Merge remote-tracking branch 'origin/main' into feat/taesd

.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

.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
+  Tutorials</a>]
-  href="https://github.com/invoke-ai/InvokeAI/">Code and
+  [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
-  Downloads</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>] 
+   [<a
+  href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>] 
 
 <div align="center">
 
@@ -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.
+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'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
 
 If you are unfamiliar with how
-to contribute to GitHub projects, here is a
+to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing: 
-[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**.
+[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.
 

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
+## Nodes
-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.
+If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
 
-#### Nodes
+## Support and Triaging
-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.
+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/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)
 * #### [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: 
 
@@ -23,60 +30,10 @@ 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.
@@ -85,6 +42,7 @@ For frontend related work, **@pyschedelicious** is the best person to reach out
 
 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.

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/features/CONCEPTS.md

@@ -21,8 +21,8 @@ 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
+[Hugging Face](https://huggingface.co/sd-concepts-library) has
-amassed a large ligrary of >800 community-contributed TI files covering a
+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
 

docs/features/CONTROLNET.md

@@ -104,7 +104,7 @@ 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:
 
@@ -117,8 +117,6 @@ 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.
 
 

docs/features/MODEL_MERGING.md

@@ -2,17 +2,50 @@
 title: Model Merging
 ---
 
-# :material-image-off: Model Merging
+InvokeAI provides the ability to merge two or three diffusers-type models into a new merged model. The
-
-## 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
 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
+  `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.
+
+* 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 +73,4 @@ 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 "Generate images with a browser-based interface"
-1: Browser-based UI
+2 "Explore InvokeAI nodes using a command-line interface"
-2: Command-line interface
+3 "Textual inversion training"
-3: Run textual inversion training
+4 "Merge models (diffusers type only)"
-4: Merge models (diffusers type only)
+5 "Download and install models"
-5: Download and install models
+6 "Change InvokeAI startup options"
-6: Change InvokeAI startup options
+7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
-7: Re-run the configure script to fix a broken install
+8 "Open the developer console"
-8: Open the developer console
+9 "Update InvokeAI"
-9: Update InvokeAI
-10: Command-line help
-Q: Quit
-
-Please enter 1-10, Q: [1] 
 ```
 
-From the command line, with the InvokeAI virtual environment active,
+Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
-you can launch the front end with the command `invokeai-ti --gui`.
+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/installation/020_INSTALL_MANUAL.md

@@ -287,7 +287,7 @@ 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
@@ -296,13 +296,14 @@ 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. Create a fork of the InvokeAI repository through the GitHub UI or [this link](https://github.com/invoke-ai/InvokeAI/fork) 
 1. 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
 installation protocol (important!)

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
+  This is a script that will install all of InvokeAI's essential
-#### [Docker Installation](040_INSTALL_DOCKER.md)
+  third party libraries and InvokeAI itself. It includes access to a
-This method is recommended for those familiar with running Docker containers
+  "developer console" which will help us debug problems with you and
-### Other Installation Guides
+  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/nodes/NODES.md

@@ -1,11 +1,30 @@
-# 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.
 
+## UI 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 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 
 
-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 
 
@@ -37,7 +56,7 @@ 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 +78,9 @@ 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. 
+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.
-
-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.
 
 ![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)
+

docs/nodes/communityNodes.md

@@ -4,9 +4,9 @@ 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
 

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 
+- 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 in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
+- 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.
+    - 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 might be asked for permission to include it in the core project.
+- 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

@@ -22,6 +22,7 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Divide Integers 			| Divides two numbers|
 |Dynamic Prompt 			| Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
 |Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
+|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|
@@ -29,6 +30,7 @@ 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.|
@@ -42,6 +44,8 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Paste Image 			| Pastes an image into another image.|
 |ImageProcessor 			| Base class for invocations that preprocess images for ControlNet|
 |Resize Image 			| Resizes an image to specific dimensions|
+|Round Float            | Rounds a float to a specified number of decimal places|
+|Float to Integer       | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
 |Scale Image 			| Scales an image by a factor|
 |Image to Latents 			| Encodes an image into latents.|
 |Add Invisible Watermark 			| Add an invisible watermark to an image|

docs/nodes/exampleWorkflows.md

@@ -1,15 +1,13 @@
 # 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
+* [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)ß
 
-## Upscaling
-
-## Inpainting / Outpainting
-
-## LoRAs

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",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Negative Prompt",
+            "value": ""
+          },
+          "style": {
+            "id": "f240d0e6-3a1c-4320-af23-20ebb707c276",
+            "name": "style",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Negative Style",
+            "value": ""
+          },
+          "original_width": {
+            "id": "05af07b0-99a0-4a68-8ad2-697bbdb7fc7e",
+            "name": "original_width",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "original_height": {
+            "id": "2c771996-a998-43b7-9dd3-3792664d4e5b",
+            "name": "original_height",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "crop_top": {
+            "id": "66519dca-a151-4e3e-ae1f-88f1f9877bde",
+            "name": "crop_top",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "crop_left": {
+            "id": "349cf2e9-f3d0-4e16-9ae2-7097d25b6a51",
+            "name": "crop_left",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "target_width": {
+            "id": "44499347-7bd6-4a73-99d6-5a982786db05",
+            "name": "target_width",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "target_height": {
+            "id": "fda359b0-ab80-4f3c-805b-c9f61319d7d2",
+            "name": "target_height",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "clip": {
+            "id": "b447adaf-a649-4a76-a827-046a9fc8d89b",
+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "clip2": {
+            "id": "86ee4e32-08f9-4baa-9163-31d93f5c0187",
+            "name": "clip2",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "conditioning": {
+            "id": "7c10118e-7b4e-4911-b98e-d3ba6347dfd0",
+            "name": "conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "SDXL Negative Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 764,
+      "position": {
+        "x": 1275,
+        "y": -350
+      }
+    },
+    {
+      "id": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "55705012-79b9-4aac-9f26-c0b10309785b",
+        "type": "noise",
+        "inputs": {
+          "seed": {
+            "id": "6431737c-918a-425d-a3b4-5d57e2f35d4d",
+            "name": "seed",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "width": {
