# zsyOAOA/ResShift

ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS@2023 Spotlight, TPAMI@2024)

Repository: https://github.com/zsyOAOA/ResShift
Canonical: https://ross.abutalabs.com/products/resshift
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-07-08T11:06:15+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 8, longevity 90
- inputs: {"age_days": 1266, "days_push": 56, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1427, forks 95 (observed 2026-08-28T04:04:41.757104+00:00)

## What it is
ResShift is an efficient diffusion model for image super-resolution that transfers between low- and high-resolution images by shifting residuals, achieving strong results with as few as 15 sampling steps. It includes training and inference code, plus Gradio, Colab, and Replicate demos, and also supports deblurring.

## Use cases
- upscale low-resolution images with a diffusion model
- fast image super-resolution with few sampling steps
- real-world image super-resolution
- deblur blurry photos with a diffusion model
- train a custom super-resolution diffusion model
- compare super-resolution models on synthetic and real datasets

## When to choose
- you need high-quality super-resolution with fast inference
- you want a research-grade diffusion SR model with training code
- you need both super-resolution and deblurring in one framework

## When to avoid
- you need a lightweight non-deep-learning upscaler for production apps
- you lack a GPU and cannot afford diffusion inference costs
- you need a simple GUI-only tool without coding

## Facets
- artifact type: library
- maturity: active
- function: image-processing, machine-learning, deep-learning
- domain: image-processing, computer-vision, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: diffusion-model, super-resolution, image-restoration, deblurring, neurips, research-code, gpu, linux

## Member repositories
- zsyOAOA/ResShift (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:41.757104+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:37:24.478612+00:00, confidence not recorded.
  - readme: https://github.com/zsyOAOA/ResShift (fetched 2026-08-28T04:04:41.757104+00:00, sha 32d9259c311f)
- Data as of 2026-08-30T08:39:29.467469+00:00.