shallowdream204/DreamClear

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1191 stars · 46 forks observed · 2026-08-28

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

DreamClear is a diffusion-transformer based real-world image restoration model for high-fidelity super-resolution, published at NeurIPS 2024. It includes training and inference code, pre-trained weights, a RealLQ250 benchmark, and a privacy-safe dataset curation pipeline.

Use cases

• restore and upscale low-quality real-world photos
• super-resolution of degraded images with diffusion models
• benchmark image restoration models on real-world LQ images
• train a diffusion transformer for image restoration
• curate privacy-safe datasets for restoration training

When to choose

• you need state-of-the-art real-world image super-resolution
• you want pretrained restoration weights and inference code
• you are researching diffusion-based image restoration

When to avoid

• you need fast real-time restoration on CPU
• you only need simple classical upscaling without GPU inference
• you need a production-ready service rather than research code

Facets

library · maturity active

image-processing machine-learning deep-learning computer-vision image-processing artificial-intelligence python super-resolution diffusion-transformer image-restoration dataset-curation neurips-2024 research-code linux gpu

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