+            "id": "38fc5b66-fe6e-47c8-bba9-daf58e454ed7",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "height": {
+            "id": "16298330-e2bf-4872-a514-d6923df53cbb",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "use_cpu": {
+            "id": "c7c436d3-7a7a-4e76-91e4-c6deb271623c",
+            "name": "use_cpu",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": true
+          }
+        },
+        "outputs": {
+          "noise": {
+            "id": "50f650dc-0184-4e23-a927-0497a96fe954",
+            "name": "noise",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "bb8a452b-133d-42d1-ae4a-3843d7e4109a",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "35cfaa12-3b8b-4b7a-a884-327ff3abddd9",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": false,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 32,
+      "position": {
+        "x": 1650,
+        "y": -300
+      }
+    },
+    {
+      "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+        "type": "l2i",
+        "inputs": {
+          "tiled": {
+            "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
+            "name": "tiled",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "fp32": {
+            "id": "b146d873-ffb9-4767-986a-5360504841a2",
+            "name": "fp32",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": true
+          },
+          "latents": {
+            "id": "65441abd-7713-4b00-9d8d-3771404002e8",
+            "name": "latents",
+            "type": "LatentsField",
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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",
+  "version": "1.0.1",
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+  "tags": "text2image, SD1.5, SD2, default",
+  "notes": "",
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+      "fieldName": "steps"
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+            "name": "use_cpu",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": true
+          }
+        },
+        "outputs": {
+          "noise": {
+            "id": "50f650dc-0184-4e23-a927-0497a96fe954",
+            "name": "noise",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "bb8a452b-133d-42d1-ae4a-3843d7e4109a",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "35cfaa12-3b8b-4b7a-a884-327ff3abddd9",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 364,
+      "position": {
+        "x": 1000,
+        "y": 350
+      }
+    },
+    {
+      "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+        "type": "l2i",
+        "inputs": {
+          "tiled": {
+            "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
+            "name": "tiled",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "fp32": {
+            "id": "b146d873-ffb9-4767-986a-5360504841a2",
+            "name": "fp32",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "latents": {
+            "id": "65441abd-7713-4b00-9d8d-3771404002e8",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "vae": {
+            "id": "a478b833-6e13-4611-9a10-842c89603c74",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "c87ae925-f858-417a-8940-8708ba9b4b53",
+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "23e41c00-a354-48e8-8f59-5875679c27ab",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": true,
+        "isIntermediate": false
+      },
+      "width": 320,
+      "height": 266,
+      "position": {
+        "x": 1800,
+        "y": 200
+      }
+    },
+    {
+      "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+        "type": "main_model_loader",
+        "inputs": {
+          "model": {
+            "id": "993eabd2-40fd-44fe-bce7-5d0c7075ddab",
+            "name": "model",
+            "type": "MainModelField",
+            "fieldKind": "input",
+            "label": "",
+            "value": {
+              "model_name": "stable-diffusion-v1-5",
+              "base_model": "sd-1",
+              "model_type": "main"
+            }
+          }
+        },
+        "outputs": {
+          "unet": {
+            "id": "5c18c9db-328d-46d0-8cb9-143391c410be",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "output"
+          },
+          "clip": {
+            "id": "6effcac0-ec2f-4bf5-a49e-a2c29cf921f4",
+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "output"
+          },
+          "vae": {
+            "id": "57683ba3-f5f5-4f58-b9a2-4b83dacad4a1",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": false,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 32,
+      "position": {
+        "x": 1000,
+        "y": 200
+      }
+    },
+    {
+      "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+        "type": "compel",
+        "inputs": {
+          "prompt": {
+            "id": "7739aff6-26cb-4016-8897-5a1fb2305e4e",
+            "name": "prompt",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Positive Prompt",
+            "value": ""
+          },
+          "clip": {
+            "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0",
+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "conditioning": {
+            "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
+            "name": "conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "Positive Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 235,
+      "position": {
+        "x": 1000,
+        "y": -75
+      }
+    },
+    {
+      "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+        "type": "rand_int",
+        "inputs": {
+          "low": {
+            "id": "3ec65a37-60ba-4b6c-a0b2-553dd7a84b84",
+            "name": "low",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "high": {
+            "id": "085f853a-1a5f-494d-8bec-e4ba29a3f2d1",
+            "name": "high",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 2147483647
+          }
+        },
+        "outputs": {
+          "value": {
+            "id": "812ade4d-7699-4261-b9fc-a6c9d2ab55ee",
+            "name": "value",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "Random Seed",
+        "isOpen": false,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 32,
+      "position": {
+        "x": 1000,
+        "y": 275
+      }
+    },
+    {
+      "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+        "type": "denoise_latents",
+        "inputs": {
+          "noise": {
+            "id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
+            "name": "noise",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "steps": {
+            "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
+            "name": "steps",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 36
+          },
+          "cfg_scale": {
+            "id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
+            "name": "cfg_scale",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 7.5
+          },
+          "denoising_start": {
+            "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
+            "name": "denoising_start",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "denoising_end": {
+            "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
+            "name": "denoising_end",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1
+          },
+          "scheduler": {
+            "id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
+            "name": "scheduler",
+            "type": "Scheduler",
+            "fieldKind": "input",
+            "label": "",
+            "value": "euler"
+          },
+          "control": {
+            "id": "7abe57cc-469d-437e-ad72-a18efa28215f",
+            "name": "control",
+            "type": "ControlField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "latents": {
+            "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "denoise_mask": {
+            "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
+            "name": "denoise_mask",
+            "type": "DenoiseMaskField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "positive_conditioning": {
+            "id": "c7160303-8a23-4f15-9197-855d48802a7f",
+            "name": "positive_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "negative_conditioning": {
+            "id": "fd750efa-1dfc-4d0b-accb-828e905ba320",
+            "name": "negative_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "unet": {
+            "id": "af1f41ba-ce2a-4314-8d7f-494bb5800381",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "latents": {
+            "id": "8508d04d-f999-4a44-94d0-388ab1401d27",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 558,
+      "position": {
+        "x": 1400,
+        "y": 200
+      }
+    }
+  ],
+  "edges": [
+    {
+      "source": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+      "sourceHandle": "value",
+      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "targetHandle": "seed",
