Blackified

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:

## Description

[fix(nodes): fix missing generation
modes](8615d53e65)

Lax typing on the metadata util functions allowed a typing issue to slip
through. Fixed the lax typing, updated core metadata node.

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #4959 (thanks @coder543)

.github/CODEOWNERS

@@ -1,34 +1,34 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @Millu
-/mkdocs.yml @lstein  @blessedcoolant
+/mkdocs.yml @lstein  @blessedcoolant @hipsterusername @Millu
 
 # nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising 
+/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
 
 # installation and configuration
-/pyproject.toml  @lstein @blessedcoolant
-/docker/  @lstein @blessedcoolant
-/scripts/ @ebr @lstein
-/installer/ @lstein @ebr
-/invokeai/assets @lstein @ebr
-/invokeai/configs @lstein
-/invokeai/version @lstein @blessedcoolant
+/pyproject.toml  @lstein @blessedcoolant @hipsterusername
+/docker/  @lstein @blessedcoolant @hipsterusername
+/scripts/ @ebr @lstein @hipsterusername
+/installer/ @lstein @ebr @hipsterusername
+/invokeai/assets @lstein @ebr @hipsterusername
+/invokeai/configs @lstein @hipsterusername
+/invokeai/version @lstein @blessedcoolant @hipsterusername
 
 # web ui
-/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp
-/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
+/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
 
 # front ends
-/invokeai/frontend/CLI @lstein
-/invokeai/frontend/install @lstein @ebr  
-/invokeai/frontend/merge @lstein @blessedcoolant 
-/invokeai/frontend/training @lstein @blessedcoolant 
-/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp 
+/invokeai/frontend/CLI @lstein @hipsterusername
+/invokeai/frontend/install @lstein @ebr @hipsterusername 
+/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername
 
 

.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml

@@ -1,5 +1,5 @@
 name: Feature Request
-description: Commit a idea or Request a new feature
+description: Contribute a idea or request a new feature
 title: '[enhancement]: '
 labels: ['enhancement']
 # assignees:
@@ -9,14 +9,14 @@ body:
   - type: markdown
     attributes:
       value: |
-        Thanks for taking the time to fill out this Feature request!
+        Thanks for taking the time to fill out this feature request!
 
   - type: checkboxes
     attributes:
       label: Is there an existing issue for this?
       description: |
         Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
-        to see if a simmilar issue already exists for the feature you want to request
+        to see if a similar issue already exists for the feature you want to request
       options:
         - label: I have searched the existing issues
           required: true
@@ -34,12 +34,9 @@ body:
     id: whatisexpected
     attributes:
       label: What should this feature add?
-      description: Please try to explain the functionality this feature should add
+      description: Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
       placeholder: |
-        Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
-        Great benefits with automatic labeling, assigning and other functionalitys not available in that form
-        via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
-        https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
+        I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
     validations:
       required: true
 
@@ -51,6 +48,6 @@ body:
 
   - type: textarea
     attributes:
-      label: Aditional Content
+      label: Additional Content
       description: Add any other context or screenshots about the feature request here.
-      placeholder: This is a Mockup of the design how I imagine it <screenshot>
+      placeholder: This is a mockup of the design how I imagine it <screenshot>

.github/workflows/pypi-release.yml

@@ -28,7 +28,7 @@ jobs:
         run: twine check dist/*
 
       - name: check PyPI versions
-        if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
+        if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
         run: |
           pip install --upgrade requests
           python -c "\

.github/workflows/style-checks.yml

@@ -1,6 +1,4 @@
 name: style checks
-# just formatting and flake8 for now
-# TODO: add isort later
 
 on:
   pull_request:
@@ -20,8 +18,8 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install black flake8 Flake8-pyproject
+          pip install black flake8 Flake8-pyproject isort
 
-      # - run: isort --check-only .
+      - run: isort --check-only .
       - run: black --check .
       - run: flake8

.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

.pre-commit-config.yaml

@@ -15,3 +15,10 @@ repos:
     language: system
     entry: flake8
     types: [python]
+
+  - id: isort
+    name: isort
+    stages: [commit]
+    language: system
+    entry: isort
+    types: [python]

README.md

@@ -46,13 +46,13 @@ the foundation for multiple commercial products.
   Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
   href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
   href="https://invoke-ai.github.io/InvokeAI/">Documentation and
-  Tutorials</a>] [<a
-  href="https://github.com/invoke-ai/InvokeAI/">Code and
-  Downloads</a>] [<a
-  href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
+  Tutorials</a>]
+  [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
   [<a
   href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
-  Ideas & Q&A</a>]
+  Ideas & Q&A</a>] 
+   [<a
+  href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>] 
 
 <div align="center">
 
@@ -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)
@@ -368,9 +368,9 @@ InvokeAI offers a locally hosted Web Server & React Frontend, with an industry l
 
 The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
 
-### *Node Architecture & Editor (Beta)*
+### *Workflows & Nodes*
 
-Invoke AI's backend is built on a graph-based execution architecture. This allows for customizable generation pipelines to be developed by professional users looking to create specific workflows to support their production use-cases, and will be extended in the future with additional capabilities.
+InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
 
 ### *Board & Gallery Management*
 
@@ -383,8 +383,9 @@ Invoke AI provides an organized gallery system for easily storing, accessing, an
 - *Upscaling Tools*
 - *Embedding Manager & Support*
 - *Model Manager & Support*
+- *Workflow creation & management*
 - *Node-Based Architecture*
-- *Node-Based Plug-&-Play UI (Beta)*
+
 
 ### Latest Changes
 
@@ -395,20 +396,18 @@ 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
-problems and other issues.
+problems and other issues. For more help, please join our [Discord][discord link]
 
 ## Contributing
 
 Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
 cleanup, testing, or code reviews, is very much encouraged to do so.
 
-To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
-
-If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
+Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
 
 If you are unfamiliar with how
-to contribute to GitHub projects, here is a
-[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.
+to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing: 
+[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
 
 We hope you enjoy using our software as much as we enjoy creating it,
 and we hope that some of those of you who are reading this will elect
@@ -424,7 +423,7 @@ their time, hard work and effort.
 
 ### Support
 
-For support, please use this repository's GitHub Issues tracking service, or join the Discord.
+For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
 
 Original portions of the software are Copyright (c) 2023 by respective contributors.
 

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
+## optional variables specific to the docker setup.
 # GPU_DRIVER=cuda
 # 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
 
@@ -37,7 +37,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
     else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/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-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 --build -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/CONTRIBUTING.md

@@ -1,39 +1,41 @@
-# How to Contribute
+# Contributing
 
-## Welcome to Invoke AI
 Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
 
 
-## Contributing to Invoke AI
+# Methods of Contributing to Invoke AI
 Anyone who wishes to contribute to InvokeAI, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation is very much encouraged to do so.
 
-To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
+## Development
+If you’d like to help with development, please see our [development guide](contribution_guides/development.md). 
 
-### Areas of contribution: 
+**New Contributors:** If you’re unfamiliar with contributing to open source projects, take a look at our [new contributor guide](contribution_guides/newContributorChecklist.md).
 
-#### Development
-If you’d like to help with development, please see our [development guide](contribution_guides/development.md). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
+## Nodes
+If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
 
-#### Nodes
-If you’d like to help with development, please see our [nodes contribution guide](/nodes/contributingNodes). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
+## Support and Triaging
+Helping support other users in [Discord](https://discord.gg/ZmtBAhwWhy) and on Github are valuable forms of contribution that we greatly appreciate. 
 
-#### Documentation
+We receive many issues and requests for help from users. We're limited in bandwidth relative to our the user base, so providing answers to questions or helping identify causes of issues is very helpful. By doing this, you enable us to spend time on the highest priority work. 
+
+## Documentation
 If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
 
-#### Translation
+## Translation
 If you'd like to help with translation, please see our [translation guide](contribution_guides/translation.md).
 
-#### Tutorials 
+## Tutorials 
 Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
 
 We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect to become part of our contributor community.
 
 
-### Contributors
+# Contributors
 
 This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
 
-### Code of Conduct
+# Code of Conduct
 
 The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
@@ -47,8 +49,7 @@ By making a contribution to this project, you certify that:
 This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
 
 This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
-
-### Support
+# Support
 
 For support, please use this repository's [GitHub Issues](https://github.com/invoke-ai/InvokeAI/issues), or join the [Discord](https://discord.gg/ZmtBAhwWhy).
 

docs/contributing/INVOCATIONS.md

@@ -244,8 +244,12 @@ copy-paste the template above.
 We can use the `@invocation` decorator to provide some additional info to the
 UI, like a custom title, tags and category.
 
