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, and runs on GPU cards with as little as 4 GB or RAM.

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

1. Installation
2. Major Features
3. Changelog
4. Troubleshooting
5. Contributing
6. Support

This fork is supported across multiple platforms. You can find individual installation instructions below.

• Linux

• Windows

• Macintosh

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.

To run in full-precision mode, start dream.py with the --full_precision flag:

(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision

• Interactive Command Line Interface

• Image To Image

• Inpainting Support

• GFPGAN and Real-ESRGAN Support

• Seamless Tiling

• Google Colab

• Web Server

• Reading Prompts From File

• Shortcut: Reusing Seeds

• Weighted Prompts

• Variations

• Personalizing Text-to-Image Generation

• Simplified API for text to image generation

• Creating Transparent Regions for Inpainting

• Preload Models

• 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 dream.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 CHANGELOGS.

Please check out our Q&A to get solutions for common installation problems and other issues.

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.

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.

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 (https://github.com/lstein)

Please see the original README for more information on this software and underlying algorithm, located in the file README-CompViz.md.