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17
.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml vendored
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@ -1,5 +1,5 @@
name: Feature Request
description: Commit a idea or Request a new feature
description: Contribute a idea or request a new feature
title: '[enhancement]: '
labels: ['enhancement']
# assignees:
@ -9,14 +9,14 @@ body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this Feature request!
Thanks for taking the time to fill out this feature request!
- type: checkboxes
attributes:
label: Is there an existing issue for this?
description: |
Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
to see if a simmilar issue already exists for the feature you want to request
to see if a similar issue already exists for the feature you want to request
options:
- label: I have searched the existing issues
required: true
@ -34,12 +34,9 @@ body:
id: whatisexpected
attributes:
label: What should this feature add?
description: Please try to explain the functionality this feature should add
description: Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
placeholder: |
Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
Great benefits with automatic labeling, assigning and other functionalitys not available in that form
via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
validations:
required: true
@ -51,6 +48,6 @@ body:
- type: textarea
attributes:
label: Aditional Content
label: Additional Content
description: Add any other context or screenshots about the feature request here.
placeholder: This is a Mockup of the design how I imagine it <screenshot>
placeholder: This is a mockup of the design how I imagine it <screenshot>

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@ -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

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

29
README.md
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@ -46,13 +46,13 @@ the foundation for multiple commercial products.
Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
href="https://invoke-ai.github.io/InvokeAI/">Documentation and
Tutorials</a>] [<a
href="https://github.com/invoke-ai/InvokeAI/">Code and
Downloads</a>] [<a
href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
Tutorials</a>]
[<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
[<a
href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
Ideas & Q&A</a>]
Ideas & Q&A</a>]
[<a
href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>]
<div align="center">
@ -368,9 +368,9 @@ InvokeAI offers a locally hosted Web Server & React Frontend, with an industry l
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
### *Node Architecture & Editor (Beta)*
### *Workflows & Nodes*
Invoke AI's backend is built on a graph-based execution architecture. This allows for customizable generation pipelines to be developed by professional users looking to create specific workflows to support their production use-cases, and will be extended in the future with additional capabilities.
InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
### *Board & Gallery Management*
@ -383,8 +383,9 @@ Invoke AI provides an organized gallery system for easily storing, accessing, an
- *Upscaling Tools*
- *Embedding Manager & Support*
- *Model Manager & Support*
- *Workflow creation & management*
- *Node-Based Architecture*
- *Node-Based Plug-&-Play UI (Beta)*
### Latest Changes
@ -395,20 +396,18 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
### Troubleshooting
Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
problems and other issues.
problems and other issues. For more help, please join our [Discord][discord link]
## Contributing
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
cleanup, testing, or code reviews, is very much encouraged to do so.
To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
If you are unfamiliar with how
to contribute to GitHub projects, here is a
[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.
to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing:
[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
We hope you enjoy using our software as much as we enjoy creating it,
and we hope that some of those of you who are reading this will elect
@ -424,7 +423,7 @@ their time, hard work and effort.
### Support
For support, please use this repository's GitHub Issues tracking service, or join the Discord.
For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
Original portions of the software are Copyright (c) 2023 by respective contributors.

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

0
docs/contributing/contribution_guides/development_guides/contributingToFrontend.md → docs/contributing/contribution_guides/contributingToFrontend.md
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64
docs/contributing/contribution_guides/development.md
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@ -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.

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@ -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**.

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@ -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
amassed a large ligrary of &gt;800 community-contributed TI files covering a
[Hugging Face](https://huggingface.co/sd-concepts-library) has
amassed a large library of &gt;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

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

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@ -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.

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@ -2,17 +2,50 @@
title: Model Merging
---
# :material-image-off: Model Merging
## How to Merge Models
As of version 2.3, InvokeAI comes with a script that allows you to
merge two or three diffusers-type models into a new merged model. The
InvokeAI provides the ability to merge two or three diffusers-type models into a new merged model. The
resulting model will combine characteristics of the original, and can
be used to teach an old model new tricks.
## How to Merge Models
Model Merging can be be done by navigating to the Model Manager and clicking the "Merge Models" tab. From there, you can select the models and settings you want to use to merge th models.
## Settings
* Model Selection: there are three multiple choice fields that
display all the diffusers-style models that InvokeAI knows about.
If you do not see the model you are looking for, then it is probably
a legacy checkpoint model and needs to be converted using the
`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 ".+-".

37
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@ -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)

29
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@ -43,27 +43,22 @@ into the directory
InvokeAI 2.3 and higher comes with a text console-based training front
end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
start the front end by selecting choice (3):
start training tool selecting choice (3):
```sh
Do you want to generate images using the
1: Browser-based UI
2: Command-line interface
3: Run textual inversion training
4: Merge models (diffusers type only)
5: Download and install models
6: Change InvokeAI startup options
7: Re-run the configure script to fix a broken install
8: Open the developer console
9: Update InvokeAI
10: Command-line help
Q: Quit
Please enter 1-10, Q: [1]
1 "Generate images with a browser-based interface"
2 "Explore InvokeAI nodes using a command-line interface"
3 "Textual inversion training"
4 "Merge models (diffusers type only)"
5 "Download and install models"
6 "Change InvokeAI startup options"
7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
8 "Open the developer console"
9 "Update InvokeAI"
```
From the command line, with the InvokeAI virtual environment active,
you can launch the front end with the command `invokeai-ti --gui`.
Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
you can then launch the front end with the command `invokeai-ti --gui`.
This will launch a text-based front end that will look like this:

336
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@ -0,0 +1,336 @@
---
title: Command-line Utilities
---
# :material-file-document: Utilities
# Command-line Utilities
InvokeAI comes with several scripts that are accessible via the
command line. To access these commands, start the "developer's
console" from the launcher (`invoke.bat` menu item [8]). Users who are
familiar with Python can alternatively activate InvokeAI's virtual
environment (typically, but not necessarily `invokeai/.venv`).
In the developer's console, type the script's name to run it. To get a
synopsis of what a utility does and the command-line arguments it
accepts, pass it the `-h` argument, e.g.
```bash
invokeai-merge -h
```
## **invokeai-web**
This script launches the web server and is effectively identical to
selecting option [1] in the launcher. An advantage of launching the
server from the command line is that you can override any setting
configuration option in `invokeai.yaml` using like-named command-line
arguments. For example, to temporarily change the size of the RAM
cache to 7 GB, you can launch as follows:
```bash
invokeai-web --ram 7
```
## **invokeai-merge**
This is the model merge script, the same as launcher option [4]. Call
it with the `--gui` command-line argument to start the interactive
console-based GUI. Alternatively, you can run it non-interactively
using command-line arguments as illustrated in the example below which
merges models named `stable-diffusion-1.5` and `inkdiffusion` into a new model named
`my_new_model`:
```bash
invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffusion --merged_model_name my_new_model
```
## **invokeai-ti**
This is the textual inversion training script that is run by launcher
option [3]. Call it with `--gui` to run the interactive console-based
front end. It can also be run non-interactively. It has about a
zillion arguments, but a typical training session can be launched
with:
```bash
invokeai-ti --model stable-diffusion-1.5 \
--placeholder_token 'jello' \
--learnable_property object \
--num_train_epochs 50 \
--train_data_dir /path/to/training/images \
--output_dir /path/to/trained/model
```
(Note that \\ is the Linux/Mac long-line continuation character. Use ^
in Windows).
## **invokeai-install**
This is the console-based model install script that is run by launcher
option [5]. If called without arguments, it will launch the
interactive console-based interface. It can also be used
non-interactively to list, add and remove models as shown by these
examples:
* This will download and install three models from CivitAI, HuggingFace,
and local disk:
```bash
invokeai-install --add https://civitai.com/api/download/models/161302 ^
gsdf/Counterfeit-V3.0 ^
D:\Models\merge_model_two.safetensors
```
(Note that ^ is the Windows long-line continuation character. Use \\ on
Linux/Mac).
* This will list installed models of type `main`:
```bash
invokeai-model-install --list-models main
```
* This will delete the models named `voxel-ish` and `realisticVision`:
```bash
invokeai-model-install --delete voxel-ish realisticVision
```
## **invokeai-configure**
This is the console-based configure script that ran when InvokeAI was
first installed. You can run it again at any time to change the
configuration, repair a broken install.
Called without any arguments, `invokeai-configure` enters interactive
mode with two screens. The first screen is a form that provides access
to most of InvokeAI's configuration options. The second screen lets
you download, add, and delete models interactively. When you exit the
second screen, the script will add any missing "support models"
needed for core functionality, and any selected "sd weights" which are
the model checkpoint/diffusers files.
This behavior can be changed via a series of command-line
arguments. Here are some of the useful ones:
* `invokeai-configure --skip-sd-weights --skip-support-models`
This will run just the configuration part of the utility, skipping
downloading of support models and stable diffusion weights.
* `invokeai-configure --yes`
This will run the configure script non-interactively. It will set the
configuration options to their default values, install/repair support
models, and download the "recommended" set of SD models.
* `invokeai-configure --yes --default_only`
This will run the configure script non-interactively. In contrast to
the previous command, it will only download the default SD model,
Stable Diffusion v1.5
* `invokeai-configure --yes --default_only --skip-sd-weights`
This is similar to the previous command, but will not download any
SD models at all. It is usually used to repair a broken install.
By default, `invokeai-configure` runs on the currently active InvokeAI
root folder. To run it against a different root, pass it the `--root
</path/to/root>` argument.
Lastly, you can use `invokeai-configure` to create a working root
directory entirely from scratch. Assuming you wish to make a root directory
named `InvokeAI-New`, run this command:
```bash
invokeai-configure --root InvokeAI-New --yes --default_only
```
This will create a minimally functional root directory. You can now
launch the web server against it with `invokeai-web --root InvokeAI-New`.
## **invokeai-update**
This is the interactive console-based script that is run by launcher
menu item [9] to update to a new version of InvokeAI. It takes no
command-line arguments.
## **invokeai-metadata**
This is a script which takes a list of InvokeAI-generated images and
outputs their metadata in the same JSON format that you get from the
`</>` button in the Web GUI. For example:
```bash
$ invokeai-metadata ffe2a115-b492-493c-afff-7679aa034b50.png
ffe2a115-b492-493c-afff-7679aa034b50.png:
{
"app_version": "3.1.0",
"cfg_scale": 8.0,
"clip_skip": 0,
"controlnets": [],
"generation_mode": "sdxl_txt2img",
"height": 1024,
"loras": [],
"model": {
"base_model": "sdxl",
"model_name": "stable-diffusion-xl-base-1.0",
"model_type": "main"
},
"negative_prompt": "",
"negative_style_prompt": "",
"positive_prompt": "military grade sushi dinner for shock troopers",
"positive_style_prompt": "",
"rand_device": "cpu",
"refiner_cfg_scale": 7.5,
"refiner_model": {
"base_model": "sdxl-refiner",
"model_name": "sd_xl_refiner_1.0",
"model_type": "main"
},
"refiner_negative_aesthetic_score": 2.5,
"refiner_positive_aesthetic_score": 6.0,
"refiner_scheduler": "euler",
"refiner_start": 0.8,
"refiner_steps": 20,
"scheduler": "euler",
"seed": 387129902,
"steps": 25,
"width": 1024
}
```
You may list multiple files on the command line.
## **invokeai-import-images**
InvokeAI uses a database to store information about images it
generated, and just copying the image files from one InvokeAI root
directory to another does not automatically import those images into
the destination's gallery. This script allows you to bulk import
images generated by one instance of InvokeAI into a gallery maintained
by another. It also works on images generated by older versions of
InvokeAI, going way back to version 1.
This script has an interactive mode only. The following example shows
it in action:
```bash
$ invokeai-import-images
===============================================================================
This script will import images generated by earlier versions of
InvokeAI into the currently installed root directory:
/home/XXXX/invokeai-main
If this is not what you want to do, type ctrl-C now to cancel.
===============================================================================
= Configuration & Settings
Found invokeai.yaml file at /home/XXXX/invokeai-main/invokeai.yaml:
Database : /home/XXXX/invokeai-main/databases/invokeai.db
Outputs : /home/XXXX/invokeai-main/outputs/images
Use these paths for import (yes) or choose different ones (no) [Yn]:
Inputs: Specify absolute path containing InvokeAI .png images to import: /home/XXXX/invokeai-2.3/outputs/images/
Include files from subfolders recursively [yN]?
Options for board selection for imported images:
1) Select an existing board name. (found 4)
2) Specify a board name to create/add to.
3) Create/add to board named 'IMPORT'.
4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_20230919T203519Z).
5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5).
Specify desired board option: 3
===============================================================================
= Import Settings Confirmation
Database File Path : /home/XXXX/invokeai-main/databases/invokeai.db
Outputs/Images Directory : /home/XXXX/invokeai-main/outputs/images
Import Image Source Directory : /home/XXXX/invokeai-2.3/outputs/images/
Recurse Source SubDirectories : No
Count of .png file(s) found : 5785
Board name option specified : IMPORT
Database backup will be taken at : /home/XXXX/invokeai-main/databases/backup
Notes about the import process:
- Source image files will not be modified, only copied to the outputs directory.
- If the same file name already exists in the destination, the file will be skipped.
- If the same file name already has a record in the database, the file will be skipped.
- Invoke AI metadata tags will be updated/written into the imported copy only.
- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)
- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer.
- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder.
Do you wish to continue with the import [Yn] ?
Making DB Backup at /home/lstein/invokeai-main/databases/backup/backup-20230919T203519Z-invokeai.db...Done!
===============================================================================
Importing /home/XXXX/invokeai-2.3/outputs/images/17d09907-297d-4db3-a18a-60b337feac66.png
... (5785 more lines) ...
===============================================================================
= Import Complete - Elpased Time: 0.28 second(s)
Source File(s) : 5785
Total Imported : 5783
Skipped b/c file already exists on disk : 1
Skipped b/c file already exists in db : 0
Errors during import : 1
```
## **invokeai-db-maintenance**
This script helps maintain the integrity of your InvokeAI database by
finding and fixing three problems that can arise over time:
1. An image was manually deleted from the outputs directory, leaving a
dangling image record in the InvokeAI database. This will cause a
black image to appear in the gallery. This is an "orphaned database
image record." The script can fix this by running a "clean"
operation on the database, removing the orphaned entries.
2. An image is present in the outputs directory but there is no
corresponding entry in the database. This can happen when the image
is added manually to the outputs directory, or if a crash occurred
after the image was generated but before the database was
completely updated. The symptom is that the image is present in the
outputs folder but doesn't appear in the InvokeAI gallery. This is
called an "orphaned image file." The script can fix this problem by
running an "archive" operation in which orphaned files are moved
into a directory named `outputs/images-archive`. If you wish, you
can then run `invokeai-image-import` to reimport these images back
into the database.
3. The thumbnail for an image is missing, again causing a black
gallery thumbnail. This is fixed by running the "thumbnaiils"
operation, which simply regenerates and re-registers the missing
thumbnail.
You can find and fix all three of these problems in a single go by
executing this command:
```bash
invokeai-db-maintenance --operation all
```
Or you can run just the clean and thumbnail operations like this:
```bash
invokeai-db-maintenance -operation clean, thumbnail
```
If called without any arguments, the script will ask you which
operations you wish to perform.
## **invokeai-migrate3**
This script will migrate settings and models (but not images!) from an
InvokeAI v2.3 root folder to an InvokeAI 3.X folder. Call it with the
source and destination root folders like this:
```bash
invokeai-migrate3 --from ~/invokeai-2.3 --to invokeai-3.1.1
```
Both directories must previously have been properly created and
initialized by `invokeai-configure`. If you wish to migrate the images
contained in the older root as well, you can use the
`invokeai-image-migrate` script described earlier.
---
Copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

