This project provides a comprehensive Gradio-based web UI for Stable Diffusion, targeting artists, researchers, and hobbyists. It offers a rich feature set for image generation and manipulation, including advanced prompting, various upscaling and face restoration tools, and extensive customization options, significantly lowering the barrier to entry for using diffusion models.

How It Works

The UI leverages Gradio for its interactive interface and integrates multiple Stable Diffusion pipelines and auxiliary models. Key features include attention mechanisms for prompt weighting, textual inversion for custom concepts, and a variety of post-processing tools like GFPGAN and CodeFormer for face enhancement. It supports advanced generation techniques such as prompt matrix, X/Y/Z plots, and composable diffusion, enabling fine-grained control and exploration of the latent space.

Quick Start & Requirements

• Installation: Clone the repository and run webui.sh (Linux/macOS) or webui-user.bat (Windows).
• Prerequisites: Python (3.10.6 recommended for Windows), Git. NVIDIA GPUs are recommended; AMD and Intel GPU/CPU support are available via external wikis. CUDA 12 is not explicitly required but recommended for optimal performance.
• Resources: Reports of 4GB VRAM working, with some users succeeding on 2GB.
• Docs: Project Wiki

Highlighted Details

• Extensive support for various Stable Diffusion models and formats (safetensors).
• Advanced features like prompt editing mid-generation and composable diffusion with weighted prompts.
• Integrated training capabilities for embeddings and hypernetworks.
• Community-driven extensions and custom scripts.
• Optimized performance with xformers support.

Maintenance & Community

The project is actively maintained by a large community, with numerous contributors and community-developed extensions. Links to community resources like Discord are available via the project wiki.

Licensing & Compatibility

The project is released under the Apache 2.0 license. This license permits commercial use and linking with closed-source projects.

Limitations & Caveats

While supporting a wide range of hardware, optimal performance and feature availability are heavily dependent on GPU capabilities. Some advanced features or specific model integrations might require manual configuration or community extensions.