+      "id": "reactflow__edge-ea94bc37-d995-4a83-aa99-4af42479f2f2value-55705012-79b9-4aac-9f26-c0b10309785bseed",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "clip",
+      "target": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-7d8bf987-284f-413a-b2fd-d825445a5d6cclip",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "clip",
+      "target": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "vae",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "vae",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
+      "type": "default"
+    },
+    {
+      "source": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "sourceHandle": "latents",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-75899702-fa44-46d2-b2d5-3e17f234c3e7latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
+      "type": "default"
+    },
+    {
+      "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "sourceHandle": "conditioning",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "sourceHandle": "conditioning",
+      "target": "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/lib/main.py

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

invokeai/app/api/dependencies.py

@@ -1,13 +1,9 @@
 # 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_image_record_storage import SqliteBoardImageRecordStorage
-)
+from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
-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
@@ -19,16 +15,16 @@ 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.invocation_services import InvocationServices
+from ..services.invocation_stats import InvocationStatsService
 from ..services.invoker import Invoker
+from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
+from ..services.model_manager_service import ModelManagerService
 from ..services.processor import DefaultInvocationProcessor
 from ..services.sqlite import SqliteItemStorage
-from ..services.model_manager_service import ModelManagerService
-from ..services.invocation_stats import InvocationStatsService
 from .events import FastAPIEventService
 
 

invokeai/app/api/routers/images.py

@@ -1,20 +1,17 @@
 import io
 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 PIL import Image
 from pydantic import BaseModel, Field
 
 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 (
+from invokeai.app.services.models.image_record import ImageDTO, ImageRecordChanges, ImageUrlsDTO
-    ImageDTO,
+
-    ImageRecordChanges,
-    ImageUrlsDTO,
-)
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])

invokeai/app/api/routers/models.py

@@ -2,7 +2,7 @@
 
 
 import pathlib
-from typing import Literal, List, Optional, Union
+from typing import List, Literal, Optional, Union
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -10,13 +10,13 @@ from pydantic import BaseModel, parse_obj_as
 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
 

invokeai/app/api/routers/sessions.py

@@ -9,13 +9,7 @@ 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 (
+from ...services.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
-    Edge,
-    EdgeConnection,
-    Graph,
-    GraphExecutionState,
-    NodeAlreadyExecutedError,
-)
 from ...services.item_storage import PaginatedResults
 from ..dependencies import ApiDependencies
 

invokeai/app/cli/commands.py

@@ -1,16 +1,18 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from abc import ABC, abstractmethod
 import argparse
+from abc import ABC, abstractmethod
 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 networkx as nx
+from pydantic import BaseModel, Field
 
 import invokeai.backend.util.logging as logger
+
 from ..invocations.baseinvocation import BaseInvocation
 from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, Edge
+from ..services.graph import Edge, GraphExecutionState, LibraryGraph
 from ..services.invoker import Invoker
 
 

invokeai/app/cli/completer.py

@@ -6,15 +6,15 @@ 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
+from typing import Dict, List, Literal, get_args, get_origin, get_type_hints
 
 import invokeai.backend.util.logging as logger
+
 from ...backend import ModelManager
 from ..invocations.baseinvocation import BaseInvocation
-from .commands import BaseCommand
 from ..services.invocation_services import InvocationServices
+from .commands import BaseCommand
 
 # singleton object, class variable
 completer = None

invokeai/app/invocations/baseinvocation.py

@@ -3,10 +3,10 @@
 from __future__ import annotations
 
 import json
+import re
 from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import signature
-import re
 from typing import (
     TYPE_CHECKING,
     AbstractSet,
@@ -23,10 +23,10 @@ from typing import (
     get_type_hints,
 )
 
-from pydantic import BaseModel, Field, validator
-from pydantic.fields import Undefined, ModelField
-from pydantic.typing import NoArgAnyCallable
 import semver
+from pydantic import BaseModel, Field, validator
+from pydantic.fields import ModelField, Undefined
+from pydantic.typing import NoArgAnyCallable
 
 from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig
 
@@ -198,6 +198,7 @@ class _InputField(BaseModel):
     ui_type: Optional[UIType]
     ui_component: Optional[UIComponent]
     ui_order: Optional[int]
+    ui_choice_labels: Optional[dict[str, str]]
     item_default: Optional[Any]
 
 
@@ -246,6 +247,7 @@ def InputField(
     ui_component: Optional[UIComponent] = None,
     ui_hidden: bool = False,
     ui_order: Optional[int] = None,
+    ui_choice_labels: Optional[dict[str, str]] = None,
     item_default: Optional[Any] = None,
     **kwargs: Any,
 ) -> Any:
@@ -312,6 +314,7 @@ def InputField(
         ui_hidden=ui_hidden,
         ui_order=ui_order,
         item_default=item_default,
+        ui_choice_labels=ui_choice_labels,
         **kwargs,
     )
 

invokeai/app/invocations/collections.py

@@ -38,14 +38,16 @@ class RangeInvocation(BaseInvocation):
     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(

invokeai/app/invocations/compel.py

@@ -5,16 +5,15 @@ from typing import List, 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.app.invocations.primitives import ConditioningField, ConditioningOutput
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
     BasicConditioningInfo,
     SDXLConditioningInfo,
 )
 
-from ...backend.model_management.models import ModelType
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.models import ModelNotFoundException
+from ...backend.model_management.models import ModelNotFoundException, ModelType
 from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
 from ...backend.util.devices import torch_dtype
 from .baseinvocation import (

invokeai/app/invocations/controlnet_image_processors.py

@@ -28,15 +28,14 @@ from pydantic import BaseModel, Field, validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
-
 from ...backend.model_management import BaseModelType
 from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
     UIType,
@@ -44,7 +43,6 @@ from .baseinvocation import (
     invocation_output,
 )
 
-
 CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
 CONTROLNET_RESIZE_VALUES = Literal[
     "just_resize",

invokeai/app/invocations/cv.py

@@ -4,9 +4,10 @@
 import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
+
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
 

invokeai/app/invocations/image.py

@@ -98,7 +98,7 @@ class ImageCropInvocation(BaseInvocation):
         )
 
 
-@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.0")
+@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
 class ImagePasteInvocation(BaseInvocation):
     """Pastes an image into another image."""
 
@@ -110,6 +110,7 @@ class ImagePasteInvocation(BaseInvocation):
     )
     x: int = InputField(default=0, description="The left x coordinate at which to paste the image")
     y: int = InputField(default=0, description="The top y coordinate at which to paste the image")
+    crop: bool = InputField(default=False, description="Crop to base image dimensions")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         base_image = context.services.images.get_pil_image(self.base_image.image_name)
@@ -129,6 +130,10 @@ class ImagePasteInvocation(BaseInvocation):
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
         new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
 
+        if self.crop:
+            base_w, base_h = base_image.size
+            new_image = new_image.crop((abs(min_x), abs(min_y), abs(min_x) + base_w, abs(min_y) + base_h))
+
         image_dto = context.services.images.create(
             image=new_image,
             image_origin=ResourceOrigin.INTERNAL,

invokeai/app/invocations/latent.py

@@ -34,6 +34,22 @@ from invokeai.app.invocations.primitives import (
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
+
+from ...backend.model_management.lora import ModelPatcher
+from ...backend.model_management.models import BaseModelType
+from ...backend.model_management.seamless import set_seamless
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.stable_diffusion.diffusers_pipeline import (
+    ConditioningData,
+    ControlNetData,
