# wyhuai/DDNM

[ICLR 2023 Oral] Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Repository: https://github.com/wyhuai/DDNM
Canonical: https://ross.abutalabs.com/products/ddnm
Language: Python
License: MIT
License Family: permissive
Topics: diffusion-models, zero-shot, image-restoration, iclr, iclr2023
Last push: 2024-04-25T08:28:06+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1406, "days_push": 860, "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 1349, forks 107 (observed 2026-08-28T04:04:27.889549+00:00)

## What it is
DDNM is a Python research codebase implementing the Denoising Diffusion Null-Space Model for zero-shot image restoration, published as an ICLR 2023 Oral paper. It restores images from degradations like super-resolution, denoising, colorization, inpainting, deblurring, and compressed sensing without any task-specific training or optimization.

## Use cases
- restore old photos with diffusion models
- zero-shot image super-resolution
- colorize black and white images
- inpaint missing regions in images
- deblur blurry photos
- denoise images without training a model
- solve compressed sensing image reconstruction

## When to choose
- you need image restoration without training a task-specific model
- you want a research-grade diffusion-based restoration baseline
- you need to handle arbitrary image sizes or multiple degradation types with one framework

## When to avoid
- you need a fast, production-ready image enhancement tool
- you lack a GPU or cannot download large pre-trained diffusion checkpoints
- you need real-time or mobile image restoration

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, deep-learning, artificial-intelligence
- platform: python
- tags: diffusion-models, zero-shot, image-restoration, super-resolution, inpainting, colorization, deblurring, denoising, research-code, iclr2023, gpu, linux

## Member repositories
- wyhuai/DDNM (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:27.889549+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 2026-08-30T04:42:23.922609+00:00, confidence not recorded.
  - readme: https://github.com/wyhuai/DDNM (fetched 2026-08-28T04:04:27.889549+00:00, sha 7a621108c5b6)
- Data as of 2026-08-30T08:39:29.467469+00:00.