3
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@ -51,6 +51,9 @@ Prevent InvokeAI from displaying unwanted racy images.
### * [Controlling Logging](LOGGING.md)
Control how InvokeAI logs status messages.
### * [Command-line Utilities](UTILITIES.md)
A list of the command-line utilities available with InvokeAI.
<!-- OUT OF DATE
### * [Miscellaneous](OTHER.md)
Run InvokeAI on Google Colab, generate images with repeating patterns,

7
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@ -15,7 +15,8 @@ title: Home
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.1/css/fontawesome.min.css">
<style>
.button {
width: 300px;
width: 100%;
max-width: 100%;
height: 50px;
background-color: #448AFF;
color: #fff;
@ -27,8 +28,9 @@ title: Home
.button-container {
display: grid;
grid-template-columns: repeat(3, 300px);
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 20px;
justify-content: center;
}
.button:hover {
@ -145,6 +147,7 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
### InvokeAI Configuration
- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
- [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
## :octicons-log-16: Important Changes Since Version 2.3

7
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@ -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!)

72
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@ -17,14 +17,32 @@ This fork is supported across Linux, Windows and Macintosh. Linux users can use
either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
driver).
### [Installation Getting Started Guide](installation)
#### **[Automated Installer](010_INSTALL_AUTOMATED.md)**
## **[Automated Installer](010_INSTALL_AUTOMATED.md)**
✅ This is the recommended installation method for first-time users.
#### [Manual Installation](020_INSTALL_MANUAL.md)
This method is recommended for experienced users and developers
#### [Docker Installation](040_INSTALL_DOCKER.md)
This method is recommended for those familiar with running Docker containers
### Other Installation Guides
This is a script that will install all of InvokeAI's essential
third party libraries and InvokeAI itself. It includes access to a
"developer console" which will help us debug problems with you and
give you to access experimental features.
## **[Manual Installation](020_INSTALL_MANUAL.md)**
This method is recommended for experienced users and developers.
In this method you will manually run the commands needed to install
InvokeAI and its dependencies. We offer two recipes: one suited to
those who prefer the `conda` tool, and one suited to those who prefer
`pip` and Python virtual environments. In our hands the pip install
is faster and more reliable, but your mileage may vary.
Note that the conda installation method is currently deprecated and
will not be supported at some point in the future.
## **[Docker Installation](040_INSTALL_DOCKER.md)**
This method is recommended for those familiar with running Docker containers.
We offer a method for creating Docker containers containing InvokeAI and its dependencies. This method is recommended for individuals with experience with Docker containers and understand the pluses and minuses of a container-based install.
## Other Installation Guides
- [PyPatchMatch](060_INSTALL_PATCHMATCH.md)
- [XFormers](070_INSTALL_XFORMERS.md)
- [CUDA and ROCm Drivers](030_INSTALL_CUDA_AND_ROCM.md)
@ -63,43 +81,3 @@ images in full-precision mode:
- GTX 1650 series cards
- GTX 1660 series cards
## Installation options
1. [Automated Installer](010_INSTALL_AUTOMATED.md)
This is a script that will install all of InvokeAI's essential
third party libraries and InvokeAI itself. It includes access to a
"developer console" which will help us debug problems with you and
give you to access experimental features.
✅ This is the recommended option for first time users.
2. [Manual Installation](020_INSTALL_MANUAL.md)
In this method you will manually run the commands needed to install
InvokeAI and its dependencies. We offer two recipes: one suited to
those who prefer the `conda` tool, and one suited to those who prefer
`pip` and Python virtual environments. In our hands the pip install
is faster and more reliable, but your mileage may vary.
Note that the conda installation method is currently deprecated and
will not be supported at some point in the future.
This method is recommended for users who have previously used `conda`
or `pip` in the past, developers, and anyone who wishes to remain on
the cutting edge of future InvokeAI development and is willing to put
up with occasional glitches and breakage.
3. [Docker Installation](040_INSTALL_DOCKER.md)
We also offer a method for creating Docker containers containing
InvokeAI and its dependencies. This method is recommended for
individuals with experience with Docker containers and understand
the pluses and minuses of a container-based install.
## Quick Guides
* [Installing CUDA and ROCm Drivers](./030_INSTALL_CUDA_AND_ROCM.md)
* [Installing XFormers](./070_INSTALL_XFORMERS.md)
* [Installing PyPatchMatch](./060_INSTALL_PATCHMATCH.md)
* [Installing New Models](./050_INSTALLING_MODELS.md)

40
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@ -1,13 +1,32 @@
# 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
## Important Concepts
There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
@ -37,7 +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.
To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
Batch or multiple image generation in the workflow editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate, meaning this example will generate 4 images. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. This noise can then be fed to the Denoise Latents node for it to iterate through the denoising process with the different seeds provided.
![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)

38
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@ -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
@ -196,6 +196,40 @@ Results after using the depth controlnet
--------------------------------
### Prompt Tools
**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These where written to accompany the PromptsFromFile node and other prompt generation nodes.
1. PromptJoin - Joins to prompts into one.
2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
3. PromptSplitNeg - splits a prompt into positive and negative using the old V2 method of [] for negative.
4. PromptToFile - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
5. PTFieldsCollect - Converts image generation fields into a Json format string that can be passed to Prompt to file.
6. PTFieldsExpand - Takes Json string and converts it to individual generation parameters This can be fed from the Prompts to file node.
7. PromptJoinThree - Joins 3 prompt together.
8. PromptStrength - This take a string and float and outputs another string in the format of (string)strength like the weighted format of compel.
9. PromptStrengthCombine - This takes a collection of prompt strength strings and outputs a string in the .and() or .blend() format that can be fed into a proper prompt node.
See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
--------------------------------
### XY Image to Grid and Images to Grids nodes
**Description:** Image to grid nodes and supporting tools.
1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then mutilple grids will be created until it runs out of images.
2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporoting nodes. See example node setups for more details.
See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
--------------------------------
### Example Node Template
**Description:** This node allows you to do super cool things with InvokeAI.

8
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@ -4,10 +4,10 @@ To learn about the specifics of creating a new node, please visit our [Node crea
Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps:
- Make sure the node is contained in a new Python (.py) file
- Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
- Make sure you are following the template below and have provided all relevant details about the node and what it does.
- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
- Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
- Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.
### Community Node Template

4
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@ -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|

16
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@ -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

1010
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735
docs/workflows/SDXL_Text_to_Image.json Normal file
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@ -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": [
{
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"fieldName": "model"
},
{
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"fieldName": "prompt"
},
{
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"fieldName": "style"
},
{
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},
{
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"fieldName": "style"
},
{
"nodeId": "87ee6243-fb0d-4f77-ad5f-56591659339e",
"fieldName": "steps"
}
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"meta": {
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},
"nodes": [
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},
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"label": "Negative Style",
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},
"original_width": {
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"value": 1024
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},
"crop_top": {
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"name": "crop_top",
"type": "integer",
"fieldKind": "input",
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},
"crop_left": {
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"name": "crop_left",
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},
"target_width": {
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},
"target_height": {
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},
"clip": {
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},
"clip2": {
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"type": "ClipField",
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}
},
"outputs": {
"conditioning": {
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"name": "conditioning",
"type": "ConditioningField",
"fieldKind": "output"
}
},
"label": "SDXL Negative Compel Prompt",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": true
},
"width": 320,
"height": 764,
"position": {
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}
},
{
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"type": "noise",
"inputs": {
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},
"outputs": {
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{
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"inputs": {
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},
{
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{
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"value": {
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"base_model": "sdxl",
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}
},
"outputs": {
"unet": {
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},
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},
{
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"type": "invocation",
"data": {
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"id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
"type": "sdxl_compel_prompt",
"inputs": {
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"name": "prompt",
"type": "string",
"fieldKind": "input",
"label": "Positive Prompt",
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},
"style": {
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"name": "style",
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},
"original_width": {
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},
"original_height": {
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},
"crop_top": {
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},
"crop_left": {
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},
"target_width": {
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},
"target_height": {
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},
"clip": {
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},
"clip2": {
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}
},
"outputs": {
"conditioning": {
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"name": "conditioning",
"type": "ConditioningField",
"fieldKind": "output"
}
},
"label": "SDXL Positive Compel Prompt",
"isOpen": true,
"notes": "",
"embedWorkflow": false,
"isIntermediate": true
},
"width": 320,
"height": 764,
"position": {
"x": 900,
"y": -350
}
},
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"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"
}
]
}

2
installer/create_installer.sh
View File

@ -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."

1
installer/lib/main.py
View File

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

10
installer/templates/invoke.bat.in
View File

@ -17,9 +17,10 @@ echo 6. Change InvokeAI startup options
echo 7. Re-run the configure script to fix a broken install or to complete a major upgrade
echo 8. Open the developer console
echo 9. Update InvokeAI
echo 10. Command-line help
echo 10. Run the InvokeAI image database maintenance script
echo 11. Command-line help
echo Q - Quit
set /P choice="Please enter 1-10, Q: [1] "
set /P choice="Please enter 1-11, Q: [1] "
if not defined choice set choice=1
IF /I "%choice%" == "1" (
echo Starting the InvokeAI browser-based UI..
@ -58,8 +59,11 @@ IF /I "%choice%" == "1" (
echo Running invokeai-update...
python -m invokeai.frontend.install.invokeai_update
) ELSE IF /I "%choice%" == "10" (
echo Running the db maintenance script...
python .venv\Scripts\invokeai-db-maintenance.exe
) ELSE IF /I "%choice%" == "11" (
echo Displaying command line help...
python .venv\Scripts\invokeai.exe --help %*
python .venv\Scripts\invokeai-web.exe --help %*
pause
exit /b
) ELSE IF /I "%choice%" == "q" (

18
installer/templates/invoke.sh.in
View File

@ -97,13 +97,13 @@ do_choice() {
;;
10)
clear
printf "Command-line help\n"
invokeai --help
printf "Running the db maintenance script\n"
invokeai-db-maintenance --root ${INVOKEAI_ROOT}
;;
"HELP 1")
11)
clear
printf "Command-line help\n"
invokeai --help
invokeai-web --help
;;
*)
clear
@ -125,7 +125,10 @@ do_dialog() {
6 "Change InvokeAI startup options"
7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
8 "Open the developer console"
9 "Update InvokeAI")
9 "Update InvokeAI"
10 "Run the InvokeAI image database maintenance script"
11 "Command-line help"
)
choice=$(dialog --clear \
--backtitle "\Zb\Zu\Z3InvokeAI" \
@ -157,9 +160,10 @@ do_line_input() {
printf "7: Re-run the configure script to fix a broken install\n"
printf "8: Open the developer console\n"
printf "9: Update InvokeAI\n"
printf "10: Command-line help\n"
printf "10: Run the InvokeAI image database maintenance script\n"
printf "11: Command-line help\n"
printf "Q: Quit\n\n"
read -p "Please enter 1-10, Q: [1] " yn
read -p "Please enter 1-11, Q: [1] " yn
choice=${yn:='1'}
do_choice $choice
clear

58
invokeai/app/api/dependencies.py
View File

@ -1,34 +1,35 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import sqlite3
from logging import Logger
from invokeai.app.services.board_image_record_storage import (
SqliteBoardImageRecordStorage,
)
from invokeai.app.services.board_images import (
BoardImagesService,
BoardImagesServiceDependencies,
)
from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.session_processor.session_processor_default import DefaultSessionProcessor
from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
from invokeai.app.services.urls import LocalUrlService
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 ..services.thread import lock
from .events import FastAPIEventService
@ -67,22 +68,32 @@ class ApiDependencies:
output_folder = config.output_path
# TODO: build a file/path manager?
db_path = config.db_path
db_path.parent.mkdir(parents=True, exist_ok=True)
db_location = str(db_path)
if config.use_memory_db:
db_location = ":memory:"
else:
db_path = config.db_path
db_path.parent.mkdir(parents=True, exist_ok=True)
db_location = str(db_path)
logger.info(f"Using database at {db_location}")
db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution
if config.log_sql:
db_conn.set_trace_callback(print)
db_conn.execute("PRAGMA foreign_keys = ON;")
graph_execution_manager = SqliteItemStorage[GraphExecutionState](
filename=db_location, table_name="graph_executions"
conn=db_conn, table_name="graph_executions", lock=lock
)
urls = LocalUrlService()
image_record_storage = SqliteImageRecordStorage(db_location)
image_record_storage = SqliteImageRecordStorage(conn=db_conn, lock=lock)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
board_record_storage = SqliteBoardRecordStorage(db_location)
board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
board_record_storage = SqliteBoardRecordStorage(conn=db_conn, lock=lock)
board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn, lock=lock)
boards = BoardService(
services=BoardServiceDependencies(
@ -124,18 +135,29 @@ class ApiDependencies:
boards=boards,
board_images=board_images,
queue=MemoryInvocationQueue(),
graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, lock=lock, table_name="graphs"),
graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(),
configuration=config,
performance_statistics=InvocationStatsService(graph_execution_manager),
logger=logger,
session_queue=SqliteSessionQueue(conn=db_conn, lock=lock),
session_processor=DefaultSessionProcessor(),
invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
)
create_system_graphs(services.graph_library)
ApiDependencies.invoker = Invoker(services)
try:
lock.acquire()
db_conn.execute("VACUUM;")
db_conn.commit()
logger.info("Cleaned database")
finally:
lock.release()
@staticmethod
def shutdown():
if ApiDependencies.invoker:

