📄 README.md

Stable Diffusion web UI

A web interface for Stable Diffusion, implemented using Gradio library. ![](screenshot.png)

Features

Detailed feature showcase with images:

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

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 and Running

Make sure the required dependencies are met and follow the instructions available for:

NVidia (recommended)

AMD GPUs.

Intel CPUs, Intel GPUs (both integrated and discrete) (external wiki page)

Ascend NPUs (external wiki page)

Alternatively, use online services (like Google Colab):

List of Online Services

Installation on Windows 10/11 with NVidia-GPUs using release package

1. Download sd.webui.zip from v1.0.0-pre and extract its contents. 2. Run update.bat. 3. Run run.bat. > For more details see Install-and-Run-on-NVidia-GPUs

Automatic Installation on Windows

1. Install Python 3.10.6 (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install git. 3. Download the stable-diffusion-webui repository, for example by running git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git. 4. Run webui-user.bat from Windows Explorer as normal, non-administrator, user.

Automatic Installation on Linux

1. Install the dependencies: ``

bash
Debian-based:
sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
Red Hat-based:
sudo dnf install wget git python3 gperftools-libs libglvnd-glx
openSUSE-based:
sudo zypper install wget git python3 libtcmalloc4 libglvnd
Arch-based:
sudo pacman -S wget git python3


If your system is very new, you need to install python3.11 or python3.10:

bash
Ubuntu 24.04
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install python3.11

Manjaro/Arch
sudo pacman -S yay
yay -S python311 # do not confuse with python3.11 package

Only for 3.11
Then set up env variable in launch script
export python_cmd="python3.11"
or in webui-user.sh
python_cmd="python3.11"


2. Navigate to the directory you would like the webui to be installed and execute the following command:

bash
wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh


Or just clone the repo wherever you want:

bash
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui



3. Run

webui.sh

.
4. Check

webui-user.sh

 for options.
Installation on Apple Silicon

Find the instructions here.

Contributing
Here's how to add code to this repo: Contributing

Documentation

The documentation was moved from this README over to the project's wiki.

For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) crawlable wiki.

Credits
Licenses for borrowed code can be found in

Settings -> Licenses screen, and also in html/licenses.html` file.

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 (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

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/diffusionrestartsampling

Hypertile - tfernd - https://github.com/tfernd/HyperTile

Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.

(You)