Stable Diffusion web UI patched

For the original description from the AUTOMATIC1111 repository see here.

A web interface for Stable Diffusion, implemented using Gradio library.

This is a fork, please do not send any bug reports / issues to the original creators!

Features

• Original txt2img and img2img modes
• One click install and run script (but you still must install python and git)
• Outpainting
• Inpainting
• Color Sketch
• Prompt Matrix
• Stable Diffusion Upscale
• Attention, specify parts of text that the model should pay more attention to
  • a man in a ((tuxedo)) - will pay more attention to tuxedo
  • a man in a (tuxedo:1.21) - alternative syntax
  • select text and press Ctrl+Up or Ctrl+Down (or Command+Up or Command+Down if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
• Loopback, run img2img processing multiple times
• X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
• Textual Inversion
  • have as many embeddings as you want and use any names you like for them
  • use multiple embeddings with different numbers of vectors per token
  • works with half precision floating point numbers
  • train embeddings on 8GB (also reports of 6GB working)
• Extras tab with:
  • GFPGAN, neural network that fixes faces
  • CodeFormer, face restoration tool as an alternative to GFPGAN
  • RealESRGAN, neural network upscaler
  • ESRGAN, neural network upscaler with a lot of third party models
  • SwinIR and Swin2SR (see here), neural network upscalers
  • LDSR, Latent diffusion super resolution upscaling
• Resizing aspect ratio options
• Sampling method selection
  • Adjust sampler eta values (noise multiplier)
  • More advanced noise setting options
• Interrupt processing at any time
• 4GB video card support (also reports of 2GB working)
• Correct seeds for batches
• Live prompt token length validation
• Generation parameters
  • parameters you used to generate images are saved with that image
  • in PNG chunks for PNG, in EXIF for JPEG
  • can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
  • can be disabled in settings
  • drag and drop an image/text-parameters to promptbox
• Read Generation Parameters Button, loads parameters in promptbox to UI
• Settings page
• Running arbitrary python code from UI (must run with --allow-code to enable)
• Mouseover hints for most UI elements
• Possible to change defaults/mix/max/step values for UI elements via text config
• Tiling support, a checkbox to create images that can be tiled like textures
• Progress bar and live image generation preview
  • Can use a separate neural network to produce previews with almost none VRAM or compute requirement
• Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
• Styles, a way to save part of prompt and easily apply them via dropdown later
• Variations, a way to generate same image but with tiny differences
• Seed resizing, a way to generate same image but at slightly different resolution
• CLIP interrogator, a button that tries to guess prompt from an image
• Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
• Batch Processing, process a group of files using img2img
• Img2img Alternative, reverse Euler method of cross attention control
• Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
• Reloading checkpoints on the fly
• Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
• Custom scripts with many extensions from community
• Composable-Diffusion, a way to use multiple prompts at once
  • separate prompts using uppercase AND
  • also supports weights for prompts: a cat :1.2 AND a dog AND a penguin :2.2
• No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
• DeepDanbooru integration, creates danbooru style tags for anime prompts
• xformers, major speed increase for select cards: (add --xformers to commandline args)
• via extension: History tab: view, direct and delete images conveniently within the UI
• Generate forever option
• Training tab
  • hypernetworks and embeddings options
  • Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
• Clip skip
• Hypernetworks
• Loras (same as Hypernetworks but more pretty)
• A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
• Can select to load a different VAE from settings screen
• Estimated completion time in progress bar
• API
• Support for dedicated inpainting model by RunwayML
• via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embeds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)
• Stable Diffusion 2.0 support - see wiki for instructions
• Alt-Diffusion support - see wiki for instructions
• Now without any bad letters!
• Load checkpoints in safetensors format
• Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
• Now with a license!
• Reorder elements in the UI from settings screen
• Segmind Stable Diffusion support

Installation

This version is tested on Debian 12 (bookworm). Python 3.12 and later are not supported!

Debian

• Install the CUDA toolkit (12.6 or later) and the GPU driver
• Install Python 3.11, venv and pip (sudo apt install python3.11 python3.11-venv python3-pip)

NixOS

• Make sure CUDA and the NVIDIA GPU driver are installed and the driver is enabled.
• Install Python 3.11 and pip.
• Start a nix-shell.

Example configuration.nix (tested with NVIDIA Quadro P6000):

    hardware.graphics.enable = true;
    hardware.nvidia.package = config.boot.kernelPackages.nvidiaPackages.stable;
    hardware.nvidia.open = false;
    services.xserver.videoDrivers = [ "nvidia" ];
    environment.systemPackages = with pkgs; [
      cudatoolkit
      cudaPackages.cudnn
      cudaPackages.cutensor
      python311Full
      python311Packages.pip
    ];

Prepare the Python venv

• Run ./setup.sh. This will create the python venv and install all pip dependencies.
• Download at least one Stable Diffusion model and put it into models/Stable-diffusion.
• Check if the web UI starts by running ./webui.sh.

Documentation

The documentation is at the original project's wiki.

New documentation specific to this fork will be added soon.

Credits

Licenses for borrowed code can be found in Settings -> Licenses screen, and also in html/licenses.html file.

• Original repo by AUTOMATIC1111 and contributors - 82a973c043
• Initial shell.nix for Nix / NixOS by virchau13 - 5b5c18e45f
• Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers, https://github.com/mcmonkey4eva/sd3-ref
• k-diffusion - https://github.com/crowsonkb/k-diffusion.git
• Spandrel - https://github.com/chaiNNer-org/spandrel implementing
  • 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 (92feee6)
• Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
• Security advice - RyotaK
• UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
• TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
• LyCORIS - KohakuBlueleaf
• Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
• Hypertile - tfernd - https://github.com/tfernd/HyperTile
• Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
• Satan
• Lucifer