+We also encourage providing a version. This must be a
+[semver](https://semver.org/) version string ("$MAJOR.$MINOR.$PATCH"). The UI
+will let users know if their workflow is using a mismatched version of the node.
+
 ```python
-@invocation("resize", title="My Resizer", tags=["resize", "image"], category="My Invocations")
+@invocation("resize", title="My Resizer", tags=["resize", "image"], category="My Invocations", version="1.0.0")
 class ResizeInvocation(BaseInvocation):
     """Resizes an image"""
 
@@ -279,8 +283,6 @@ take a look a at our [contributing nodes overview](contributingNodes).
 
 ## Advanced
 
--->
-
 ### Custom Output Types
 
 Like with custom inputs, sometimes you might find yourself needing custom

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -47,34 +47,9 @@ pip install ".[dev,test]"
 These are optional groups of packages which are defined within the `pyproject.toml`
 and will be required for testing the changes you make to the code.
 
-### Running Tests
-
-We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
-be found under the `./tests` folder and can be run with a single `pytest`
-command. Optionally, to review test coverage you can append `--cov`.
-
-```zsh
-pytest --cov
-```
-
-Test outcomes and coverage will be reported in the terminal. In addition a more
-detailed report is created in both XML and HTML format in the `./coverage`
-folder. The HTML one in particular can help identify missing statements
-requiring tests to ensure coverage. This can be run by opening
-`./coverage/html/index.html`.
-
-For example.
-
-```zsh
-pytest --cov; open ./coverage/html/index.html
-```
-
-??? info "HTML coverage report output"
-
-    ![html-overview](../assets/contributing/html-overview.png)
-
-    ![html-detail](../assets/contributing/html-detail.png)
+### Tests
 
+See the [tests documentation](./TESTS.md) for information about running and writing tests.
 ### Reloading Changes
 
 Experimenting with changes to the Python source code is a drag if you have to re-start the server —
@@ -167,6 +142,23 @@ and so you'll have access to the same python environment as the InvokeAI app.
 
 This is _super_ handy.
 
+#### Enabling Type-Checking with Pylance
+
+We use python's typing system in InvokeAI. PR reviews will include checking that types are present and correct. We don't enforce types with `mypy` at this time, but that is on the horizon.
+
+Using a code analysis tool to automatically type check your code (and types) is very important when writing with types. These tools provide immediate feedback in your editor when types are incorrect, and following their suggestions lead to fewer runtime bugs.
+
+Pylance, installed at the beginning of this guide, is the de-facto python LSP (language server protocol). It provides type checking in the editor (among many other features). Once installed, you do need to enable type checking manually:
+
+- Open a python file
+- Look along the status bar in VSCode for `{ } Python`
+- Click the `{ }`
+- Turn type checking on - basic is fine
+
+You'll now see red squiggly lines where type issues are detected. Hover your cursor over the indicated symbols to see what's wrong.
+
+In 99% of cases when the type checker says there is a problem, there really is a problem, and you should take some time to understand and resolve what it is pointing out.
+
 #### Debugging configs with `launch.json`
 
 Debugging configs are managed in a `launch.json` file. Like most VSCode configs,

docs/contributing/TESTS.md

@@ -0,0 +1,89 @@
+# InvokeAI Backend Tests
+
+We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
+
+## Fast vs. Slow
+All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
+
+'Fast' tests are run to validate every PR, and are fast enough that they can be run routinely during development.
+
+'Slow' tests are currently only run manually on an ad-hoc basis. In the future, they may be automated to run nightly. Most developers are only expected to run the 'slow' tests that directly relate to the feature(s) that they are working on.
+
+As a rule of thumb, tests should be marked as 'slow' if there is a chance that they take >1s (e.g. on a CPU-only machine with slow internet connection). Common examples of slow tests are tests that depend on downloading a model, or running model inference.
+
+## Running Tests
+
+Below are some common test commands:
+```bash
+# Run the fast tests. (This implicitly uses the configured default option: `-m "not slow"`.)
+pytest tests/
+
+# Equivalent command to run the fast tests.
+pytest tests/ -m "not slow"
+
+# Run the slow tests.
+pytest tests/ -m "slow"
+
+# Run the slow tests from a specific file.
+pytest tests/path/to/slow_test.py -m "slow"
+
+# Run all tests (fast and slow).
+pytest tests -m ""
+```
+
+## Test Organization
+
+All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
+
+TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
+
+## Tests that depend on models
+
+There are a few things to keep in mind when adding tests that depend on models.
+
+1. If a required model is not already present, it should automatically be downloaded as part of the test setup.
+2. If a model is already downloaded, it should not be re-downloaded unnecessarily.
+3. Take reasonable care to keep the total number of models required for the tests low. Whenever possible, re-use models that are already required for other tests. If you are adding a new model, consider including a comment to explain why it is required/unique.
+
+There are several utilities to help with model setup for tests. Here is a sample test that depends on a model:
+```python
+import pytest
+import torch
+
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType
+from invokeai.backend.util.test_utils import install_and_load_model
+
+@pytest.mark.slow
+def test_model(model_installer, torch_device):
+    model_info = install_and_load_model(
+        model_installer=model_installer,
+        model_path_id_or_url="HF/dummy_model_id",
+        model_name="dummy_model",
+        base_model=BaseModelType.StableDiffusion1,
+        model_type=ModelType.Dummy,
+    )
+
+    dummy_input = build_dummy_input(torch_device)
+
+    with torch.no_grad(), model_info as model:
+        model.to(torch_device, dtype=torch.float32)
+        output = model(dummy_input)
+
+    # Validate output...
+
+```
+
+## Test Coverage
+
+To review test coverage, append `--cov` to your pytest command:
+```bash
+pytest tests/ --cov
+```
+
+Test outcomes and coverage will be reported in the terminal. In addition, a more detailed report is created in both XML and HTML format in the `./coverage` folder. The HTML output is particularly helpful in identifying untested statements where coverage should be improved. The HTML report can be viewed by opening `./coverage/html/index.html`.
+
+??? info "HTML coverage report output"
+
+    ![html-overview](../assets/contributing/html-overview.png)
+
+    ![html-detail](../assets/contributing/html-detail.png)

docs/contributing/contribution_guides/development.md

@@ -4,14 +4,21 @@
 
 If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
 
-For more information, please review our area specific documentation:
+
+## **Get Started**
+
+To get started, take a look at our [new contributors checklist](newContributorChecklist.md)
+
+Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
+* #### [Frontend Documentation](./contributingToFrontend.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
-If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md) or [translation](translation.md).
+
+
+If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md), [translation](translation.md) or helping support other users and triage issues as they're reported in GitHub.
 
 There are two paths to making a development contribution: 
 
@@ -23,69 +30,20 @@ There are two paths to making a development contribution:
 
 ## Best Practices: 
 * Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
-* Comments! Commenting your code helps reviwers easily understand your contribution
+* Comments! Commenting your code helps reviewers easily understand your contribution
 * Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
 * Make all communications public. This ensure knowledge is shared with the whole community
 
-## **How do I make a contribution?**
-
-Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
-
-Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
-
-1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
-2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
-3. Clone the repository to your local machine using:
-
-```bash
-git clone https://github.com/your-GitHub-username/InvokeAI.git
-```
-
-If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
-
-4. Create a new branch for your fix using:
-
-```bash
-git checkout -b branch-name-here
-```
-
-5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
-6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
-
-```bash
-git add insert-paths-of-changed-files-here
-```
-
-7. Store the contents of the index with a descriptive message.
-
-```bash
-git commit -m "Insert a short message of the changes made here"
-```
-
-8. Push the changes to the remote repository using
-
-```markdown
-git push origin branch-name-here
-```
-
-9. Submit a pull request to the **main** branch of the InvokeAI repository.
-10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
-11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
-12. Wait for the pull request to be reviewed by other collaborators.
-13. Make changes to the pull request if the reviewer(s) recommend them.
-14. Celebrate your success after your pull request is merged!
-
-If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
-
 ## **Where can I go for help?**
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@pyschedelicious** is the best person to reach out to. 
+For frontend related work, **@psychedelicious** is the best person to reach out to. 
+
+For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
 
-For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.
 
 ## **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/contributing/contribution_guides/documentation.md

@@ -10,4 +10,4 @@ When updating or creating documentation, please keep in mind InvokeAI is a tool
 
 ## Help & Questions
 
-Please ping @imic1 or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.
+Please ping @imic or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

docs/contributing/contribution_guides/newContributorChecklist.md

@@ -0,0 +1,68 @@
+# New Contributor Guide
+
+If you're a new contributor to InvokeAI or Open Source Projects, this is the guide for you. 
+
+## New Contributor Checklist
+- [x] Set up your local development environment & fork of InvokAI by following [the steps outlined here](../../installation/020_INSTALL_MANUAL.md#developer-install)
+- [x] Set up your local tooling with [this guide](InvokeAI/contributing/LOCAL_DEVELOPMENT/#developing-invokeai-in-vscode). Feel free to skip this step if you already have tooling you're comfortable with. 
+- [x] Familiarize yourself with [Git](https://www.atlassian.com/git) & our project structure by reading through the [development documentation](development.md)
+- [x] Join the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord
+- [x] Choose an issue to work on! This can be achieved by asking in the #dev-chat channel, tackling a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) or finding an item on the [roadmap](https://github.com/orgs/invoke-ai/projects/7). If nothing in any of those places catches your eye, feel free to work on something of interest to you! 
+- [x] Make your first Pull Request with the guide below
+- [x] Happy development! Don't be afraid to ask for help - we're happy to help you contribute!
+
+
+## How do I make a contribution?
+
+Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
+
+Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
+
+1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
+2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
+3. Clone the repository to your local machine using:
+```bash
+git clone https://github.com/your-GitHub-username/InvokeAI.git
+```
+If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
+4. Create a new branch for your fix using:
+```bash
+git checkout -b branch-name-here
+```
+5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
+6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
+```bash
+git add -A
+```
+7. Store the contents of the index with a descriptive message.
+```bash
+git commit -m "Insert a short message of the changes made here"
+```
+8. Push the changes to the remote repository using
+```bash
+git push origin branch-name-here
+```
+9. Submit a pull request to the **main** branch of the InvokeAI repository. If you're not sure how to, [follow this guide](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)
+10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
+11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
+12. Wait for the pull request to be reviewed by other collaborators.
+13. Make changes to the pull request if the reviewer(s) recommend them.
+14. Celebrate your success after your pull request is merged!
+
+If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
+
+
+## Best Practices: 
+* Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
+* Comments! Commenting your code helps reviewers easily understand your contribution
+* Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
+* Make all communications public. This ensure knowledge is shared with the whole community
+
+
+## **Where can I go for help?**
+
+If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
+
+For frontend related work, **@pyschedelicious** is the best person to reach out to. 
+
+For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.

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/CONCEPTS.md

@@ -21,15 +21,16 @@ TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
 different TI training packages. InvokeAI supports both formats, but its
 [built-in TI training system](TRAINING.md) produces `.pt`.
 
-The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
-amassed a large ligrary of >800 community-contributed TI files covering a
+[Hugging Face](https://huggingface.co/sd-concepts-library) has
+amassed a large library of >800 community-contributed TI files covering a
 broad range of subjects and styles. You can also install your own or others' TI files 
 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> |
 | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |

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
@@ -159,7 +159,7 @@ groups in `invokeia.yaml`:
 | `host`     | `localhost`      | Name or IP address of the network interface that the web server will listen on  |
 | `port`     | `9090`           | Network port number that the web server will listen on  |
 | `allow_origins`  | `[]`       | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
-| `allow_credentials | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
+| `allow_credentials` | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
 | `allow_methods` | `*`         | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API  |
 | `allow_headers` | `*`         | List of HTTP headers that the web server will accept when accessing the API  |
 

docs/features/CONTROLNET.md

@@ -1,13 +1,11 @@
 ---
-title: ControlNet
+title: Control Adapters
 ---
 
-# :material-loupe: ControlNet
+# :material-loupe: Control Adapters
 
 ## ControlNet
 
-ControlNet
-
 ControlNet is a powerful set of features developed by the open-source
 community (notably, Stanford researcher
 [**@ilyasviel**](https://github.com/lllyasviel)) that allows you to
@@ -19,9 +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
@@ -29,35 +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
@@ -65,6 +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**:
@@ -96,6 +88,8 @@ A model that generates normal maps from input images, allowing for more realisti
 **Image Segmentation**: 
 A model that divides input images into segments or regions, each of which corresponds to a different object or part of the image. (More details coming soon)
 
+**QR Code Monster**:
+A model that helps generate creative QR codes that still scan. Can also be used to create images with text, logos or shapes within them. 
 
 **Openpose**: 
 The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
@@ -104,7 +98,7 @@ The OpenPose control model allows for the identification of the general pose of
 
 The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.
 
-**Tile (experimental)**:
+**Tile**:
 
 The Tile model fills out details in the image to match the image, rather than the prompt. The Tile Model is a versatile tool that offers a range of functionalities. Its primary capabilities can be boiled down to two main behaviors:
 
@@ -117,12 +111,10 @@ The Tile Model can be a powerful tool in your arsenal for enhancing image qualit
 
 With Pix2Pix, you can input an image into the controlnet, and then "instruct" the model to change it using your prompt. For example, you can say "Make it winter" to add more wintry elements to a scene.
 
-**Inpaint**: Coming Soon - Currently this model is available but not functional on the Canvas. An upcoming release will provide additional capabilities for using this model when inpainting.
-
 Each of these models can be adjusted and combined with other ControlNet models to achieve different results, giving you even more control over your image generation process.
 
 
-## Using ControlNet
+### Using ControlNet
 
 To use ControlNet, you can simply select the desired model and adjust both the ControlNet and Pre-processor settings to achieve the desired result. You can also use multiple ControlNet models at the same time, allowing you to achieve even more complex effects or styles in your generated images.
 
@@ -134,3 +126,55 @@ Weight - Strength of the Controlnet model applied to the generation for the sect
 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 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.
+
+**Note:** T2I-Adapter models and ControlNet models cannot currently be used together.
+
+## IP-Adapter
+
+[IP-Adapter](https://ip-adapter.github.io) is a tooling that allows for image prompt capabilities with text-to-image diffusion models. IP-Adapter works by analyzing the given image prompt to extract features, then passing those features to the UNet along with any other conditioning provided. 
+
+![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.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 [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
+
+IP-Adapter can be used by navigating to the *Control Adapters* options and enabling IP-Adapter. 
+
+IP-Adapter requires an image to be used as the Image Prompt. It can also be used in conjunction with text prompts, Image-to-Image, Inpainting, Outpainting, ControlNets and LoRAs.
+
+
+Each IP-Adapter has two settings that are applied to the IP-Adapter:
+
+* Weight - Strength of the IP-Adapter 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 IP-Adapter applied.

docs/features/MODEL_MERGING.md

@@ -2,17 +2,51 @@
 title: Model Merging
 ---
 
-# :material-image-off: Model Merging
-
-## How to Merge Models
-
-As of version 2.3, InvokeAI comes with a script that allows you to
-merge two or three diffusers-type models into a new merged model. The
+InvokeAI provides the ability to merge two or three diffusers-type models into a new merged model. The
 resulting model will combine characteristics of the original, and can
 be used to teach an old model new tricks.
 
+## How to Merge Models
+
+Model Merging can be be done by navigating to the Model Manager and clicking the "Merge Models" tab. From there, you can select the models and settings you want to use to merge th models. 
+
+## Settings
+
+* Model Selection: there are three multiple choice fields that
+  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
+  "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
+  2d and (optionally) 3d models. So if you have two models named "A"
+  and "B", an alpha value of 0.25 will give you a merged model that is
+  25% A and 75% B.
+
+* Interpolation Method: This is the method used to combine
+  weights. The options are "weighted_sum" (the default), "sigmoid",
+  "inv_sigmoid" and "add_difference". Each produces slightly different
+  results. When three models are in use, only "add_difference" is
+  available.
+
+* Save Location: The location you want the merged model to be saved in. Default is in the InvokeAI root folder
+
+* Name for merged model: This is the name for the new model. Please
+  use InvokeAI conventions - only alphanumeric letters and the
+  characters ".+-".
+
+* Ignore Mismatches / Force: Not all models are compatible with each other. The merge
+  script will check for compatibility and refuse to merge ones that
+  are incompatible. Set this checkbox to try merging anyway.
+
+
+
 You may run the merge script by starting the invoke launcher
-(`invoke.sh` or `invoke.bat`) and choosing the option for _merge
+(`invoke.sh` or `invoke.bat`) and choosing the option (4) for _merge
 models_. This will launch a text-based interactive user interface that
 prompts you to select the models to merge, how to merge them, and the
 merged model name.
@@ -40,34 +74,4 @@ this to get back.
 If the merge runs successfully, it will create a new diffusers model
 under the selected name and register it with InvokeAI.
 
-## The Settings
-
-* Model Selection -- there are three multiple choice fields that
-  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.
-
-* 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
-  2d and (optionally) 3d models. So if you have two models named "A"
-  and "B", an alpha value of 0.25 will give you a merged model that is
-  25% A and 75% B.
-
-* Interpolation Method -- This is the method used to combine
-  weights. The options are "weighted_sum" (the default), "sigmoid",
-  "inv_sigmoid" and "add_difference". Each produces slightly different
-  results. When three models are in use, only "add_difference" is
-  available. (TODO: cite a reference that describes what these
-  interpolation methods actually do and how to decide among them).
-
-* Force -- Not all models are compatible with each other. The merge
-  script will check for compatibility and refuse to merge ones that
-  are incompatible. Set this checkbox to try merging anyway.
-
-* Name for merged model - This is the name for the new model. Please
-  use InvokeAI conventions - only alphanumeric letters and the
-  characters ".+-".
 

docs/features/PROMPTS.md

@@ -142,7 +142,7 @@ Prompt2prompt `.swap()` is not compatible with xformers, which will be temporari
 The `prompt2prompt` code is based off
 [bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
 
-### Escaping parentheses () and speech marks ""
+### Escaping parentheses and speech marks 
 
 If the model you are using has parentheses () or speech marks "" as part of its
 syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
@@ -246,7 +246,7 @@ To create a Dynamic Prompt, follow these steps:
     Within the braces, separate each option using a vertical bar |.
     If you want to include multiple options from a single group, prefix with the desired number and $$.
 
-For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {2$$style1|style2|style3}.
+For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {style1|style2|style3}.
 ### How Dynamic Prompts Work
 
 Once a Dynamic Prompt is configured, the system generates an array of combinations using the options provided. Each group of options in curly braces is treated independently, with the system selecting one option from each group. For a prefixed set (e.g., 2$$), the system will select two distinct options.
@@ -273,3 +273,36 @@ Below are some useful strategies for creating Dynamic Prompts:
     Experiment with different quantities for the prefix. For example, 3$$ will select three distinct options.
     Be aware of coherence in your prompts. Although the system can generate all possible combinations, not all may semantically make sense. Therefore, carefully choose the options for each group.
     Always review and fine-tune the generated prompts as needed. While Dynamic Prompts can help you generate a multitude of combinations, the final polishing and refining remain in your hands.
+
+
+## SDXL Prompting
+
+Prompting with SDXL is slightly different than prompting with SD1.5 or SD2.1 models - SDXL expects a prompt _and_ a style. 
+
+
+### Prompting 
+<figure markdown>
+
+![SDXL prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt.png)
+
+</figure>
+
+In the prompt box, enter a positive or negative prompt as you normally would. 
+
+For the style box you can enter a style that you want the image to be generated in. You can use styles from this example list, or any other style you wish: anime, photographic, digital art, comic book, fantasy art, analog film, neon punk, isometric, low poly, origami, line art, cinematic, 3d model, pixel art, etc.
+
+
+### Concatenated Prompts
+
+
+InvokeAI also has the option to concatenate the prompt and style inputs, by pressing the "link" button in the Positive Prompt box. 
+
+This concatenates the prompt & style inputs, and passes the joined prompt and style to the SDXL model. 
+![SDXL concatenated prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt-concatenated.png)
+
+
+
+
+
+
+

docs/features/TRAINING.md

@@ -43,27 +43,22 @@ into the directory
 
 InvokeAI 2.3 and higher comes with a text console-based training front
 end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
-start the front end by selecting choice (3):
+start training tool selecting choice (3):
 
