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Stable Diffusion web UI

A Gradio-based web user interface for Stable Diffusion maintained by AUTOMATIC1111. It provides an extensive local/server UI for image generation and editing (txt2img, img2img, inpainting, outpainting), textual inversion, LoRA/hypernetworks support, upscalers, face restoration, many samplers and advanced prompt controls, extensions and API — widely used as an open-source frontend for Stable Diffusion workflows.

Introduction

Stable Diffusion web UI (AUTOMATIC1111)

Stable Diffusion web UI is an open-source, Gradio-based web interface for running and interacting with Stable Diffusion models. Created and maintained by the GitHub user AUTOMATIC1111 and a large community of contributors, the project focuses on providing a full-featured, highly configurable frontend for generating and editing images using diffusion models.

Key capabilities
  • Core modes: txt2img (text-to-image) and img2img (image-to-image).
  • Editing & composition: inpainting, outpainting, color sketching, prompt editing mid-generation, loopback image processing.
  • Advanced prompt controls: attention syntax ((...)), weighted tokens, prompt matrix for batch variations, no hard token limit, negative prompts and live token length validation.
  • Model & training support: textual inversion (train embeddings), hypernetworks / LoRA support, checkpoint reloading and merging, support for Stable Diffusion v2 and alternative checkpoints/formats (including safetensors).
  • Extras & restoration: GFPGAN, CodeFormer, RealESRGAN, ESRGAN, SwinIR and other upscalers/face-restoration options available via the Extras tab or extensions.
  • Performance & compatibility: xformers acceleration support, options for low-VRAM cards (4GB, sometimes 2GB), broader platform install guides (NVIDIA, AMD, Apple Silicon, Intel), and community-contributed guides for NPUs.
  • Extensibility: an active extensions ecosystem (history/image browser, aesthetic gradients, many custom scripts), an API for automation, and ability to run arbitrary Python code with a flag.
  • Usability features: progress preview, estimated completion time, save/load of generation parameters (embedded in PNG/JPEG metadata), styles and presets, batch processing, tiling, prompt library, and UI customization.
Installation & usage

The repository provides one-click install scripts and detailed platform-specific instructions (Windows, Linux, macOS/Apple Silicon), plus many community guides and online hosting examples (e.g., Google Colab). Typical usage is cloning the repo, installing dependencies (Python, git, required libraries), and running the provided launch script (webui-user.bat / webui.sh). Optional flags (like --xformers or --allow-code) enable specific features.

Ecosystem & community

The project acts as a hub for many community tools and ideas: integrations with CLIP-interrogator, DeepDanbooru (for anime tagging), composable-diffusion approaches, numerous upscaler and optimization libraries, and a wide set of community-contributed extensions and scripts. It is widely used by practitioners, hobbyists, and researchers experimenting with image-generation pipelines and training lightweight embeddings or hypernetworks.

Why it matters

Stable Diffusion web UI lowers the barrier to experiment with diffusion-based image generation by combining advanced model features with an accessible GUI, extensive customization, and a rich extension ecosystem. This makes it a go-to open-source frontend for creative workflows, reproducible experiments, and local deployments of Stable Diffusion.

Information

  • Websitegithub.com
  • AuthorsAUTOMATIC1111, Community contributors
  • Published date2022/08/22

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