+    StableDiffusionGeneratorPipeline,
+    image_resized_to_grid_as_tensor,
+)
+from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
+from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from ...backend.util.devices import choose_precision, choose_torch_device
+from ...backend.util.logging import InvokeAILogger
+from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -49,21 +65,6 @@ from .baseinvocation import (
 from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
-from ..models.image import ImageCategory, ResourceOrigin
-from ...backend.model_management import BaseModelType
-from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.seamless import set_seamless
-from ...backend.stable_diffusion import PipelineIntermediateState
-from ...backend.stable_diffusion.diffusers_pipeline import (
-    ConditioningData,
-    ControlNetData,
-    StableDiffusionGeneratorPipeline,
-    image_resized_to_grid_as_tensor,
-)
-from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
-from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_precision, choose_torch_device
-from ...backend.util.logging import InvokeAILogger
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 

invokeai/app/invocations/math.py

@@ -1,8 +1,11 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-import numpy as np
+from typing import Literal
 
-from invokeai.app.invocations.primitives import IntegerOutput
+import numpy as np
+from pydantic import validator
+
+from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
 
 from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
 
@@ -60,3 +63,201 @@ class RandomIntInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IntegerOutput:
         return IntegerOutput(value=np.random.randint(self.low, self.high))
+
+
+@invocation(
+    "float_to_int",
+    title="Float To Integer",
+    tags=["math", "round", "integer", "float", "convert"],
+    category="math",
+    version="1.0.0",
+)
+class FloatToIntegerInvocation(BaseInvocation):
+    """Rounds a float number to (a multiple of) an integer."""
+
+    value: float = InputField(default=0, description="The value to round")
+    multiple: int = InputField(default=1, ge=1, title="Multiple of", description="The multiple to round to")
+    method: Literal["Nearest", "Floor", "Ceiling", "Truncate"] = InputField(
+        default="Nearest", description="The method to use for rounding"
+    )
+
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
+        if self.method == "Nearest":
+            return IntegerOutput(value=round(self.value / self.multiple) * self.multiple)
+        elif self.method == "Floor":
+            return IntegerOutput(value=np.floor(self.value / self.multiple) * self.multiple)
+        elif self.method == "Ceiling":
+            return IntegerOutput(value=np.ceil(self.value / self.multiple) * self.multiple)
+        else:  # self.method == "Truncate"
+            return IntegerOutput(value=int(self.value / self.multiple) * self.multiple)
+
+
+@invocation("round_float", title="Round Float", tags=["math", "round"], category="math", version="1.0.0")
+class RoundInvocation(BaseInvocation):
+    """Rounds a float to a specified number of decimal places."""
+
+    value: float = InputField(default=0, description="The float value")
+    decimals: int = InputField(default=0, description="The number of decimal places")
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        return FloatOutput(value=round(self.value, self.decimals))
+
+
+INTEGER_OPERATIONS = Literal[
+    "ADD",
+    "SUB",
+    "MUL",
+    "DIV",
+    "EXP",
+    "MOD",
+    "ABS",
+    "MIN",
+    "MAX",
+]
+
+
+INTEGER_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    MOD="Modulus A%B",
+    ABS="Absolute Value of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
+
+
+@invocation(
+    "integer_math",
+    title="Integer Math",
+    tags=[
+        "math",
+        "integer",
+        "add",
+        "subtract",
+        "multiply",
+        "divide",
+        "modulus",
+        "power",
+        "absolute value",
+        "min",
+        "max",
+    ],
+    category="math",
+    version="1.0.0",
+)
+class IntegerMathInvocation(BaseInvocation):
+    """Performs integer math."""
+
+    operation: INTEGER_OPERATIONS = InputField(
+        default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
+    )
+    a: int = InputField(default=0, description=FieldDescriptions.num_1)
+    b: int = InputField(default=0, description=FieldDescriptions.num_2)
+
+    @validator("b")
+    def no_unrepresentable_results(cls, v, values):
+        if values["operation"] == "DIV" and v == 0:
+            raise ValueError("Cannot divide by zero")
+        elif values["operation"] == "MOD" and v == 0:
+            raise ValueError("Cannot divide by zero")
+        elif values["operation"] == "EXP" and v < 0:
+            raise ValueError("Result of exponentiation is not an integer")
+        return v
+
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
+        # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
+        if self.operation == "ADD":
+            return IntegerOutput(value=self.a + self.b)
+        elif self.operation == "SUB":
+            return IntegerOutput(value=self.a - self.b)
+        elif self.operation == "MUL":
+            return IntegerOutput(value=self.a * self.b)
+        elif self.operation == "DIV":
+            return IntegerOutput(value=int(self.a / self.b))
+        elif self.operation == "EXP":
+            return IntegerOutput(value=self.a**self.b)
+        elif self.operation == "MOD":
+            return IntegerOutput(value=self.a % self.b)
+        elif self.operation == "ABS":
+            return IntegerOutput(value=abs(self.a))
+        elif self.operation == "MIN":
+            return IntegerOutput(value=min(self.a, self.b))
+        else:  # self.operation == "MAX":
+            return IntegerOutput(value=max(self.a, self.b))
+
+
+FLOAT_OPERATIONS = Literal[
+    "ADD",
+    "SUB",
+    "MUL",
+    "DIV",
+    "EXP",
+    "ABS",
+    "SQRT",
+    "MIN",
+    "MAX",
+]
+
+
+FLOAT_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    ABS="Absolute Value of A",
+    SQRT="Square Root of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
+
+
+@invocation(
+    "float_math",
+    title="Float Math",
+    tags=["math", "float", "add", "subtract", "multiply", "divide", "power", "root", "absolute value", "min", "max"],
+    category="math",
+    version="1.0.0",
+)
+class FloatMathInvocation(BaseInvocation):
+    """Performs floating point math."""
+
+    operation: FLOAT_OPERATIONS = InputField(
+        default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
+    )
+    a: float = InputField(default=0, description=FieldDescriptions.num_1)
+    b: float = InputField(default=0, description=FieldDescriptions.num_2)
+
+    @validator("b")
+    def no_unrepresentable_results(cls, v, values):
+        if values["operation"] == "DIV" and v == 0:
+            raise ValueError("Cannot divide by zero")
+        elif values["operation"] == "EXP" and values["a"] == 0 and v < 0:
+            raise ValueError("Cannot raise zero to a negative power")
+        elif values["operation"] == "EXP" and type(values["a"] ** v) is complex:
+            raise ValueError("Root operation resulted in a complex number")
+        return v
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
+        if self.operation == "ADD":
+            return FloatOutput(value=self.a + self.b)
+        elif self.operation == "SUB":
+            return FloatOutput(value=self.a - self.b)
+        elif self.operation == "MUL":
+            return FloatOutput(value=self.a * self.b)
+        elif self.operation == "DIV":
+            return FloatOutput(value=self.a / self.b)
+        elif self.operation == "EXP":
+            return FloatOutput(value=self.a**self.b)
+        elif self.operation == "SQRT":
+            return FloatOutput(value=np.sqrt(self.a))
+        elif self.operation == "ABS":
+            return FloatOutput(value=abs(self.a))
+        elif self.operation == "MIN":
+            return FloatOutput(value=min(self.a, self.b))
+        else:  # self.operation == "MAX":
+            return FloatOutput(value=max(self.a, self.b))

invokeai/app/invocations/onnx.py

@@ -25,8 +25,8 @@ from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
     UIComponent,

invokeai/app/invocations/param_easing.py

@@ -3,7 +3,6 @@ from typing import Literal, Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
-
 import PIL.Image
 from easing_functions import (
     BackEaseIn,

invokeai/app/invocations/strings.py

@@ -0,0 +1,139 @@
+# 2023 skunkworxdark (https://github.com/skunkworxdark)
+
+import re
+
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
+    invocation,
+    invocation_output,
+)
+from .primitives import StringOutput
+
+
+@invocation_output("string_pos_neg_output")
+class StringPosNegOutput(BaseInvocationOutput):
+    """Base class for invocations that output a positive and negative string"""
+
+    positive_string: str = OutputField(description="Positive string")