 ```sh
-Do you want to generate images using the
-1: Browser-based UI
-2: Command-line interface
-3: Run textual inversion training
-4: Merge models (diffusers type only)
-5: Download and install models
-6: Change InvokeAI startup options
-7: Re-run the configure script to fix a broken install
-8: Open the developer console
-9: Update InvokeAI
-10: Command-line help
-Q: Quit
-
-Please enter 1-10, Q: [1] 
+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"
 ```
 
-From the command line, with the InvokeAI virtual environment active,
-you can launch the front end with the command `invokeai-ti --gui`.
+Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
+you can then launch the front end with the command `invokeai-ti --gui`.
 
 This will launch a text-based front end that will look like this:
 

docs/features/UTILITIES.md

@@ -0,0 +1,336 @@
+---
+title: Command-line Utilities
+---
+
+# :material-file-document: Utilities
+
+# 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 [7]). Users who are
+familiar with Python can alternatively activate InvokeAI's virtual
+environment (typically, but not necessarily `invokeai/.venv`).
+
+In the developer's console, type the script's name to run it. To get a
+synopsis of what a utility does and the command-line arguments it
+accepts, pass it the `-h` argument, e.g.
+
+```bash
+invokeai-merge -h
+```
+## **invokeai-web**
+
+This script launches the web server and is effectively identical to
+selecting option [1] in the launcher. An advantage of launching the
+server from the command line is that you can override any setting
+configuration option in `invokeai.yaml` using like-named command-line
+arguments. For example, to temporarily change the size of the RAM
+cache to 7 GB, you can launch as follows:
+
+```bash
+invokeai-web --ram 7
+```
+
+## **invokeai-merge**
+
+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
+merges models named `stable-diffusion-1.5` and `inkdiffusion` into a new model named
+`my_new_model`:
+
+```bash
+invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffusion --merged_model_name my_new_model
+```
+
+## **invokeai-ti**
+
+This is the textual inversion training script that is run by launcher
+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:
+
+```bash
+invokeai-ti --model stable-diffusion-1.5 \
+            --placeholder_token 'jello' \
+            --learnable_property object \
+			--num_train_epochs 50 \
+			--train_data_dir /path/to/training/images \
+			--output_dir /path/to/trained/model
+```
+
+(Note that \\ is the Linux/Mac long-line continuation character. Use ^
+in Windows).
+
+## **invokeai-install**
+
+This is the console-based model install script that is run by launcher
+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:
+
+* This will download and install three models from CivitAI, HuggingFace,
+and local disk:
+
+```bash
+invokeai-install --add https://civitai.com/api/download/models/161302 ^
+                  gsdf/Counterfeit-V3.0  ^
+				  D:\Models\merge_model_two.safetensors
+```
+(Note that ^ is the Windows long-line continuation character. Use \\ on
+Linux/Mac).
+
+* This will list installed models of type `main`:
+
+```bash
+invokeai-model-install --list-models main
+```
+
+* This will delete the models named `voxel-ish` and `realisticVision`:
+
+```bash
+invokeai-model-install --delete voxel-ish realisticVision
+```
+
+## **invokeai-configure**
+
+This is the console-based configure script that ran when InvokeAI was
+first installed. You can run it again at any time to change the
+configuration, repair a broken install.
+
+Called without any arguments, `invokeai-configure` enters interactive
+mode with two screens. The first screen is a form that provides access
+to most of InvokeAI's configuration options. The second screen lets
+you download, add, and delete models interactively. When you exit the
+second screen, the script will add any missing "support models"
+needed for core functionality, and any selected "sd weights" which are
+the model checkpoint/diffusers files.
+
+This behavior can be changed via a series of command-line
+arguments. Here are some of the useful ones:
+
+* `invokeai-configure --skip-sd-weights --skip-support-models`
+This will run just the configuration part of the utility, skipping
+downloading of support models and stable diffusion weights.
+
+* `invokeai-configure --yes`
+This will run the configure script non-interactively. It will set the
+configuration options to their default values, install/repair support
+models, and download the "recommended" set of SD models.
+
+* `invokeai-configure --yes --default_only` 
+This will run the configure script non-interactively. In contrast to
+the previous command, it will only download the default SD model,
+Stable Diffusion v1.5
+
+* `invokeai-configure --yes --default_only --skip-sd-weights` 
+This is similar to the previous command, but will not download any
+SD models at all. It is usually used to repair a broken install.
+
+By default, `invokeai-configure` runs on the currently active InvokeAI
+root folder. To run it against a different root, pass it the `--root
+</path/to/root>` argument.
+
+Lastly, you can use `invokeai-configure` to create a working root
+directory entirely from scratch. Assuming you wish to make a root directory
+named `InvokeAI-New`, run this command:
+
+```bash
+invokeai-configure --root InvokeAI-New --yes --default_only
+```
+This will create a minimally functional root directory. You can now
+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 [8] to update to a new version of InvokeAI. It takes no
+command-line arguments.
+
+## **invokeai-metadata**
+
+This is a script which takes a list of InvokeAI-generated images and
+outputs their metadata in the same JSON format that you get from the
+`</>` button in the Web GUI. For example:
+
+```bash
+$ invokeai-metadata ffe2a115-b492-493c-afff-7679aa034b50.png
+ffe2a115-b492-493c-afff-7679aa034b50.png:
+{
+    "app_version": "3.1.0",
+    "cfg_scale": 8.0,
+    "clip_skip": 0,
+    "controlnets": [],
+    "generation_mode": "sdxl_txt2img",
+    "height": 1024,
+    "loras": [],
+    "model": {
+        "base_model": "sdxl",
+        "model_name": "stable-diffusion-xl-base-1.0",
+        "model_type": "main"
+    },
+    "negative_prompt": "",
+    "negative_style_prompt": "",
+    "positive_prompt": "military grade sushi dinner for shock troopers",
+    "positive_style_prompt": "",
+    "rand_device": "cpu",
+    "refiner_cfg_scale": 7.5,
+    "refiner_model": {
+        "base_model": "sdxl-refiner",
+        "model_name": "sd_xl_refiner_1.0",
+        "model_type": "main"
+    },
+    "refiner_negative_aesthetic_score": 2.5,
+    "refiner_positive_aesthetic_score": 6.0,
+    "refiner_scheduler": "euler",
+    "refiner_start": 0.8,
+    "refiner_steps": 20,
+    "scheduler": "euler",
+    "seed": 387129902,
+    "steps": 25,
+    "width": 1024
+}
+```
+
+You may list multiple files on the command line.
+
+## **invokeai-import-images**
+
+InvokeAI uses a database to store information about images it
+generated, and just copying the image files from one InvokeAI root
+directory to another does not automatically import those images into
+the destination's gallery. This script allows you to bulk import
+images generated by one instance of InvokeAI into a gallery maintained
+by another. It also works on images generated by older versions of
+InvokeAI, going way back to version 1.
+
+This script has an interactive mode only. The following example shows
+it in action:
+
+```bash
+$ invokeai-import-images
+===============================================================================
+This script will import images generated by earlier versions of
+InvokeAI into the currently installed root directory:
+   /home/XXXX/invokeai-main
+If this is not what you want to do, type ctrl-C now to cancel.
+===============================================================================
+= Configuration & Settings
+Found invokeai.yaml file at /home/XXXX/invokeai-main/invokeai.yaml:
+  Database : /home/XXXX/invokeai-main/databases/invokeai.db
+  Outputs  : /home/XXXX/invokeai-main/outputs/images
+ 
+Use these paths for import (yes) or choose different ones (no) [Yn]:
+Inputs: Specify absolute path containing InvokeAI .png images to import: /home/XXXX/invokeai-2.3/outputs/images/
+Include files from subfolders recursively [yN]?
+
+Options for board selection for imported images:
+1) Select an existing board name. (found 4)
+2) Specify a board name to create/add to.
+3) Create/add to board named 'IMPORT'.
+4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_20230919T203519Z).
+5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5).
+Specify desired board option: 3
+
+===============================================================================
+= Import Settings Confirmation
+
+Database File Path               : /home/XXXX/invokeai-main/databases/invokeai.db
+Outputs/Images Directory         : /home/XXXX/invokeai-main/outputs/images
+Import Image Source Directory    : /home/XXXX/invokeai-2.3/outputs/images/
+  Recurse Source SubDirectories  : No
+Count of .png file(s) found      : 5785
+Board name option specified      : IMPORT
+Database backup will be taken at : /home/XXXX/invokeai-main/databases/backup
+
+Notes about the import process:
+- Source image files will not be modified, only copied to the outputs directory.
+- If the same file name already exists in the destination, the file will be skipped.
+- If the same file name already has a record in the database, the file will be skipped.
+- Invoke AI metadata tags will be updated/written into the imported copy only.
+- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)
+- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer.
+- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder.
+ 
+Do you wish to continue with the import [Yn] ?
+
+Making DB Backup at /home/lstein/invokeai-main/databases/backup/backup-20230919T203519Z-invokeai.db...Done!
+
+===============================================================================
+Importing /home/XXXX/invokeai-2.3/outputs/images/17d09907-297d-4db3-a18a-60b337feac66.png
+... (5785 more lines) ...
+===============================================================================
+= Import Complete - Elpased Time: 0.28 second(s)
+
+Source File(s)                          : 5785
+Total Imported                          : 5783
+Skipped b/c file already exists on disk : 1
+Skipped b/c file already exists in db   : 0
+Errors during import                    : 1
+```
+## **invokeai-db-maintenance**
+
+This script helps maintain the integrity of your InvokeAI database by
+finding and fixing three problems that can arise over time:
+
+1. An image was manually deleted from the outputs directory, leaving a
+   dangling image record in the InvokeAI database. This will cause a
+   black image to appear in the gallery. This is an "orphaned database
+   image record." The script can fix this by running a "clean"
+   operation on the database, removing the orphaned entries.
+   
+2. An image is present in the outputs directory but there is no
+   corresponding entry in the database. This can happen when the image
+   is added manually to the outputs directory, or if a crash occurred
+   after the image was generated but before the database was
+   completely updated. The symptom is that the image is present in the
+   outputs folder but doesn't appear in the InvokeAI gallery. This is
+   called an "orphaned image file." The script can fix this problem by
+   running an "archive" operation in which orphaned files are moved
+   into a directory named `outputs/images-archive`. If you wish, you
+   can then run `invokeai-image-import` to reimport these images back
+   into the database.
+   
+3. The thumbnail for an image is missing, again causing a black
+   gallery thumbnail. This is fixed by running the "thumbnaiils"
+   operation, which simply regenerates and re-registers the missing
+   thumbnail.
+   
+You can find and fix all three of these problems in a single go by
+executing this command:
+
+```bash
+invokeai-db-maintenance --operation all
+```
+
+Or you can run just the clean and thumbnail operations like this:
+
+```bash
+invokeai-db-maintenance -operation clean, thumbnail
+```
+
+If called without any arguments, the script will ask you which
+operations you wish to perform.
+
+## **invokeai-migrate3**
+
+This script will migrate settings and models (but not images!) from an
+InvokeAI v2.3 root folder to an InvokeAI 3.X folder. Call it with the
+source and destination root folders like this:
+
+```bash
+invokeai-migrate3 --from ~/invokeai-2.3 --to invokeai-3.1.1
+```
+
+Both directories must previously have been properly created and
+initialized by `invokeai-configure`. If you wish to migrate the images
+contained in the older root as well, you can use the
+`invokeai-image-migrate` script described earlier.
+
+---
+
+Copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/features/index.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
 
@@ -51,6 +51,9 @@ Prevent InvokeAI from displaying unwanted racy images.
 ### * [Controlling Logging](LOGGING.md)
 Control how InvokeAI logs status messages.
 
+### * [Command-line Utilities](UTILITIES.md)
+A list of the command-line utilities available with InvokeAI.
+
 <!-- OUT OF DATE
 ### * [Miscellaneous](OTHER.md)
 Run InvokeAI on Google Colab, generate images with repeating patterns,

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

@@ -15,7 +15,8 @@ title: Home
 <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.1/css/fontawesome.min.css">
 <style>
     .button {
-      width: 300px;
+      width: 100%;
+      max-width: 100%;
       height: 50px;
       background-color: #448AFF;
       color: #fff;
@@ -27,8 +28,9 @@ title: Home
 
     .button-container {
       display: grid;
-      grid-template-columns: repeat(3, 300px);
+      grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
       gap: 20px;
+      justify-content: center;
     }
 
     .button:hover {
@@ -141,10 +143,10 @@ 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)
+- [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
 
 ## :octicons-log-16: Important Changes Since Version 2.3
 
@@ -163,10 +165,8 @@ 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`.

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

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
@@ -256,6 +256,10 @@ manager, please follow these steps:
         *highly recommended** if your virtual environment is located outside of
         your runtime directory.
 
+    !!! tip
+
+        On linux, it is recommended to run invokeai with the following env var: `MALLOC_MMAP_THRESHOLD_=1048576`. For example: `MALLOC_MMAP_THRESHOLD_=1048576 invokeai --web`. This helps to prevent memory fragmentation that can lead to memory accumulation over time. This env var is set automatically when running via `invoke.sh`.
+
 10.  Render away!
 
     Browse the [features](../features/index.md) section to learn about all the
@@ -287,7 +291,7 @@ manager, please follow these steps:
     Leave off the `--gui` option to run the script using command-line arguments. Pass the `--help` argument
     to get usage instructions.
 
-### Developer Install
+## Developer Install
 
 If you have an interest in how InvokeAI works, or you would like to
 add features or bugfixes, you are encouraged to install the source
@@ -296,18 +300,29 @@ code for InvokeAI. For this to work, you will need to install the
 on your system, please see the [Git Installation
 Guide](https://github.com/git-guides/install-git)
 
-1. From the command line, run this command:
+You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
+
+If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
+
+> **Why do I need the frontend toolchain**?
+>
+> The InvokeAI project uses trunk-based development. That means our `main` branch is the development branch, and releases are tags on that branch. Because development is very active, we don't keep an updated build of the UI in `main` - we only build it for production releases.
+>
+> That means that between releases, to have a functioning application when running directly from the repo, you will need to run the UI in dev mode or build it regularly (any time the UI code changes).
+
+1. Create a fork of the InvokeAI repository through the GitHub UI or [this link](https://github.com/invoke-ai/InvokeAI/fork) 
+2. From the command line, run this command:
    ```bash
-   git clone https://github.com/invoke-ai/InvokeAI.git
+   git clone https://github.com/<your_github_username>/InvokeAI.git
    ```
 
     This will create a directory named `InvokeAI` and populate it with the
-    full source code from the InvokeAI repository.
+    full source code from your fork of the InvokeAI repository.
 
-2. Activate the InvokeAI virtual environment as per step (4) of the manual
+3. Activate the InvokeAI virtual environment as per step (4) of the manual
 installation protocol (important!)
 
-3. Enter the InvokeAI repository directory and run one of these
+4. Enter the InvokeAI repository directory and run one of these
    commands, based on your GPU:
 
     === "CUDA (NVidia)"
@@ -333,11 +348,15 @@ installation protocol (important!)
     Be sure to pass `-e` (for an editable install) and don't forget the
     dot ("."). It is part of the command.
 
-    You can now run `invokeai` and its related commands. The code will be
+5.  Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
+
+6.  You can now run `invokeai` and its related commands. The code will be
     read from the repository, so that you can edit the .py source files
     and watch the code's behavior change.
 
-4.  If you wish to contribute to the InvokeAI project, you are
+    When you pull in new changes to the repo, be sure to re-build the UI.
+
+7.  If you wish to contribute to the InvokeAI project, you are
     encouraged to establish a GitHub account and "fork"
     https://github.com/invoke-ai/InvokeAI into your own copy of the
     repository. You can then use GitHub functions to create and submit

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -57,6 +57,30 @@ familiar with containerization technologies such as Docker.
 For downloads and instructions, visit the [NVIDIA CUDA Container
 Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
 
+### cuDNN Installation for 40/30 Series Optimization* (Optional)
+
+1. Find the InvokeAI folder
+2. Click on .venv folder - e.g., YourInvokeFolderHere\\.venv
+3. Click on Lib folder - e.g., YourInvokeFolderHere\\.venv\Lib
+4. Click on site-packages folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages
+5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\\.venv\Lib\site-packages\torch
+6. Click on the lib folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib
+7. Copy everything inside the folder and save it elsewhere as a backup.
+8. Go to __https://developer.nvidia.com/cudnn__
+9. Login or create an Account. 
+10. Choose the newer version of cuDNN. **Note:** 
+There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs.
+You can find which version you should download from [this link](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html).
+13. Download the latest version and extract it from the download location
+14. Find the bin folder E\cudnn-windows-x86_64-__Whatever Version__\bin
+15. Copy and paste the .dll files into YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib **Make sure to copy, and not move the files**
+16. If prompted, replace any existing files 
+
+**Notes:** 
+* If no change is seen or any issues are encountered, follow the same steps as above and paste the torch/lib backup folder you made earlier and replace it. If you didn't make a backup, you can also uninstall and reinstall torch through the command line to repair this folder. 
+* This optimization is  intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.
+
+
 ### Torch Installation
 
 When installing torch and torchvision manually with `pip`, remember to provide

docs/installation/040_INSTALL_DOCKER.md

@@ -4,30 +4,31 @@ 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
 [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
@@ -36,6 +37,16 @@ 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 +69,33 @@ 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
-```
-
-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.
-
-!!! 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
-```
+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)

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
@@ -171,3 +171,16 @@ subfolders and organize them as you wish.
 
 The location of the autoimport directories are controlled by settings
 in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
+
+### Installing models that live in HuggingFace subfolders
+
+On rare occasions you may need to install a diffusers-style model that
+lives in a subfolder of a HuggingFace repo id. In this event, simply
+add ":_subfolder-name_" to the end of the repo id. For example, if the
+repo id is "monster-labs/control_v1p_sd15_qrcode_monster" and the model
+you wish to fetch lives in a subfolder named "v2", then the repo id to
+pass to the various model installers should be 
+
+```
+monster-labs/control_v1p_sd15_qrcode_monster:v2
+```

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/INSTALLATION.md

@@ -17,14 +17,32 @@ This fork is supported across Linux, Windows and Macintosh. Linux users can use
 either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
