# Janspiry/Image-Super-Resolution-via-Iterative-Refinement

Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch

Repository: https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
Canonical: https://ross.abutalabs.com/products/image-super-resolution-via-iterative-refinement
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pytorch, diffusion-probabilistic, super-resolution, ddpm, image-generation
Last push: 2023-11-04T00:38:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1862, "days_push": 1034, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3923, forks 484 (observed 2026-08-28T04:08:29.806456+00:00)

## What it is
An unofficial PyTorch implementation of SR3 (Image Super-Resolution via Iterative Refinement), a diffusion-based model for image super-resolution. It supports conditional upscaling (e.g., 64x64 to 512x512), unconditional face generation, and includes training utilities with multi-GPU support and logging.

## Use cases
- upscale low-resolution images with a diffusion model
- train an SR3 super-resolution model in PyTorch
- generate faces unconditionally with DDPM
- reproduce the SR3 paper results
- run pretrained super-resolution on Colab
- experiment with conditional diffusion models

## When to choose
- you want a PyTorch implementation of SR3 to study or extend
- you need diffusion-based image upscaling with pretrained weights
- you want a codebase for experimenting with conditional DDPMs

## When to avoid
- you need a production-ready or officially supported super-resolution tool
- you want the latest diffusion SR techniques rather than a 2021-era reproduction
- you lack a GPU, since training and inference are compute-heavy

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, stable-diffusion
- domain: computer-vision, image-processing, deep-learning, machine-learning
- platform: python
- tags: super-resolution, diffusion-models, ddpm, sr3, pytorch, image-generation, unofficial-implementation, gpu, linux

## Member repositories
- Janspiry/Image-Super-Resolution-via-Iterative-Refinement (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:29.806456+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 2026-08-29T18:24:45.177051+00:00, confidence not recorded.
  - readme: https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement (fetched 2026-08-28T04:08:29.806456+00:00, sha 4c6b76dfb60a)
- Data as of 2026-08-30T08:39:29.467469+00:00.