41
invokeai/app/api/routers/app_info.py
View File

@ -7,6 +7,7 @@ from fastapi.routing import APIRouter
from pydantic import BaseModel, Field
from invokeai.app.invocations.upscale import ESRGAN_MODELS
from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.patchmatch import PatchMatch
from invokeai.backend.image_util.safety_checker import SafetyChecker
@ -103,3 +104,43 @@ async def set_log_level(
"""Sets the log verbosity level"""
ApiDependencies.invoker.services.logger.setLevel(level)
return LogLevel(ApiDependencies.invoker.services.logger.level)
@app_router.delete(
"/invocation_cache",
operation_id="clear_invocation_cache",
responses={200: {"description": "The operation was successful"}},
)
async def clear_invocation_cache() -> None:
"""Clears the invocation cache"""
ApiDependencies.invoker.services.invocation_cache.clear()
@app_router.put(
"/invocation_cache/enable",
operation_id="enable_invocation_cache",
responses={200: {"description": "The operation was successful"}},
)
async def enable_invocation_cache() -> None:
"""Clears the invocation cache"""
ApiDependencies.invoker.services.invocation_cache.enable()
@app_router.put(
"/invocation_cache/disable",
operation_id="disable_invocation_cache",
responses={200: {"description": "The operation was successful"}},
)
async def disable_invocation_cache() -> None:
"""Clears the invocation cache"""
ApiDependencies.invoker.services.invocation_cache.disable()
@app_router.get(
"/invocation_cache/status",
operation_id="get_invocation_cache_status",
responses={200: {"model": InvocationCacheStatus}},
)
async def get_invocation_cache_status() -> InvocationCacheStatus:
"""Clears the invocation cache"""
return ApiDependencies.invoker.services.invocation_cache.get_status()

9
invokeai/app/api/routers/images.py
View File

@ -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 (
ImageDTO,
ImageRecordChanges,
ImageUrlsDTO,
)
from invokeai.app.services.models.image_record import ImageDTO, ImageRecordChanges, ImageUrlsDTO
from ..dependencies import ApiDependencies
images_router = APIRouter(prefix="/v1/images", tags=["images"])

8
invokeai/app/api/routers/models.py
View File

@ -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

247
invokeai/app/api/routers/session_queue.py Normal file
View File

@ -0,0 +1,247 @@
from typing import Optional
from fastapi import Body, Path, Query
from fastapi.routing import APIRouter
from pydantic import BaseModel
from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
from invokeai.app.services.session_queue.session_queue_common import (
QUEUE_ITEM_STATUS,
Batch,
BatchStatus,
CancelByBatchIDsResult,
ClearResult,
EnqueueBatchResult,
EnqueueGraphResult,
PruneResult,
SessionQueueItem,
SessionQueueItemDTO,
SessionQueueStatus,
)
from invokeai.app.services.shared.models import CursorPaginatedResults
from ...services.graph import Graph
from ..dependencies import ApiDependencies
session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])
class SessionQueueAndProcessorStatus(BaseModel):
"""The overall status of session queue and processor"""
queue: SessionQueueStatus
processor: SessionProcessorStatus
@session_queue_router.post(
"/{queue_id}/enqueue_graph",
operation_id="enqueue_graph",
responses={
201: {"model": EnqueueGraphResult},
},
)
async def enqueue_graph(
queue_id: str = Path(description="The queue id to perform this operation on"),
graph: Graph = Body(description="The graph to enqueue"),
prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
) -> EnqueueGraphResult:
"""Enqueues a graph for single execution."""
return ApiDependencies.invoker.services.session_queue.enqueue_graph(queue_id=queue_id, graph=graph, prepend=prepend)
@session_queue_router.post(
"/{queue_id}/enqueue_batch",
operation_id="enqueue_batch",
responses={
201: {"model": EnqueueBatchResult},
},
)
async def enqueue_batch(
queue_id: str = Path(description="The queue id to perform this operation on"),
batch: Batch = Body(description="Batch to process"),
prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
) -> EnqueueBatchResult:
"""Processes a batch and enqueues the output graphs for execution."""
return ApiDependencies.invoker.services.session_queue.enqueue_batch(queue_id=queue_id, batch=batch, prepend=prepend)
@session_queue_router.get(
"/{queue_id}/list",
operation_id="list_queue_items",
responses={
200: {"model": CursorPaginatedResults[SessionQueueItemDTO]},
},
)
async def list_queue_items(
queue_id: str = Path(description="The queue id to perform this operation on"),
limit: int = Query(default=50, description="The number of items to fetch"),
status: Optional[QUEUE_ITEM_STATUS] = Query(default=None, description="The status of items to fetch"),
cursor: Optional[int] = Query(default=None, description="The pagination cursor"),
priority: int = Query(default=0, description="The pagination cursor priority"),
) -> CursorPaginatedResults[SessionQueueItemDTO]:
"""Gets all queue items (without graphs)"""
return ApiDependencies.invoker.services.session_queue.list_queue_items(
queue_id=queue_id, limit=limit, status=status, cursor=cursor, priority=priority
)
@session_queue_router.put(
"/{queue_id}/processor/resume",
operation_id="resume",
responses={200: {"model": SessionProcessorStatus}},
)
async def resume(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> SessionProcessorStatus:
"""Resumes session processor"""
return ApiDependencies.invoker.services.session_processor.resume()
@session_queue_router.put(
"/{queue_id}/processor/pause",
operation_id="pause",
responses={200: {"model": SessionProcessorStatus}},
)
async def Pause(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> SessionProcessorStatus:
"""Pauses session processor"""
return ApiDependencies.invoker.services.session_processor.pause()
@session_queue_router.put(
"/{queue_id}/cancel_by_batch_ids",
operation_id="cancel_by_batch_ids",
responses={200: {"model": CancelByBatchIDsResult}},
)
async def cancel_by_batch_ids(
queue_id: str = Path(description="The queue id to perform this operation on"),
batch_ids: list[str] = Body(description="The list of batch_ids to cancel all queue items for", embed=True),
) -> CancelByBatchIDsResult:
"""Immediately cancels all queue items from the given batch ids"""
return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
@session_queue_router.put(
"/{queue_id}/clear",
operation_id="clear",
responses={
200: {"model": ClearResult},
},
)
async def clear(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> ClearResult:
"""Clears the queue entirely, immediately canceling the currently-executing session"""
queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
if queue_item is not None:
ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
return clear_result
@session_queue_router.put(
"/{queue_id}/prune",
operation_id="prune",
responses={
200: {"model": PruneResult},
},
)
async def prune(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> PruneResult:
"""Prunes all completed or errored queue items"""
return ApiDependencies.invoker.services.session_queue.prune(queue_id)
@session_queue_router.get(
"/{queue_id}/current",
operation_id="get_current_queue_item",
responses={
200: {"model": Optional[SessionQueueItem]},
},
)
async def get_current_queue_item(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> Optional[SessionQueueItem]:
"""Gets the currently execution queue item"""
return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
@session_queue_router.get(
"/{queue_id}/next",
operation_id="get_next_queue_item",
responses={
200: {"model": Optional[SessionQueueItem]},
},
)
async def get_next_queue_item(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> Optional[SessionQueueItem]:
"""Gets the next queue item, without executing it"""
return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
@session_queue_router.get(
"/{queue_id}/status",
operation_id="get_queue_status",
responses={
200: {"model": SessionQueueAndProcessorStatus},
},
)
async def get_queue_status(
queue_id: str = Path(description="The queue id to perform this operation on"),
) -> SessionQueueAndProcessorStatus:
"""Gets the status of the session queue"""
queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
processor = ApiDependencies.invoker.services.session_processor.get_status()
return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
@session_queue_router.get(
"/{queue_id}/b/{batch_id}/status",
operation_id="get_batch_status",
responses={
200: {"model": BatchStatus},
},
)
async def get_batch_status(
queue_id: str = Path(description="The queue id to perform this operation on"),
batch_id: str = Path(description="The batch to get the status of"),
) -> BatchStatus:
"""Gets the status of the session queue"""
return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
@session_queue_router.get(
"/{queue_id}/i/{item_id}",
operation_id="get_queue_item",
responses={
200: {"model": SessionQueueItem},
},
)
async def get_queue_item(
queue_id: str = Path(description="The queue id to perform this operation on"),
item_id: int = Path(description="The queue item to get"),
) -> SessionQueueItem:
"""Gets a queue item"""
return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
@session_queue_router.put(
"/{queue_id}/i/{item_id}/cancel",
operation_id="cancel_queue_item",
responses={
200: {"model": SessionQueueItem},
},
)
async def cancel_queue_item(
queue_id: str = Path(description="The queue id to perform this operation on"),
item_id: int = Path(description="The queue item to cancel"),
) -> SessionQueueItem:
"""Deletes a queue item"""
return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)

26
invokeai/app/api/routers/sessions.py
View File

@ -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 (
Edge,
EdgeConnection,
Graph,
GraphExecutionState,
NodeAlreadyExecutedError,
)
from ...services.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
from ...services.item_storage import PaginatedResults
from ..dependencies import ApiDependencies
@ -29,12 +23,14 @@ session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
200: {"model": GraphExecutionState},
400: {"description": "Invalid json"},
},
deprecated=True,
)
async def create_session(
graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with")
queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"),
) -> GraphExecutionState:
"""Creates a new session, optionally initializing it with an invocation graph"""
session = ApiDependencies.invoker.create_execution_state(graph)
session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
return session
@ -42,6 +38,7 @@ async def create_session(
"/",
operation_id="list_sessions",
responses={200: {"model": PaginatedResults[GraphExecutionState]}},
deprecated=True,
)
async def list_sessions(
page: int = Query(default=0, description="The page of results to get"),
@ -63,6 +60,7 @@ async def list_sessions(
200: {"model": GraphExecutionState},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def get_session(
session_id: str = Path(description="The id of the session to get"),
@ -83,6 +81,7 @@ async def get_session(
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def add_node(
session_id: str = Path(description="The id of the session"),
@ -115,6 +114,7 @@ async def add_node(
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def update_node(
session_id: str = Path(description="The id of the session"),
@ -148,6 +148,7 @@ async def update_node(
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def delete_node(
session_id: str = Path(description="The id of the session"),
@ -178,6 +179,7 @@ async def delete_node(
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def add_edge(
session_id: str = Path(description="The id of the session"),
@ -209,6 +211,7 @@ async def add_edge(
400: {"description": "Invalid node or link"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def delete_edge(
session_id: str = Path(description="The id of the session"),
@ -247,8 +250,10 @@ async def delete_edge(
400: {"description": "The session has no invocations ready to invoke"},
404: {"description": "Session not found"},
},
deprecated=True,
)
async def invoke_session(
queue_id: str = Query(description="The id of the queue to associate the session with"),
session_id: str = Path(description="The id of the session to invoke"),
all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
) -> Response:
@ -260,7 +265,7 @@ async def invoke_session(
if session.is_complete():
raise HTTPException(status_code=400)
ApiDependencies.invoker.invoke(session, invoke_all=all)
ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
return Response(status_code=202)
@ -268,6 +273,7 @@ async def invoke_session(
"/{session_id}/invoke",
operation_id="cancel_session_invoke",
responses={202: {"description": "The invocation is canceled"}},
deprecated=True,
)
async def cancel_session_invoke(
session_id: str = Path(description="The id of the session to cancel"),

41
invokeai/app/api/routers/utilities.py Normal file
View File

@ -0,0 +1,41 @@
from typing import Optional
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from fastapi import Body
from fastapi.routing import APIRouter
from pydantic import BaseModel
from pyparsing import ParseException
utilities_router = APIRouter(prefix="/v1/utilities", tags=["utilities"])
class DynamicPromptsResponse(BaseModel):
prompts: list[str]
error: Optional[str] = None
@utilities_router.post(
"/dynamicprompts",
operation_id="parse_dynamicprompts",
responses={
200: {"model": DynamicPromptsResponse},
},
)
async def parse_dynamicprompts(
prompt: str = Body(description="The prompt to parse with dynamicprompts"),
max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
) -> DynamicPromptsResponse:
"""Creates a batch process"""
try:
error: Optional[str] = None
if combinatorial:
generator = CombinatorialPromptGenerator()
prompts = generator.generate(prompt, max_prompts=max_prompts)
else:
generator = RandomPromptGenerator()
prompts = generator.generate(prompt, num_images=max_prompts)
except ParseException as e:
prompts = [prompt]
error = str(e)
return DynamicPromptsResponse(prompts=prompts if prompts else [""], error=error)

33
invokeai/app/api/sockets.py
View File

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

70
invokeai/app/api_app.py
View File

@ -1,40 +1,44 @@
# Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import asyncio
import socket
from inspect import signature
from pathlib import Path
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
from fastapi.openapi.utils import get_openapi
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema
from .services.config import InvokeAIAppConfig
from ..backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
import invokeai.frontend.web as web_dir
import mimetypes
if True: # hack to make flake8 happy with imports coming after setting up the config
import asyncio
import logging
import mimetypes
import socket
from inspect import signature
from pathlib import Path
from .api.dependencies import ApiDependencies
from .api.routers import sessions, models, images, boards, board_images, app_info
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
import torch
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
from fastapi.openapi.utils import get_openapi
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema
import torch
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
if torch.backends.mps.is_available():
# noinspection PyUnresolvedReferences
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.frontend.web as web_dir
from invokeai.version.invokeai_version import __version__
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
from .api.routers import app_info, board_images, boards, images, models, session_queue, sessions, utilities
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
if torch.backends.mps.is_available():
# noinspection PyUnresolvedReferences
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
app_config = InvokeAIAppConfig.get_config()
@ -89,6 +93,8 @@ async def shutdown_event():
app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api")
app.include_router(models.models_router, prefix="/api")
app.include_router(images.images_router, prefix="/api")
@ -99,6 +105,8 @@ app.include_router(board_images.board_images_router, prefix="/api")
app.include_router(app_info.app_router, prefix="/api")
app.include_router(session_queue.session_queue_router, prefix="/api")
# Build a custom OpenAPI to include all outputs
# TODO: can outputs be included on metadata of invocation schemas somehow?

