andreas128/RePaint

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2267 stars · 198 forks observed · 2026-08-28

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

Official PyTorch implementation of RePaint, a CVPR 2022 paper on image inpainting using denoising diffusion probabilistic models. It provides pretrained models for ImageNet, CelebA-HQ, and Places2, filling in missing image regions guided by known pixels.

Use cases

• fill in missing parts of images with a diffusion model
• inpaint faces or diverse photos using pretrained models
• reproduce CVPR 2022 RePaint paper results
• run image inpainting with masks like thin, thick, or half
• experiment with diffusion model resampling schedules
• super-resolution or image expansion via inpainting

When to choose

• you want research-grade diffusion-based inpainting with pretrained checkpoints
• you need to reproduce or build on the RePaint paper
• your images match CelebA-HQ, ImageNet, or Places2 style domains

When to avoid

• you need a production-ready tool with a maintained license
• you want a general-purpose inpainting GUI or plugin
• you need support for arbitrary domains without retraining

Facets

library · maturity maintenance

image-processing machine-learning deep-learning computer-vision image-processing deep-learning artificial-intelligence python diffusion-models inpainting pytorch cvpr2022 research-code image-generation gpu linux macos

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