-### [Installation Getting Started Guide](installation)
-#### **[Automated Installer](010_INSTALL_AUTOMATED.md)**
+
+## **[Automated Installer](010_INSTALL_AUTOMATED.md)**
 ✅ This is the recommended installation method for first-time users. 
-#### [Manual Installation](020_INSTALL_MANUAL.md)
-This method is recommended for experienced users and developers
-#### [Docker Installation](040_INSTALL_DOCKER.md)
-This method is recommended for those familiar with running Docker containers
-### Other Installation Guides
+
+  This is a script that will install all of InvokeAI's essential
+  third party libraries and InvokeAI itself. It includes access to a
+  "developer console" which will help us debug problems with you and
+  give you to access experimental features.
+
+## **[Manual Installation](020_INSTALL_MANUAL.md)**
+This method is recommended for experienced users and developers.
+
+  In this method you will manually run the commands needed to install
+  InvokeAI and its dependencies. We offer two recipes: one suited to
+  those who prefer the `conda` tool, and one suited to those who prefer
+  `pip` and Python virtual environments. In our hands the pip install
+  is faster and more reliable, but your mileage may vary.
+  Note that the conda installation method is currently deprecated and
+  will not be supported at some point in the future.
+
+## **[Docker Installation](040_INSTALL_DOCKER.md)**
+This method is recommended for those familiar with running Docker containers.
+
+We offer a method for creating Docker containers containing InvokeAI and its dependencies. This method is recommended for individuals with experience with Docker containers and understand the pluses and minuses of a container-based install.
+
+## Other Installation Guides
   - [PyPatchMatch](060_INSTALL_PATCHMATCH.md)
   - [XFormers](070_INSTALL_XFORMERS.md)
   - [CUDA and ROCm Drivers](030_INSTALL_CUDA_AND_ROCM.md)
@@ -63,43 +81,3 @@ images in full-precision mode:
 - GTX 1650 series cards
 - GTX 1660 series cards
 
-## Installation options
-
-1. [Automated Installer](010_INSTALL_AUTOMATED.md)
-
-    This is a script that will install all of InvokeAI's essential
-    third party libraries and InvokeAI itself. It includes access to a
-    "developer console" which will help us debug problems with you and
-    give you to access experimental features.
-
-
-    ✅ This is the recommended option for first time users. 
-
-2. [Manual Installation](020_INSTALL_MANUAL.md)
-
-    In this method you will manually run the commands needed to install
-    InvokeAI and its dependencies. We offer two recipes: one suited to
-    those who prefer the `conda` tool, and one suited to those who prefer
-    `pip` and Python virtual environments. In our hands the pip install
-    is faster and more reliable, but your mileage may vary.
-    Note that the conda installation method is currently deprecated and
-    will not be supported at some point in the future.
-
-    This method is recommended for users who have previously used `conda`
-    or `pip` in the past, developers, and anyone who wishes to remain on
-    the cutting edge of future InvokeAI development and is willing to put
-    up with occasional glitches and breakage.
-
-3. [Docker Installation](040_INSTALL_DOCKER.md)
-
-    We also offer a method for creating Docker containers containing
-    InvokeAI and its dependencies. This method is recommended for
-    individuals with experience with Docker containers and understand
-    the pluses and minuses of a container-based install.
-
-## Quick Guides
-
-* [Installing CUDA and ROCm Drivers](./030_INSTALL_CUDA_AND_ROCM.md)
-* [Installing XFormers](./070_INSTALL_XFORMERS.md)
-* [Installing PyPatchMatch](./060_INSTALL_PATCHMATCH.md)
-* [Installing New Models](./050_INSTALLING_MODELS.md)

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/NODES.md

@@ -1,13 +1,36 @@
-# Using the Node Editor
+# Using the Workflow Editor
 
-The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. Nodes take in inputs on the left side of the node, and return an output on the right side of the node. A node graph is composed of multiple nodes that are connected together to create a workflow. Nodes' inputs and outputs are connected by dragging connectors from node to node. Inputs and outputs are color coded for ease of use.
+The workflow editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. Nodes take in inputs on the left side of the node, and return an output on the right side of the node. A node graph is composed of multiple nodes that are connected together to create a workflow. Nodes' inputs and outputs are connected by dragging connectors from node to node. Inputs and outputs are color coded for ease of use.
 
-To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
+If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Workflow Editor and build workflows to suit your needs.
+
+## Features
+
+### Linear View
+The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations. 
+
+To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
+
+The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity. 
+
+![linearview](../assets/nodes/linearview.png)
+
+### Renaming Fields and Nodes
+Any node or input field can be renamed in the workflow editor. If the input field you have renamed has been added to the Linear View, the changed name will be reflected in the Linear View and the node. 
+
+### Managing Nodes
+
+* Ctrl+C to copy a node
+* Ctrl+V to paste a node
+* Backspace/Delete to delete a node
+* Shift+Click to drag and select multiple nodes 
+
+### Node Caching 
+
+Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing. 
 
 
-If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Nodes Editor and build workflows to suit your needs.
-
-## Important Concepts
+## Important Concepts 
 
 There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
 
@@ -37,7 +60,7 @@ It is common to want to use both the same seed (for continuity) and random seeds
 
 ### ControlNet
 
-The ControlNet node outputs a Control, which can be provided as input to non-image *ToLatents nodes. Depending on the type of ControlNet desired, ControlNet nodes usually require an image processor node, such as a Canny Processor or Depth Processor, which prepares an input image for use with ControlNet.
+The ControlNet node outputs a Control, which can be provided as input to a Denoise Latents node. Depending on the type of ControlNet desired, ControlNet nodes usually require an image processor node, such as a Canny Processor or Depth Processor, which prepares an input image for use with ControlNet.
 
 ![groupscontrol](../assets/nodes/groupscontrol.png)
 
@@ -59,10 +82,9 @@ Iteration is a common concept in any processing, and means to repeat a process w
 
 ![groupsiterate](../assets/nodes/groupsiterate.png)
 
-### Multiple Image Generation + Random Seeds
+### Batch / Multiple Image Generation + Random Seeds
 
-Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. 
-
-To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
+Batch or multiple image generation in the workflow editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate, meaning this example will generate 4 images. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. This noise can then be fed to the Denoise Latents node for it to iterate through the denoising process with the different seeds provided.
 
 ![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)
+

docs/nodes/communityNodes.md

@@ -4,41 +4,68 @@ 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. Along with the node, an example node graph should be provided to help you get started with the node. 
+To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node. 
 
-To use a community node graph, download the the `.json` node graph file and load it into Invoke AI via the **Load Nodes** button on the Node Editor. 
+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
-
-### FaceTools
-
-**Description:** FaceTools is a collection of nodes created to manipulate faces as you would in Unified Canvas. It includes FaceMask, FaceOff, and FacePlace. FaceMask autodetects a face in the image using MediaPipe and creates a mask from it. FaceOff similarly detects a face, then takes the face off of the image by adding a square bounding box around it and cropping/scaling it. FacePlace puts the bounded face image from FaceOff back onto the original image. Using these nodes with other inpainting node(s), you can put new faces on existing things, put new things around existing faces, and work closer with a face as a bounded image. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control on FaceMask and FaceOff. See GitHub repository below for usage examples.
-
-**Node Link:** https://github.com/ymgenesis/FaceTools/
-
-**FaceMask Output Examples** 
-
-![5cc8abce-53b0-487a-b891-3bf94dcc8960](https://github.com/invoke-ai/InvokeAI/assets/25252829/43f36d24-1429-4ab1-bd06-a4bedfe0955e)
-![b920b710-1882-49a0-8d02-82dff2cca907](https://github.com/invoke-ai/InvokeAI/assets/25252829/7660c1ed-bf7d-4d0a-947f-1fc1679557ba)
-![71a91805-fda5-481c-b380-264665703133](https://github.com/invoke-ai/InvokeAI/assets/25252829/f8f6a2ee-2b68-4482-87da-b90221d5c3e2)
-
-### Ideal Size
-
-**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
-
-**Node Link:** https://github.com/JPPhoto/ideal-size-node
+- Community Nodes
+    + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
+    + [Film Grain](#film-grain)
+    + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
+    + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
+    + [Grid to Gif](#grid-to-gif)
+    + [Halftone](#halftone)
+    + [Ideal Size](#ideal-size)
+    + [Image and Mask Composition Pack](#image-and-mask-composition-pack)
+    + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
+    + [Image Picker](#image-picker)
+    + [Load Video Frame](#load-video-frame)
+    + [Make 3D](#make-3d)
+    + [Oobabooga](#oobabooga)
+    + [Prompt Tools](#prompt-tools)
+    + [Retroize](#retroize)
+    + [Size Stepper Nodes](#size-stepper-nodes)
+    + [Text font to Image](#text-font-to-image)
+    + [Thresholding](#thresholding)
+    + [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)
 
 
 --------------------------------
-### Retroize
+### Depth Map from Wavefront OBJ
 
-**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
+**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation.
 
-**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
+To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations.
 
-**Retroize Output Examples**
+**Node Link:** https://github.com/dwringer/depth-from-obj-node
 
-![image](https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974)
+**Example Usage:**
+</br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
+
+--------------------------------
+### Film Grain
+
+**Description:** This node adds a film grain effect to the input image based on the weights, seeds, and blur radii parameters. It works with RGB input images only.
+
+**Node Link:** https://github.com/JPPhoto/film-grain-node
+
+--------------------------------
+### Generative Grammar-Based Prompt Nodes
+
+**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no nonterminal terms remain in the string.
+
+This includes 3 Nodes:
+- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
+- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
+- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
+
+**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
+
+**Example Usage:**
+</br><img src="https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg" width="500" />
 
 --------------------------------
 ### GPT2RandomPromptMaker
@@ -51,31 +78,133 @@ To use a community node graph, download the the `.json` node graph file and load
 
 Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment.
 
-![9acf5aef-7254-40dd-95b3-8eac431dfab0 (1)](https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c)
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c" width="200" />
+
+--------------------------------
+### Grid to Gif
+
+**Description:** One node that turns a grid image into an image collection, one node that turns an image collection into a gif.
+
+**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py
+
+**Example Node Graph:**  https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json
+
+**Output Examples** 
+
+<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/input.png" width="300" />
+<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/output.gif" width="300" />
+
+--------------------------------
+### Halftone
+
+**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing.
+
+**Node Link:** https://github.com/JPPhoto/halftone-node
+
+**Example**
+
+Input:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4" width="300" />
+
+Halftone Output:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f" width="300" />
+
+CMYK Halftone Output:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
+
+--------------------------------
+### Ideal Size
+
+**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
+
+**Node Link:** https://github.com/JPPhoto/ideal-size-node
+
+--------------------------------
+### Image and Mask Composition Pack
+
+**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
+
+This includes 15 Nodes:
+
+- *Adjust Image Hue Plus* - Rotate the hue of an image in one of several different color spaces.
+- *Blend Latents/Noise (Masked)* - Use a mask to blend part of one latents tensor [including Noise outputs] into another. Can be used to "renoise" sections during a multi-stage [masked] denoising process.
+- *Enhance Image* - Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
+- *Equivalent Achromatic Lightness* - Calculates image lightness accounting for Helmholtz-Kohlrausch effect based on a method described by High, Green, and Nussbaum (2023).
+- *Text to Mask (Clipseg)* - Input a prompt and an image to generate a mask representing areas of the image matched by the prompt.
+- *Text to Mask Advanced (Clipseg)* - Output up to four prompt masks combined with logical "and", logical "or", or as separate channels of an RGBA image.
+- *Image Layer Blend* - Perform a layered blend of two images using alpha compositing. Opacity of top layer is selectable, with optional mask and several different blend modes/color spaces.
+- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
+- *Image Dilate or Erode* - Dilate or expand a mask (or any image!). This is equivalent to an expand/contract operation.
+- *Image Value Thresholds* - Clip an image to pure black/white beyond specified thresholds.
+- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
+- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
+- *Rotate/Flip Image* - Rotate an image in degrees clockwise/counterclockwise about its center, optionally resizing the image boundaries to fit, or flipping it about the vertical and/or horizontal axes.
+- *Shadows/Highlights/Midtones* - Extract three masks (with adjustable hard or soft thresholds) representing shadows, midtones, and highlights regions of an image.
+- *Text Mask (simple 2D)* - create and position a white on black (or black on white) line of text using any font locally available to Invoke.
+
+**Node Link:** https://github.com/dwringer/composition-nodes
+  
+</br><img src="https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg" width="500" />
+
+--------------------------------
+### Image to Character Art Image Nodes
+
+**Description:** Group of nodes to convert an input image into ascii/unicode art Image
+
+**Node Link:** https://github.com/mickr777/imagetoasciiimage
+
+**Output Examples**
+
+<img src="https://user-images.githubusercontent.com/115216705/271817646-8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056.png" width="300" /><img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/3c4990eb-2f42-46b9-90f9-0088b939dc6a" width="300" /></br>
+<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/fee7f800-a4a8-41e2-a66b-c66e4343307e" width="300" />
+<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/1d9c1003-a45f-45c2-aac7-46470bb89330" width="300" />
+
+--------------------------------
+
+### Image Picker
+
+**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose.
+
+**Node Link:** https://github.com/JPPhoto/image-picker-node
 
 --------------------------------
 ### Load Video Frame
 
-**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs.
+**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs.
 
 **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:** 
-=======
-![Example animation](https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif)
+
+<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/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)
 
 --------------------------------
+### Make 3D
 
+**Description:** Create compelling 3D stereo images from 2D originals.
+
+**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
+
+**Example Node Graph:**  https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
+
+**Output Examples** 
+
+<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" />
+
+--------------------------------
 ### Oobabooga
 
 **Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input.
 
 **Link:** https://github.com/sammyf/oobabooga-node
 
-
 **Example:**
 
 "describe a new mystical  creature in its natural environment"
@@ -85,7 +214,7 @@ Generated Prompt: An enchanted weapon will be usable by any character regardless
 "The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface.
 As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace."
 
-![glimmerwing_small](https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed)
+<img src="https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed" width="300" />
 
 **Requirement**
 
@@ -93,10 +222,105 @@ a Text-Generation-Webui instance (might work remotely too, but I never tried it)
 
 **Note**
 
-This node works best with SDXL models, especially as the style can be described independantly of the LLM's output.
+This node works best with SDXL models, especially as the style can be described independently of the LLM's output.
 
 --------------------------------
+### 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.
+
+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
+
+--------------------------------
+### Retroize
+
+**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
+
+**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
+
+**Retroize Output Examples**
+
+<img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
+
+--------------------------------
+### Size Stepper Nodes
+
+**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes.
+
+A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection.
+
+**Node Link:** https://github.com/dwringer/size-stepper-nodes
+
+**Example Usage:**
+</br><img src="https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg" width="500" />
+
+--------------------------------
+### Text font to Image
+
+**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
+
+**Node Link:** https://github.com/mickr777/textfontimage
+
+**Output Examples**
+
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36" width="300" />
+
+Results after using the depth controlnet
+
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a" width="300" />
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc" width="300" />
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89" width="300" />
+
+--------------------------------
+### Thresholding
+
+**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image.
+
+**Node Link:** https://github.com/JPPhoto/thresholding-node
+
+**Examples**
+
+Input:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632" width="300" />
+
+Highlights/Midtones/Shadows:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" width="300" />
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" width="300" />
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" width="300" />
+
+Highlights/Midtones/Shadows (with LUT blur enabled):
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" width="300" />
+<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" />
+
+--------------------------------
+### XY Image to Grid and Images to Grids nodes
+
+**Description:** Image to grid nodes and supporting tools.
+
+1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images.
+2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details.
+
+See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
+
+**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
+
+--------------------------------
 ### Example Node Template
 
 **Description:** This node allows you to do super cool things with InvokeAI.
@@ -107,7 +331,7 @@ This node works best with SDXL models, especially as the style can be described
 
 **Output Examples** 
 
-![Example Image](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png){: style="height:115px;width:240px"}
+</br><img src="https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png" width="500" />
 
 
 ## Disclaimer

docs/nodes/contributingNodes.md

@@ -4,10 +4,10 @@ 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 
-- Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
-    - Make sure you are following the template below and have provided all relevant details about the node and what it does.
-- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
+- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
+- Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
+    - Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
+- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.
 
 ### Community Node Template
 

docs/nodes/defaultNodes.md

@@ -1,6 +1,6 @@
 # List of Default Nodes
 
-The table below contains a list of the default nodes shipped with InvokeAI and their descriptions. 
+The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
 
 | Node <img width=160 align="right"> | Function                                                                              |
 |: ---------------------------------- | :--------------------------------------------------------------------------------------|
@@ -17,11 +17,13 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Conditioning Primitive 			| A conditioning tensor primitive value|
 |Content Shuffle Processor 			| Applies content shuffle processing to image|
 |ControlNet 			| Collects ControlNet info to pass to other nodes|
-|OpenCV Inpaint 			| Simple inpaint using opencv.|
 |Denoise Latents 			| Denoises noisy latents to decodable images|
 |Divide Integers 			| Divides two numbers|
 |Dynamic Prompt 			| Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
-|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
+|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
+|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier)             | Identifies and labels faces in an image|
+|[FaceOff](./detailedNodes/faceTools.md#faceoff)             | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
+|Float Math             | Perform basic math operations on two floats|
 |Float Primitive Collection 			| A collection of float primitive values|
 |Float Primitive 			| A float primitive value|
 |Float Range 			| Creates a range|
@@ -29,19 +31,22 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Blur Image 			| Blurs an image|
 |Extract Image Channel 			| Gets a channel from an image.|
 |Image Primitive Collection 			| A collection of image primitive values|
+|Integer Math           | Perform basic math operations on two integers|
 |Convert Image Mode 			| Converts an image to a different mode.|
 |Crop Image 			| Crops an image to a specified box. The box can be outside of the image.|
 |Image Hue Adjustment 			| Adjusts the Hue of an image.|
 |Inverse Lerp Image 			| Inverse linear interpolation of all pixels of an image|
 |Image Primitive 			| An image primitive value|
 |Lerp Image 			| Linear interpolation of all pixels of an image|
-|Image Luminosity Adjustment 			| Adjusts the Luminosity (Value) of an image.|
+|Offset Image Channel 			| Add to or subtract from an image color channel by a uniform value.|
+|Multiply Image Channel 			| Multiply or Invert an image color channel by a scalar value.|
 |Multiply Images 			| Multiplies two images together using `PIL.ImageChops.multiply()`.|
 |Blur NSFW Image 			| Add blur to NSFW-flagged images|
 |Paste Image 			| Pastes an image into another image.|
 |ImageProcessor 			| Base class for invocations that preprocess images for ControlNet|
 |Resize Image 			| Resizes an image to specific dimensions|
-|Image Saturation Adjustment 			| Adjusts the Saturation of an image.|
+|Round Float            | Rounds a float to a specified number of decimal places|
+|Float to Integer       | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
 |Scale Image 			| Scales an image by a factor|
 |Image to Latents 			| Encodes an image into latents.|
 |Add Invisible Watermark 			| Add an invisible watermark to an image|
@@ -72,6 +77,7 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |ONNX Prompt (Raw) 			| A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
 |ONNX Text to Latents 			| Generates latents from conditionings.|
 |ONNX Model Loader 			| Loads a main model, outputting its submodels.|
+|OpenCV Inpaint 			| Simple inpaint using opencv.|
 |Openpose Processor 			| Applies Openpose processing to image|
 |PIDI Processor 			| Applies PIDI processing to image|
 |Prompts from File 			| Loads prompts from a text file|
@@ -93,5 +99,6 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |String Primitive 			| A string primitive value|
 |Subtract Integers 			| Subtracts two numbers|
 |Tile Resample Processor 			| Tile resampler processor|
+|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
 |VAE Loader 			| Loads a VAE model, outputting a VaeLoaderOutput|
 |Zoe (Depth) Processor 			| Applies Zoe depth processing to image|

docs/nodes/detailedNodes/faceTools.md

@@ -0,0 +1,154 @@
+# Face Nodes
+
+## FaceOff
+
+FaceOff mimics a user finding a face in an image and resizing the bounding box
+around the head in Canvas.
+
+Enter a face ID (found with FaceIdentifier) to choose which face to mask.
+
+Just as you would add more context inside the bounding box by making it larger
+in Canvas, the node gives you a padding input (in pixels) which will
+simultaneously add more context, and increase the resolution of the bounding box
+so the face remains the same size inside it.
+
+The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
+threshold a detected face must reach for it to be processed. Lowering this value
+may help if detection is failing. If the detected masks are imperfect and stray
+too far outside/inside of faces, the node gives you X & Y offsets to shrink/grow
+the masks by a multiplier.
+
+FaceOff will output the face in a bounded image, taking the face off of the
+original image for input into any node that accepts image inputs. The node also
+outputs a face mask with the dimensions of the bounded image. The X & Y outputs
+are for connecting to the X & Y inputs of the Paste Image node, which will place
+the bounded image back on the original image using these coordinates.
+
+###### Inputs/Outputs
+
+| Input              | Description                                                                                                                                                                              |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Image              | Image for face detection                                                                                                                                                                 |
+| Face ID            | The face ID to process, numbered from 0. Multiple faces not supported. Find a face's ID with FaceIdentifier node.                                                                        |
+| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
+| X Offset           | X-axis offset of the mask                                                                                                                                                                |
+| Y Offset           | Y-axis offset of the mask                                                                                                                                                                |
+| Padding            | All-axis padding around the mask in pixels                                                                                                                                               |
+| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
+
+| Output        | Description                                      |
+| ------------- | ------------------------------------------------ |
+| Bounded Image | Original image bound, cropped, and resized       |
+| Width         | The width of the bounded image in pixels         |
+| Height        | The height of the bounded image in pixels        |
+| Mask          | The output mask                                  |
+| X             | The x coordinate of the bounding box's left side |
+| Y             | The y coordinate of the bounding box's top side  |
+
+## FaceMask
+
+FaceMask mimics a user drawing masks on faces in an image in Canvas.
+
+The "Face IDs" input allows the user to select specific faces to be masked.
+Leave empty to detect and mask all faces, or a comma-separated list for a
+specific combination of faces (ex: `1,2,4`). A single integer will detect and
+mask that specific face. Find face IDs with the FaceIdentifier node.
+
+The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
+threshold a detected face must reach for it to be processed. Lowering this value
+may help if detection is failing.
+
+If the detected masks are imperfect and stray too far outside/inside of faces,
+the node gives you X & Y offsets to shrink/grow the masks by a multiplier. All
+masks shrink/grow together by the X & Y offset values.
+
+By default, masks are created to change faces. When masks are inverted, they
+change surrounding areas, protecting faces.
+
+###### Inputs/Outputs
+
+| Input              | Description                                                                                                                                                                              |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Image              | Image for face detection                                                                                                                                                                 |
+| Face IDs           | Comma-separated list of face ids to mask eg '0,2,7'. Numbered from 0. Leave empty to mask all. Find face IDs with FaceIdentifier node.                                                   |
+| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
+| X Offset           | X-axis offset of the mask                                                                                                                                                                |
+| Y Offset           | Y-axis offset of the mask                                                                                                                                                                |
+| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
+| Invert Mask        | Toggle to invert the face mask                                                                                                                                                           |
+
+| Output | Description                       |
+| ------ | --------------------------------- |
+| Image  | The original image                |
+| Width  | The width of the image in pixels  |
+| Height | The height of the image in pixels |
+| Mask   | The output face mask              |
+
+## FaceIdentifier
+
+FaceIdentifier outputs an image with detected face IDs printed in white numbers
+onto each face.
+
+Face IDs can then be used in FaceMask and FaceOff to selectively mask all, a
+specific combination, or single faces.
+
+The FaceIdentifier output image is generated for user reference, and isn't meant
+to be passed on to other image-processing nodes.
+
+The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
+threshold a detected face must reach for it to be processed. Lowering this value
+may help if detection is failing. If an image is changed in the slightest, run
+it through FaceIdentifier again to get updated FaceIDs.
+
+###### Inputs/Outputs
+
+| Input              | Description                                                                                                                                                                              |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Image              | Image for face detection                                                                                                                                                                 |
+| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
+| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
+
+| Output | Description                                                                                      |
+| ------ | ------------------------------------------------------------------------------------------------ |
+| Image  | The original image with small face ID numbers printed in white onto each face for user reference |
+| Width  | The width of the original image in pixels                                                        |
+| Height | The height of the original image in pixels                                                       |
+
+## Tips
+
+- If not all target faces are being detected, activate Chunk to bypass full
+  image face detection and greatly improve detection success.
+- Final results will vary between full-image detection and chunking for faces
+  that are detectable by both due to the nature of the process. Try either to
+  your taste.
+- Be sure Minimum Confidence is set the same when using FaceIdentifier with
+  FaceOff/FaceMask.
+- For FaceOff, use the color correction node before faceplace to correct edges
+  being noticeable in the final image (see example screenshot).
+- Non-inpainting models may struggle to paint/generate correctly around faces.
+- If your face won't change the way you want it to no matter what you change,
+  consider that the change you're trying to make is too much at that resolution.
+  For example, if an image is only 512x768 total, the face might only be 128x128
+  or 256x256, much smaller than the 512x512 your SD1.5 model was probably
+  trained on. Try increasing the resolution of the image by upscaling or
+  resizing, add padding to increase the bounding box's resolution, or use an
+  image where the face takes up more pixels.
+- If the resulting face seems out of place pasted back on the original image
+  (ie. too large, not proportional), add more padding on the FaceOff node to
+  give inpainting more context. Context and good prompting are important to
+  keeping things proportional.
+- If you find the mask is too big/small and going too far outside/inside the
+  area you want to affect, adjust the x & y offsets to shrink/grow the mask area
+- Use a higher denoise start value to resemble aspects of the original face or
+  surroundings. Denoise start = 0 & denoise end = 1 will make something new,
+  while denoise start = 0.50 & denoise end = 1 will be 50% old and 50% new.
+- mediapipe isn't good at detecting faces with lots of face paint, hair covering
+  the face, etc. Anything that obstructs the face will likely result in no faces
+  being detected.
+- If you find your face isn't being detected, try lowering the minimum
+  confidence value from 0.5. This could result in false positives, however
+  (random areas being detected as faces and masked).
+- After altering an image and wanting to process a different face in the newly
+  altered image, run the altered image through FaceIdentifier again to see the
+  new Face IDs. MediaPipe will most likely detect faces in a different order
+  after an image has been changed in the slightest.

docs/nodes/exampleWorkflows.md

@@ -1,15 +1,14 @@
 # Example Workflows
 
-TODO: Will update once uploading workflows is available.
+We've curated some example workflows for you to get started with Workflows in InvokeAI
 
-## Text2Image
+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!
 
-## Image2Image
+If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
 
-## ControlNet
-
-## Upscaling
-
-## Inpainting / Outpainting
-
-## LoRAs
+* [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)
+* [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)

docs/workflows/SDXL_Text_to_Image.json

@@ -0,0 +1,735 @@
+{
+  "name": "SDXL Text to Image",
+  "author": "InvokeAI",
+  "description": "Sample text to image workflow for SDXL",
+  "version": "1.0.1",
+  "contact": "invoke@invoke.ai",
+  "tags": "text2image, SDXL, default",
+  "notes": "",
+  "exposedFields": [
+    {
+      "nodeId": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
+      "fieldName": "model"
+    },
+    {
+      "nodeId": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "fieldName": "style"
+    },
+    {
+      "nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "fieldName": "style"
+    },
+    {
+      "nodeId": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "fieldName": "steps"
+    }
+  ],
+  "meta": {
+    "version": "1.0.0"
+  },
+  "nodes": [
+    {
+      "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+        "type": "sdxl_compel_prompt",
+        "inputs": {
+          "prompt": {
+            "id": "5a6889e6-95cb-462f-8f4a-6b93ae7afaec",
+            "name": "prompt",
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+}

docs/workflows/Text_to_Image.json

@@ -0,0 +1,573 @@
+{
+  "name": "Text to Image",
+  "author": "InvokeAI",
+  "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
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+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "unet",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
+      "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",
+      "type": "default"
+    }
+  ]
+}

installer/create_installer.sh

@@ -14,7 +14,7 @@ fi
 VERSION=$(cd ..; python -c "from invokeai.version import __version__ as version; print(version)")
 PATCH=""
 VERSION="v${VERSION}${PATCH}"
-LATEST_TAG="v3.0-latest"
+LATEST_TAG="v3-latest"
 
 echo Building installer for version $VERSION
 echo "Be certain that you're in the 'installer' directory before continuing."

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(
@@ -332,6 +324,7 @@ class InvokeAiInstance:
         Configure the InvokeAI runtime directory
         """
 
+        auto_install = False
         # set sys.argv to a consistent state
         new_argv = [sys.argv[0]]
         for i in range(1, len(sys.argv)):
@@ -340,13 +333,17 @@ class InvokeAiInstance:
                 new_argv.append(el)
                 new_argv.append(sys.argv[i + 1])
             elif el in ["-y", "--yes", "--yes-to-all"]:
-                new_argv.append(el)
+                auto_install = True
         sys.argv = new_argv
 
+        import messages
         import requests  # to catch download exceptions
-        from messages import introduction
 
-        introduction()
+        auto_install = auto_install or messages.user_wants_auto_configuration()
+        if auto_install:
+            sys.argv.append("--yes")
+        else:
+            messages.introduction()
 
         from invokeai.frontend.install.invokeai_configure import invokeai_configure
 

installer/lib/main.py

@@ -5,6 +5,7 @@ InvokeAI Installer
 import argparse
 import os
 from pathlib import Path
+
 from installer import Installer
 
 if __name__ == "__main__":

installer/lib/messages.py

@@ -7,7 +7,7 @@ import os
 import platform
 from pathlib import Path
 
-from prompt_toolkit import prompt
+from prompt_toolkit import HTML, prompt
 from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
@@ -65,17 +65,50 @@ def confirm_install(dest: Path) -> bool:
     if dest.exists():
         print(f":exclamation: Directory {dest} already exists :exclamation:")
         dest_confirmed = Confirm.ask(
-            ":stop_sign: Are you sure you want to (re)install in this location?",
+            ":stop_sign: (re)install in this location?",
             default=False,
         )
     else:
         print(f"InvokeAI will be installed in {dest}")
-        dest_confirmed = not Confirm.ask("Would you like to pick a different location?", default=False)
+        dest_confirmed = Confirm.ask("Use this location?", default=True)
     console.line()
 
     return dest_confirmed
 
 
+def user_wants_auto_configuration() -> bool:
+    """Prompt the user to choose between manual and auto configuration."""
+    console.rule("InvokeAI Configuration Section")
+    console.print(
+        Panel(
+            Group(
+                "\n".join(
+                    [
+                        "Libraries are installed and InvokeAI will now set up its root directory and configuration. Choose between:",
+                        "",
+                        "  * AUTOMATIC configuration:  install reasonable defaults and a minimal set of starter models.",
+                        "  * MANUAL configuration: manually inspect and adjust configuration options and pick from a larger set of starter models.",
+                        "",
+                        "Later you can fine tune your configuration by selecting option [6] 'Change InvokeAI startup options' from the invoke.bat/invoke.sh launcher script.",
+                    ]
+                ),
+            ),
+            box=box.MINIMAL,
+            padding=(1, 1),
+        )
+    )
+    choice = (
+        prompt(
+            HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
+            validator=Validator.from_callable(
+                lambda n: n == "" or n.startswith(("a", "A", "m", "M")), error_message="Please select 'a' or 'm'"
+            ),
+        )
+        or "a"
+    )
+    return choice.lower().startswith("a")
+
+
 def dest_path(dest=None) -> Path:
     """
     Prompt the user for the destination path and create the path

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

@@ -9,14 +9,14 @@ 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 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 1-10, Q: [1] "
@@ -25,24 +25,21 @@ 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
@@ -54,12 +51,15 @@ 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%" == "9" (
+   echo Running the db maintenance script...
+   python .venv\Scripts\invokeai-db-maintenance.exe
 ) ELSE IF /I "%choice%" == "10" (
     echo Displaying command line help...
-    python .venv\Scripts\invokeai.exe --help %*
+    python .venv\Scripts\invokeai-web.exe --help %*
     pause
     exit /b
 ) ELSE IF /I "%choice%" == "q" (

installer/templates/invoke.sh.in

@@ -46,6 +46,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
+# Avoid glibc memory fragmentation. See invokeai/backend/model_management/README.md for details.