10
invokeai/app/cli/commands.py
View File

@ -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

6
invokeai/app/cli/completer.py
View File

@ -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

117
invokeai/app/cli_app.py
View File

@ -1,64 +1,63 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import argparse
import re
import shlex
import sys
import time
from typing import Union, get_type_hints, Optional
from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from pydantic import BaseModel, ValidationError
from pydantic.fields import Field
# This should come early so that the logger can pick up its configuration options
from .services.config import InvokeAIAppConfig
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
from invokeai.app.services.board_image_record_storage import (
SqliteBoardImageRecordStorage,
)
from invokeai.app.services.board_images import (
BoardImagesService,
BoardImagesServiceDependencies,
)
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from invokeai.app.services.invocation_stats import InvocationStatsService
from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
if True: # hack to make flake8 happy with imports coming after setting up the config
import argparse
import re
import shlex
import sqlite3
import sys
import time
from typing import Optional, Union, get_type_hints
from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
from .cli.completer import set_autocompleter
from .invocations.baseinvocation import BaseInvocation
from .services.events import EventServiceBase
from .services.graph import (
Edge,
EdgeConnection,
GraphExecutionState,
GraphInvocation,
LibraryGraph,
are_connection_types_compatible,
)
from .services.image_file_storage import DiskImageFileStorage
from .services.invocation_queue import MemoryInvocationQueue
from .services.invocation_services import InvocationServices
from .services.invoker import Invoker
from .services.model_manager_service import ModelManagerService
from .services.processor import DefaultInvocationProcessor
from .services.sqlite import SqliteItemStorage
import torch
from pydantic import BaseModel, ValidationError
from pydantic.fields import Field
import torch
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.invocation_stats import InvocationStatsService
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
from .cli.completer import set_autocompleter
from .invocations.baseinvocation import BaseInvocation
from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id
from .services.events import EventServiceBase
from .services.graph import (
Edge,
EdgeConnection,
GraphExecutionState,
GraphInvocation,
LibraryGraph,
are_connection_types_compatible,
)
from .services.image_file_storage import DiskImageFileStorage
from .services.invocation_queue import MemoryInvocationQueue
from .services.invocation_services import InvocationServices
from .services.invoker import Invoker
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
from .services.model_manager_service import ModelManagerService
from .services.processor import DefaultInvocationProcessor
from .services.sqlite import SqliteItemStorage
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
config = InvokeAIAppConfig.get_config()
config.parse_args()
@ -252,19 +251,18 @@ def invoke_cli():
db_location = config.db_path
db_location.parent.mkdir(parents=True, exist_ok=True)
db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution
logger.info(f'InvokeAI database location is "{db_location}"')
graph_execution_manager = SqliteItemStorage[GraphExecutionState](
filename=db_location, table_name="graph_executions"
)
graph_execution_manager = SqliteItemStorage[GraphExecutionState](conn=db_conn, table_name="graph_executions")
urls = LocalUrlService()
image_record_storage = SqliteImageRecordStorage(db_location)
image_record_storage = SqliteImageRecordStorage(conn=db_conn)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
board_record_storage = SqliteBoardRecordStorage(db_location)
board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
board_record_storage = SqliteBoardRecordStorage(conn=db_conn)
board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn)
boards = BoardService(
services=BoardServiceDependencies(
@ -306,12 +304,13 @@ def invoke_cli():
boards=boards,
board_images=board_images,
queue=MemoryInvocationQueue(),
graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, table_name="graphs"),
graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(),
performance_statistics=InvocationStatsService(graph_execution_manager),
logger=logger,
configuration=config,
invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
)
system_graphs = create_system_graphs(services.graph_library)

88
invokeai/app/invocations/baseinvocation.py
View File

@ -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 +23,12 @@ 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
if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices
@ -65,6 +67,7 @@ class FieldDescriptions:
width = "Width of output (px)"
height = "Height of output (px)"
control = "ControlNet(s) to apply"
ip_adapter = "IP-Adapter to apply"
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)"
@ -85,6 +88,9 @@ class FieldDescriptions:
num_1 = "The first number"
num_2 = "The second number"
mask = "The mask to use for the operation"
board = "The board to save the image to"
image = "The image to process"
tile_size = "Tile size"
class Input(str, Enum):
@ -153,6 +159,7 @@ class UIType(str, Enum):
VaeModel = "VaeModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
UNet = "UNetField"
Vae = "VaeField"
CLIP = "ClipField"
@ -169,6 +176,7 @@ class UIType(str, Enum):
WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate"
MetadataField = "MetadataField"
BoardField = "BoardField"
# endregion
@ -196,6 +204,7 @@ 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]
@ -244,6 +253,7 @@ 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:
@ -310,6 +320,7 @@ def InputField(
ui_hidden=ui_hidden,
ui_order=ui_order,
item_default=item_default,
ui_choice_labels=ui_choice_labels,
**kwargs,
)
@ -412,12 +423,27 @@ class UIConfigBase(BaseModel):
class InvocationContext:
"""Initialized and provided to on execution of invocations."""
services: InvocationServices
graph_execution_state_id: str
queue_id: str
queue_item_id: int
queue_batch_id: str
def __init__(self, services: InvocationServices, graph_execution_state_id: str):
def __init__(
self,
services: InvocationServices,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
):
self.services = services
self.graph_execution_state_id = graph_execution_state_id
self.queue_id = queue_id
self.queue_item_id = queue_item_id
self.queue_batch_id = queue_batch_id
class BaseInvocationOutput(BaseModel):
@ -470,6 +496,7 @@ class BaseInvocation(ABC, BaseModel):
@classmethod
def get_all_subclasses(cls):
app_config = InvokeAIAppConfig.get_config()
subclasses = []
toprocess = [cls]
while len(toprocess) > 0:
@ -477,7 +504,23 @@ class BaseInvocation(ABC, BaseModel):
next_subclasses = next.__subclasses__()
subclasses.extend(next_subclasses)
toprocess.extend(next_subclasses)
return subclasses
allowed_invocations = []
for sc in subclasses:
is_in_allowlist = (
sc.__fields__.get("type").default in app_config.allow_nodes
if isinstance(app_config.allow_nodes, list)
else True
)
is_in_denylist = (
sc.__fields__.get("type").default in app_config.deny_nodes
if isinstance(app_config.deny_nodes, list)
else False
)
if is_in_allowlist and not is_in_denylist:
allowed_invocations.append(sc)
return allowed_invocations
@classmethod
def get_invocations(cls):
@ -498,6 +541,9 @@ class BaseInvocation(ABC, BaseModel):
return signature(cls.invoke).return_annotation
class Config:
validate_assignment = True
validate_all = True
@staticmethod
def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
uiconfig = getattr(model_class, "UIConfig", None)
@ -546,7 +592,29 @@ class BaseInvocation(ABC, BaseModel):
raise RequiredConnectionException(self.__fields__["type"].default, field_name)
elif _input == Input.Any:
raise MissingInputException(self.__fields__["type"].default, field_name)
return self.invoke(context)
# skip node cache codepath if it's disabled
if context.services.configuration.node_cache_size == 0:
return self.invoke(context)
output: BaseInvocationOutput
if self.use_cache:
key = context.services.invocation_cache.create_key(self)
cached_value = context.services.invocation_cache.get(key)
if cached_value is None:
context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
output = self.invoke(context)
context.services.invocation_cache.save(key, output)
return output
else:
context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
return cached_value
else:
context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
return self.invoke(context)
def get_type(self) -> str:
return self.__fields__["type"].default
id: str = Field(
description="The id of this instance of an invocation. Must be unique among all instances of invocations."
@ -559,6 +627,7 @@ class BaseInvocation(ABC, BaseModel):
description="The workflow to save with the image",
ui_type=UIType.WorkflowField,
)
use_cache: bool = InputField(default=True, description="Whether or not to use the cache")
@validator("workflow", pre=True)
def validate_workflow_is_json(cls, v):
@ -582,6 +651,7 @@ def invocation(
tags: Optional[list[str]] = None,
category: Optional[str] = None,
version: Optional[str] = None,
use_cache: Optional[bool] = True,
) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]:
"""
Adds metadata to an invocation.
@ -590,6 +660,8 @@ def invocation(
:param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
:param Optional[list[str]] tags: Adds tags to the invocation. Invocations may be searched for by their tags. Defaults to None.
:param Optional[str] category: Adds a category to the invocation. Used to group the invocations in the UI. Defaults to None.
:param Optional[str] version: Adds a version to the invocation. Must be a valid semver string. Defaults to None.
:param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
"""
def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]:
@ -614,6 +686,8 @@ def invocation(
except ValueError as e:
raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
cls.UIConfig.version = version
if use_cache is not None:
cls.__fields__["use_cache"].default = use_cache
# Add the invocation type to the pydantic model of the invocation
invocation_type_annotation = Literal[invocation_type] # type: ignore

9
invokeai/app/invocations/collections.py
View File

@ -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(
@ -54,6 +56,7 @@ class RangeOfSizeInvocation(BaseInvocation):
tags=["range", "integer", "random", "collection"],
category="collections",
version="1.0.0",
use_cache=False,
)
class RandomRangeInvocation(BaseInvocation):
"""Creates a collection of random numbers"""

55
invokeai/app/invocations/compel.py
View File

@ -5,17 +5,16 @@ 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.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
ExtraConditioningInfo,
SDXLConditioningInfo,
)
from ...backend.model_management.models import ModelType
from ...backend.model_management.lora import ModelPatcher
from ...backend.model_management.models import ModelNotFoundException
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from ...backend.model_management.models import ModelNotFoundException, ModelType
from ...backend.util.devices import torch_dtype
from .baseinvocation import (
BaseInvocation,
@ -100,14 +99,15 @@ class CompelInvocation(BaseInvocation):
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
with ModelPatcher.apply_lora_text_encoder(
text_encoder_info.context.model, _lora_loader()
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
), ModelPatcher.apply_clip_skip(
text_encoder_info.context.model, self.clip.skipped_layers
), text_encoder_info as text_encoder:
with (
ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
),
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
text_encoder_info as text_encoder,
):
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
@ -123,7 +123,7 @@ class CompelInvocation(BaseInvocation):
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
ec = ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
cross_attention_control_args=options.get("cross_attention_control", None),
)
@ -214,14 +214,15 @@ class SDXLPromptInvocationBase:
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
with ModelPatcher.apply_lora(
text_encoder_info.context.model, _lora_loader(), lora_prefix
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
), ModelPatcher.apply_clip_skip(
text_encoder_info.context.model, clip_field.skipped_layers
), text_encoder_info as text_encoder:
with (
ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
),
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
text_encoder_info as text_encoder,
):
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
@ -245,7 +246,7 @@ class SDXLPromptInvocationBase:
else:
c_pooled = None
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
ec = ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
cross_attention_control_args=options.get("cross_attention_control", None),
)
@ -437,9 +438,11 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
text_fragments = [
x.text
if type(x) is Fragment
else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
(
x.text
if type(x) is Fragment
else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
)
for x in parsed_prompt.children
]
text = " ".join(text_fragments)

37
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -28,23 +28,20 @@ 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,
invocation,
invocation_output,
)
CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
CONTROLNET_RESIZE_VALUES = Literal[
"just_resize",
@ -102,7 +99,7 @@ class ControlNetInvocation(BaseInvocation):
image: ImageField = InputField(description="The control image")
control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
control_weight: Union[float, List[float]] = InputField(
default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
default=1.0, description="The weight given to the ControlNet"
)
begin_step_percent: float = InputField(
default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
@ -562,3 +559,33 @@ class SamDetectorReproducibleColors(SamDetector):
img[:, :] = ann_color
final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
return np.array(final_img, dtype=np.uint8)
@invocation(
"color_map_image_processor",
title="Color Map Processor",
tags=["controlnet"],
category="controlnet",
version="1.0.0",
)
class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
"""Generates a color map from the provided image"""
color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
def run_processor(self, image: Image.Image):
image = image.convert("RGB")
image = np.array(image, dtype=np.uint8)
height, width = image.shape[:2]
width_tile_size = min(self.color_map_tile_size, width)
height_tile_size = min(self.color_map_tile_size, height)
color_map = cv2.resize(
image,
(width // width_tile_size, height // height_tile_size),
interpolation=cv2.INTER_CUBIC,
)
color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
color_map = Image.fromarray(color_map)
return color_map