+    negative_string: str = OutputField(description="Negative string")
+
+
+@invocation(
+    "string_split_neg",
+    title="String Split Negative",
+    tags=["string", "split", "negative"],
+    category="string",
+    version="1.0.0",
+)
+class StringSplitNegInvocation(BaseInvocation):
+    """Splits string into two strings, inside [] goes into negative string everthing else goes into positive string. Each [ and ] character is replaced with a space"""
+
+    string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringPosNegOutput:
+        p_string = ""
+        n_string = ""
+        brackets_depth = 0
+        escaped = False
+
+        for char in self.string or "":
+            if char == "[" and not escaped:
+                n_string += " "
+                brackets_depth += 1
+            elif char == "]" and not escaped:
+                brackets_depth -= 1
+                char = " "
+            elif brackets_depth > 0:
+                n_string += char
+            else:
+                p_string += char
+
+            # keep track of the escape char but only if it isn't escaped already
+            if char == "\\" and not escaped:
+                escaped = True
+            else:
+                escaped = False
+
+        return StringPosNegOutput(positive_string=p_string, negative_string=n_string)
+
+
+@invocation_output("string_2_output")
+class String2Output(BaseInvocationOutput):
+    """Base class for invocations that output two strings"""
+
+    string_1: str = OutputField(description="string 1")
+    string_2: str = OutputField(description="string 2")
+
+
+@invocation("string_split", title="String Split", tags=["string", "split"], category="string", version="1.0.0")
+class StringSplitInvocation(BaseInvocation):
+    """Splits string into two strings, based on the first occurance of the delimiter. The delimiter will be removed from the string"""
+
+    string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea)
+    delimiter: str = InputField(
+        default="", description="Delimiter to spilt with. blank will split on the first whitespace"
+    )
+
+    def invoke(self, context: InvocationContext) -> String2Output:
+        result = self.string.split(self.delimiter, 1)
+        if len(result) == 2:
+            part1, part2 = result
+        else:
+            part1 = result[0]
+            part2 = ""
+
+        return String2Output(string_1=part1, string_2=part2)
+
+
+@invocation("string_join", title="String Join", tags=["string", "join"], category="string", version="1.0.0")
+class StringJoinInvocation(BaseInvocation):
+    """Joins string left to string right"""
+
+    string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea)
+    string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        return StringOutput(value=((self.string_left or "") + (self.string_right or "")))
+
+
+@invocation("string_join_three", title="String Join Three", tags=["string", "join"], category="string", version="1.0.0")
+class StringJoinThreeInvocation(BaseInvocation):
+    """Joins string left to string middle to string right"""
+
+    string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea)
+    string_middle: str = InputField(default="", description="String Middle", ui_component=UIComponent.Textarea)
+    string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        return StringOutput(value=((self.string_left or "") + (self.string_middle or "") + (self.string_right or "")))
+
+
+@invocation(
+    "string_replace", title="String Replace", tags=["string", "replace", "regex"], category="string", version="1.0.0"
+)
+class StringReplaceInvocation(BaseInvocation):
+    """Replaces the search string with the replace string"""
+
+    string: str = InputField(default="", description="String to work on", ui_component=UIComponent.Textarea)
+    search_string: str = InputField(default="", description="String to search for", ui_component=UIComponent.Textarea)
+    replace_string: str = InputField(
+        default="", description="String to replace the search", ui_component=UIComponent.Textarea
+    )
+    use_regex: bool = InputField(
+        default=False, description="Use search string as a regex expression (non regex is case insensitive)"
+    )
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        pattern = self.search_string or ""
+        new_string = self.string or ""
+        if len(pattern) > 0:
+            if not self.use_regex:
+                # None regex so make case insensitve
+                pattern = "(?i)" + re.escape(pattern)
+            new_string = re.sub(pattern, (self.replace_string or ""), new_string)
+        return StringOutput(value=new_string)

invokeai/app/invocations/upscale.py

@@ -7,8 +7,8 @@ import numpy as np
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from realesrgan import RealESRGANer
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation

invokeai/app/services/board_image_record_storage.py

@@ -1,13 +1,10 @@
-from abc import ABC, abstractmethod
 import sqlite3
 import threading
+from abc import ABC, abstractmethod
 from typing import Optional, cast
 
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import (
+from invokeai.app.services.models.image_record import ImageRecord, deserialize_image_record
-    ImageRecord,
-    deserialize_image_record,
-)
 
 
 class BoardImageRecordStorageBase(ABC):

invokeai/app/services/board_images.py

@@ -1,12 +1,9 @@
 from abc import ABC, abstractmethod
 from logging import Logger
 from typing import Optional
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
-from invokeai.app.services.board_record_storage import (
-    BoardRecord,
-    BoardRecordStorageBase,
-)
 
+from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
+from invokeai.app.services.board_record_storage import BoardRecord, BoardRecordStorageBase
 from invokeai.app.services.image_record_storage import ImageRecordStorageBase
 from invokeai.app.services.models.board_record import BoardDTO
 from invokeai.app.services.urls import UrlServiceBase

invokeai/app/services/board_record_storage.py

@@ -1,15 +1,13 @@
+import sqlite3
 import threading
 import uuid
 from abc import ABC, abstractmethod
 from typing import Optional, Union, cast
 
-import sqlite3
+from pydantic import BaseModel, Extra, Field
+
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.board_record import (
+from invokeai.app.services.models.board_record import BoardRecord, deserialize_board_record
-    BoardRecord,
-    deserialize_board_record,
-)
-from pydantic import BaseModel, Field, Extra
 
 
 class BoardChanges(BaseModel, extra=Extra.forbid):

invokeai/app/services/boards.py

@@ -1,17 +1,10 @@
 from abc import ABC, abstractmethod
-
 from logging import Logger
+
 from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
 from invokeai.app.services.board_images import board_record_to_dto
-
+from invokeai.app.services.board_record_storage import BoardChanges, BoardRecordStorageBase
-from invokeai.app.services.board_record_storage import (
+from invokeai.app.services.image_record_storage import ImageRecordStorageBase, OffsetPaginatedResults
-    BoardChanges,
-    BoardRecordStorageBase,
-)
-from invokeai.app.services.image_record_storage import (
-    ImageRecordStorageBase,
-    OffsetPaginatedResults,
-)
 from invokeai.app.services.models.board_record import BoardDTO
 from invokeai.app.services.urls import UrlServiceBase
 

invokeai/app/services/config/__init__.py

@@ -2,8 +2,5 @@
 Init file for InvokeAI configure package
 """
 
-from .invokeai_config import (  # noqa F401
-    InvokeAIAppConfig,
-    get_invokeai_config,
-)
 from .base import PagingArgumentParser  # noqa F401
+from .invokeai_config import InvokeAIAppConfig, get_invokeai_config  # noqa F401

invokeai/app/services/config/base.py

@@ -9,15 +9,17 @@ the command line.
 """
 
 from __future__ import annotations
+
 import argparse
 import os
 import pydoc
 import sys
 from argparse import ArgumentParser
-from omegaconf import OmegaConf, DictConfig, ListConfig
 from pathlib import Path
+from typing import ClassVar, Dict, List, Literal, Union, get_args, get_origin, get_type_hints
+
+from omegaconf import DictConfig, ListConfig, OmegaConf
 from pydantic import BaseSettings
-from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
 
 
 class PagingArgumentParser(argparse.ArgumentParser):

invokeai/app/services/config/invokeai_config.py

@@ -172,9 +172,9 @@ from __future__ import annotations
 
 import os
 from pathlib import Path
-from typing import ClassVar, Dict, List, Literal, Union, get_type_hints, Optional
+from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
 
-from omegaconf import OmegaConf, DictConfig
+from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, parse_obj_as
 
 from .base import InvokeAISettings

invokeai/app/services/default_graphs.py

@@ -1,12 +1,11 @@
-from ..invocations.latent import LatentsToImageInvocation, DenoiseLatentsInvocation
-from ..invocations.image import ImageNSFWBlurInvocation
-from ..invocations.noise import NoiseInvocation
