hfarkhari/automatic_1111 repository overview

⁠Welcome to Stable Diffusion WebUI (AUTOMATIC1111 v1.6)

This repository provides an easy-to-use Docker image for running Stable Diffusion WebUI, which is a tool for stable image-to-image transformation using diffusion models. If you encounter difficulties with the traditional installation process, worry not! With our Docker image, you can quickly get up and running.

This Docker Hub repository offers a Docker image for Stable Diffusion WebUI, built on AUTOMATIC1111 v1.6.0⁠, Gradio v3.43.2⁠, and contain SadTalker⁠ extension.It also enabling the execution of SD-XL v1.0⁠ model without requiring Nvidia driver or CUDA toolkit downgrades, with PyTorch 2.0 and CUDA 11.7 included. Tested on Ubuntu 22.04 with latest Nvidia Driver and CUDA 12.2.

⁠Installation

To install Stable Diffusion WebUI, you can follow the instructions in the main project repository here⁠ or here⁠:

⁠Run with Docker (No Need to Install)

If you prefer a hassle-free setup, we've got you covered! Follow these steps to run Stable Diffusion WebUI using Docker:

1. First, download the Docker image by running the following command:

docker pull hfarkhari/automatic_1111:v1.6_gradio_3.43.2_user_p7860_sadtalker

1. Next, clone this repository to your local machine, placing it in a folder named "stable-diffusion-webui":

git clone https://github.com/HFarkhari/stable-diffusion-webui  <path-to-local-cloned-git-folder>/stable-diffusion-webui

1. Now, you're all set to run the Docker image. Use the following command:

docker run --gpus all  -p 7860:7860 -it --rm  -v $(realpath <path-to-local-cloned-git-folder>/stable-diffusion-webui):/workspace hfarkhari/automatic_1111:v1.6_gradio_3.43.2_user_p7860_sadtalker

Make sure to replace <path-to-local-cloned-git-folder> with the actual path to the "stable-diffusion-webui" folder on your computer.

⁠Custom Configuration

If you face any problem to run this docker file you can run with one of these three ways and also it is important to check guidance here⁠:

1. Use "python webui.py" and the proper commands:

docker run --gpus all  -p 7860:7860 -it --rm  -v $(realpath <path-to-local-cloned-git-folder>/stable-diffusion-webui):/workspace hfarkhari/automatic_1111:v1.6_gradio_3.43.2_user_p7860_sadtalker python webui.py --listen --port 7860 --xformers --api

1. Use "python launch.py" and the proper commands:

docker run --gpus all  -p 7860:7860 -it --rm  -v $(realpath <path-to-local-cloned-git-folder>/stable-diffusion-webui):/workspace hfarkhari/automatic_1111:v1.6_gradio_3.43.2_user_p7860_sadtalker python launch.py --listen --port 7860 --xformers --api

1. Use "bash webui.sh" if you use "Ubuntu" linux and the proper commands:

docker run --gpus all  -p 7860:7860 -it --rm  -v $(realpath <path-to-local-cloned-git-folder>/stable-diffusion-webui):/workspace hfarkhari/automatic_1111:v1.6_gradio_3.43.2_user_p7860_sadtalker bash webui.sh  --listen --port 7860 --xformers --api

⁠Advanced Options for Better Performance

For improved performance, you can include some extra commands when running the Docker image:

docker run --gpus all --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -p 7860:7860 -it --rm --shm-size=16g  -v $(realpath ~/Desktop/stable-diffusion-webui):/workspace hfarkhari/automatic_1111:v1.6_gradio_3.43.2_user_p7860_sadtalker

⁠Accessing the WebUI

Once you have successfully run the Docker image, open your web browser and navigate to the following address:

http://127.0.0.1:7860

⁠Downloading Model Checkpoints

If you wish to use specific model checkpoints, such as "SD 1.5" or "SDXL 1.0", simply download them and place them in the "stable-diffusion-webui/models/Stable-diffusion" folder.

⁠Future Updates

We are continuously working on improving this Docker file and will provide a presentation and video to explain its usage in the near future. Stay tuned for updates here!

Thank you for using Stable Diffusion WebUI. If you encounter any issues or have questions, feel free to reach out via the GitHub repository. Happy image transformation!

⁠Stable Diffusion web UI (AUTOMATIC1111)

A browser interface based on Gradio library for Stable Diffusion.

⁠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 sparate 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 domension must be a multiple of 8 rather than 64
• Now with a license!
• Reorder elements in the UI from settings screen