+export MALLOC_MMAP_THRESHOLD_=1048576
+
 # Primary function for the case statement to determine user input
 do_choice() {
     case $1 in
@@ -55,55 +58,50 @@ 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
         ;;
+    9)
+        clear
+        printf "Running the db maintenance script\n"
+        invokeai-db-maintenance --root ${INVOKEAI_ROOT}
+        ;;
     10)
         clear
         printf "Command-line help\n"
-        invokeai --help
-        ;;
-    "HELP 1")
-        clear
-        printf "Command-line help\n"
-        invokeai --help
+        invokeai-web --help
         ;;
     *)
         clear
@@ -118,14 +116,16 @@ 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")
+        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 \
         --backtitle "\Zb\Zu\Z3InvokeAI" \
@@ -149,14 +149,14 @@ 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 "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 1-10, Q: [1] " yn

invokeai/app/api/dependencies.py

@@ -1,34 +1,37 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from logging import Logger
-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.config import InvokeAIAppConfig
-from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-from invokeai.app.services.images import ImageService, ImageServiceDependencies
-from invokeai.app.services.resource_name import SimpleNameService
-from invokeai.app.services.urls import LocalUrlService
+
+from 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__
 
-from ..services.default_graphs import create_system_graphs
-from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-from ..services.graph import GraphExecutionState, LibraryGraph
-from ..services.image_file_storage import DiskImageFileStorage
-from ..services.invocation_queue import MemoryInvocationQueue
+from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
+from ..services.board_images.board_images_default import BoardImagesService
+from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
+from ..services.boards.boards_default import BoardService
+from ..services.config import InvokeAIAppConfig
+from ..services.image_files.image_files_disk import DiskImageFileStorage
+from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
+from ..services.images.images_default import ImageService
+from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
+from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
+from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
+from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
-from ..services.processor import DefaultInvocationProcessor
-from ..services.sqlite import SqliteItemStorage
-from ..services.model_manager_service import ModelManagerService
-from ..services.invocation_stats import InvocationStatsService
+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.names.names_default import SimpleNameService
+from ..services.session_processor.session_processor_default import DefaultSessionProcessor
+from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
+from ..services.shared.default_graphs import create_system_graphs
+from ..services.shared.graph import GraphExecutionState, LibraryGraph
+from ..services.shared.sqlite import SqliteDatabase
+from ..services.urls.urls_default import LocalUrlService
+from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
 
 
@@ -48,7 +51,7 @@ def check_internet() -> bool:
         return False
 
 
-logger = InvokeAILogger.getLogger()
+logger = InvokeAILogger.get_logger()
 
 
 class ApiDependencies:
@@ -62,80 +65,69 @@ class ApiDependencies:
         logger.info(f"Root directory = {str(config.root_path)}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
-        events = FastAPIEventService(event_handler_id)
-
         output_folder = config.output_path
 
-        # TODO: build a file/path manager?
-        db_path = config.db_path
-        db_path.parent.mkdir(parents=True, exist_ok=True)
-        db_location = str(db_path)
+        db = SqliteDatabase(config, logger)
 
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-            filename=db_location, table_name="graph_executions"
-        )
+        configuration = config
+        logger = logger
 
-        urls = LocalUrlService()
-        image_record_storage = SqliteImageRecordStorage(db_location)
-        image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-        names = SimpleNameService()
+        board_image_records = SqliteBoardImageRecordStorage(db=db)
+        board_images = BoardImagesService()
+        board_records = SqliteBoardRecordStorage(db=db)
+        boards = BoardService()
+        events = FastAPIEventService(event_handler_id)
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
+        graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
+        image_files = DiskImageFileStorage(f"{output_folder}/images")
+        image_records = SqliteImageRecordStorage(db=db)
+        images = ImageService()
+        invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
-
-        board_record_storage = SqliteBoardRecordStorage(db_location)
-        board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
-
-        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,
-            )
-        )
+        model_manager = ModelManagerService(config, logger)
+        names = SimpleNameService()
+        performance_statistics = InvocationStatsService()
+        processor = DefaultInvocationProcessor()
+        queue = MemoryInvocationQueue()
+        session_processor = DefaultSessionProcessor()
+        session_queue = SqliteSessionQueue(db=db)
+        urls = LocalUrlService()
+        workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
+        workflow_records = SqliteWorkflowRecordsStorage(db=db)
 
         services = InvocationServices(
-            model_manager=ModelManagerService(config, logger),
-            events=events,
-            latents=latents,
-            images=images,
-            boards=boards,
+            board_image_records=board_image_records,
             board_images=board_images,
-            queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
+            board_records=board_records,
+            boards=boards,
+            configuration=configuration,
+            events=events,
             graph_execution_manager=graph_execution_manager,
-            processor=DefaultInvocationProcessor(),
-            configuration=config,
-            performance_statistics=InvocationStatsService(graph_execution_manager),
+            graph_library=graph_library,
+            image_files=image_files,
+            image_records=image_records,
+            images=images,
+            invocation_cache=invocation_cache,
+            latents=latents,
             logger=logger,
+            model_manager=model_manager,
+            names=names,
+            performance_statistics=performance_statistics,
+            processor=processor,
+            queue=queue,
+            session_processor=session_processor,
+            session_queue=session_queue,
+            urls=urls,
+            workflow_image_records=workflow_image_records,
+            workflow_records=workflow_records,
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
 
+        db.clean()
+
     @staticmethod
     def shutdown():
         if ApiDependencies.invoker:

invokeai/app/api/events.py

@@ -7,7 +7,7 @@ from typing import Any
 
 from fastapi_events.dispatcher import dispatch
 
-from ..services.events import EventServiceBase
+from ..services.events.events_base import EventServiceBase
 
 
 class FastAPIEventService(EventServiceBase):

invokeai/app/api/routers/app_info.py

@@ -1,19 +1,20 @@
 import typing
 from enum import Enum
+from pathlib import Path
+
 from fastapi import Body
 from fastapi.routing import APIRouter
-from pathlib import Path
 from pydantic import BaseModel, Field
 
+from invokeai.app.invocations.upscale import ESRGAN_MODELS
+from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
+from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.patchmatch import PatchMatch
 from invokeai.backend.image_util.safety_checker import SafetyChecker
-from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
-from invokeai.app.invocations.upscale import ESRGAN_MODELS
-
+from invokeai.backend.util.logging import logging
 from invokeai.version import __version__
 
 from ..dependencies import ApiDependencies
-from invokeai.backend.util.logging import logging
 
 
 class LogLevel(int, Enum):
@@ -55,7 +56,7 @@ async def get_version() -> AppVersion:
 
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
-    infill_methods = ["tile", "lama"]
+    infill_methods = ["tile", "lama", "cv2"]
     if PatchMatch.patchmatch_available():
         infill_methods.append("patchmatch")
 
@@ -103,3 +104,43 @@ async def set_log_level(
     """Sets the log verbosity level"""
     ApiDependencies.invoker.services.logger.setLevel(level)
     return LogLevel(ApiDependencies.invoker.services.logger.level)
+
+
+@app_router.delete(
+    "/invocation_cache",
+    operation_id="clear_invocation_cache",
+    responses={200: {"description": "The operation was successful"}},
+)
+async def clear_invocation_cache() -> None:
+    """Clears the invocation cache"""
+    ApiDependencies.invoker.services.invocation_cache.clear()
+
+
+@app_router.put(
+    "/invocation_cache/enable",
+    operation_id="enable_invocation_cache",
+    responses={200: {"description": "The operation was successful"}},
+)
+async def enable_invocation_cache() -> None:
+    """Clears the invocation cache"""
+    ApiDependencies.invoker.services.invocation_cache.enable()
+
+
+@app_router.put(
+    "/invocation_cache/disable",
+    operation_id="disable_invocation_cache",
+    responses={200: {"description": "The operation was successful"}},
+)
+async def disable_invocation_cache() -> None:
+    """Clears the invocation cache"""
+    ApiDependencies.invoker.services.invocation_cache.disable()
+
+
+@app_router.get(
+    "/invocation_cache/status",
+    operation_id="get_invocation_cache_status",
+    responses={200: {"model": InvocationCacheStatus}},
+)
+async def get_invocation_cache_status() -> InvocationCacheStatus:
+    """Clears the invocation cache"""
+    return ApiDependencies.invoker.services.invocation_cache.get_status()

invokeai/app/api/routers/boards.py

@@ -4,9 +4,9 @@ from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
-from invokeai.app.services.board_record_storage import BoardChanges
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.board_record import BoardDTO
+from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.boards.boards_common import BoardDTO
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 
 from ..dependencies import ApiDependencies
 

invokeai/app/api/routers/images.py

@@ -1,20 +1,18 @@
 import io
+import traceback
 from typing import Optional
 
-from PIL import Image
 from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
-from pydantic import BaseModel, Field
+from PIL import Image
+from pydantic import BaseModel, Field, ValidationError
+
+from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
+from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 
-from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import (
-    ImageDTO,
-    ImageRecordChanges,
-    ImageUrlsDTO,
-)
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
@@ -45,20 +43,41 @@ async def upload_image(
     crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
 ) -> ImageDTO:
     """Uploads an image"""
-    if not file.content_type.startswith("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,
@@ -66,6 +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,
         )
 
@@ -74,6 +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")
 
 
@@ -90,7 +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"""
 
@@ -102,6 +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",
@@ -138,11 +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:
@@ -325,3 +358,20 @@ async def unstar_images_in_list(
         return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to unstar images")
+
+
+class ImagesDownloaded(BaseModel):
+    response: Optional[str] = Field(
+        description="If defined, the message to display to the user when images begin downloading"
+    )
+
+
+@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
+async def download_images_from_list(
+    image_names: list[str] = Body(description="The list of names of images to download", embed=True),
+    board_id: Optional[str] = Body(
+        default=None, description="The board from which image should be downloaded from", embed=True
+    ),
+) -> ImagesDownloaded:
+    # return ImagesDownloaded(response="Your images are downloading")
+    raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")

invokeai/app/api/routers/models.py

@@ -2,29 +2,35 @@
 
 
 import pathlib
-from typing import Literal, List, Optional, Union
+from typing import Annotated, List, Literal, Optional, Union
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
-from pydantic import BaseModel, parse_obj_as
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
 from starlette.exceptions import HTTPException
 
 from invokeai.backend import BaseModelType, ModelType
+from invokeai.backend.model_management import MergeInterpolationMethod
 from invokeai.backend.model_management.models import (
     OPENAPI_MODEL_CONFIGS,
-    SchedulerPredictionType,
-    ModelNotFoundException,
     InvalidModelException,
+    ModelNotFoundException,
+    SchedulerPredictionType,
 )
-from invokeai.backend.model_management import MergeInterpolationMethod
 
 from ..dependencies import ApiDependencies
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
 
 UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
+
 ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
+
 ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
+
 MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 
@@ -32,6 +38,11 @@ ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 class ModelsList(BaseModel):
     models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
 
+    model_config = ConfigDict(use_enum_values=True)
+
+
+ModelsListValidator = TypeAdapter(ModelsList)
+
 
 @models_router.get(
     "/",
@@ -49,7 +60,7 @@ async def list_models(
             models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
     else:
         models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
-    models = parse_obj_as(ModelsList, {"models": models_raw})
+    models = ModelsListValidator.validate_python({"models": models_raw})
     return models
 
 
@@ -105,11 +116,14 @@ async def update_model(
                 info.path = new_info.get("path")
 
         # replace empty string values with None/null to avoid phenomenon of vae: ''
-        info_dict = info.dict()
+        info_dict = info.model_dump()
         info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
 
         ApiDependencies.invoker.services.model_manager.update_model(
-            model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+            model_attributes=info_dict,
         )
 
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@@ -117,7 +131,7 @@ async def update_model(
             base_model=base_model,
             model_type=model_type,
         )
-        model_response = parse_obj_as(UpdateModelResponse, 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:
@@ -146,18 +160,21 @@ async def update_model(
 async def import_model(
     location: str = Body(description="A model path, repo_id or URL to import"),
     prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
-        description="Prediction type for SDv2 checkpoint files", default="v_prediction"
+        description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
+        default=None,
     ),
 ) -> ImportModelResponse:
     """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
 
+    location = location.strip("\"' ")
     items_to_import = {location}
     prediction_types = {x.value: x for x in SchedulerPredictionType}
     logger = ApiDependencies.invoker.services.logger
 
     try:
         installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
-            items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
+            items_to_import=items_to_import,
+            prediction_type_helper=lambda x: prediction_types.get(prediction_type),
         )
         info = installed_models.get(location)
 
@@ -169,7 +186,7 @@ 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 parse_obj_as(ImportModelResponse, model_raw)
+        return ImportModelResponseValidator.validate_python(model_raw)
 
     except ModelNotFoundException as e:
         logger.error(str(e))
@@ -203,13 +220,18 @@ async def add_model(
 
     try:
         ApiDependencies.invoker.services.model_manager.add_model(
-            info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
+            info.model_name,
+            info.base_model,
+            info.model_type,
+            model_attributes=info.model_dump(),
         )
         logger.info(f"Successfully added {info.model_name}")
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=info.model_name, base_model=info.base_model, model_type=info.model_type
+            model_name=info.model_name,
+            base_model=info.base_model,
+            model_type=info.model_type,
         )
-        return parse_obj_as(ImportModelResponse, model_raw)
+        return ImportModelResponseValidator.validate_python(model_raw)
     except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
@@ -221,7 +243,10 @@ async def add_model(
 @models_router.delete(
     "/{base_model}/{model_type}/{model_name}",
     operation_id="del_model",
-    responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
+    responses={
+        204: {"description": "Model deleted successfully"},
+        404: {"description": "Model not found"},
+    },
     status_code=204,
     response_model=None,
 )
@@ -277,7 +302,7 @@ async def convert_model(
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
             model_name, base_model=base_model, model_type=model_type
         )
-        response = parse_obj_as(ConvertModelResponse, 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:
@@ -300,7 +325,8 @@ async def search_for_models(
 ) -> List[pathlib.Path]:
     if not search_path.is_dir():
         raise HTTPException(
-            status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
+            status_code=404,
+            detail=f"The search path '{search_path}' does not exist or is not directory",
         )
     return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
 
@@ -335,6 +361,26 @@ async def sync_to_config() -> bool:
     return True
 
 
+# There's some weird pydantic-fastapi behaviour that requires this to be a separate class
+# TODO: After a few updates, see if it works inside the route operation handler?
+class MergeModelsBody(BaseModel):
+    model_names: List[str] = Field(description="model name", min_length=2, max_length=3)
+    merged_model_name: Optional[str] = Field(description="Name of destination model")
+    alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5)
+    interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method")
+    force: Optional[bool] = Field(
+        description="Force merging of models created with different versions of diffusers",
+        default=False,
+    )
+
+    merge_dest_directory: Optional[str] = Field(
+        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
+        default=None,
+    )
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
 @models_router.put(
     "/merge/{base_model}",
     operation_id="merge_models",
@@ -347,31 +393,23 @@ async def sync_to_config() -> bool:
     response_model=MergeModelResponse,
 )
 async def merge_models(
+    body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)],
     base_model: BaseModelType = Path(description="Base model"),
-    model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
-    merged_model_name: Optional[str] = Body(description="Name of destination model"),
-    alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
-    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
-    force: Optional[bool] = Body(
-        description="Force merging of models created with different versions of diffusers", default=False
-    ),
-    merge_dest_directory: Optional[str] = Body(
-        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
-        default=None,
-    ),
 ) -> MergeModelResponse:
     """Convert a checkpoint model into a diffusers model"""
     logger = ApiDependencies.invoker.services.logger
     try:
-        logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
-        dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
+        logger.info(
+            f"Merging models: {body.model_names} into {body.merge_dest_directory or '<MODELS>'}/{body.merged_model_name}"
+        )
+        dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None
         result = ApiDependencies.invoker.services.model_manager.merge_models(
-            model_names,
-            base_model,
-            merged_model_name=merged_model_name or "+".join(model_names),
-            alpha=alpha,
-            interp=interp,
-            force=force,
+            model_names=body.model_names,
+            base_model=base_model,
+            merged_model_name=body.merged_model_name or "+".join(body.model_names),
+            alpha=body.alpha,
+            interp=body.interp,
+            force=body.force,