 from ..invocations.compel import CompelInvocation
+from ..invocations.image import ImageNSFWBlurInvocation
+from ..invocations.latent import DenoiseLatentsInvocation, LatentsToImageInvocation
+from ..invocations.noise import NoiseInvocation
 from ..invocations.primitives import IntegerInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
 from .item_storage import ItemStorageABC
 
-
 default_text_to_image_graph_id = "539b2af5-2b4d-4d8c-8071-e54a3255fc74"
 
 

invokeai/app/services/events.py

@@ -1,14 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from typing import Any, Optional
+
 from invokeai.app.models.image import ProgressImage
+from invokeai.app.services.model_manager_service import BaseModelType, ModelInfo, ModelType, SubModelType
 from invokeai.app.util.misc import get_timestamp
-from invokeai.app.services.model_manager_service import (
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelInfo,
-)
 
 
 class EventServiceBase:

invokeai/app/services/graph.py

@@ -14,12 +14,12 @@ from ..invocations import *  # noqa: F401 F403
 from ..invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    invocation,
     Input,
     InputField,
     InvocationContext,
     OutputField,
     UIType,
+    invocation,
     invocation_output,
 )
 

invokeai/app/services/image_record_storage.py

@@ -9,11 +9,7 @@ from pydantic import BaseModel, Field
 from pydantic.generics import GenericModel
 
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.services.models.image_record import (
+from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
-    ImageRecord,
-    ImageRecordChanges,
-    deserialize_image_record,
-)
 
 T = TypeVar("T", bound=BaseModel)
 

invokeai/app/services/images.py

@@ -26,12 +26,7 @@ from invokeai.app.services.image_record_storage import (
     OffsetPaginatedResults,
 )
 from invokeai.app.services.item_storage import ItemStorageABC
-from invokeai.app.services.models.image_record import (
+from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
-    ImageDTO,
-    ImageRecord,
-    ImageRecordChanges,
-    image_record_to_dto,
-)
 from invokeai.app.services.resource_name import NameServiceBase
 from invokeai.app.services.urls import UrlServiceBase
 from invokeai.app.util.metadata import get_metadata_graph_from_raw_session

invokeai/app/services/invocation_queue.py

@@ -3,9 +3,9 @@
 import time
 from abc import ABC, abstractmethod
 from queue import Queue
+from typing import Optional
 
 from pydantic import BaseModel, Field
-from typing import Optional
 
 
 class InvocationQueueItem(BaseModel):

invokeai/app/services/invocation_services.py

@@ -1,21 +1,23 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
 from __future__ import annotations
+
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
     from logging import Logger
+
     from invokeai.app.services.board_images import BoardImagesServiceABC
     from invokeai.app.services.boards import BoardServiceABC
-    from invokeai.app.services.images import ImageServiceABC
-    from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
-    from invokeai.app.services.model_manager_service import ModelManagerServiceBase
-    from invokeai.app.services.events import EventServiceBase
-    from invokeai.app.services.latent_storage import LatentsStorageBase
-    from invokeai.app.services.invocation_queue import InvocationQueueABC
-    from invokeai.app.services.item_storage import ItemStorageABC
     from invokeai.app.services.config import InvokeAIAppConfig
+    from invokeai.app.services.events import EventServiceBase
     from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
+    from invokeai.app.services.images import ImageServiceABC
+    from invokeai.app.services.invocation_queue import InvocationQueueABC
+    from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
     from invokeai.app.services.invoker import InvocationProcessorABC
+    from invokeai.app.services.item_storage import ItemStorageABC
+    from invokeai.app.services.latent_storage import LatentsStorageBase
+    from invokeai.app.services.model_manager_service import ModelManagerServiceBase
 
 
 class InvocationServices:

invokeai/app/services/invocation_stats.py

@@ -28,22 +28,22 @@ The abstract base class for this class is InvocationStatsServiceBase. An impleme
 writes to the system log is stored in InvocationServices.performance_statistics.
 """
 
-import psutil
 import time
 from abc import ABC, abstractmethod
 from contextlib import AbstractContextManager
 from dataclasses import dataclass, field
 from typing import Dict
 
+import psutil
 import torch
 
 import invokeai.backend.util.logging as logger
+from invokeai.backend.model_management.model_cache import CacheStats
 
 from ..invocations.baseinvocation import BaseInvocation
 from .graph import GraphExecutionState
 from .item_storage import ItemStorageABC
 from .model_manager_service import ModelManagerService
-from invokeai.backend.model_management.model_cache import CacheStats
 
 # size of GIG in bytes
 GIG = 1073741824

invokeai/app/services/latent_storage.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
-from typing import Dict, Union, Optional
+from typing import Dict, Optional, Union
 
 import torch
 

invokeai/app/services/model_manager_service.py

@@ -5,27 +5,28 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from logging import Logger
 from pathlib import Path
-from pydantic import Field
-from typing import Literal, Optional, Union, Callable, List, Tuple, TYPE_CHECKING
 from types import ModuleType
-
+from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
-from invokeai.backend.model_management import (
-    ModelManager,
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelInfo,
-    AddModelResult,
-    SchedulerPredictionType,
-    ModelMerger,
-    MergeInterpolationMethod,
-    ModelNotFoundException,
-)
-from invokeai.backend.model_management.model_search import FindModels
-from invokeai.backend.model_management.model_cache import CacheStats
 
 import torch
+from pydantic import Field
+
 from invokeai.app.models.exceptions import CanceledException
+from invokeai.backend.model_management import (
+    AddModelResult,
+    BaseModelType,
+    MergeInterpolationMethod,
+    ModelInfo,
+    ModelManager,
+    ModelMerger,
+    ModelNotFoundException,
+    ModelType,
+    SchedulerPredictionType,
+    SubModelType,
+)
+from invokeai.backend.model_management.model_cache import CacheStats
+from invokeai.backend.model_management.model_search import FindModels
+
 from ...backend.util import choose_precision, choose_torch_device
 from .config import InvokeAIAppConfig
 

invokeai/app/services/models/board_record.py

@@ -1,6 +1,8 @@
-from typing import Optional, Union
 from datetime import datetime
+from typing import Optional, Union
+
 from pydantic import Field
+
 from invokeai.app.util.misc import get_iso_timestamp
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 

invokeai/app/services/resource_name.py

@@ -1,6 +1,6 @@
+import uuid
 from abc import ABC, abstractmethod
 from enum import Enum, EnumMeta
-import uuid
 
 
 class ResourceType(str, Enum, metaclass=EnumMeta):

invokeai/app/util/controlnet_utils.py

@@ -1,12 +1,12 @@
 from typing import Union
-import torch
-import numpy as np
-import cv2
-from PIL import Image
-from diffusers.utils import PIL_INTERPOLATION
 
-from einops import rearrange
+import cv2
+import numpy as np
+import torch
 from controlnet_aux.util import HWC3
+from diffusers.utils import PIL_INTERPOLATION
+from einops import rearrange
+from PIL import Image
 
 ###################################################################
 # Copy of scripts/lvminthin.py from Mikubill/sd-webui-controlnet

invokeai/app/util/misc.py

@@ -1,4 +1,5 @@
 import datetime
+
 import numpy as np
 
 

invokeai/app/util/model_exclude_null.py

@@ -1,6 +1,6 @@
 from typing import Any
-from pydantic import BaseModel
 
+from pydantic import BaseModel
 
 """
 We want to exclude null values from objects that make their way to the client.

invokeai/app/util/step_callback.py

@@ -1,11 +1,13 @@
 import torch
 from PIL import Image
+
 from invokeai.app.models.exceptions import CanceledException
 from invokeai.app.models.image import ProgressImage
-from ..invocations.baseinvocation import InvocationContext
+
-from ...backend.util.util import image_to_dataURL
-from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.model_management.models import BaseModelType
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.util.util import image_to_dataURL
+from ..invocations.baseinvocation import InvocationContext
 
 
 def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):

invokeai/app/util/thumbnails.py

@@ -1,4 +1,5 @@
 import os
+
 from PIL import Image
 
 

invokeai/backend/__init__.py

@@ -1,5 +1,5 @@
 """
 Initialization file for invokeai.backend
 """
-from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo  # noqa: F401
+from .model_management import BaseModelType, ModelCache, ModelInfo, ModelManager, ModelType, SubModelType  # noqa: F401
