automatic/README.md

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 still exists 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)
  

<<<<<<< HEAD

• Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
• k-diffusion - https://github.com/crowsonkb/k-diffusion.git
• GFPGAN - https://github.com/TencentARC/GFPGAN.git
• CodeFormer - https://github.com/sczhou/CodeFormer
• ESRGAN - https://github.com/xinntao/ESRGAN
• SwinIR - https://github.com/JingyunLiang/SwinIR
• Swin2SR - https://github.com/mv-lab/swin2sr
• LDSR - https://github.com/Hafiidz/latent-diffusion
• MiDaS - https://github.com/isl-org/MiDaS
• Ideas for optimizations - https://github.com/basujindal/stable-diffusion
• Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
• Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
• Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
• Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
• Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
• Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
• CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
• Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
• xformers - https://github.com/facebookresearch/xformers
• DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
• Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
• Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
• Security advice - RyotaK
• Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
• (You) =======

./automatic.sh install

• Install requirements and exit

./automatic.sh public

• Start with listen on public IP with authentication enabled

./automatic.sh clean

• Start with all optimizations disabled
  Use this for troubleshooting

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

92b4fbe (update requirements)