3
invokeai/app/invocations/cv.py
View File

@ -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

56
invokeai/app/invocations/image.py
View File

@ -8,12 +8,12 @@ import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@ -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,
@ -330,8 +335,8 @@ class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions"""
image: ImageField = InputField(description="The image to resize")
width: int = InputField(default=512, ge=64, multiple_of=8, description="The width to resize to (px)")
height: int = InputField(default=512, ge=64, multiple_of=8, description="The height to resize to (px)")
width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
@ -960,3 +965,44 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
width=image_dto.width,
height=image_dto.height,
)
@invocation(
"save_image",
title="Save Image",
tags=["primitives", "image"],
category="primitives",
version="1.0.1",
use_cache=False,
)
class SaveImageInvocation(BaseInvocation):
"""Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: CoreMetadata = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image_dto = context.services.images.create(
image=image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
board_id=self.board.board_id if self.board else None,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
workflow=self.workflow,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)

103
invokeai/app/invocations/ip_adapter.py Normal file
View File

@ -0,0 +1,103 @@
import os
from builtins import float
from typing import List, Union
from pydantic import BaseModel, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
from invokeai.app.invocations.primitives import ImageField
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
class CLIPVisionModelField(BaseModel):
model_name: str = Field(description="Name of the CLIP Vision image encoder model")
base_model: BaseModelType = Field(description="Base model (usually 'Any')")
class IPAdapterField(BaseModel):
image: ImageField = Field(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
# weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
)
@invocation_output("ip_adapter_output")
class IPAdapterOutput(BaseInvocationOutput):
# Outputs
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""
# Inputs
image: ImageField = InputField(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = InputField(
description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
)
# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
weight: Union[float, List[float]] = InputField(
default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
)
begin_step_percent: float = InputField(
default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
)
end_step_percent: float = InputField(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
)
def invoke(self, context: InvocationContext) -> IPAdapterOutput:
# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
ip_adapter_info = context.services.model_manager.model_info(
self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
)
# HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
# directly, and 2) we are reading from disk every time this invocation is called without caching the result.
# A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
# is currently messy due to differences between how the model info is generated when installing a model from
# disk vs. downloading the model.
image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
)
image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
image_encoder_model = CLIPVisionModelField(
model_name=image_encoder_model_name,
base_model=BaseModelType.Any,
)
return IPAdapterOutput(
ip_adapter=IPAdapterField(
image=self.image,
ip_adapter_model=self.ip_adapter_model,
image_encoder_model=image_encoder_model,
weight=self.weight,
begin_step_percent=self.begin_step_percent,
end_step_percent=self.end_step_percent,
),
)

224
invokeai/app/invocations/latent.py
View File

@ -1,13 +1,16 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from contextlib import ExitStack
from functools import singledispatchmethod
from typing import List, Literal, Optional, Union
import einops
import numpy as np
import torch
import torchvision.transforms as T
from diffusers import AutoencoderKL, AutoencoderTiny
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import UNet2DConditionModel
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
@ -19,6 +22,7 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
@ -31,15 +35,17 @@ 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.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
from invokeai.backend.model_management.models import ModelType, SilenceWarnings
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
from ...backend.model_management.lora import ModelPatcher
from ...backend.model_management.seamless import set_seamless
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,
IPAdapterData,
StableDiffusionGeneratorPipeline,
image_resized_to_grid_as_tensor,
)
@ -63,8 +69,10 @@ from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .model import ModelInfo, UNetField, VaeField
DEFAULT_PRECISION = choose_precision(choose_torch_device())
if choose_torch_device() == torch.device("mps"):
from torch import mps
DEFAULT_PRECISION = choose_precision(choose_torch_device())
SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
@ -188,7 +196,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.0.0",
version="1.1.0",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -202,7 +210,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3)
steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
cfg_scale: Union[float, List[float]] = InputField(
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, ui_type=UIType.Float, title="CFG Scale"
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
)
denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
@ -212,13 +220,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
control: Union[ControlField, list[ControlField]] = InputField(
default=None,
description=FieldDescriptions.control,
input=Input.Connection,
ui_order=5,
)
ip_adapter: Optional[IPAdapterField] = InputField(
description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
)
latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
denoise_mask: Optional[DenoiseMaskField] = InputField(
default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=7
)
@validator("cfg_scale")
@ -320,8 +330,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
def prep_control_data(
self,
context: InvocationContext,
# really only need model for dtype and device
model: StableDiffusionGeneratorPipeline,
control_input: Union[ControlField, List[ControlField]],
latents_shape: List[int],
exit_stack: ExitStack,
@ -341,57 +349,107 @@ class DenoiseLatentsInvocation(BaseInvocation):
else:
control_list = None
if control_list is None:
control_data = None
# from above handling, any control that is not None should now be of type list[ControlField]
else:
# FIXME: add checks to skip entry if model or image is None
# and if weight is None, populate with default 1.0?
control_data = []
control_models = []
for control_info in control_list:
control_model = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=control_info.control_model.model_name,
model_type=ModelType.ControlNet,
base_model=control_info.control_model.base_model,
context=context,
)
)
return None
# After above handling, any control that is not None should now be of type list[ControlField].
control_models.append(control_model)
control_image_field = control_info.image
input_image = context.services.images.get_pil_image(control_image_field.image_name)
# self.image.image_type, self.image.image_name
# FIXME: still need to test with different widths, heights, devices, dtypes
# and add in batch_size, num_images_per_prompt?
# and do real check for classifier_free_guidance?
# prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
control_image = prepare_control_image(
image=input_image,
do_classifier_free_guidance=do_classifier_free_guidance,
width=control_width_resize,
height=control_height_resize,
# batch_size=batch_size * num_images_per_prompt,
# num_images_per_prompt=num_images_per_prompt,
device=control_model.device,
dtype=control_model.dtype,
control_mode=control_info.control_mode,
resize_mode=control_info.resize_mode,
# FIXME: add checks to skip entry if model or image is None
# and if weight is None, populate with default 1.0?
controlnet_data = []
for control_info in control_list:
control_model = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=control_info.control_model.model_name,
model_type=ModelType.ControlNet,
base_model=control_info.control_model.base_model,
context=context,
)
control_item = ControlNetData(
model=control_model,
image_tensor=control_image,
weight=control_info.control_weight,
begin_step_percent=control_info.begin_step_percent,
end_step_percent=control_info.end_step_percent,
control_mode=control_info.control_mode,
# any resizing needed should currently be happening in prepare_control_image(),
# but adding resize_mode to ControlNetData in case needed in the future
resize_mode=control_info.resize_mode,
)
control_data.append(control_item)
# MultiControlNetModel has been refactored out, just need list[ControlNetData]
return control_data
)
# control_models.append(control_model)
control_image_field = control_info.image
input_image = context.services.images.get_pil_image(control_image_field.image_name)
# self.image.image_type, self.image.image_name
# FIXME: still need to test with different widths, heights, devices, dtypes
# and add in batch_size, num_images_per_prompt?
# and do real check for classifier_free_guidance?
# prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
control_image = prepare_control_image(
image=input_image,
do_classifier_free_guidance=do_classifier_free_guidance,
width=control_width_resize,
height=control_height_resize,
# batch_size=batch_size * num_images_per_prompt,
# num_images_per_prompt=num_images_per_prompt,
device=control_model.device,
dtype=control_model.dtype,
control_mode=control_info.control_mode,
resize_mode=control_info.resize_mode,
)
control_item = ControlNetData(
model=control_model, # model object
image_tensor=control_image,
weight=control_info.control_weight,
begin_step_percent=control_info.begin_step_percent,
end_step_percent=control_info.end_step_percent,
control_mode=control_info.control_mode,
# any resizing needed should currently be happening in prepare_control_image(),
# but adding resize_mode to ControlNetData in case needed in the future
resize_mode=control_info.resize_mode,
)
controlnet_data.append(control_item)
# MultiControlNetModel has been refactored out, just need list[ControlNetData]
return controlnet_data
def prep_ip_adapter_data(
self,
context: InvocationContext,
ip_adapter: Optional[IPAdapterField],
conditioning_data: ConditioningData,
unet: UNet2DConditionModel,
exit_stack: ExitStack,
) -> Optional[IPAdapterData]:
"""If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
to the `conditioning_data` (in-place).
"""
if ip_adapter is None:
return None
image_encoder_model_info = context.services.model_manager.get_model(
model_name=ip_adapter.image_encoder_model.model_name,
model_type=ModelType.CLIPVision,
base_model=ip_adapter.image_encoder_model.base_model,
context=context,
)
ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=ip_adapter.ip_adapter_model.model_name,
model_type=ModelType.IPAdapter,
base_model=ip_adapter.ip_adapter_model.base_model,
context=context,
)
)
input_image = context.services.images.get_pil_image(ip_adapter.image.image_name)
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
with image_encoder_model_info as image_encoder_model:
# Get image embeddings from CLIP and ImageProjModel.
image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
input_image, image_encoder_model
)
conditioning_data.ip_adapter_conditioning = IPAdapterConditioningInfo(
image_prompt_embeds, uncond_image_prompt_embeds
)
return IPAdapterData(
ip_adapter_model=ip_adapter_model,
weight=ip_adapter.weight,
begin_step_percent=ip_adapter.begin_step_percent,
end_step_percent=ip_adapter.end_step_percent,
)
# original idea by https://github.com/AmericanPresidentJimmyCarter
# TODO: research more for second order schedulers timesteps
@ -485,9 +543,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
**self.unet.unet.dict(),
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
with (
ExitStack() as exit_stack,
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
set_seamless(unet_info.context.model, self.unet.seamless_axes),
unet_info as unet,
):
latents = latents.to(device=unet.device, dtype=unet.dtype)
if noise is not None:
noise = noise.to(device=unet.device, dtype=unet.dtype)
@ -506,8 +567,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
control_data = self.prep_control_data(
model=pipeline,
controlnet_data = self.prep_control_data(
context=context,
control_input=self.control,
latents_shape=latents.shape,
@ -516,6 +576,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
exit_stack=exit_stack,
)
ip_adapter_data = self.prep_ip_adapter_data(
context=context,
ip_adapter=self.ip_adapter,
conditioning_data=conditioning_data,
unet=unet,
exit_stack=exit_stack,
)
num_inference_steps, timesteps, init_timestep = self.init_scheduler(
scheduler,
device=unet.device,
@ -534,13 +602,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
masked_latents=masked_latents,
num_inference_steps=num_inference_steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
control_data=controlnet_data, # list[ControlNetData],
ip_adapter_data=ip_adapter_data, # IPAdapterData,
callback=step_callback,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
@ -612,6 +683,8 @@ class LatentsToImageInvocation(BaseInvocation):
# clear memory as vae decode can request a lot
torch.cuda.empty_cache()
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
with torch.inference_mode():
# copied from diffusers pipeline
@ -624,6 +697,8 @@ class LatentsToImageInvocation(BaseInvocation):
image = VaeImageProcessor.numpy_to_pil(np_image)[0]
torch.cuda.empty_cache()
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
image_dto = context.services.images.create(
image=image,
@ -683,6 +758,8 @@ class ResizeLatentsInvocation(BaseInvocation):
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
resized_latents = resized_latents.to("cpu")
torch.cuda.empty_cache()
if device == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
@ -719,6 +796,8 @@ class ScaleLatentsInvocation(BaseInvocation):
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
resized_latents = resized_latents.to("cpu")
torch.cuda.empty_cache()
if device == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
@ -779,8 +858,7 @@ class ImageToLatentsInvocation(BaseInvocation):
# non_noised_latents_from_image
image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
with torch.inference_mode():
image_tensor_dist = vae.encode(image_tensor).latent_dist
latents = image_tensor_dist.sample().to(dtype=vae.dtype) # FIXME: uses torch.randn. make reproducible!
latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
latents = vae.config.scaling_factor * latents
latents = latents.to(dtype=orig_dtype)
@ -807,6 +885,18 @@ class ImageToLatentsInvocation(BaseInvocation):
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents, seed=None)
@singledispatchmethod
@staticmethod
def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
image_tensor_dist = vae.encode(image_tensor).latent_dist
latents = image_tensor_dist.sample().to(dtype=vae.dtype) # FIXME: uses torch.randn. make reproducible!
return latents
@_encode_to_tensor.register
@staticmethod
def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
return vae.encode(image_tensor).latents
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
class BlendLatentsInvocation(BaseInvocation):
@ -875,6 +965,8 @@ class BlendLatentsInvocation(BaseInvocation):
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
blended_latents = blended_latents.to("cpu")
torch.cuda.empty_cache()
if device == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)

214
invokeai/app/invocations/math.py
View File

@ -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
@ -51,7 +54,14 @@ class DivideInvocation(BaseInvocation):
return IntegerOutput(value=int(self.a / self.b))
@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math", version="1.0.0")
@invocation(
"rand_int",
title="Random Integer",
tags=["math", "random"],
category="math",
version="1.0.0",
use_cache=False,
)
class RandomIntInvocation(BaseInvocation):
"""Outputs a single random integer."""
@ -60,3 +70,201 @@ 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))

6
invokeai/app/invocations/metadata.py
View File

@ -42,7 +42,8 @@ class CoreMetadata(BaseModelExcludeNull):
cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
steps: int = Field(description="The number of steps used for inference")
scheduler: str = Field(description="The scheduler used for inference")
clip_skip: int = Field(
clip_skip: Optional[int] = Field(
default=None,
description="The number of skipped CLIP layers",
)
model: MainModelField = Field(description="The main model used for inference")
@ -116,7 +117,8 @@ class MetadataAccumulatorInvocation(BaseInvocation):
cfg_scale: float = InputField(description="The classifier-free guidance scale parameter")
steps: int = InputField(description="The number of steps used for inference")
scheduler: str = InputField(description="The scheduler used for inference")
clip_skip: int = InputField(
clip_skip: Optional[int] = Field(
default=None,
description="The number of skipped CLIP layers",
)
model: MainModelField = InputField(description="The main model used for inference")