 from .model_management.models import SilenceWarnings  # noqa: F401

invokeai/backend/image_util/invisible_watermark.py

@@ -3,12 +3,13 @@ This module defines a singleton object, "invisible_watermark" that
 wraps the invisible watermark model. It respects the global "invisible_watermark"
 configuration variable, that allows the watermarking to be supressed.
 """
-import numpy as np
 import cv2
-from PIL import Image
+import numpy as np
 from imwatermark import WatermarkEncoder
-from invokeai.app.services.config import InvokeAIAppConfig
+from PIL import Image
+
 import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
 
 config = InvokeAIAppConfig.get_config()
 

invokeai/backend/image_util/patchmatch.py

@@ -5,6 +5,7 @@ wraps the actual patchmatch object. It respects the global
 be suppressed or deferred
 """
 import numpy as np
+
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 

invokeai/backend/image_util/safety_checker.py

@@ -5,10 +5,11 @@ configuration variable, that allows the checker to be supressed.
 """
 import numpy as np
 from PIL import Image
-from invokeai.backend import SilenceWarnings
+
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.util.devices import choose_torch_device
 import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend import SilenceWarnings
+from invokeai.backend.util.devices import choose_torch_device
 
 config = InvokeAIAppConfig.get_config()
 

invokeai/backend/install/check_root.py

@@ -2,9 +2,8 @@
 Check that the invokeai_root is correctly configured and exit if not.
 """
 import sys
-from invokeai.app.services.config import (
+
-    InvokeAIAppConfig,
+from invokeai.app.services.config import InvokeAIAppConfig
-)
 
 
 def check_invokeai_root(config: InvokeAIAppConfig):

invokeai/backend/install/invokeai_configure.py

@@ -6,68 +6,56 @@
 #
 # Coauthor: Kevin Turner http://github.com/keturn
 #
-import sys
 import argparse
 import io
 import os
-import psutil
 import shutil
+import sys
 import textwrap
-import torch
 import traceback
-import yaml
 import warnings
 from argparse import Namespace
 from enum import Enum
 from pathlib import Path
 from shutil import get_terminal_size
-from typing import get_type_hints, get_args, Any
+from typing import Any, get_args, get_type_hints
 from urllib import request
 
 import npyscreen
-import transformers
 import omegaconf
+import psutil
+import torch
+import transformers
+import yaml
 from diffusers import AutoencoderKL
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
 from omegaconf import OmegaConf
+from pydantic.error_wrappers import ValidationError
 from tqdm import tqdm
-from transformers import (
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-    CLIPTextModel,
-    CLIPTextConfig,
-    CLIPTokenizer,
-    AutoFeatureExtractor,
-    BertTokenizerFast,
-)
-import invokeai.configs as configs
 
-from invokeai.app.services.config import (
+import invokeai.configs as configs
-    InvokeAIAppConfig,
+from invokeai.app.services.config import InvokeAIAppConfig
-)
+from invokeai.backend.install.legacy_arg_parsing import legacy_parser
+from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, hf_download_from_pretrained
+from invokeai.backend.model_management.model_probe import BaseModelType, ModelType
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
 
 # TO DO - Move all the frontend code into invokeai.frontend.install
 from invokeai.frontend.install.widgets import (
-    SingleSelectColumnsSimple,
-    MultiSelectColumns,
-    CenteredButtonPress,
-    FileBox,
-    set_min_terminal_size,
-    CyclingForm,
     MIN_COLS,
     MIN_LINES,
+    CenteredButtonPress,
+    CyclingForm,
+    FileBox,
+    MultiSelectColumns,
+    SingleSelectColumnsSimple,
     WindowTooSmallException,
+    set_min_terminal_size,
 )
-from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-from invokeai.backend.install.model_install_backend import (
-    hf_download_from_pretrained,
-    InstallSelections,
-    ModelInstall,
-)
-from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
-from pydantic.error_wrappers import ValidationError
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()

invokeai/backend/install/migrate_to_3.py

@@ -3,33 +3,26 @@ Migrate the models directory and models.yaml file from an existing
 InvokeAI 2.3 installation to 3.0.0.
 """
 
-import os
 import argparse
+import os
 import shutil
-import yaml
-
-import transformers
-import diffusers
 import warnings
-
 from dataclasses import dataclass
 from pathlib import Path
-from omegaconf import OmegaConf, DictConfig
 from typing import Union
 
-from diffusers import StableDiffusionPipeline, AutoencoderKL
+import diffusers
+import transformers
+import yaml
+from diffusers import AutoencoderKL, StableDiffusionPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import (
+from omegaconf import DictConfig, OmegaConf
-    CLIPTextModel,
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
-    CLIPTokenizer,
-    AutoFeatureExtractor,
-    BertTokenizerFast,
-)
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import ModelManager
-from invokeai.backend.model_management.model_probe import ModelProbe, ModelType, BaseModelType, ModelProbeInfo
+from invokeai.backend.model_management.model_probe import BaseModelType, ModelProbe, ModelProbeInfo, ModelType
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()

invokeai/backend/install/model_install_backend.py

@@ -7,23 +7,23 @@ import warnings
 from dataclasses import dataclass, field
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Optional, List, Dict, Callable, Union, Set
+from typing import Callable, Dict, List, Optional, Set, Union
 
 import requests
+import torch
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
-import torch
+from huggingface_hub import HfApi, HfFolder, hf_hub_url
-from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
 from tqdm import tqdm
 
 import invokeai.configs as configs
-
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
+from invokeai.backend.model_management import AddModelResult, BaseModelType, ModelManager, ModelType, ModelVariantType
-from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo
+from invokeai.backend.model_management.model_probe import ModelProbe, ModelProbeInfo, SchedulerPredictionType
 from invokeai.backend.util import download_with_resume
-from invokeai.backend.util.devices import torch_dtype, choose_torch_device
+from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+
 from ..util.logging import InvokeAILogger
 
 warnings.filterwarnings("ignore")

invokeai/backend/model_management/__init__.py

@@ -1,15 +1,19 @@
 """
 Initialization file for invokeai.backend.model_management
 """
-from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType  # noqa: F401
+# This import must be first
-from .model_cache import ModelCache  # noqa: F401
+from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType  # noqa: F401 isort: split
+
 from .lora import ModelPatcher, ONNXModelPatcher  # noqa: F401
+from .model_cache import ModelCache  # noqa: F401
 from .models import (  # noqa: F401
     BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelVariantType,
-    ModelNotFoundException,
     DuplicateModelException,
+    ModelNotFoundException,
+    ModelType,
+    ModelVariantType,
+    SubModelType,
 )
-from .model_merge import ModelMerger, MergeInterpolationMethod  # noqa: F401
+
+# This import must be last
+from .model_merge import ModelMerger, MergeInterpolationMethod  # noqa: F401 isort: split

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -25,12 +25,7 @@ from typing import Optional, Union
 
 import requests
 import torch
-from diffusers.models import (
+from diffusers.models import AutoencoderKL, ControlNetModel, PriorTransformer, UNet2DConditionModel
-    AutoencoderKL,
-    ControlNetModel,
-    PriorTransformer,
-    UNet2DConditionModel,
-)
 from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
@@ -64,6 +59,7 @@ from transformers import (
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.util.logging import InvokeAILogger
+
 from .models import BaseModelType, ModelVariantType
 
 try:
@@ -1203,8 +1199,8 @@ def download_from_original_stable_diffusion_ckpt(
         StableDiffusionControlNetPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionPipeline,
-        StableDiffusionXLPipeline,
         StableDiffusionXLImg2ImgPipeline,
+        StableDiffusionXLPipeline,
         StableUnCLIPImg2ImgPipeline,
         StableUnCLIPPipeline,
     )

invokeai/backend/model_management/lora.py

@@ -2,8 +2,8 @@ from __future__ import annotations
 
 import copy
 from contextlib import contextmanager
-from typing import Optional, Dict, Tuple, Any, Union, List
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -14,7 +14,6 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 from .models.lora import LoRAModel
 
-
 """
 loras = [
     (lora_model1, 0.7),
@@ -307,9 +306,10 @@ class TextualInversionManager(BaseTextualInversionManager):
 
 
 class ONNXModelPatcher:
-    from .models.base import IAIOnnxRuntimeModel
     from diffusers import OnnxRuntimeModel
 
