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https://github.com/automatic1111/stable-diffusion-webui

Stable Diffusion web UI
https://github.com/automatic1111/stable-diffusion-webui

ai ai-art deep-learning diffusion gradio image-generation image2image img2img pytorch stable-diffusion text2image torch txt2img unstable upscaling web

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Stable Diffusion web UI

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README 

        
# Stable Diffusion web UI

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



![](screenshot.png)



## Features

[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), 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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community

- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), 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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)

- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): 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](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML

- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-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](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))

- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions

- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) 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](https://huggingface.co/segmind/SSD-1B) support



## Installation and Running

Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:

- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)

- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.

- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)

- [Ascend NPUs](https://github.com/wangshuai09/stable-diffusion-webui/wiki/Install-and-run-on-Ascend-NPUs) (external wiki page)



Alternatively, use online services (like Google Colab):



- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)



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

1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)



### Automatic Installation on Windows

1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".

2. Install [git](https://git-scm.com/download/win).

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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).



## Contributing

Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)



## Documentation



The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/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](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/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/diffusion_restart_sampling

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

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

- (You)