yinboc/liif

Health v2 · maintenance only

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1388 stars · 159 forks observed · 2026-08-28

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

LIIF is the official PyTorch implementation of the CVPR 2021 paper 'Learning Continuous Image Representation with Local Implicit Image Function'. It represents images as continuous functions of coordinates, enabling rendering at arbitrary resolutions including extrapolation far beyond training scales.

Use cases

• upscale images to arbitrary resolutions with a neural network
• super-resolution beyond 30x training scale
• learn continuous implicit representations of 2D images
• reproduce CVPR 2021 LIIF experiments on DIV2K and benchmarks
• train size-varied ground-truth super-resolution models

When to choose

• you need resolution-free image upscaling or arbitrary-scale super-resolution
• you want to experiment with implicit neural representations for images
• you need the reference implementation for research citation

When to avoid

• you need a production-ready image upscaler with active maintenance
• you lack a GPU or PyTorch environment
• you need general image editing beyond super-resolution

Facets

library · maturity maintenance

machine-learning image-processing deep-learning machine-learning computer-vision image-processing python super-resolution implicit-neural-representation pytorch arbitrary-resolution research-code cvpr-2021 gpu

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