+    from .models.base import IAIOnnxRuntimeModel
+
     @classmethod
     @contextmanager
     def apply_lora_unet(

invokeai/backend/model_management/model_cache.py

@@ -17,18 +17,19 @@ context. Use like this:
 """
 
 import gc
+import hashlib
 import os
 import sys
-import hashlib
 from contextlib import suppress
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Dict, Union, types, Optional, Type, Any
+from typing import Any, Dict, Optional, Type, Union, types
 
 import torch
 
 import invokeai.backend.util.logging as logger
-from .models import BaseModelType, ModelType, SubModelType, ModelBase
+
+from .models import BaseModelType, ModelBase, ModelType, SubModelType
 
 # Maximum size of the cache, in gigs
 # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously

invokeai/backend/model_management/model_manager.py

@@ -234,8 +234,8 @@ import textwrap
 import types
 from dataclasses import dataclass
 from pathlib import Path
-from shutil import rmtree, move
+from shutil import move, rmtree
-from typing import Optional, List, Literal, Tuple, Union, Dict, Set, Callable
+from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union
 
 import torch
 import yaml
@@ -246,20 +246,21 @@ from pydantic import BaseModel, Field
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.util import CUDA_DEVICE, Chdir
+
 from .model_cache import ModelCache, ModelLocker
 from .model_search import ModelSearch
 from .models import (
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelError,
-    SchedulerPredictionType,
     MODEL_CLASSES,
-    ModelConfigBase,
+    BaseModelType,
-    ModelNotFoundException,
-    InvalidModelException,
     DuplicateModelException,
+    InvalidModelException,
     ModelBase,
+    ModelConfigBase,
+    ModelError,
+    ModelNotFoundException,
+    ModelType,
+    SchedulerPredictionType,
+    SubModelType,
 )
 
 # We are only starting to number the config file with release 3.

invokeai/backend/model_management/model_merge.py

@@ -9,13 +9,14 @@ Copyright (c) 2023 Lincoln Stein and the InvokeAI Development Team
 import warnings
 from enum import Enum
 from pathlib import Path
+from typing import List, Optional, Union
+
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
-from typing import List, Union, Optional
 
 import invokeai.backend.util.logging as logger
 
-from ...backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
+from ...backend.model_management import AddModelResult, BaseModelType, ModelManager, ModelType, ModelVariantType
 
 
 class MergeInterpolationMethod(str, Enum):

invokeai/backend/model_management/model_probe.py

@@ -1,20 +1,20 @@
 import json
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Callable, Literal, Union, Dict, Optional
+from typing import Callable, Dict, Literal, Optional, Union
 
 import safetensors.torch
 import torch
-from diffusers import ModelMixin, ConfigMixin
+from diffusers import ConfigMixin, ModelMixin
 from picklescan.scanner import scan_file_path
 
 from .models import (
     BaseModelType,
+    InvalidModelException,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
     SilenceWarnings,
-    InvalidModelException,
 )
 from .models.base import read_checkpoint_meta
 from .util import lora_token_vector_length

invokeai/backend/model_management/model_search.py

@@ -5,8 +5,8 @@ Abstract base class for recursive directory search for models.
 
 import os
 from abc import ABC, abstractmethod
-from typing import List, Set, types
 from pathlib import Path
+from typing import List, Set, types
 
 import invokeai.backend.util.logging as logger
 

invokeai/backend/model_management/models/__init__.py

@@ -1,29 +1,30 @@
 import inspect
 from enum import Enum
-from pydantic import BaseModel
 from typing import Literal, get_origin
+
+from pydantic import BaseModel
+
 from .base import (  # noqa: F401
     BaseModelType,
-    ModelType,
+    DuplicateModelException,
-    SubModelType,
+    InvalidModelException,
     ModelBase,
     ModelConfigBase,
+    ModelError,
+    ModelNotFoundException,
+    ModelType,
     ModelVariantType,
     SchedulerPredictionType,
-    ModelError,
     SilenceWarnings,
-    ModelNotFoundException,
+    SubModelType,
-    InvalidModelException,
-    DuplicateModelException,
 )
-from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
-from .sdxl import StableDiffusionXLModel
-from .vae import VaeModel
-from .lora import LoRAModel
 from .controlnet import ControlNetModel  # TODO:
-from .textual_inversion import TextualInversionModel
+from .lora import LoRAModel
-
+from .sdxl import StableDiffusionXLModel
+from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
 from .stable_diffusion_onnx import ONNXStableDiffusion1Model, ONNXStableDiffusion2Model
+from .textual_inversion import TextualInversionModel
+from .vae import VaeModel
 
 MODEL_CLASSES = {
     BaseModelType.StableDiffusion1: {

invokeai/backend/model_management/models/base.py

@@ -1,29 +1,25 @@
+import inspect
 import json
 import os
 import sys
 import typing
-import inspect
 import warnings
 from abc import ABCMeta, abstractmethod
 from contextlib import suppress
 from enum import Enum
 from pathlib import Path
-from picklescan.scanner import scan_file_path
+from typing import Any, Callable, Dict, Generic, List, Literal, Optional, Type, TypeVar, Union
 
-import torch
 import numpy as np
 import onnx
 import safetensors.torch
-from diffusers import DiffusionPipeline, ConfigMixin
+import torch
-from onnx import numpy_helper
+from diffusers import ConfigMixin, DiffusionPipeline
-from onnxruntime import (
-    InferenceSession,
-    SessionOptions,
-    get_available_providers,
-)
-from pydantic import BaseModel, Field
-from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 from diffusers import logging as diffusers_logging
+from onnx import numpy_helper
+from onnxruntime import InferenceSession, SessionOptions, get_available_providers
+from picklescan.scanner import scan_file_path
+from pydantic import BaseModel, Field
 from transformers import logging as transformers_logging
 
 

invokeai/backend/model_management/models/controlnet.py

@@ -1,23 +1,26 @@
 import os
-import torch
 from enum import Enum
 from pathlib import Path
-from typing import Optional, Literal
+from typing import Literal, Optional
+
+import torch
+
+import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
+
 from .base import (
+    BaseModelType,
+    EmptyConfigLoader,
+    InvalidModelException,
     ModelBase,
     ModelConfigBase,
-    BaseModelType,
+    ModelNotFoundException,
     ModelType,
     SubModelType,
-    EmptyConfigLoader,
-    calc_model_size_by_fs,
     calc_model_size_by_data,
+    calc_model_size_by_fs,
     classproperty,
-    InvalidModelException,
-    ModelNotFoundException,
 )
-from invokeai.app.services.config import InvokeAIAppConfig
-import invokeai.backend.util.logging as logger
 
 
 class ControlNetModelFormat(str, Enum):

invokeai/backend/model_management/models/sdxl.py

@@ -1,19 +1,21 @@
-import os
 import json
+import os
 from enum import Enum
-from pydantic import Field
 from typing import Literal, Optional
+
+from omegaconf import OmegaConf
+from pydantic import Field
+
 from .base import (
-    ModelConfigBase,
     BaseModelType,
+    DiffusersModel,
+    InvalidModelException,
+    ModelConfigBase,
     ModelType,
     ModelVariantType,
-    DiffusersModel,
-    read_checkpoint_meta,
     classproperty,
-    InvalidModelException,
+    read_checkpoint_meta,
 )
-from omegaconf import OmegaConf
 
 
 class StableDiffusionXLModelFormat(str, Enum):

invokeai/backend/model_management/models/stable_diffusion.py

@@ -1,26 +1,29 @@
-import os
 import json
+import os
 from enum import Enum
-from pydantic import Field
 from pathlib import Path
 from typing import Literal, Optional, Union
+
 from diffusers import StableDiffusionInpaintPipeline, StableDiffusionPipeline
-from .base import (
+from omegaconf import OmegaConf
-    ModelConfigBase,
+from pydantic import Field
-    BaseModelType,
+
-    ModelType,
-    ModelVariantType,
-    DiffusersModel,
-    SilenceWarnings,
-    read_checkpoint_meta,
-    classproperty,
-    InvalidModelException,
-    ModelNotFoundException,
-)
-from .sdxl import StableDiffusionXLModel
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
-from omegaconf import OmegaConf
+
+from .base import (
+    BaseModelType,
+    DiffusersModel,
+    InvalidModelException,
+    ModelConfigBase,
+    ModelNotFoundException,
+    ModelType,
+    ModelVariantType,
+    SilenceWarnings,
+    classproperty,
+    read_checkpoint_meta,
+)
+from .sdxl import StableDiffusionXLModel
 
 
 class StableDiffusion1ModelFormat(str, Enum):
@@ -272,8 +275,8 @@ def _convert_ckpt_and_cache(
         return output_path
 
     # to avoid circular import errors
-    from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
     from ...util.devices import choose_torch_device, torch_dtype
+    from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
 
     model_base_to_model_type = {
         BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",