model")
-    begin_step_percent: float = Field(
-        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
+    ip_adapter_model: IPAdapterModelField = Field(
+        description="The IP-Adapter model.",
     )
-    end_step_percent: float = Field(
-        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
+    weight: Union[float, list[float]] = Field(
+        description="The weight given to the IP-Adapter",
+    )
+    begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
+    end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
+
+
+@invocation_output("metadata_item_output")
+class MetadataItemOutput(BaseInvocationOutput):
+    """Metadata Item Output"""
+
+    item: MetadataItemField = OutputField(description="Metadata Item")
+
+
+@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
+class MetadataItemInvocation(BaseInvocation):
+    """Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
+
+    label: str = InputField(description=FieldDescriptions.metadata_item_label)
+    value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
+
+    def invoke(self, context: InvocationContext) -> MetadataItemOutput:
+        return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
+
+
+@invocation_output("metadata_output")
+class MetadataOutput(BaseInvocationOutput):
+    metadata: MetadataField = OutputField(description="Metadata Dict")
+
+
+@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
+class MetadataInvocation(BaseInvocation):
+    """Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
+
+    items: Union[list[MetadataItemField], MetadataItemField] = InputField(
+        description=FieldDescriptions.metadata_item_polymorphic
     )
 
+    def invoke(self, context: InvocationContext) -> MetadataOutput:
+        if isinstance(self.items, MetadataItemField):
+            # single metadata item
+            data = {self.items.label: self.items.value}
+        else:
+            # collection of metadata items
+            data = {item.label: item.value for item in self.items}
 
-class CoreMetadata(BaseModelExcludeNull):
-    """Core generation metadata for an image generated in InvokeAI."""
-
-    app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
-    generation_mode: Optional[str] = Field(
-        default=None,
-        description="The generation mode that output this image",
-    )
-    created_by: Optional[str] = Field(description="The name of the creator of the image")
-    positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
-    negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
-    width: Optional[int] = Field(default=None, description="The width parameter")
-    height: Optional[int] = Field(default=None, description="The height parameter")
-    seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
-    rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
-    cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
-    steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
-    scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
-    clip_skip: Optional[int] = Field(
-        default=None,
-        description="The number of skipped CLIP layers",
-    )
-    model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
-    controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
-    ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
-        default=None, description="The IP Adapters used for inference"
-    )
-    t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
-    loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
-    vae: Optional[VAEModelField] = Field(
-        default=None,
-        description="The VAE used for decoding, if the main model's default was not used",
-    )
-
-    # Latents-to-Latents
-    strength: Optional[float] = Field(
-        default=None,
-        description="The strength used for latents-to-latents",
-    )
-    init_image: Optional[str] = Field(default=None, description="The name of the initial image")
-
-    # SDXL
-    positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
-    negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
-
-    # SDXL Refiner
-    refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
-    refiner_cfg_scale: Optional[float] = Field(
-        default=None,
-        description="The classifier-free guidance scale parameter used for the refiner",
-    )
-    refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
-    refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
-    refiner_positive_aesthetic_score: Optional[float] = Field(
-        default=None, description="The aesthetic score used for the refiner"
-    )
-    refiner_negative_aesthetic_score: Optional[float] = Field(
-        default=None, description="The aesthetic score used for the refiner"
-    )
-    refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
+        # add app version
+        data.update({"app_version": __version__})
+        return MetadataOutput(metadata=MetadataField.model_validate(data))
 
 
-class ImageMetadata(BaseModelExcludeNull):
-    """An image's generation metadata"""
+@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
+class MergeMetadataInvocation(BaseInvocation):
+    """Merged a collection of MetadataDict into a single MetadataDict."""
 
-    metadata: Optional[dict] = Field(
-        default=None,
-        description="The image's core metadata, if it was created in the Linear or Canvas UI",
-    )
-    graph: Optional[dict] = Field(default=None, description="The graph that created the image")
+    collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
+
+    def invoke(self, context: InvocationContext) -> MetadataOutput:
+        data = {}
+        for item in self.collection:
+            data.update(item.model_dump())
+
+        return MetadataOutput(metadata=MetadataField.model_validate(data))
 
 
-@invocation_output("metadata_accumulator_output")
-class MetadataAccumulatorOutput(BaseInvocationOutput):
-    """The output of the MetadataAccumulator node"""
-
-    metadata: CoreMetadata = OutputField(description="The core metadata for the image")
+GENERATION_MODES = Literal[
+    "txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint"
+]
 
 
-@invocation(
-    "metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
-)
-class MetadataAccumulatorInvocation(BaseInvocation):
-    """Outputs a Core Metadata Object"""
+@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.1")
+class CoreMetadataInvocation(BaseInvocation):
+    """Collects core generation metadata into a MetadataField"""
 
