Images that are used as parameters (e.g. init image, canvas images) are stored as full `ImageDTO` objects in state, separate from and duplicating any object representing those same objects in the `imagesSlice`. We cannot store only image names as parameters, then pull the full `ImageDTO` from `imagesSlice`, because if an image is not on a loaded page, it doesn't exist in `imagesSlice`. For example, if you scroll down a few pages in the gallery and send that image to canvas, on reloading the app, the canvas will be unable to load that image. We solved this temporarily by storing the full `ImageDTO` object wherever it was needed, but this is both inefficient and allows for stale `ImageDTO`s across the app. One other possible solution was to just fetch the `ImageDTO` for all images at startup, and insert them into the `imagesSlice`, but then we run into an issue where we are displaying images in the gallery totally out of context. For example, if an image from several pages into the gallery was sent to canvas, and the user refreshes, we'd display the first 20 images in gallery. Then to populate the canvas, we'd fetch that image we sent to canvas and add it to `imagesSlice`. Now we'd have 21 images in the gallery: 1 to 20 and whichever image we sent to canvas. Weird. Using `rtk-query` solves this by allowing us to very easily fetch individual images in the components that need them, and not directly interact with `imagesSlice`. This commit changes all references to images-as-parameters to store only the name of the image, and not the full `ImageDTO` object. Then, we use an `rtk-query` generated `useGetImageDTOQuery()` hook in each of those components to fetch the image. We can use cache invalidation when we mutate any image to trigger automated re-running of the query and all the images are automatically kept up to date. This also obviates the need for the convoluted URL fetching scheme for images that are used as parameters. The `imagesSlice` still need this handling unfortunately. |
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.dev_scripts | ||
.github | ||
coverage | ||
docker | ||
docs | ||
installer | ||
invokeai | ||
notebooks | ||
scripts | ||
tests | ||
.dockerignore | ||
.editorconfig | ||
.git-blame-ignore-revs | ||
.gitattributes | ||
.gitignore | ||
.gitmodules | ||
.prettierrc.yaml | ||
CODE_OF_CONDUCT.md | ||
InvokeAI_Statement_of_Values.md | ||
LICENSE | ||
LICENSE-ModelWeights.txt | ||
mkdocs.yml | ||
pyproject.toml | ||
README.md | ||
shell.nix | ||
Stable_Diffusion_v1_Model_Card.md |
Note: The UI is not fully functional on main
. If you need a stable UI based on main
, use the pre-nodes
tag while we migrate to a new backend.
InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
Quick links: [How to Install] [Discord Server] [Documentation and Tutorials] [Code and Downloads] [Bug Reports] [Discussion, Ideas & Q&A]
Note: InvokeAI is rapidly evolving. Please use the Issues tab to report bugs and make feature requests. Be sure to use the provided templates. They will help us diagnose issues faster.
FOR DEVELOPERS - MIGRATING TO THE 3.0.0 MODELS FORMAT
The models directory and models.yaml have changed. To migrate to the new layout, please follow this recipe:
-
Run `python scripts/migrate_models_to_3.0.py <path_to_root_directory>
-
This will create a new models directory named
models-3.0
and a new config directory namedmodels.yaml-3.0
, both in the current working directory. If you prefer to name them something else, pass the--dest-directory
and/or--dest-yaml
arguments. -
Check that the new models directory and yaml file look ok.
-
Replace the existing directory and file, keeping backup copies just in case.
Table of Contents
- Quick Start
- Installation
- Hardware Requirements
- Features
- Latest Changes
- Troubleshooting
- Contributing
- Contributors
- Support
- Further Reading
Getting Started with InvokeAI
For full installation and upgrade instructions, please see: InvokeAI Installation Overview
Automatic Installer (suggested for 1st time users)
-
Go to the bottom of the Latest Release Page
-
Download the .zip file for your OS (Windows/macOS/Linux).
-
Unzip the file.
-
If you are on Windows, double-click on the
install.bat
script. On macOS, open a Terminal window, drag the fileinstall.sh
from Finder into the Terminal, and press return. On Linux, runinstall.sh
. -
You'll be asked to confirm the location of the folder in which to install InvokeAI and its image generation model files. Pick a location with at least 15 GB of free memory. More if you plan on installing lots of models.
