# hojonathanho/diffusion

Denoising Diffusion Probabilistic Models

Repository: https://github.com/hojonathanho/diffusion
Canonical: https://ross.abutalabs.com/products/diffusion
Language: Python
License Family: other
Last push: 2023-08-29T16:48:57+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": 2267, "days_push": 1100, "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 5300, forks 492 (observed 2026-08-28T04:09:15.312202+00:00)

## What it is
The official reference implementation of Denoising Diffusion Probabilistic Models (DDPM) from the 2020 paper by Jonathan Ho et al., written in TensorFlow 1.15. It provides training and evaluation scripts for diffusion-based image generation on TPUs, with pretrained models and samples available for download.

## Use cases
- reproduce the DDPM paper results
- train a denoising diffusion model on image datasets
- generate images with a pretrained diffusion model
- study the original DDPM codebase for research
- evaluate diffusion models with FID and other metrics

## When to choose
- you need the canonical DDPM implementation for research reproduction
- you have TPU/GCP infrastructure and legacy TensorFlow 1.x environments
- you want to study the original diffusion model code

## When to avoid
- you want modern PyTorch or JAX diffusion libraries like Hugging Face diffusers
- you need a maintained, licensed, production-ready image generation tool
- you cannot run TensorFlow 1.15 and Python 3.5

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, machine-learning, image-processing
- platform: python
- tags: diffusion-models, ddpm, generative-models, tensorflow, research-code, image-generation, gpu, linux

## Member repositories
- hojonathanho/diffusion (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:15.312202+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 2026-08-29T17:59:09.006165+00:00, confidence not recorded.
  - readme: https://github.com/hojonathanho/diffusion (fetched 2026-08-28T04:09:15.312202+00:00, sha b929f79a4a75)
- Data as of 2026-08-30T08:39:29.467469+00:00.