             merge_dest_directory=dest,
         )
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@@ -379,9 +417,12 @@ async def merge_models(
             base_model=base_model,
             model_type=ModelType.Main,
         )
-        response = parse_obj_as(ConvertModelResponse, model_raw)
+        response = ConvertModelResponseValidator.validate_python(model_raw)
     except ModelNotFoundException:
-        raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
+        raise HTTPException(
+            status_code=404,
+            detail=f"One or more of the models '{body.model_names}' not found",
+        )
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     return response

invokeai/app/api/routers/session_queue.py

@@ -0,0 +1,228 @@
+from typing import Optional
+
+from fastapi import Body, Path, Query
+from fastapi.routing import APIRouter
+from pydantic import BaseModel
+
+from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
+from invokeai.app.services.session_queue.session_queue_common import (
+    QUEUE_ITEM_STATUS,
+    Batch,
+    BatchStatus,
+    CancelByBatchIDsResult,
+    ClearResult,
+    EnqueueBatchResult,
+    PruneResult,
+    SessionQueueItem,
+    SessionQueueItemDTO,
+    SessionQueueStatus,
+)
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
+
+from ..dependencies import ApiDependencies
+
+session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])
+
+
+class SessionQueueAndProcessorStatus(BaseModel):
+    """The overall status of session queue and processor"""
+
+    queue: SessionQueueStatus
+    processor: SessionProcessorStatus
+
+
+@session_queue_router.post(
+    "/{queue_id}/enqueue_batch",
+    operation_id="enqueue_batch",
+    responses={
+        201: {"model": EnqueueBatchResult},
+    },
+)
+async def enqueue_batch(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    batch: Batch = Body(description="Batch to process"),
+    prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
+) -> EnqueueBatchResult:
+    """Processes a batch and enqueues the output graphs for execution."""
+
+    return ApiDependencies.invoker.services.session_queue.enqueue_batch(queue_id=queue_id, batch=batch, prepend=prepend)
+
+
+@session_queue_router.get(
+    "/{queue_id}/list",
+    operation_id="list_queue_items",
+    responses={
+        200: {"model": CursorPaginatedResults[SessionQueueItemDTO]},
+    },
+)
+async def list_queue_items(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    limit: int = Query(default=50, description="The number of items to fetch"),
+    status: Optional[QUEUE_ITEM_STATUS] = Query(default=None, description="The status of items to fetch"),
+    cursor: Optional[int] = Query(default=None, description="The pagination cursor"),
+    priority: int = Query(default=0, description="The pagination cursor priority"),
+) -> CursorPaginatedResults[SessionQueueItemDTO]:
+    """Gets all queue items (without graphs)"""
+
+    return ApiDependencies.invoker.services.session_queue.list_queue_items(
+        queue_id=queue_id, limit=limit, status=status, cursor=cursor, priority=priority
+    )
+
+
+@session_queue_router.put(
+    "/{queue_id}/processor/resume",
+    operation_id="resume",
+    responses={200: {"model": SessionProcessorStatus}},
+)
+async def resume(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionProcessorStatus:
+    """Resumes session processor"""
+    return ApiDependencies.invoker.services.session_processor.resume()
+
+
+@session_queue_router.put(
+    "/{queue_id}/processor/pause",
+    operation_id="pause",
+    responses={200: {"model": SessionProcessorStatus}},
+)
+async def Pause(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionProcessorStatus:
+    """Pauses session processor"""
+    return ApiDependencies.invoker.services.session_processor.pause()
+
+
+@session_queue_router.put(
+    "/{queue_id}/cancel_by_batch_ids",
+    operation_id="cancel_by_batch_ids",
+    responses={200: {"model": CancelByBatchIDsResult}},
+)
+async def cancel_by_batch_ids(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    batch_ids: list[str] = Body(description="The list of batch_ids to cancel all queue items for", embed=True),
+) -> CancelByBatchIDsResult:
+    """Immediately cancels all queue items from the given batch ids"""
+    return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
+
+
+@session_queue_router.put(
+    "/{queue_id}/clear",
+    operation_id="clear",
+    responses={
+        200: {"model": ClearResult},
+    },
+)
+async def clear(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> ClearResult:
+    """Clears the queue entirely, immediately canceling the currently-executing session"""
+    queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+    if queue_item is not None:
+        ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
+    clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
+    return clear_result
+
+
+@session_queue_router.put(
+    "/{queue_id}/prune",
+    operation_id="prune",
+    responses={
+        200: {"model": PruneResult},
+    },
+)
+async def prune(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> PruneResult:
+    """Prunes all completed or errored queue items"""
+    return ApiDependencies.invoker.services.session_queue.prune(queue_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/current",
+    operation_id="get_current_queue_item",
+    responses={
+        200: {"model": Optional[SessionQueueItem]},
+    },
+)
+async def get_current_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> Optional[SessionQueueItem]:
+    """Gets the currently execution queue item"""
+    return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/next",
+    operation_id="get_next_queue_item",
+    responses={
+        200: {"model": Optional[SessionQueueItem]},
+    },
+)
+async def get_next_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> Optional[SessionQueueItem]:
+    """Gets the next queue item, without executing it"""
+    return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/status",
+    operation_id="get_queue_status",
+    responses={
+        200: {"model": SessionQueueAndProcessorStatus},
+    },
+)
+async def get_queue_status(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionQueueAndProcessorStatus:
+    """Gets the status of the session queue"""
+    queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
+    processor = ApiDependencies.invoker.services.session_processor.get_status()
+    return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
+
+
+@session_queue_router.get(
+    "/{queue_id}/b/{batch_id}/status",
+    operation_id="get_batch_status",
+    responses={
+        200: {"model": BatchStatus},
+    },
+)
+async def get_batch_status(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    batch_id: str = Path(description="The batch to get the status of"),
+) -> BatchStatus:
+    """Gets the status of the session queue"""
+    return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/i/{item_id}",
+    operation_id="get_queue_item",
+    responses={
+        200: {"model": SessionQueueItem},
+    },
+)
+async def get_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    item_id: int = Path(description="The queue item to get"),
+) -> SessionQueueItem:
+    """Gets a queue item"""
+    return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
+
+
+@session_queue_router.put(
+    "/{queue_id}/i/{item_id}/cancel",
+    operation_id="cancel_queue_item",
+    responses={
+        200: {"model": SessionQueueItem},
+    },
+)
+async def cancel_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    item_id: int = Path(description="The queue item to cancel"),
+) -> SessionQueueItem:
+    """Deletes a queue item"""
+
+    return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)

invokeai/app/api/routers/sessions.py

@@ -1,59 +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
 
-# Importing * is bad karma but needed here for node detection
-from ...invocations import *  # noqa: F401 F403
-from ...invocations.baseinvocation import BaseInvocation
-from ...services.graph import (
-    Edge,
-    EdgeConnection,
-    Graph,
-    GraphExecutionState,
-    NodeAlreadyExecutedError,
-)
-from ...services.item_storage import PaginatedResults
+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"},
-    },
-)
-async def create_session(
-    graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with")
-) -> GraphExecutionState:
-    """Creates a new session, optionally initializing it with an invocation graph"""
-    session = ApiDependencies.invoker.create_execution_state(graph)
-    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]}},
-)
-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(
@@ -75,203 +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"},
-    },
-)
-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"},
-    },
-)
-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"},
-    },
-)
-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"},
-    },
-)
-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"},
-    },
-)
-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"},
-    },
-)
-async def invoke_session(
-    session_id: str = Path(description="The id of the session to invoke"),
-    all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
-) -> 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(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"}},
-)
-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/utilities.py

@@ -0,0 +1,42 @@
+from typing import Optional, Union
+
+from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
+from fastapi import Body
+from fastapi.routing import APIRouter
+from pydantic import BaseModel
+from pyparsing import ParseException
+
+utilities_router = APIRouter(prefix="/v1/utilities", tags=["utilities"])
+
+
+class DynamicPromptsResponse(BaseModel):
+    prompts: list[str]
+    error: Optional[str] = None
+
+
+@utilities_router.post(
+    "/dynamicprompts",
+    operation_id="parse_dynamicprompts",
+    responses={
+        200: {"model": DynamicPromptsResponse},
+    },
+)
+async def parse_dynamicprompts(
+    prompt: str = Body(description="The prompt to parse with dynamicprompts"),
+    max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
+    combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
+) -> DynamicPromptsResponse:
+    """Creates a batch process"""
+    generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
+    try:
+        error: Optional[str] = None
+        if combinatorial:
+            generator = CombinatorialPromptGenerator()
+            prompts = generator.generate(prompt, max_prompts=max_prompts)
+        else:
+            generator = RandomPromptGenerator()
+            prompts = generator.generate(prompt, num_images=max_prompts)
+    except ParseException as e:
+        prompts = [prompt]
+        error = str(e)
+    return DynamicPromptsResponse(prompts=prompts if prompts else [""], error=error)

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

@@ -3,34 +3,35 @@
 from fastapi import FastAPI
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event
-from fastapi_socketio import SocketManager
+from socketio import ASGIApp, AsyncServer
 
-from ..services.events import EventServiceBase
+from ..services.events.events_base import EventServiceBase
 
 
 class SocketIO:
-    __sio: SocketManager
+    __sio: AsyncServer
+    __app: ASGIApp
 
     def __init__(self, app: FastAPI):
-        self.__sio = SocketManager(app=app)
-        self.__sio.on("subscribe", handler=self._handle_sub)
-        self.__sio.on("unsubscribe", handler=self._handle_unsub)
+        self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
+        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
+        app.mount("/ws", self.__app)
 
-        local_handler.register(event_name=EventServiceBase.session_event, _func=self._handle_session_event)
+        self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)
+        self.__sio.on("unsubscribe_queue", handler=self._handle_unsub_queue)
+        local_handler.register(event_name=EventServiceBase.queue_event, _func=self._handle_queue_event)
 
-    async def _handle_session_event(self, event: Event):
+    async def _handle_queue_event(self, event: Event):
         await self.__sio.emit(
             event=event[1]["event"],
             data=event[1]["data"],
-            room=event[1]["data"]["graph_execution_state_id"],
+            room=event[1]["data"]["queue_id"],
         )
 
-    async def _handle_sub(self, sid, data, *args, **kwargs):
-        if "session" in data:
-            self.__sio.enter_room(sid, data["session"])
+    async def _handle_sub_queue(self, sid, data, *args, **kwargs):
+        if "queue_id" in data:
+            await self.__sio.enter_room(sid, data["queue_id"])
 
-        # @app.sio.on('unsubscribe')
-
-    async def _handle_unsub(self, sid, data, *args, **kwargs):
-        if "session" in data:
-            self.__sio.leave_room(sid, data["session"])
+    async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
+        if "queue_id" in data:
+            await self.__sio.enter_room(sid, data["queue_id"])

invokeai/app/api_app.py

@@ -1,47 +1,64 @@
-# Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-import asyncio
-import logging
-import socket
-from inspect import signature
-from pathlib import Path
+from typing import Any
 
-import uvicorn
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
-from fastapi.openapi.utils import get_openapi
-from fastapi.staticfiles import StaticFiles
-from fastapi_events.handlers.local import local_handler
-from fastapi_events.middleware import EventHandlerASGIMiddleware
-from pydantic.schema import schema
+from fastapi.responses import HTMLResponse
 
 from .services.config import InvokeAIAppConfig
-from ..backend.util.logging import InvokeAILogger
 
-from invokeai.version.invokeai_version import __version__
+# parse_args() must be called before any other imports. if it is not called first, consumers of the config
+# which are imported/used before parse_args() is called will get the default config values instead of the
+# values from the command line or config file.
+app_config = InvokeAIAppConfig.get_config()
+app_config.parse_args()
 
-import invokeai.frontend.web as web_dir
-import mimetypes
+if True:  # hack to make flake8 happy with imports coming after setting up the config
+    import asyncio
+    import mimetypes
+    import socket
+    from inspect import signature
+    from pathlib import Path
 
-from .api.dependencies import ApiDependencies
-from .api.routers import sessions, models, images, boards, board_images, app_info
-from .api.sockets import SocketIO
-from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
+    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
+    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
 
-import torch
-
-# noinspection PyUnresolvedReferences
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-
-if torch.backends.mps.is_available():
+    # for PyCharm:
     # noinspection PyUnresolvedReferences
-    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+    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,
+        sessions,
+        utilities,
+        workflows,
+    )
+    from .api.sockets import SocketIO
+    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
+
+    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.getLogger(config=app_config)
-
+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")
@@ -49,7 +66,7 @@ mimetypes.add_type("text/css", ".css")
 
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
+app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
 
 # Add event handler
 event_handler_id: int = id(app)
@@ -61,49 +78,45 @@ app.add_middleware(
 
 socket_io = SocketIO(app)
 
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=app_config.allow_origins,
+    allow_credentials=app_config.allow_credentials,
+    allow_methods=app_config.allow_methods,
+    allow_headers=app_config.allow_headers,
+)
+
+app.add_middleware(GZipMiddleware, minimum_size=1000)
+
 
 # Add startup event to load dependencies
 @app.on_event("startup")
-async def startup_event():
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=app_config.allow_origins,
-        allow_credentials=app_config.allow_credentials,
-        allow_methods=app_config.allow_methods,
-        allow_headers=app_config.allow_headers,
-    )
-
+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
-# TODO: REMOVE
-# app.include_router(
-#     invocation.invocation_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(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(
@@ -111,6 +124,7 @@ def custom_openapi():
         description="An API for invoking AI image operations",
         version="1.0.0",
         routes=app.routes,
+        separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
     )
 
     # Add all outputs
@@ -121,29 +135,32 @@ def custom_openapi():
         output_type = signature(invoker.invoke).return_annotation
         output_types.add(output_type)
 
-    output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
-    for schema_key, output_schema in output_schemas["definitions"].items():
-        output_schema["class"] = "output"
-        openapi_schema["components"]["schemas"][schema_key] = output_schema
-
+    output_schemas = models_json_schema(
+        models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
+    )
+    for schema_key, output_schema in output_schemas[1]["$defs"].items():
         # TODO: note that we assume the schema_key here is the TYPE.__name__
         # This could break in some cases, figure out a better way to do it
         output_type_titles[schema_key] = output_schema["title"]
 
     # Add Node Editor UI helper schemas
-    ui_config_schemas = schema([UIConfigBase, _InputField, _OutputField], ref_prefix="#/components/schemas/")
-    for schema_key, ui_config_schema in ui_config_schemas["definitions"].items():
+    ui_config_schemas = models_json_schema(
+        [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
+        ref_template="#/components/schemas/{model}",
+    )
+    for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
         openapi_schema["components"]["schemas"][schema_key] = ui_config_schema
 
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
         invoker_name = invoker.__name__
-        output_type = signature(invoker.invoke).return_annotation
+        output_type = signature(obj=invoker.invoke).return_annotation
         output_type_title = output_type_titles[output_type.__name__]
-        invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
+        invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
         outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
         invoker_schema["output"] = outputs_ref
         invoker_schema["class"] = "invocation"
+        openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output"
 
     from invokeai.backend.model_management.models import get_model_config_enums
 
@@ -154,7 +171,6 @@ 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.",
@@ -166,36 +182,45 @@ def custom_openapi():
     return app.openapi_schema
 
 
-app.openapi = custom_openapi
-
-# Override API doc favicons
-app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
+app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
 
 
 @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
@@ -218,7 +243,7 @@ def invoke_api():
                 exc_info=e,
             )
         else:
-            jurigged.watch(logger=InvokeAILogger.getLogger(name="jurigged").info)
+            jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
 
     port = find_port(app_config.port)
     if port != app_config.port:
@@ -230,14 +255,14 @@ 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)
 
     # replace uvicorn's loggers with InvokeAI's for consistent appearance
     for logname in ["uvicorn.access", "uvicorn"]:
-        log = logging.getLogger(logname)
+        log = InvokeAILogger.get_logger(logname)
         log.handlers.clear()
         for ch in logger.handlers:
             log.addHandler(ch)

invokeai/app/cli/commands.py

@@ -1,311 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC, abstractmethod
-import argparse
-from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
-from pydantic import BaseModel, Field
-import networkx as nx
-import matplotlib.pyplot as plt
-
-import invokeai.backend.util.logging as logger
-from ..invocations.baseinvocation import BaseInvocation
-from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, Edge
-from ..services.invoker import Invoker
-
-
-def add_field_argument(command_parser, name: str, field, default_override=None):
-    default = (
-        default_override
-        if default_override is not None
-        else field.default
-        if field.default_factory is None
-        else field.default_factory()
-    )
-    if get_origin(field.type_) == Literal:
-        allowed_values = get_args(field.type_)
-        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.field_info.description,
-        )
-    else:
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field.type_,
-            default=default,
-            help=field.field_info.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:
-    # type: Literal['your_command_name'] = 'your_command_name'
-
-    @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 List, Dict, Literal, get_args, get_type_hints, get_origin
-
-import invokeai.backend.util.logging as logger
