TencentARC/BrushNet

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1745 stars · 146 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BrushNet is the official PyTorch implementation of an ECCV 2024 plug-and-play image inpainting model that embeds pixel-level masked image features into pre-trained diffusion models via a decomposed dual-branch architecture. It includes training, inference, and evaluation code, checkpoints for SD 1.5 and SDXL, and ComfyUI/Gradio demos.

Use cases

• remove objects from photos and fill the region with generated content
• inpaint masked areas of an image guided by a text prompt
• add a plug-and-play inpainting branch to an existing stable diffusion model
• restore corrupted or damaged parts of images
• benchmark and evaluate diffusion-based inpainting models
• train a custom inpainting model on my own dataset

When to choose

• you need high-quality, text-coherent image inpainting on top of Stable Diffusion
• you want a plug-and-play inpainting module without modifying the base diffusion model
• you need training, inference, and evaluation code plus datasets (BrushData, BrushBench) for inpainting research

When to avoid

• you need non-diffusion or lightweight classical inpainting (e.g., OpenCV-style patch fill)
• you lack a GPU or cannot run large diffusion checkpoints
• you need a production-ready end-user app rather than a research codebase

Facets

library · maturity active

image-processing machine-learning deep-learning stable-diffusion image-processing artificial-intelligence deep-learning computer-vision python image-inpainting diffusion-models text-to-image eccv2024 plug-and-play generative-ai gpu linux

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