XPixelGroup/HYPIR

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1241 stars · 102 forks observed · 2026-08-28

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

Official PyTorch implementation of HYPIR, a SIGGRAPH 2025 method that harnesses diffusion-yielded score priors for image restoration. It provides pretrained models (based on Stable Diffusion 2.1) for restoration, super-resolution, and upscaling, with inference and training code.

Use cases

• restore old or degraded photos with diffusion priors
• upscale and enhance low-resolution images
• super-resolve images to high fidelity
• denoise and repair corrupted images
• train a custom image restoration model
• run image enhancement inference on GPU

When to choose

• you need state-of-the-art diffusion-based image restoration or super-resolution
• you want pretrained restoration models with inference and training code
• you are doing research on diffusion priors for image enhancement

When to avoid

• you need fast CPU-only image enhancement without a GPU
• you need a simple classical upscaler like bicubic or Real-ESRGAN with minimal compute
• you cannot accept a non-standard (NOASSERTION) license for commercial use without review

Facets

library · maturity active

image-processing machine-learning deep-learning image-processing computer-vision artificial-intelligence python cross-platform diffusion-models image-restoration super-resolution image-upscaling siggraph-2025 pretrained-models gpu

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markdown · JSON · MCP: product_card(name="XPixelGroup/HYPIR")