-from ...backend import ModelManager
-from ..invocations.baseinvocation import BaseInvocation
-from .commands import BaseCommand
-from ..services.invocation_services import InvocationServices
-
-# singleton object, class variable
-completer = None
-
-
-class Completer(object):
-    def __init__(self, model_manager: ModelManager):
-        self.commands = self.get_commands()
-        self.matches = None
-        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

@@ -1,485 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import argparse
-import re
-import shlex
-import sys
-import time
-from typing import Union, get_type_hints, Optional
-
-from pydantic import BaseModel, ValidationError
-from pydantic.fields import Field
-
-# This should come early so that the logger can pick up its configuration options
-from .services.config import InvokeAIAppConfig
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.version.invokeai_version import __version__
-
-
-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.resource_name import SimpleNameService
-from invokeai.app.services.urls import LocalUrlService
-from invokeai.app.services.invocation_stats import InvocationStatsService
-from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
-from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-
-from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
-from .cli.completer import set_autocompleter
-from .invocations.baseinvocation import BaseInvocation
-from .services.events import EventServiceBase
-from .services.graph import (
-    Edge,
-    EdgeConnection,
-    GraphExecutionState,
-    GraphInvocation,
-    LibraryGraph,
-    are_connection_types_compatible,
-)
-from .services.image_file_storage import DiskImageFileStorage
-from .services.invocation_queue import MemoryInvocationQueue
-from .services.invocation_services import InvocationServices
-from .services.invoker import Invoker
-from .services.model_manager_service import ModelManagerService
-from .services.processor import DefaultInvocationProcessor
-from .services.sqlite import SqliteItemStorage
-
-import torch
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-
-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().getLogger(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)
-
-    logger.info(f'InvokeAI database location is "{db_location}"')
-
-    graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-        filename=db_location, table_name="graph_executions"
-    )
-
-    urls = LocalUrlService()
-    image_record_storage = SqliteImageRecordStorage(db_location)
-    image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-    names = SimpleNameService()
-
-    board_record_storage = SqliteBoardRecordStorage(db_location)
-    board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
-
-    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](filename=db_location, table_name="graphs"),
-        graph_execution_manager=graph_execution_manager,
-        processor=DefaultInvocationProcessor(),
-        performance_statistics=InvocationStatsService(graph_execution_manager),
-        logger=logger,
-        configuration=config,
-    )
-
-    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.absolute())
+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__ = list(f.stem for f in python_files)  # type: ignore

invokeai/app/invocations/collections.py

@@ -2,7 +2,7 @@
 
 
 import numpy as np
-from pydantic import validator
+from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
@@ -10,7 +10,9 @@ from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
 
-@invocation("range", title="Integer Range", tags=["collection", "integer", "range"], category="collections")
+@invocation(
+    "range", title="Integer Range", tags=["collection", "integer", "range"], category="collections", version="1.0.0"
+)
 class RangeInvocation(BaseInvocation):
     """Creates a range of numbers from start to stop with step"""
 
@@ -18,9 +20,9 @@ class RangeInvocation(BaseInvocation):
     stop: int = InputField(default=10, description="The stop of the range")
     step: int = InputField(default=1, description="The step of the range")
 
-    @validator("stop")
-    def stop_gt_start(cls, v, values):
-        if "start" in values and v <= values["start"]:
+    @field_validator("stop")
+    def stop_gt_start(cls, v: int, info: ValidationInfo):
+        if "start" in info.data and v <= info.data["start"]:
             raise ValueError("stop must be greater than start")
         return v
 
@@ -33,16 +35,19 @@ class RangeInvocation(BaseInvocation):
     title="Integer Range of Size",
     tags=["collection", "integer", "size", "range"],
     category="collections",
+    version="1.0.0",
 )
 class RangeOfSizeInvocation(BaseInvocation):
-    """Creates a range from start to start + size with step"""
+    """Creates a range from start to start + (size * step) incremented by step"""
 
     start: int = InputField(default=0, description="The start of the range")
-    size: int = InputField(default=1, description="The number of values")
+    size: int = InputField(default=1, gt=0, description="The number of values")
     step: int = InputField(default=1, description="The step of the range")
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
-        return IntegerCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
+        return IntegerCollectionOutput(
+            collection=list(range(self.start, self.start + (self.step * self.size), self.step))
+        )
 
 
 @invocation(
@@ -50,6 +55,8 @@ class RangeOfSizeInvocation(BaseInvocation):
     title="Random Range",
     tags=["range", "integer", "random", "collection"],
     category="collections",
+    version="1.0.0",
+    use_cache=False,
 )
 class RandomRangeInvocation(BaseInvocation):
     """Creates a collection of random numbers"""

invokeai/app/invocations/compel.py

@@ -1,21 +1,20 @@
 import re
 from dataclasses import dataclass
-from typing import List, Union
+from typing import List, Optional, Union
 
 import torch
 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.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
+from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
+    ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
 
-from ...backend.model_management.models import ModelType
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.models import ModelNotFoundException
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
+from ...backend.model_management.models import ModelNotFoundException, ModelType
 from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
@@ -44,7 +43,13 @@ class ConditioningFieldData:
 #    PerpNeg = "perp_neg"
 
 
-@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning")
+@invocation(
+    "compel",
+    title="Prompt",
+    tags=["prompt", "compel"],
+    category="conditioning",
+    version="1.0.0",
+)
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
 
@@ -62,17 +67,19 @@ class CompelInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.dict(),
+            **self.clip.tokenizer.model_dump(),
             context=context,
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.dict(),
+            **self.clip.text_encoder.model_dump(),
             context=context,
         )
 
         def _lora_loader():
             for lora in self.clip.loras:
-                lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
+                lora_info = context.services.model_manager.get_model(
+                    **lora.model_dump(exclude={"weight"}), context=context
+                )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
@@ -100,14 +107,15 @@ class CompelInvocation(BaseInvocation):
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with ModelPatcher.apply_lora_text_encoder(
-            text_encoder_info.context.model, _lora_loader()
-        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
-            tokenizer,
-            ti_manager,
-        ), ModelPatcher.apply_clip_skip(
-            text_encoder_info.context.model, self.clip.skipped_layers
-        ), text_encoder_info as text_encoder:
+        with (
+            ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
+            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+                tokenizer,
+                ti_manager,
+            ),
+            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
+            text_encoder_info as text_encoder,
+        ):
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -123,7 +131,7 @@ class CompelInvocation(BaseInvocation):
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
-            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            ec = ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -160,11 +168,11 @@ class SDXLPromptInvocationBase:
         zero_on_empty: bool,
     ):
         tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.dict(),
+            **clip_field.tokenizer.model_dump(),
             context=context,
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.dict(),
+            **clip_field.text_encoder.model_dump(),
             context=context,
         )
 
@@ -172,7 +180,11 @@ class SDXLPromptInvocationBase:
         if prompt == "" and zero_on_empty:
             cpu_text_encoder = text_encoder_info.context.model
             c = torch.zeros(
-                (1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
+                (
+                    1,
+                    cpu_text_encoder.config.max_position_embeddings,
+                    cpu_text_encoder.config.hidden_size,
+                ),
                 dtype=text_encoder_info.context.cache.precision,
             )
             if get_pooled:
@@ -186,7 +198,9 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader():
             for lora in clip_field.loras:
-                lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
+                lora_info = context.services.model_manager.get_model(
+                    **lora.model_dump(exclude={"weight"}), context=context
+                )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
@@ -214,14 +228,15 @@ class SDXLPromptInvocationBase:
                 # print(traceback.format_exc())
                 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:
+        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,
+        ):
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -245,7 +260,7 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
-            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            ec = ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -267,20 +282,29 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
+    version="1.0.0",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
 
-    prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
-    style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
+    prompt: str = InputField(
+        default="",
+        description=FieldDescriptions.compel_prompt,
+        ui_component=UIComponent.Textarea,
+    )
+    style: str = InputField(
+        default="",
+        description=FieldDescriptions.compel_prompt,
+        ui_component=UIComponent.Textarea,
+    )
     original_width: int = InputField(default=1024, description="")
     original_height: int = InputField(default=1024, description="")
     crop_top: int = InputField(default=0, description="")
     crop_left: int = InputField(default=0, description="")
     target_width: int = InputField(default=1024, description="")
     target_height: int = InputField(default=1024, description="")
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
-    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
+    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
+    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -308,7 +332,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
                 [
                     c1,
                     torch.zeros(
-                        (c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]), device=c1.device, dtype=c1.dtype
+                        (c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]),
+                        device=c1.device,
+                        dtype=c1.dtype,
                     ),
                 ],
                 dim=1,
@@ -319,7 +345,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
                 [
                     c2,
                     torch.zeros(
-                        (c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]), device=c2.device, dtype=c2.dtype
+                        (c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]),
+                        device=c2.device,
+                        dtype=c2.dtype,
                     ),
                 ],
                 dim=1,
@@ -351,12 +379,15 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     title="SDXL Refiner Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
+    version="1.0.0",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
 
     style: str = InputField(
-        default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea
+        default="",
+        description=FieldDescriptions.compel_prompt,
+        ui_component=UIComponent.Textarea,
     )  # TODO: ?
     original_width: int = InputField(default=1024, description="")
     original_height: int = InputField(default=1024, description="")
@@ -400,10 +431,16 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 class ClipSkipInvocationOutput(BaseInvocationOutput):
     """Clip skip node output"""
 
-    clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning")
+@invocation(
+    "clip_skip",
+    title="CLIP Skip",
+    tags=["clipskip", "clip", "skip"],
+    category="conditioning",
+    version="1.0.0",
+)
 class ClipSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""
 
@@ -418,7 +455,9 @@ class ClipSkipInvocation(BaseInvocation):
 
 
 def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
+    tokenizer,
+    prompt: Union[FlattenedPrompt, Blend, Conjunction],
+    truncate_if_too_long=False,
 ) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
@@ -435,9 +474,11 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
         raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
 
     text_fragments = [
-        x.text
-        if type(x) is Fragment
-        else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
+        (
+            x.text
+            if type(x) is Fragment
+            else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
+        )
         for x in parsed_prompt.children
     ]
     text = " ".join(text_fragments)

invokeai/app/invocations/controlnet_image_processors.py

@@ -2,7 +2,7 @@
 # initial implementation by Gregg Helt, 2023
 # heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
 from builtins import bool, float
-from typing import Dict, List, Literal, Optional, Union
+from typing import Dict, List, Literal, Union
 
 import cv2
 import numpy as np
@@ -24,27 +24,26 @@ from controlnet_aux import (
 )
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
-from pydantic import BaseModel, Field, validator
+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 ...backend.model_management import BaseModelType
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
-    UIType,
+    WithMetadata,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
 
-
 CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
 CONTROLNET_RESIZE_VALUES = Literal[
     "just_resize",
@@ -60,6 +59,8 @@ class ControlNetModelField(BaseModel):
     model_name: str = Field(description="Name of the ControlNet model")
     base_model: BaseModelType = Field(description="Base model")
 
+    model_config = ConfigDict(protected_namespaces=())
+
 
 class ControlField(BaseModel):
     image: ImageField = Field(description="The control image")
@@ -74,7 +75,7 @@ class ControlField(BaseModel):
     control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
     resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
-    @validator("control_weight")
+    @field_validator("control_weight")
     def validate_control_weight(cls, v):
         """Validate that all control weights in the valid range"""
         if isinstance(v, list):
@@ -95,16 +96,14 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
-    control_model: ControlNetModelField = InputField(
-        default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
-    )
+    control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
-        default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
+        default=1.0, description="The weight given to the ControlNet"
     )
     begin_step_percent: float = InputField(
         default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
@@ -129,13 +128,13 @@ class ControlNetInvocation(BaseInvocation):
         )
 
 
-@invocation("image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet")
-class ImageProcessorInvocation(BaseInvocation):
+# This invocation exists for other invocations to subclass it - do not register with @invocation!
+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
 
@@ -153,6 +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,
         )
 
@@ -173,6 +173,7 @@ class ImageProcessorInvocation(BaseInvocation):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
+    version="1.0.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -195,6 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
+    version="1.0.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -223,6 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
+    version="1.0.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -244,6 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
+    version="1.0.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -266,6 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Openpose Processor",
     tags=["controlnet", "openpose", "pose"],
     category="controlnet",
+    version="1.0.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Openpose processing to image"""
@@ -290,6 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
+    version="1.0.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -316,6 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
+    version="1.0.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -331,7 +338,9 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
         return processed_image
 
 
-@invocation("mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet")
+@invocation(
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
+)
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
 
@@ -352,7 +361,9 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
         return processed_image
 
 
-@invocation("pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet")
+@invocation(
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
+)
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
 
@@ -378,15 +389,16 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
+    version="1.0.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
 
     detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
     image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
-    h: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
-    w: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
-    f: Optional[int] = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
+    h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
+    w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
+    f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
 
     def run_processor(self, image):
         content_shuffle_processor = ContentShuffleDetector()
@@ -407,6 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
+    version="1.0.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -422,6 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
+    version="1.0.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -444,6 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
+    version="1.0.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -472,6 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
+    version="1.0.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -511,6 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
+    version="1.0.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -545,3 +562,33 @@ class SamDetectorReproducibleColors(SamDetector):
             img[:, :] = ann_color
             final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
         return np.array(final_img, dtype=np.uint8)
+
+
+@invocation(
+    "color_map_image_processor",
+    title="Color Map Processor",
+    tags=["controlnet"],
+    category="controlnet",
+    version="1.0.0",
+)
+class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates a color map from the provided image"""
+
+    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
+
+    def run_processor(self, image: Image.Image):
+        image = image.convert("RGB")
+        np_image = np.array(image, dtype=np.uint8)
+        height, width = np_image.shape[:2]
+
+        width_tile_size = min(self.color_map_tile_size, width)
+        height_tile_size = min(self.color_map_tile_size, height)
+
+        color_map = cv2.resize(
+            np_image,
+            (width // width_tile_size, height // height_tile_size),
+            interpolation=cv2.INTER_CUBIC,
+        )
+        color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
+        color_map = Image.fromarray(color_map)
+        return color_map