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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

  1. 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