-
Wait while the installer does its thing. After installing the software, the installer will launch a script that lets you configure InvokeAI and select a set of starting image generation models.
-
Find the folder that InvokeAI was installed into (it is not the same as the unpacked zip file directory!) The default location of this folder (if you didn't change it in step 5) is
~/invokeai
on Linux/Mac systems, andC:\Users\YourName\invokeai
on Windows. This directory will contain launcher scripts namedinvoke.sh
andinvoke.bat
. -
On Windows systems, double-click on the
invoke.bat
file. On macOS, open a Terminal window, draginvoke.sh
from the folder into the Terminal, and press return. On Linux, runinvoke.sh
-
Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090.
-
Type
banana sushi
in the box on the top left and clickInvoke
Command-Line Installation (for users familiar with Terminals)
You must have Python 3.9 or 3.10 installed on your machine. Earlier or later versions are not supported.
-
Open a command-line window on your machine. The PowerShell is recommended for Windows.
-
Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
mkdir invokeai
-
Create a virtual environment named
.venv
inside this directory and activate it:cd invokeai python -m venv .venv --prompt InvokeAI
-
Activate the virtual environment (do it every time you run InvokeAI)
For Linux/Mac users:
source .venv/bin/activate
For Windows users:
.venv\Scripts\activate
-
Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
For Windows/Linux with an NVIDIA GPU:
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
For Linux with an AMD GPU:
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
For non-GPU systems:
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
For Macintoshes, either Intel or M1/M2:
pip install InvokeAI --use-pep517
-
Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
invokeai-configure
-
Launch the web server (do it every time you run InvokeAI):
invokeai --web
-
Point your browser to http://localhost:9090 to bring up the web interface.
-
Type
banana sushi
in the box on the top left and clickInvoke
.
Be sure to activate the virtual environment each time before re-launching InvokeAI,
using source .venv/bin/activate
or .venv\Scripts\activate
.
Detailed Installation Instructions
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). For full installation and upgrade instructions, please see: InvokeAI Installation Overview
Hardware Requirements
InvokeAI is supported across Linux, Windows and macOS. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver).
System
You will need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
- An Apple computer with an M1 chip.
- An AMD-based graphics card with 4GB or more VRAM memory. (Linux only)
We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images.
Memory
- At least 12 GB Main Memory RAM.
Disk
- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
Features
Feature documentation can be reviewed by navigating to the InvokeAI Documentation page
Web Server & UI
InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
Unified Canvas
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.
Advanced Prompt Syntax
InvokeAI's advanced prompt syntax allows for token weighting, cross-attention control, and prompt blending, allowing for fine-tuned tweaking of your invocations and exploration of the latent space.
Command Line Interface
For users utilizing a terminal-based environment, or who want to take advantage of CLI features, InvokeAI offers an extensive and actively supported command-line interface that provides the full suite of generation functionality available in the tool.
Other features
- Support for both ckpt and diffusers models
- SD 2.0, 2.1 support
- Noise Control & Tresholding
- Popular Sampler Support
- Upscaling & Face Restoration Tools
- Embedding Manager & Support
- Model Manager & Support
Coming Soon
- Node-Based Architecture & UI
- And more...
Latest Changes
For our latest changes, view our Release Notes and the CHANGELOG.
Troubleshooting
Please check out our Q&A to get solutions for common installation problems and other issues.
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.
If you are unfamiliar with how to contribute to GitHub projects, here is a Getting Started Guide. A full set of contribution guidelines, along with templates, are in progress. You can make your pull request against the "main" branch.
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 community.
Welcome to InvokeAI!
Contributors
This fork is a combined effort of various people from across the world. Check out the list of all these amazing people. We thank them for their time, hard work and effort.
Thanks to Weblate for generously providing translation services to this project.
Support
For support, please use this repository's GitHub Issues tracking service, or join the Discord.
Original portions of the software are Copyright (c) 2023 by respective contributors.