# diff-usion/Awesome-Diffusion-Models

A collection of resources and papers on Diffusion Models

Repository: https://github.com/diff-usion/Awesome-Diffusion-Models
Canonical: https://ross.abutalabs.com/products/awesome-diffusion-models
Homepage: https://diff-usion.github.io/Awesome-Diffusion-Models/
Language: HTML
License: MIT
License Family: permissive
Topics: diffusion-models, generative-model, machine-learning, score-matching, artificial-intelligence, score-based
Last push: 2024-08-01T07:11:20+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": 1811, "days_push": 762, "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 12363, forks 1010 (observed 2026-08-28T04:10:52.479082+00:00)

## What it is
A curated awesome-list collecting papers, tutorials, videos, and notebooks about diffusion models and score-based generative models. It organizes research across vision, audio, NLP, time series, graphs, and theory.

## Use cases
- find papers on diffusion models
- learn how diffusion models work
- get started with score-based generative models
- survey diffusion model applications in medical imaging
- find tutorials and notebooks on denoising diffusion
- research text-to-speech with diffusion models

## When to choose
- you need a curated reading list on diffusion and score-based generative models
- you are surveying the research landscape before starting a project
- you want introductory tutorials, videos, and annotated implementations

## When to avoid
- you need runnable diffusion model code or a library rather than links
- you want production tooling for training or inference
- you need non-diffusion generative model resources like GANs or VAEs

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, documentation
- domain: artificial-intelligence, machine-learning, deep-learning, image-processing, tutorials, awesome-lists
- platform: -
- tags: awesome-list, diffusion-models, generative-models, score-based-models, papers, curated-resources, natural-language-processing, web-server

## Member repositories
- diff-usion/Awesome-Diffusion-Models (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:52.479082+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 2026-08-29T17:14:07.860118+00:00, confidence not recorded.
  - readme: https://github.com/diff-usion/Awesome-Diffusion-Models (fetched 2026-08-28T04:10:52.479082+00:00, sha 276f77e39a09)
  - homepage: https://diff-usion.github.io/Awesome-Diffusion-Models/ (fetched 2026-08-29T08:11:08.927036+00:00, sha d4fc47a7de60)
- Data as of 2026-08-30T08:39:29.467469+00:00.