-    generation_mode: Optional[str] = InputField(
+    generation_mode: Optional[GENERATION_MODES] = InputField(
         default=None,
         description="The generation mode that output this image",
     )
@@ -138,6 +129,8 @@ class MetadataAccumulatorInvocation(BaseInvocation):
     cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
     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")
+    seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis")
     clip_skip: Optional[int] = InputField(
         default=None,
         description="The number of skipped CLIP layers",
@@ -167,13 +160,14 @@ class MetadataAccumulatorInvocation(BaseInvocation):
     )
 
     # High resolution fix metadata.
-    hrf_width: Optional[int] = InputField(
+    hrf_enabled: Optional[bool] = InputField(
         default=None,
-        description="The high resolution fix height and width multipler.",
+        description="Whether or not high resolution fix was enabled.",
     )
-    hrf_height: Optional[int] = InputField(
+    # TODO: should this be stricter or do we just let the UI handle it?
+    hrf_method: Optional[str] = InputField(
         default=None,
-        description="The high resolution fix height and width multipler.",
+        description="The high resolution fix upscale method.",
     )
     hrf_strength: Optional[float] = InputField(
         default=None,
@@ -220,7 +214,13 @@ class MetadataAccumulatorInvocation(BaseInvocation):
         description="The start value used for refiner denoising",
     )
 
-    def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
+    def invoke(self, context: InvocationContext) -> MetadataOutput:
         """Collects and outputs a CoreMetadata object"""
 
-        return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))
+        return MetadataOutput(
+            metadata=MetadataField.model_validate(
+                self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
+            )
+        )
+
+    model_config = ConfigDict(extra="allow")

invokeai/app/invocations/model.py

@@ -3,16 +3,17 @@ 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,
 )
@@ -36,6 +37,7 @@ 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):
@@ -51,15 +53,34 @@ class VaeField(BaseModel):
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
-@invocation_output("model_loader_output")
-class ModelLoaderOutput(BaseInvocationOutput):
-    """Model loader output"""
+@invocation_output("unet_output")
+class UNetOutput(BaseInvocationOutput):
+    """Base class for invocations that output a UNet field"""
 
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+
+
+@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):
+    """Model loader output"""
+
+    pass
+
+
 class MainModelField(BaseModel):
     """Main model field"""
 
@@ -98,7 +119,7 @@ class MainModelLoaderInvocation(BaseInvocation):
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.model_exists(
+        if not context.services.model_manager.model_exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
@@ -366,13 +387,6 @@ 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"""
@@ -380,11 +394,10 @@ 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) -> VaeLoaderOutput:
+    def invoke(self, context: InvocationContext) -> VAEOutput:
         base_model = self.vae_model.base_model
         model_name = self.vae_model.model_name
         model_type = ModelType.Vae
@@ -395,7 +408,7 @@ class VaeLoaderInvocation(BaseInvocation):
             model_type=model_type,
         ):
             raise Exception(f"Unkown vae name: {model_name}!")
-        return VaeLoaderOutput(
+        return VAEOutput(
             vae=VaeField(
                 vae=ModelInfo(
                     model_name=model_name,
@@ -457,3 +470,24 @@ 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.util.misc import SEED_MAX, get_random_seed
+from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.util.misc import SEED_MAX
 
 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="1.0.0",
+    version="1.0.1",
 )
 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,
@@ -124,5 +124,6 @@ class NoiseInvocation(BaseInvocation):
             seed=self.seed,
             use_cpu=self.use_cpu,
         )
-        latents_name = context.save_latents(noise)
-        return build_noise_output(latents_name=latents_name, latents=noise, seed=self.seed)
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, noise)
+        return build_noise_output(latents_name=name, latents=noise, seed=self.seed)

invokeai/app/invocations/onnx.py

@@ -4,7 +4,7 @@ import inspect
 import re
 
 # from contextlib import ExitStack
-from typing import List, Literal, Optional, Union
+from typing import List, Literal, Union
 
 import numpy as np
 import torch
@@ -12,9 +12,9 @@ from diffusers.image_processor import VaeImageProcessor
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 from tqdm import tqdm
 
-from invokeai.app.invocations.metadata import CoreMetadata
 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
 
@@ -24,13 +24,14 @@ from ...backend.util import choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
     OutputField,
     UIComponent,
     UIType,
+    WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -53,7 +54,7 @@ ORT_TO_NP_TYPE = {
     "tensor(double)": np.float64,
 }
 
-PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]
+PRECISION_VALUES = Literal[tuple(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 = dict()
+        extra_step_kwargs = {}
         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="1.0.0",
+    version="1.1.0",
 )
-class ONNXLatentsToImageInvocation(BaseInvocation):
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -338,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None,
-        description=FieldDescriptions.core_metadata,
-        ui_hidden=True,
-    )
     # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -381,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.model_dump() if self.metadata else None,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 

invokeai/app/invocations/param_easing.py

@@ -100,7 +100,7 @@ EASING_FUNCTIONS_MAP = {
     "BounceInOut": BounceEaseInOut,
 }
 
-EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
+EASING_FUNCTION_KEYS = Literal[tuple(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 = list()
+        easing_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 = list()
+            base_easing_vals = []
             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

@@ -5,10 +5,11 @@ 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,
@@ -61,12 +62,12 @@ class BooleanInvocation(BaseInvocation):
     title="Boolean Collection Primitive",
     tags=["primitives", "boolean", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class BooleanCollectionInvocation(BaseInvocation):
     """A collection of boolean primitive values"""
 
-    collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")
+    collection: list[bool] = InputField(default=[], description="The collection of boolean values")
 
     def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
         return BooleanCollectionOutput(collection=self.collection)
@@ -110,12 +111,12 @@ class IntegerInvocation(BaseInvocation):
     title="Integer Collection Primitive",
     tags=["primitives", "integer", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class IntegerCollectionInvocation(BaseInvocation):
     """A collection of integer primitive values"""
 
-    collection: list[int] = InputField(default_factory=list, description="The collection of integer values")
+    collection: list[int] = InputField(default=[], description="The collection of integer values")
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(collection=self.collection)
@@ -157,12 +158,12 @@ class FloatInvocation(BaseInvocation):
     title="Float Collection Primitive",
     tags=["primitives", "float", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FloatCollectionInvocation(BaseInvocation):
     """A collection of float primitive values"""
 
-    collection: list[float] = InputField(default_factory=list, description="The collection of float values")
+    collection: list[float] = InputField(default=[], description="The collection of float values")
 
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         return FloatCollectionOutput(collection=self.collection)
@@ -204,12 +205,12 @@ class StringInvocation(BaseInvocation):
     title="String Collection Primitive",
     tags=["primitives", "string", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class StringCollectionInvocation(BaseInvocation):
     """A collection of string primitive values"""
 
-    collection: list[str] = InputField(default_factory=list, description="The collection of string values")
+    collection: list[str] = InputField(default=[], description="The collection of string values")
 
     def invoke(self, context: InvocationContext) -> StringCollectionOutput:
         return StringCollectionOutput(collection=self.collection)
@@ -251,7 +252,9 @@ class ImageCollectionOutput(BaseInvocationOutput):
 
 
 @invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
-class ImageInvocation(BaseInvocation):
+class ImageInvocation(
+    BaseInvocation,
+):
     """An image primitive value"""
 
     image: ImageField = InputField(description="The image to load")
@@ -291,7 +294,7 @@ class DenoiseMaskField(BaseModel):
     """An inpaint mask field"""
 
     mask_name: str = Field(description="The name of the mask image")
-    masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
+    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
 
 
 @invocation_output("denoise_mask_output")
@@ -464,13 +467,13 @@ class ConditioningInvocation(BaseInvocation):
     title="Conditioning Collection Primitive",
     tags=["primitives", "conditioning", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ConditioningCollectionInvocation(BaseInvocation):
     """A collection of conditioning tensor primitive values"""
 
     collection: list[ConditioningField] = InputField(
-        default_factory=list,
+        default=[],
         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="1.0.0",
+    version="1.0.1",
 )
 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) as f:
+        with open(file_path, encoding="utf-8") 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

@@ -1,8 +1,9 @@
+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

@@ -5,17 +5,16 @@ 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.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
 
 
@@ -59,7 +58,7 @@ class T2IAdapterInvocation(BaseInvocation):
         ui_order=-1,
     )
     weight: Union[float, list[float]] = InputField(
-        default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
+        default=1, ge=0, description="The weight given to the T2I-Adapter", 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/upscale.py

@@ -2,19 +2,19 @@
 from pathlib import Path
 from typing import Literal
 
-import cv2 as cv
+import cv2
 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, 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="1.1.0")
-class ESRGANInvocation(BaseInvocation):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.2.0")
+class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -92,9 +92,9 @@ class ESRGANInvocation(BaseInvocation):
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
 
-        upsampler = RealESRGANer(
+        upscaler = RealESRGAN(
             scale=netscale,
-            model_path=str(models_path / esrgan_model_path),
+            model_path=models_path / esrgan_model_path,
             model=rrdbnet_model,
             half=False,
             tile=self.tile_size,
@@ -102,15 +102,9 @@ class ESRGANInvocation(BaseInvocation):
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
         # TODO: This strips the alpha... is that okay?
-        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")
+        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")
 
         torch.cuda.empty_cache()
         if choose_torch_device() == torch.device("mps"):
@@ -123,6 +117,7 @@ class ESRGANInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
             workflow=self.workflow,
         )
 

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -139,7 +139,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 (board_id,),
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            images = list(map(lambda r: deserialize_image_record(dict(r)), result))
+            images = [deserialize_image_record(dict(r)) for r in result]
 
             self._cursor.execute(
                 """--sql
@@ -167,7 +167,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 (board_id,),
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = list(map(lambda r: r[0], result))
+            image_names = [r[0] for r in result]
             return image_names
         except sqlite3.Error as e:
             self._conn.rollback()