stable-diffusion-webui

Stable Diffusion web UI

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Quick Overview

AUTOMATIC1111/stable-diffusion-webui is a browser interface for Stable Diffusion, a popular AI image generation model. This project provides a user-friendly GUI with extended functionality, allowing users to generate, edit, and manipulate images using various AI models and techniques.

Pros

Easy-to-use web interface for Stable Diffusion
Extensive features including inpainting, outpainting, and prompt editing
Active community with frequent updates and improvements
Support for multiple models and extensions

Cons

Can be resource-intensive, requiring a powerful GPU for optimal performance
Setup process may be challenging for less technical users
Some features may require additional downloads or setup
Occasional stability issues due to rapid development and updates

Getting Started

Clone the repository:

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

Navigate to the project directory:
```
cd stable-diffusion-webui
```
Run the appropriate script for your operating system:
- Windows: webui-user.bat
- Linux/macOS: ./webui.sh
Open a web browser and go to http://localhost:7860 to access the interface.

Note: Ensure you have Python 3.10+ installed and a compatible GPU with the necessary drivers. Some models may need to be downloaded separately due to licensing restrictions.

Competitor Comparisons

InvokeAI

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Pros of InvokeAI

More user-friendly interface, especially for beginners
Better documentation and community support
Integrated support for multiple AI models beyond Stable Diffusion

Cons of InvokeAI

Fewer advanced features and customization options
Slower development cycle and updates
Less extensive plugin ecosystem

Code Comparison

InvokeAI:

from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, InvocationContext, invocation

stable-diffusion-webui:

from modules import sd_samplers, sd_models, images, scripts, shared
from modules.processing import StableDiffusionProcessing
from modules.sd_samplers import samplers

The code snippets show that InvokeAI uses a more modular and structured approach, while stable-diffusion-webui has a more direct import style. This reflects InvokeAI's focus on extensibility and stable-diffusion-webui's emphasis on performance and flexibility.

stablediffusion

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High-Resolution Image Synthesis with Latent Diffusion Models

Pros of stablediffusion

Official implementation from Stability AI, ensuring compatibility with the latest model updates
More focused on research and development of the core Stable Diffusion technology
Provides a clean, modular codebase for easier integration into other projects

Cons of stablediffusion

Less user-friendly interface compared to stable-diffusion-webui
Fewer built-in features and extensions for image generation and manipulation
Requires more technical knowledge to set up and use effectively

Code Comparison

stable-diffusion-webui:

from modules import sd_samplers
sampler = sd_samplers.create_sampler(name, model)
samples = sampler.sample(model, x, conditioning, unconditional_conditioning, steps=steps)

stablediffusion:

from ldm.models.diffusion import DDIMSampler
sampler = DDIMSampler(model)
samples, _ = sampler.sample(S=steps, conditioning=c, batch_size=1, shape=shape, verbose=False)

The code comparison shows that stable-diffusion-webui provides a more abstracted and user-friendly approach to sampling, while stablediffusion offers a more direct implementation that may be better suited for research and customization.

InvokeAI

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Pros of InvokeAI

More user-friendly interface, especially for beginners
Better documentation and community support
Integrated support for multiple AI models beyond Stable Diffusion

Cons of InvokeAI

Fewer advanced features and customization options
Slower development cycle and updates
Less extensive plugin ecosystem

Code Comparison

InvokeAI:

from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, InvocationContext, invocation

stable-diffusion-webui:

from modules import sd_samplers, sd_models, images, scripts, shared
from modules.processing import StableDiffusionProcessing
from modules.sd_samplers import samplers

ComfyUI

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The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.

Pros of ComfyUI

Highly flexible and customizable node-based interface
Better suited for advanced users and complex workflows
More efficient resource utilization for batch processing

Cons of ComfyUI

Steeper learning curve for beginners
Less user-friendly interface compared to stable-diffusion-webui
Fewer built-in features and extensions out of the box

Code Comparison

ComfyUI (Python):

class KSampler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model": ("MODEL",),
                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
                     "positive": ("CONDITIONING", ),
                     "negative": ("CONDITIONING", ),
                     "latent_image": ("LATENT", ),
                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                     }
                }

stable-diffusion-webui (Python):

def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
    steps = steps or p.steps

    if isinstance(conditioning, dict):
        img2img_conditioning = conditioning.pop("c_crossattn", None)
        img2img_conditioning_mask = conditioning.pop("c_concat", None)
    else:
        img2img_conditioning = None
        img2img_conditioning_mask = None

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README

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):

List of Online Services

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

Download sd.webui.zip from v1.0.0-pre and extract its contents.
Run update.bat.
Run run.bat.

For more details see Install-and-Run-on-NVidia-GPUs

Automatic Installation on Windows

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

Automatic Installation on Linux

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"

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

Run webui.sh.
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)

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