stable-diffusion-webui

AUTOMATIC1111 / stable-diffusion-webui

Stable Diffusion 的浏览器界面，提供可视化的 AI 绘画操作方式，让用户无需代码即可生成和编辑图像。

Python AIGC AI 基础设施 开发工具 Stable Diffusion Web UI AI绘画 图像生成 机器学习

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这是 AI 绘画领域最具影响力的开源项目之一，集成了模型管理、提示词编辑、多种采样算法和扩展插件生态，极大降低了使用门槛。适合艺术家、设计师和 AI 爱好者快速上手。注意项目对硬件有一定要求，且功能繁杂，新手建议先阅读文档并关注显存占用。

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项目文档

来自 GitHub README · master 分支

Stable Diffusion web UI

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

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 - 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 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):

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:
# 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:

# 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"
  1. Navigate to the directory you would like the webui to be installed and execute the following command:
wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh

Or just clone the repo wherever you want:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
  1. Run webui.sh.
  2. 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/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)

文档抓取自 GitHub 仓库 README，版权归原作者所有；已过滤徽章等噪音并经安全消毒后展示。