# arpitbansal297/Cold-Diffusion-Models

Official implementation of Cold-Diffusion for different transformations in pytorch.

Repository: https://github.com/arpitbansal297/Cold-Diffusion-Models
Canonical: https://ross.abutalabs.com/products/cold-diffusion-models
Homepage: https://arxiv.org/abs/2208.09392
Language: Python
License Family: other
Last push: 2022-10-13T00:23:35+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": 1477, "days_push": 1421, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1136, forks 83 (observed 2026-08-28T04:03:43.668270+00:00)

## What it is
Official PyTorch implementation of Cold Diffusion, a research paper showing that diffusion-style generative models can invert arbitrary image degradations (blur, masking, downsampling, desaturation) rather than only Gaussian noise. It includes training and sampling scripts plus pretrained models for MNIST, CIFAR-10, CelebA, and AFHQ.

## Use cases
- train a diffusion model that uses blur instead of noise
- generate images with generalized diffusion models
- reproduce the Cold Diffusion paper experiments
- experiment with non-Gaussian degradation processes in diffusion
- compare hot vs cold diffusion sampling algorithms
- generate CelebA or AFHQ faces with pretrained models

## When to choose
- you want to research or extend generalized diffusion models with arbitrary degradations
- you need a PyTorch codebase for the Cold Diffusion paper
- you want to test whether deterministic degradations suffice for generation

## When to avoid
- you need a production-ready, licensed generative image library
- you want standard DDPM denoising with active maintenance and community support
- you need a stable API or long-term support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: machine-learning, computer-vision, image-processing, deep-learning
- platform: python
- tags: diffusion-models, generative-models, pytorch, research-code, cold-diffusion, image-generation

## Member repositories
- arpitbansal297/Cold-Diffusion-Models (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:43.668270+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 2026-08-30T06:36:30.152215+00:00, confidence not recorded.
  - readme: https://github.com/arpitbansal297/Cold-Diffusion-Models (fetched 2026-08-28T04:03:43.668270+00:00, sha 58e6aa558e3a)
  - homepage: https://arxiv.org/abs/2208.09392 (fetched 2026-08-29T12:41:39.716704+00:00, sha b8bd585e69d4)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T12:41:39.719787+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T12:41:39.724616+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T12:41:39.727970+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T12:41:39.722449+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.