11
invokeai/app/invocations/onnx.py
View File

@ -25,8 +25,8 @@ from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
InputField,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
@ -95,9 +95,10 @@ class ONNXPromptInvocation(BaseInvocation):
print(f'Warn: trigger: "{trigger}" not found')
if loras or ti_list:
text_encoder.release_session()
with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras), ONNXModelPatcher.apply_ti(
orig_tokenizer, text_encoder, ti_list
) as (tokenizer, ti_manager):
with (
ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
):
text_encoder.create_session()
# copy from
@ -165,7 +166,6 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
default=7.5,
ge=1,
description=FieldDescriptions.cfg_scale,
ui_type=UIType.Float,
)
scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, input=Input.Direct, ui_type=UIType.Scheduler
@ -178,7 +178,6 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
control: Optional[Union[ControlField, list[ControlField]]] = InputField(
default=None,
description=FieldDescriptions.control,
ui_type=UIType.Control,
)
# seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")

1
invokeai/app/invocations/param_easing.py
View File

@ -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,

6
invokeai/app/invocations/primitives.py
View File

@ -226,6 +226,12 @@ class ImageField(BaseModel):
image_name: str = Field(description="The name of the image")
class BoardField(BaseModel):
"""A board primitive field"""
board_id: str = Field(description="The id of the board")
@invocation_output("image_output")
class ImageOutput(BaseInvocationOutput):
"""Base class for nodes that output a single image"""

9
invokeai/app/invocations/prompt.py
View File

@ -10,7 +10,14 @@ from invokeai.app.invocations.primitives import StringCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt", version="1.0.0")
@invocation(
"dynamic_prompt",
title="Dynamic Prompt",
tags=["prompt", "collection"],
category="prompt",
version="1.0.0",
use_cache=False,
)
class DynamicPromptInvocation(BaseInvocation):
"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""

139
invokeai/app/invocations/strings.py Normal file
View File

@ -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)

2
invokeai/app/invocations/upscale.py
View File

@ -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

17
invokeai/app/services/board_image_record_storage.py
View File

@ -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 (
ImageRecord,
deserialize_image_record,
)
from invokeai.app.services.models.image_record import ImageRecord, deserialize_image_record
class BoardImageRecordStorageBase(ABC):
@ -56,24 +53,20 @@ class BoardImageRecordStorageBase(ABC):
class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
_filename: str
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.Lock
def __init__(self, filename: str) -> None:
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
super().__init__()
self._filename = filename
self._conn = sqlite3.connect(filename, check_same_thread=False)
self._conn = conn
# Enable row factory to get rows as dictionaries (must be done before making the cursor!)
self._conn.row_factory = sqlite3.Row
self._cursor = self._conn.cursor()
self._lock = threading.Lock()
self._lock = lock
try:
self._lock.acquire()
# Enable foreign keys
self._conn.execute("PRAGMA foreign_keys = ON;")
self._create_tables()
self._conn.commit()
finally:

7
invokeai/app/services/board_images.py
View File

@ -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

24
invokeai/app/services/board_record_storage.py
View File

@ -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 (
BoardRecord,
deserialize_board_record,
)
from pydantic import BaseModel, Field, Extra
from invokeai.app.services.models.board_record import BoardRecord, deserialize_board_record
from invokeai.app.util.misc import uuid_string
class BoardChanges(BaseModel, extra=Extra.forbid):
@ -89,24 +87,20 @@ class BoardRecordStorageBase(ABC):
class SqliteBoardRecordStorage(BoardRecordStorageBase):
_filename: str
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.Lock
def __init__(self, filename: str) -> None:
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
super().__init__()
self._filename = filename
self._conn = sqlite3.connect(filename, check_same_thread=False)
self._conn = conn
# Enable row factory to get rows as dictionaries (must be done before making the cursor!)
self._conn.row_factory = sqlite3.Row
self._cursor = self._conn.cursor()
self._lock = threading.Lock()
self._lock = lock
try:
self._lock.acquire()
# Enable foreign keys
self._conn.execute("PRAGMA foreign_keys = ON;")
self._create_tables()
self._conn.commit()
finally:
@ -176,7 +170,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
board_name: str,
) -> BoardRecord:
try:
board_id = str(uuid.uuid4())
board_id = uuid_string()
self._lock.acquire()
self._cursor.execute(
"""--sql

13
invokeai/app/services/boards.py
View File

@ -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.image_record_storage import (
ImageRecordStorageBase,
OffsetPaginatedResults,
)
from invokeai.app.services.board_record_storage import 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

5
invokeai/app/services/config/__init__.py
View File

@ -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

29
invokeai/app/services/config/base.py
View File

@ -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, Optional, 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):
@ -37,12 +39,14 @@ class InvokeAISettings(BaseSettings):
read from an omegaconf .yaml file.
"""
initconf: ClassVar[DictConfig] = None
initconf: ClassVar[Optional[DictConfig]] = None
argparse_groups: ClassVar[Dict] = {}
def parse_args(self, argv: list = sys.argv[1:]):
def parse_args(self, argv: Optional[list] = sys.argv[1:]):
parser = self.get_parser()
opt = parser.parse_args(argv)
opt, unknown_opts = parser.parse_known_args(argv)
if len(unknown_opts) > 0:
print("Unknown args:", unknown_opts)
for name in self.__fields__:
if name not in self._excluded():
value = getattr(opt, name)
@ -79,7 +83,8 @@ class InvokeAISettings(BaseSettings):
else:
settings_stanza = "Uncategorized"
env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
env_prefix = getattr(cls.Config, "env_prefix", None)
env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper()
initconf = (
cls.initconf.get(settings_stanza)
@ -112,8 +117,8 @@ class InvokeAISettings(BaseSettings):
field.default = current_default
@classmethod
def cmd_name(self, command_field: str = "type") -> str:
hints = get_type_hints(self)
def cmd_name(cls, command_field: str = "type") -> str:
hints = get_type_hints(cls)
if command_field in hints:
return get_args(hints[command_field])[0]
else:
@ -129,16 +134,12 @@ class InvokeAISettings(BaseSettings):
return parser
@classmethod
def add_subparser(cls, parser: argparse.ArgumentParser):
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
@classmethod
def _excluded(self) -> List[str]:
def _excluded(cls) -> List[str]:
# internal fields that shouldn't be exposed as command line options
return ["type", "initconf"]
@classmethod
def _excluded_from_yaml(self) -> List[str]:
def _excluded_from_yaml(cls) -> List[str]:
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
return [
"type",

48
invokeai/app/services/config/invokeai_config.py
View File

@ -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
@ -194,8 +194,8 @@ class InvokeAIAppConfig(InvokeAISettings):
setting environment variables INVOKEAI_<setting>.
"""
singleton_config: ClassVar[InvokeAIAppConfig] = None
singleton_init: ClassVar[Dict] = None
singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
singleton_init: ClassVar[Optional[Dict]] = None
# fmt: off
type: Literal["InvokeAI"] = "InvokeAI"
@ -234,6 +234,7 @@ class InvokeAIAppConfig(InvokeAISettings):
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
log_sql : bool = Field(default=False, description="Log SQL queries", category="Logging")
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development")
@ -245,15 +246,24 @@ class InvokeAIAppConfig(InvokeAISettings):
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
# DEVICE
device : Literal[tuple(["auto", "cpu", "cuda", "cuda:1", "mps"])] = Field(default="auto", description="Generation device", category="Device", )
precision: Literal[tuple(["auto", "float16", "float32", "autocast"])] = Field(default="auto", description="Floating point precision", category="Device", )
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", category="Device", )
precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", category="Device", )
# GENERATION
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", )
attention_type : Literal[tuple(["auto", "normal", "xformers", "sliced", "torch-sdp"])] = Field(default="auto", description="Attention type", category="Generation", )
attention_slice_size: Literal[tuple(["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", category="Generation", )
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
# QUEUE
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )
# NODES
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes")
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes")
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", category="Nodes", )
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
@ -267,8 +277,9 @@ class InvokeAIAppConfig(InvokeAISettings):
class Config:
validate_assignment = True
env_prefix = "INVOKEAI"
def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
def parse_args(self, argv: Optional[list[str]] = None, conf: Optional[DictConfig] = None, clobber=False):
"""
Update settings with contents of init file, environment, and
command-line settings.
@ -279,12 +290,16 @@ class InvokeAIAppConfig(InvokeAISettings):
# Set the runtime root directory. We parse command-line switches here
# in order to pick up the --root_dir option.
super().parse_args(argv)
loaded_conf = None
if conf is None:
try:
conf = OmegaConf.load(self.root_dir / INIT_FILE)
loaded_conf = OmegaConf.load(self.root_dir / INIT_FILE)
except Exception:
pass
InvokeAISettings.initconf = conf
if isinstance(loaded_conf, DictConfig):
InvokeAISettings.initconf = loaded_conf
else:
InvokeAISettings.initconf = conf
# parse args again in order to pick up settings in configuration file
super().parse_args(argv)
@ -372,13 +387,6 @@ class InvokeAIAppConfig(InvokeAISettings):
"""
return self._resolve(self.models_dir)
@property
def autoconvert_path(self) -> Path:
"""
Path to the directory containing models to be imported automatically at startup.
"""
return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
# the following methods support legacy calls leftover from the Globals era
@property
def full_precision(self) -> bool:
@ -401,11 +409,11 @@ class InvokeAIAppConfig(InvokeAISettings):
return True
@property
def ram_cache_size(self) -> float:
def ram_cache_size(self) -> Union[Literal["auto"], float]:
return self.max_cache_size or self.ram
@property
def vram_cache_size(self) -> float:
def vram_cache_size(self) -> Union[Literal["auto"], float]:
return self.max_vram_cache_size or self.vram
@property

102
invokeai/app/services/default_graphs.py
View File

@ -1,67 +1,67 @@
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"
def create_text_to_image() -> LibraryGraph:
graph = Graph(
nodes={
"width": IntegerInvocation(id="width", value=512),
"height": IntegerInvocation(id="height", value=512),
"seed": IntegerInvocation(id="seed", value=-1),
"3": NoiseInvocation(id="3"),
"4": CompelInvocation(id="4"),
"5": CompelInvocation(id="5"),
"6": DenoiseLatentsInvocation(id="6"),
"7": LatentsToImageInvocation(id="7"),
"8": ImageNSFWBlurInvocation(id="8"),
},
edges=[
Edge(
source=EdgeConnection(node_id="width", field="value"),
destination=EdgeConnection(node_id="3", field="width"),
),
Edge(
source=EdgeConnection(node_id="height", field="value"),
destination=EdgeConnection(node_id="3", field="height"),
),
Edge(
source=EdgeConnection(node_id="seed", field="value"),
destination=EdgeConnection(node_id="3", field="seed"),
),
Edge(
source=EdgeConnection(node_id="3", field="noise"),
destination=EdgeConnection(node_id="6", field="noise"),
),
Edge(
source=EdgeConnection(node_id="6", field="latents"),
destination=EdgeConnection(node_id="7", field="latents"),
),
Edge(
source=EdgeConnection(node_id="4", field="conditioning"),
destination=EdgeConnection(node_id="6", field="positive_conditioning"),
),
Edge(
source=EdgeConnection(node_id="5", field="conditioning"),
destination=EdgeConnection(node_id="6", field="negative_conditioning"),
),
Edge(
source=EdgeConnection(node_id="7", field="image"),
destination=EdgeConnection(node_id="8", field="image"),
),
],
)
return LibraryGraph(
id=default_text_to_image_graph_id,
name="t2i",
description="Converts text to an image",
graph=Graph(
nodes={
"width": IntegerInvocation(id="width", value=512),
"height": IntegerInvocation(id="height", value=512),
"seed": IntegerInvocation(id="seed", value=-1),
"3": NoiseInvocation(id="3"),
"4": CompelInvocation(id="4"),
"5": CompelInvocation(id="5"),
"6": DenoiseLatentsInvocation(id="6"),
"7": LatentsToImageInvocation(id="7"),
"8": ImageNSFWBlurInvocation(id="8"),
},
edges=[
Edge(
source=EdgeConnection(node_id="width", field="value"),
destination=EdgeConnection(node_id="3", field="width"),
),
Edge(
source=EdgeConnection(node_id="height", field="value"),
destination=EdgeConnection(node_id="3", field="height"),
),
Edge(
source=EdgeConnection(node_id="seed", field="value"),
destination=EdgeConnection(node_id="3", field="seed"),
),
Edge(
source=EdgeConnection(node_id="3", field="noise"),
destination=EdgeConnection(node_id="6", field="noise"),
),
Edge(
source=EdgeConnection(node_id="6", field="latents"),
destination=EdgeConnection(node_id="7", field="latents"),
),
Edge(
source=EdgeConnection(node_id="4", field="conditioning"),
destination=EdgeConnection(node_id="6", field="positive_conditioning"),
),
Edge(
source=EdgeConnection(node_id="5", field="conditioning"),
destination=EdgeConnection(node_id="6", field="negative_conditioning"),
),
Edge(
source=EdgeConnection(node_id="7", field="image"),
destination=EdgeConnection(node_id="8", field="image"),
),
],
),
graph=graph,
exposed_inputs=[
ExposedNodeInput(node_path="4", field="prompt", alias="positive_prompt"),
ExposedNodeInput(node_path="5", field="prompt", alias="negative_prompt"),

