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AUTOMATIC1111/stable-diffusion-webui

Stable Diffusion web UI

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AUTOMATIC1111/stable-diffusion-webui has 165k+ stars and 33k+ forks on GitHub, is written in Python, and was last pushed on Mar 2, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 49 minutes ago
Type:ApplicationCategory(s):Image, Video & Audio GenerationAI Agents & LLM Apps
Added to GitGenius on February 26th, 2026
Created on August 22nd, 2022
Open Issues & Pull Requests: 2,511 (+0)
GitHub issues: Enabled
Number of forks: 33,624
Total Stargazers: 165,075 (+0)
Total Subscribers: 1,260 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 9.0 days
Mean response time: 179.1 days
90th percentile: 585.8 days
Tracked items: 1,046

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Maintainer activity

2 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 18, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

Roughly one issue in four opened in the past year never receives a reply. 99% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "bug" is answered fastest, typically in about 3 days, while "asking-for-help-with-local-system-issues" waits about 11 months. 77% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 29% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 1,356
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 846 days
Stale 30+ days: 1,345
Stale 90+ days: 1,329

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug-report (2,176)
  • enhancement (797)
  • asking-for-help-with-local-system-issues (289)
  • bug (216)
  • not-an-issue (113)
  • question (39)
  • platform:mac (35)
  • cannot-reproduce (28)

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Detailed Description

The Stable Diffusion web UI is a Gradio-based web interface for Stable Diffusion, written in Python, that enables local deployment of text-to-image and image-to-image generation capabilities.

The interface implements comprehensive image generation features including original txt2img and img2img modes, outpainting, inpainting, and color sketch functionality. Users can employ prompt matrices to explore parameter variations and leverage attention mechanisms to emphasize specific text portions using syntax like double parentheses or weighted notation. The loopback feature enables iterative img2img processing, while X/Y/Z plotting allows visualization of images generated with different parameters in three-dimensional space.

Advanced training capabilities include textual inversion for custom embeddings, hypernetworks, and LoRAs, with preprocessing options for image cropping, mirroring, and autotagging using BLIP or DeepDanbooru. The extras tab provides multiple face restoration and upscaling options including GFPGAN, CodeFormer, RealESRGAN, ESRGAN, SwinIR, Swin2SR, and LDSR. The system supports checkpoint merging, allowing users to combine up to three model checkpoints into a single file, and includes a checkpoint reloading feature for on-the-fly model switching.

The interface accommodates hardware constraints with reported support for 4GB video cards and lower, includes negative prompts for excluding unwanted elements, and provides seed resizing and variation generation for controlled image modifications. Prompt editing enables mid-generation prompt switching, while the highres fix option produces high-resolution output without typical distortions. The CLIP interrogator estimates prompts from existing images, and composable diffusion supports multiple weighted prompts using uppercase AND separators without token limits.

Installation follows platform-specific workflows with one-click scripts for Windows, Linux, and Apple Silicon, though Python and Git remain required dependencies. The project supports multiple GPU architectures including NVidia, AMD, and Intel processors, with alternative deployment options via online services like Google Colab. Settings allow customization of UI element defaults and reordering of interface components.

Custom scripts and community extensions extend functionality, with features like the history tab for image browsing and aesthetic gradients for style-specific generation available through the extension ecosystem.