Stable Diffusion - Automatic

Heavily opinionated custom fork of https://github.com/AUTOMATIC1111/stable-diffusion-webui

Notes

Fork is as close as up-to-date with origin as time allows
All code changes are merged upstream whenever possible

Fork adds extra functionality:

• Ships with additional extensions
  e.g. System Info, Steps Animation, etc.
• Ships with set of CLI tools that rely on SD API for execution:
  e.g. generate, train, bench, etc.
  Full list

Simplified start script: automatic.sh
Existing webui.sh/webui.bat still exist for backward compatibility, fresh installs to auto-install dependencies, etc.

./automatic.sh

• Start in default mode with optimizations enabled

./automatic.sh env

• Print env info and exit
  Example:

  Version: c07487a Tue Jan 24 08:04:31 2023 -0500
  Platform: Ubuntu 22.04.1 LTS 5.15.79.1-microsoft-standard-WSL2 x86_64
  Python 3.10.6
  Torch: 2.0.0.dev20230118+cu118 CUDA: 11.8 cuDNN: 8700 GPU: NVIDIA GeForce RTX 3060 Arch: (8, 6)
  

./automatic.sh public

• Start with listen on public IP with authentication enabled

./automatic.sh clean

• Start with all optimizations disabled
  Use this for troubleshooting

Install

1. Install PyTorch first
2. Clone and initialize repository

git clone --depth 1 https://github.com/vladmandic/automatic
git submodule update --init --recursive

3. Install dependencies

./automatic.sh install

Differences

Fork does differ in few things:

• Drops compatibility with python 3.7 and requires 3.9
• Updated Python libraries to latest known compatible versions
  e.g. accelerate, transformers, numpy, etc.
• Includes opinionated System and Options configuration
  e.g. samplers, upscalers, etc.
• Includes reskinned UI
  Black and orange dark theme with fixed width options panels and larger previews
• Includes SD2 configuration files
• Uses simplified folder structure
  e.g. /train, /outputs/*
• Modified training templates

Only Python library which is not auto-updated is PyTorch itself as that is very system specific
For some Torch optimizations notes, see Wiki

Fork is compatible with regular PyTorch 1.13 as well as pre-release of PyTorch 2.0
See Wiki for Torch optimization notes

Docs

Everything is in Wiki
Except my current TODO