152
invokeai/app/services/events.py
View File

@ -1,28 +1,26 @@
# 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.services.session_queue.session_queue_common import EnqueueBatchResult, SessionQueueItem
from invokeai.app.util.misc import get_timestamp
from invokeai.app.services.model_manager_service import (
BaseModelType,
ModelType,
SubModelType,
ModelInfo,
)
class EventServiceBase:
session_event: str = "session_event"
queue_event: str = "queue_event"
"""Basic event bus, to have an empty stand-in when not needed"""
def dispatch(self, event_name: str, payload: Any) -> None:
pass
def __emit_session_event(self, event_name: str, payload: dict) -> None:
def __emit_queue_event(self, event_name: str, payload: dict) -> None:
"""Queue events are emitted to a room with queue_id as the room name"""
payload["timestamp"] = get_timestamp()
self.dispatch(
event_name=EventServiceBase.session_event,
event_name=EventServiceBase.queue_event,
payload=dict(event=event_name, data=payload),
)
@ -30,6 +28,9 @@ class EventServiceBase:
# This will make them easier to integrate until we find a schema generator.
def emit_generator_progress(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
@ -39,11 +40,14 @@ class EventServiceBase:
total_steps: int,
) -> None:
"""Emitted when there is generation progress"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="generator_progress",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
node=node,
node_id=node.get("id"),
source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None,
step=step,
@ -54,15 +58,21 @@ class EventServiceBase:
def emit_invocation_complete(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
result: dict,
node: dict,
source_node_id: str,
) -> None:
"""Emitted when an invocation has completed"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="invocation_complete",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
@ -72,6 +82,9 @@ class EventServiceBase:
def emit_invocation_error(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
@ -79,9 +92,12 @@ class EventServiceBase:
error: str,
) -> None:
"""Emitted when an invocation has completed"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="invocation_error",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
@ -90,28 +106,47 @@ class EventServiceBase:
),
)
def emit_invocation_started(self, graph_execution_state_id: str, node: dict, source_node_id: str) -> None:
def emit_invocation_started(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
) -> None:
"""Emitted when an invocation has started"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="invocation_started",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
),
)
def emit_graph_execution_complete(self, graph_execution_state_id: str) -> None:
def emit_graph_execution_complete(
self, queue_id: str, queue_item_id: int, queue_batch_id: str, graph_execution_state_id: str
) -> None:
"""Emitted when a session has completed all invocations"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="graph_execution_state_complete",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
),
)
def emit_model_load_started(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
model_name: str,
base_model: BaseModelType,
@ -119,9 +154,12 @@ class EventServiceBase:
submodel: SubModelType,
) -> None:
"""Emitted when a model is requested"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="model_load_started",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
model_name=model_name,
base_model=base_model,
@ -132,6 +170,9 @@ class EventServiceBase:
def emit_model_load_completed(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
model_name: str,
base_model: BaseModelType,
@ -140,9 +181,12 @@ class EventServiceBase:
model_info: ModelInfo,
) -> None:
"""Emitted when a model is correctly loaded (returns model info)"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="model_load_completed",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
model_name=model_name,
base_model=base_model,
@ -156,14 +200,20 @@ class EventServiceBase:
def emit_session_retrieval_error(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
error_type: str,
error: str,
) -> None:
"""Emitted when session retrieval fails"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="session_retrieval_error",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
error_type=error_type,
error=error,
@ -172,18 +222,78 @@ class EventServiceBase:
def emit_invocation_retrieval_error(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
node_id: str,
error_type: str,
error: str,
) -> None:
"""Emitted when invocation retrieval fails"""
self.__emit_session_event(
self.__emit_queue_event(
event_name="invocation_retrieval_error",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
node_id=node_id,
error_type=error_type,
error=error,
),
)
def emit_session_canceled(
self,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
) -> None:
"""Emitted when a session is canceled"""
self.__emit_queue_event(
event_name="session_canceled",
payload=dict(
queue_id=queue_id,
queue_item_id=queue_item_id,
queue_batch_id=queue_batch_id,
graph_execution_state_id=graph_execution_state_id,
),
)
def emit_queue_item_status_changed(self, session_queue_item: SessionQueueItem) -> None:
"""Emitted when a queue item's status changes"""
self.__emit_queue_event(
event_name="queue_item_status_changed",
payload=dict(
queue_id=session_queue_item.queue_id,
queue_item_id=session_queue_item.item_id,
status=session_queue_item.status,
batch_id=session_queue_item.batch_id,
session_id=session_queue_item.session_id,
error=session_queue_item.error,
created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
),
)
def emit_batch_enqueued(self, enqueue_result: EnqueueBatchResult) -> None:
"""Emitted when a batch is enqueued"""
self.__emit_queue_event(
event_name="batch_enqueued",
payload=dict(
queue_id=enqueue_result.queue_id,
batch_id=enqueue_result.batch.batch_id,
enqueued=enqueue_result.enqueued,
),
)
def emit_queue_cleared(self, queue_id: str) -> None:
"""Emitted when the queue is cleared"""
self.__emit_queue_event(
event_name="queue_cleared",
payload=dict(queue_id=queue_id),
)

93
invokeai/app/services/graph.py
View File

@ -2,24 +2,25 @@
import copy
import itertools
import uuid
from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
from typing import Annotated, Any, Optional, Union, cast, get_args, get_origin, get_type_hints
import networkx as nx
from pydantic import BaseModel, root_validator, validator
from pydantic.fields import Field
from invokeai.app.util.misc import uuid_string
# Importing * is bad karma but needed here for node detection
from ..invocations import * # noqa: F401 F403
from ..invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
invocation,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -116,6 +117,10 @@ def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
if from_type is int and to_type is float:
return True
# allow int|float -> str, pydantic will cast for us
if (from_type is int or from_type is float) and to_type is str:
return True
# if not issubclass(from_type, to_type):
if not is_union_subtype(from_type, to_type):
return False
@ -137,19 +142,31 @@ def are_connections_compatible(
return are_connection_types_compatible(from_node_field, to_node_field)
class NodeAlreadyInGraphError(Exception):
class NodeAlreadyInGraphError(ValueError):
pass
class InvalidEdgeError(Exception):
class InvalidEdgeError(ValueError):
pass
class NodeNotFoundError(Exception):
class NodeNotFoundError(ValueError):
pass
class NodeAlreadyExecutedError(Exception):
class NodeAlreadyExecutedError(ValueError):
pass
class DuplicateNodeIdError(ValueError):
pass
class NodeFieldNotFoundError(ValueError):
pass
class NodeIdMismatchError(ValueError):
pass
@ -227,7 +244,7 @@ InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()]
class Graph(BaseModel):
id: str = Field(description="The id of this graph", default_factory=lambda: uuid.uuid4().__str__())
id: str = Field(description="The id of this graph", default_factory=uuid_string)
# TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
nodes: dict[str, Annotated[InvocationsUnion, Field(discriminator="type")]] = Field(
description="The nodes in this graph", default_factory=dict
@ -237,6 +254,59 @@ class Graph(BaseModel):
default_factory=list,
)
@root_validator
def validate_nodes_and_edges(cls, values):
"""Validates that all edges match nodes in the graph"""
nodes = cast(Optional[dict[str, BaseInvocation]], values.get("nodes"))
edges = cast(Optional[list[Edge]], values.get("edges"))
if nodes is not None:
# Validate that all node ids are unique
node_ids = [n.id for n in nodes.values()]
duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
if duplicate_node_ids:
raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
# Validate that all node ids match the keys in the nodes dict
for k, v in nodes.items():
if k != v.id:
raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
if edges is not None and nodes is not None:
# Validate that all edges match nodes in the graph
node_ids = set([e.source.node_id for e in edges] + [e.destination.node_id for e in edges])
missing_node_ids = [node_id for node_id in node_ids if node_id not in nodes]
if missing_node_ids:
raise NodeNotFoundError(
f"All edges must reference nodes in the graph, missing nodes: {missing_node_ids}"
)
# Validate that all edge fields match node fields in the graph
for edge in edges:
source_node = nodes.get(edge.source.node_id, None)
if source_node is None:
raise NodeFieldNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
destination_node = nodes.get(edge.destination.node_id, None)
if destination_node is None:
raise NodeFieldNotFoundError(
f"Edge destination node {edge.destination.node_id} does not exist in the graph"
)
# output fields are not on the node object directly, they are on the output type
if edge.source.field not in source_node.get_output_type().__fields__:
raise NodeFieldNotFoundError(
f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
)
# input fields are on the node
if edge.destination.field not in destination_node.__fields__:
raise NodeFieldNotFoundError(
f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
)
return values
def add_node(self, node: BaseInvocation) -> None:
"""Adds a node to a graph
@ -697,8 +767,7 @@ class Graph(BaseModel):
class GraphExecutionState(BaseModel):
"""Tracks the state of a graph execution"""
id: str = Field(description="The id of the execution state", default_factory=lambda: uuid.uuid4().__str__())
id: str = Field(description="The id of the execution state", default_factory=uuid_string)
# TODO: Store a reference to the graph instead of the actual graph?
graph: Graph = Field(description="The graph being executed")
@ -847,7 +916,7 @@ class GraphExecutionState(BaseModel):
new_node = copy.deepcopy(node)
# Create the node id (use a random uuid)
new_node.id = str(uuid.uuid4())
new_node.id = uuid_string()
# Set the iteration index for iteration invocations
if isinstance(new_node, IterateInvocation):
@ -1082,7 +1151,7 @@ class ExposedNodeOutput(BaseModel):
class LibraryGraph(BaseModel):
id: str = Field(description="The unique identifier for this library graph", default_factory=uuid.uuid4)
id: str = Field(description="The unique identifier for this library graph", default_factory=uuid_string)
graph: Graph = Field(description="The graph")
name: str = Field(description="The name of the graph")
description: str = Field(description="The description of the graph")

16
invokeai/app/services/image_record_storage.py
View File

@ -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 (
ImageRecord,
ImageRecordChanges,
deserialize_image_record,
)
from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
T = TypeVar("T", bound=BaseModel)
@ -152,24 +148,20 @@ class ImageRecordStorageBase(ABC):
class SqliteImageRecordStorage(ImageRecordStorageBase):
_filename: str
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.Lock
def __init__(self, filename: str) -> None:
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
super().__init__()
self._filename = filename
self._conn = sqlite3.connect(filename, check_same_thread=False)
self._conn = conn
# Enable row factory to get rows as dictionaries (must be done before making the cursor!)
self._conn.row_factory = sqlite3.Row
self._cursor = self._conn.cursor()
self._lock = threading.Lock()
self._lock = lock
try:
self._lock.acquire()
# Enable foreign keys
self._conn.execute("PRAGMA foreign_keys = ON;")
self._create_tables()
self._conn.commit()
finally:

42
invokeai/app/services/images.py
View File

@ -1,6 +1,6 @@
from abc import ABC, abstractmethod
from logging import Logger
from typing import TYPE_CHECKING, Optional
from typing import TYPE_CHECKING, Callable, Optional
from PIL.Image import Image as PILImageType
@ -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 (
ImageDTO,
ImageRecord,
ImageRecordChanges,
image_record_to_dto,
)
from invokeai.app.services.models.image_record import 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
@ -43,6 +38,29 @@ if TYPE_CHECKING:
class ImageServiceABC(ABC):
"""High-level service for image management."""
_on_changed_callbacks: list[Callable[[ImageDTO], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
"""Register a callback for when an image is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an image is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: ImageDTO) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
@abstractmethod
def create(
self,
@ -166,6 +184,7 @@ class ImageService(ImageServiceABC):
_services: ImageServiceDependencies
def __init__(self, services: ImageServiceDependencies):
super().__init__()
self._services = services
def create(
@ -222,6 +241,7 @@ class ImageService(ImageServiceABC):
self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, workflow=workflow)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self._services.logger.error("Failed to save image record")
@ -240,7 +260,9 @@ class ImageService(ImageServiceABC):
) -> ImageDTO:
try:
self._services.image_records.update(image_name, changes)
return self.get_dto(image_name)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self._services.logger.error("Failed to update image record")
raise
@ -379,6 +401,7 @@ class ImageService(ImageServiceABC):
try:
self._services.image_files.delete(image_name)
self._services.image_records.delete(image_name)
self._on_deleted(image_name)
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image record")
raise
@ -395,6 +418,8 @@ class ImageService(ImageServiceABC):
for image_name in image_names:
self._services.image_files.delete(image_name)
self._services.image_records.delete_many(image_names)
for image_name in image_names:
self._on_deleted(image_name)
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image records")
raise
@ -411,6 +436,7 @@ class ImageService(ImageServiceABC):
count = len(image_names)
for image_name in image_names:
self._services.image_files.delete(image_name)
self._on_deleted(image_name)
return count
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image records")

0
invokeai/app/services/invocation_cache/__init__.py Normal file
View File

62
invokeai/app/services/invocation_cache/invocation_cache_base.py Normal file
View File

@ -0,0 +1,62 @@
from abc import ABC, abstractmethod
from typing import Optional, Union
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
class InvocationCacheBase(ABC):
"""
Base class for invocation caches.
When an invocation is executed, it is hashed and its output stored in the cache.
When new invocations are executed, if they are flagged with `use_cache`, they
will attempt to pull their value from the cache before executing.
Implementations should register for the `on_deleted` event of the `images` and `latents`
services, and delete any cached outputs that reference the deleted image or latent.
See the memory implementation for an example.
Implementations should respect the `node_cache_size` configuration value, and skip all
cache logic if the value is set to 0.
"""
@abstractmethod
def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
"""Retrieves an invocation output from the cache"""
pass
@abstractmethod
def save(self, key: Union[int, str], invocation_output: BaseInvocationOutput) -> None:
"""Stores an invocation output in the cache"""
pass
@abstractmethod
def delete(self, key: Union[int, str]) -> None:
"""Deletes an invocation output from the cache"""
pass
@abstractmethod
def clear(self) -> None:
"""Clears the cache"""
pass
@abstractmethod
def create_key(self, invocation: BaseInvocation) -> int:
"""Gets the key for the invocation's cache item"""
pass
@abstractmethod
def disable(self) -> None:
"""Disables the cache, overriding the max cache size"""
pass
@abstractmethod
def enable(self) -> None:
"""Enables the cache, letting the the max cache size take effect"""
pass
@abstractmethod
def get_status(self) -> InvocationCacheStatus:
"""Returns the status of the cache"""
pass

9
invokeai/app/services/invocation_cache/invocation_cache_common.py Normal file
View File

@ -0,0 +1,9 @@
from pydantic import BaseModel, Field
class InvocationCacheStatus(BaseModel):
size: int = Field(description="The current size of the invocation cache")
hits: int = Field(description="The number of cache hits")
misses: int = Field(description="The number of cache misses")
enabled: bool = Field(description="Whether the invocation cache is enabled")
max_size: int = Field(description="The maximum size of the invocation cache")

111
invokeai/app/services/invocation_cache/invocation_cache_memory.py Normal file
View File

