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- !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
InvokeAI: A Stable Diffusion Toolkit
Note: This fork 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 aid diagnose issues faster.
This repository was formally known as lstein/stable-diffusion
Table of Contents
This is a fork of CompVis/stable-diffusion, the open source text-to-image generator. It provides a streamlined process with various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, with GPU cards with as little as 4 GB or RAM. It provides both a polished Web interface, and an easy-to-use command-line interface.
Note: This fork 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 aid diagnose issues faster.
Table of Contents
- Installation
- Hardware Requirements
- Features
- Latest Changes
- Troubleshooting
- Contributing
- Contributors
- Support
- Further Reading
Installation
This fork is supported across multiple platforms. You can find individual installation instructions below.
Hardware Requirements
System
You wil need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
- An Apple computer with an M1 chip.
Memory
- At least 12 GB Main Memory RAM.
Disk
- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
Note
If you have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in full-precision mode as shown below.
Similarly, specify full-precision mode on Apple M1 hardware.
Precision is auto configured based on the device. If however you encounter
errors like 'expected type Float but found Half' or 'not implemented for Half'
you can try starting invoke.py with the --precision=float32 flag:
(ldm) ~/stable-diffusion$ python scripts/invoke.py --precision=float32
Features
Major Features
- Web Server
- Interactive Command Line Interface
- Image To Image
- Inpainting Support
- Outpainting Support
- Upscaling, face-restoration and outpainting
- Seamless Tiling
- Google Colab
- Reading Prompts From File
- Shortcut: Reusing Seeds
- Prompt Blending
- Thresholding and Perlin Noise Initialization Options
- Negative/Unconditioned Prompts
- Variations
- Personalizing Text-to-Image Generation
- Simplified API for text to image generation
Other Features
Latest Changes
-
vNEXT (TODO 2022)
- Deprecated
--full_precision/-F. Simply omit it andinvoke.pywill auto configure. To switch away from auto use the new flag like--precision=float32.
- Deprecated
-
v1.14 (11 September 2022)
- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
- Full support for Apple hardware with M1 or M2 chips.
- Add "seamless mode" for circular tiling of image. Generates beautiful effects. (prixt).
- Inpainting support.
- Improved web server GUI.
- Lots of code and documentation cleanups.
-
v1.13 (3 September 2022
- Support image variations (see VARIATIONS (Kevin Gibbons and many contributors and reviewers)
- Supports a Google Colab notebook for a standalone server running on Google hardware Arturo Mendivil
- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling Kevin Gibbons
- WebUI supports incremental display of in-progress images during generation Kevin Gibbons
- A new configuration file scheme that allows new models (including upcoming stable-diffusion-v1.5) to be added without altering the code. (David Wager)
- Can specify --grid on invoke.py command line as the default.
- Miscellaneous internal bug and stability fixes.
- Works on M1 Apple hardware.
- Multiple bug fixes.
For older changelogs, please visit 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. 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, but for now the most important thing is to make your pull request against the "development" branch, and not against "main". This will help keep public breakage to a minimum and will allow you to propose more radical changes.
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.
Support
For support, please use this repository's GitHub Issues tracking service. Feel free to send me an email if you use and like the script.
Original portions of the software are Copyright (c) 2020 Lincoln D. Stein
Further Reading
Please see the original README for more information on this software and underlying algorithm, located in the file README-CompViz.md.