XPixelGroup/HAT

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1583 stars · 185 forks observed · 2026-08-28

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

HAT (Hybrid Attention Transformer) is a PyTorch implementation of a state-of-the-art transformer model for image super-resolution and restoration, published at CVPR 2023 and extended in TPAMI. It includes pretrained models, real-world GAN-based SR variants, and inference tooling with tile mode for limited GPU memory.

Use cases

• upscale low-resolution images with a transformer model
• restore real-world degraded photos to high resolution
• benchmark super-resolution models on Set5, Set14, Urban100, Manga109
• train a custom image super-resolution model
• sharpen and denoise images with GAN-based SR

When to choose

• you need top-tier image super-resolution quality
• you want pretrained models for 4x upscaling or real-world SR
• you're doing research on transformer-based image restoration

When to avoid

• you need fast, lightweight upscaling on CPU or mobile
• you only need simple bicubic or Lanczos resizing
• you lack a GPU and can't tolerate slow inference

Facets

library · maturity stable

image-processing machine-learning deep-learning computer-vision image-processing deep-learning python super-resolution transformer image-restoration cvpr2023 pytorch gpu linux

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