@ -0,0 +1,111 @@
from queue import Queue
from typing import Optional, Union
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
from invokeai.app.services.invoker import Invoker
class MemoryInvocationCache(InvocationCacheBase):
_cache: dict[Union[int, str], tuple[BaseInvocationOutput, str]]
_max_cache_size: int
_disabled: bool
_hits: int
_misses: int
_cache_ids: Queue
_invoker: Invoker
def __init__(self, max_cache_size: int = 0) -> None:
self._cache = dict()
self._max_cache_size = max_cache_size
self._disabled = False
self._hits = 0
self._misses = 0
self._cache_ids = Queue()
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
if self._max_cache_size == 0:
return
self._invoker.services.images.on_deleted(self._delete_by_match)
self._invoker.services.latents.on_deleted(self._delete_by_match)
def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
if self._max_cache_size == 0 or self._disabled:
return
item = self._cache.get(key, None)
if item is not None:
self._hits += 1
return item[0]
self._misses += 1
def save(self, key: Union[int, str], invocation_output: BaseInvocationOutput) -> None:
if self._max_cache_size == 0 or self._disabled:
return
if key not in self._cache:
self._cache[key] = (invocation_output, invocation_output.json())
self._cache_ids.put(key)
if self._cache_ids.qsize() > self._max_cache_size:
try:
self._cache.pop(self._cache_ids.get())
except KeyError:
# this means the cache_ids are somehow out of sync w/ the cache
pass
def delete(self, key: Union[int, str]) -> None:
if self._max_cache_size == 0:
return
if key in self._cache:
del self._cache[key]
def clear(self, *args, **kwargs) -> None:
if self._max_cache_size == 0:
return
self._cache.clear()
self._cache_ids = Queue()
self._misses = 0
self._hits = 0
def create_key(self, invocation: BaseInvocation) -> int:
return hash(invocation.json(exclude={"id"}))
def disable(self) -> None:
if self._max_cache_size == 0:
return
self._disabled = True
def enable(self) -> None:
if self._max_cache_size == 0:
return
self._disabled = False
def get_status(self) -> InvocationCacheStatus:
return InvocationCacheStatus(
hits=self._hits,
misses=self._misses,
enabled=not self._disabled and self._max_cache_size > 0,
size=len(self._cache),
max_size=self._max_cache_size,
)
def _delete_by_match(self, to_match: str) -> None:
if self._max_cache_size == 0:
return
keys_to_delete = set()
for key, value_tuple in self._cache.items():
if to_match in value_tuple[1]:
keys_to_delete.add(key)
if not keys_to_delete:
return
for key in keys_to_delete:
self.delete(key)
self._invoker.services.logger.debug(f"Deleted {len(keys_to_delete)} cached invocation outputs for {to_match}")

9
invokeai/app/services/invocation_queue.py
View File

@ -3,14 +3,21 @@
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):
graph_execution_state_id: str = Field(description="The ID of the graph execution state")
invocation_id: str = Field(description="The ID of the node being invoked")
session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
session_queue_item_id: int = Field(
description="The ID of session queue item from which this invocation queue item came"
)
session_queue_batch_id: str = Field(
description="The ID of the session batch from which this invocation queue item came"
)
invoke_all: bool = Field(default=False)
timestamp: float = Field(default_factory=time.time)

37
invokeai/app/services/invocation_services.py
View File

@ -1,21 +1,26 @@
# 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_cache.invocation_cache_base import InvocationCacheBase
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
from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
class InvocationServices:
@ -26,8 +31,8 @@ class InvocationServices:
boards: "BoardServiceABC"
configuration: "InvokeAIAppConfig"
events: "EventServiceBase"
graph_execution_manager: "ItemStorageABC"["GraphExecutionState"]
graph_library: "ItemStorageABC"["LibraryGraph"]
graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
graph_library: "ItemStorageABC[LibraryGraph]"
images: "ImageServiceABC"
latents: "LatentsStorageBase"
logger: "Logger"
@ -35,6 +40,9 @@ class InvocationServices:
processor: "InvocationProcessorABC"
performance_statistics: "InvocationStatsServiceBase"
queue: "InvocationQueueABC"
session_queue: "SessionQueueBase"
session_processor: "SessionProcessorBase"
invocation_cache: "InvocationCacheBase"
def __init__(
self,
@ -42,8 +50,8 @@ class InvocationServices:
boards: "BoardServiceABC",
configuration: "InvokeAIAppConfig",
events: "EventServiceBase",
graph_execution_manager: "ItemStorageABC"["GraphExecutionState"],
graph_library: "ItemStorageABC"["LibraryGraph"],
graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
graph_library: "ItemStorageABC[LibraryGraph]",
images: "ImageServiceABC",
latents: "LatentsStorageBase",
logger: "Logger",
@ -51,10 +59,12 @@ class InvocationServices:
processor: "InvocationProcessorABC",
performance_statistics: "InvocationStatsServiceBase",
queue: "InvocationQueueABC",
session_queue: "SessionQueueBase",
session_processor: "SessionProcessorBase",
invocation_cache: "InvocationCacheBase",
):
self.board_images = board_images
self.boards = boards
self.boards = boards
self.configuration = configuration
self.events = events
self.graph_execution_manager = graph_execution_manager
@ -66,3 +76,6 @@ class InvocationServices:
self.processor = processor
self.performance_statistics = performance_statistics
self.queue = queue
self.session_queue = session_queue
self.session_processor = session_processor
self.invocation_cache = invocation_cache

4
invokeai/app/services/invocation_stats.py
View File

@ -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

13
invokeai/app/services/invoker.py
View File

@ -17,7 +17,14 @@ class Invoker:
self.services = services
self._start()
def invoke(self, graph_execution_state: GraphExecutionState, invoke_all: bool = False) -> Optional[str]:
def invoke(
self,
session_queue_id: str,
session_queue_item_id: int,
session_queue_batch_id: str,
graph_execution_state: GraphExecutionState,
invoke_all: bool = False,
) -> Optional[str]:
"""Determines the next node to invoke and enqueues it, preparing if needed.
Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
@ -32,7 +39,9 @@ class Invoker:
# Queue the invocation
self.services.queue.put(
InvocationQueueItem(
# session_id = session.id,
session_queue_id=session_queue_id,
session_queue_item_id=session_queue_item_id,
session_queue_batch_id=session_queue_batch_id,
graph_execution_state_id=graph_execution_state.id,
invocation_id=invocation.id,
invoke_all=invoke_all,

28
invokeai/app/services/latent_storage.py
View File

@ -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 Callable, Dict, Optional, Union
import torch
@ -11,6 +11,13 @@ import torch
class LatentsStorageBase(ABC):
"""Responsible for storing and retrieving latents."""
_on_changed_callbacks: list[Callable[[torch.Tensor], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
@abstractmethod
def get(self, name: str) -> torch.Tensor:
pass
@ -23,6 +30,22 @@ class LatentsStorageBase(ABC):
def delete(self, name: str) -> None:
pass
def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
"""Register a callback for when an item is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an item is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: torch.Tensor) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
class ForwardCacheLatentsStorage(LatentsStorageBase):
"""Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
@ -33,6 +56,7 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
__underlying_storage: LatentsStorageBase
def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
super().__init__()
self.__underlying_storage = underlying_storage
self.__cache = dict()
self.__cache_ids = Queue()
@ -50,11 +74,13 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
def save(self, name: str, data: torch.Tensor) -> None:
self.__underlying_storage.save(name, data)
self.__set_cache(name, data)
self._on_changed(data)
def delete(self, name: str) -> None:
self.__underlying_storage.delete(name)
if name in self.__cache:
del self.__cache[name]
self._on_deleted(name)
def __get_cache(self, name: str) -> Optional[torch.Tensor]:
return None if name not in self.__cache else self.__cache[name]

43
invokeai/app/services/model_manager_service.py
View File

@ -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 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
from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
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
@ -524,7 +525,7 @@ class ModelManagerService(ModelManagerServiceBase):
def _emit_load_event(
self,
context,
context: InvocationContext,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
@ -536,6 +537,9 @@ class ModelManagerService(ModelManagerServiceBase):
if model_info:
context.services.events.emit_model_load_completed(
queue_id=context.queue_id,
queue_item_id=context.queue_item_id,
queue_batch_id=context.queue_batch_id,
graph_execution_state_id=context.graph_execution_state_id,
model_name=model_name,
base_model=base_model,
@ -545,6 +549,9 @@ class ModelManagerService(ModelManagerServiceBase):
)
else:
context.services.events.emit_model_load_started(
queue_id=context.queue_id,
queue_item_id=context.queue_item_id,
queue_batch_id=context.queue_batch_id,
graph_execution_state_id=context.graph_execution_state_id,
model_name=model_name,
base_model=base_model,

4
invokeai/app/services/models/board_record.py
View File

@ -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

48
invokeai/app/services/processor.py
View File

@ -1,6 +1,7 @@
import time
import traceback
from threading import BoundedSemaphore, Event, Thread
from typing import Optional
import invokeai.backend.util.logging as logger
@ -37,10 +38,11 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
try:
self.__threadLimit.acquire()
statistics: InvocationStatsServiceBase = self.__invoker.services.performance_statistics
queue_item: Optional[InvocationQueueItem] = None
while not stop_event.is_set():
try:
queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
queue_item = self.__invoker.services.queue.get()
except Exception as e:
self.__invoker.services.logger.error("Exception while getting from queue:\n%s" % e)
@ -48,7 +50,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# do not hammer the queue
time.sleep(0.5)
continue
try:
graph_execution_state = self.__invoker.services.graph_execution_manager.get(
queue_item.graph_execution_state_id
@ -56,6 +57,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
except Exception as e:
self.__invoker.services.logger.error("Exception while retrieving session:\n%s" % e)
self.__invoker.services.events.emit_session_retrieval_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=queue_item.graph_execution_state_id,
error_type=e.__class__.__name__,
error=traceback.format_exc(),
@ -67,6 +71,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
except Exception as e:
self.__invoker.services.logger.error("Exception while retrieving invocation:\n%s" % e)
self.__invoker.services.events.emit_invocation_retrieval_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=queue_item.graph_execution_state_id,
node_id=queue_item.invocation_id,
error_type=e.__class__.__name__,
@ -79,6 +86,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# Send starting event
self.__invoker.services.events.emit_invocation_started(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
source_node_id=source_node_id,
@ -89,13 +99,17 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
graph_id = graph_execution_state.id
model_manager = self.__invoker.services.model_manager
with statistics.collect_stats(invocation, graph_id, model_manager):
# use the internal invoke_internal(), which wraps the node's invoke() method in
# this accomodates nodes which require a value, but get it only from a
# connection
# use the internal invoke_internal(), which wraps the node's invoke() method,
# which handles a few things:
# - nodes that require a value, but get it only from a connection
# - referencing the invocation cache instead of executing the node
outputs = invocation.invoke_internal(
InvocationContext(
services=self.__invoker.services,
graph_execution_state_id=graph_execution_state.id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
queue_batch_id=queue_item.session_queue_batch_id,
)
)
@ -111,6 +125,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# Send complete event
self.__invoker.services.events.emit_invocation_complete(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
source_node_id=source_node_id,
@ -138,6 +155,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
# Send error event
self.__invoker.services.events.emit_invocation_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
source_node_id=source_node_id,
@ -155,10 +175,19 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
is_complete = graph_execution_state.is_complete()
if queue_item.invoke_all and not is_complete:
try:
self.__invoker.invoke(graph_execution_state, invoke_all=True)
self.__invoker.invoke(
session_queue_batch_id=queue_item.session_queue_batch_id,
session_queue_item_id=queue_item.session_queue_item_id,
session_queue_id=queue_item.session_queue_id,
graph_execution_state=graph_execution_state,
invoke_all=True,
)
except Exception as e:
self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
self.__invoker.services.events.emit_invocation_error(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
source_node_id=source_node_id,
@ -166,7 +195,12 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
error=traceback.format_exc(),
)
elif is_complete:
self.__invoker.services.events.emit_graph_execution_complete(graph_execution_state.id)
self.__invoker.services.events.emit_graph_execution_complete(
queue_batch_id=queue_item.session_queue_batch_id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
)
except KeyboardInterrupt:
pass # Log something? KeyboardInterrupt is probably not going to be seen by the processor

5
invokeai/app/services/resource_name.py
View File

@ -1,6 +1,7 @@
from abc import ABC, abstractmethod
from enum import Enum, EnumMeta
import uuid
from invokeai.app.util.misc import uuid_string
class ResourceType(str, Enum, metaclass=EnumMeta):
@ -25,6 +26,6 @@ class SimpleNameService(NameServiceBase):
# TODO: Add customizable naming schemes
def create_image_name(self) -> str:
uuid_str = str(uuid.uuid4())
uuid_str = uuid_string()
filename = f"{uuid_str}.png"
return filename

0
invokeai/app/services/session_processor/__init__.py Normal file
View File

28
invokeai/app/services/session_processor/session_processor_base.py Normal file
View File

@ -0,0 +1,28 @@
from abc import ABC, abstractmethod
from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
class SessionProcessorBase(ABC):
"""
Base class for session processor.
The session processor is responsible for executing sessions. It runs a simple polling loop,
checking the session queue for new sessions to execute. It must coordinate with the
invocation queue to ensure only one session is executing at a time.
"""
@abstractmethod
def resume(self) -> SessionProcessorStatus:
"""Starts or resumes the session processor"""
pass
@abstractmethod
def pause(self) -> SessionProcessorStatus:
"""Pauses the session processor"""
pass
@abstractmethod
def get_status(self) -> SessionProcessorStatus:
"""Gets the status of the session processor"""
pass

6
invokeai/app/services/session_processor/session_processor_common.py Normal file
View File

@ -0,0 +1,6 @@
from pydantic import BaseModel, Field
class SessionProcessorStatus(BaseModel):
is_started: bool = Field(description="Whether the session processor is started")
is_processing: bool = Field(description="Whether a session is being processed")

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