Awesome-Diffusion-Models
A curated list of links to articles, papers, videos and notebooks about diffusion models.
- Stars
- 12.4k
- Forks
- 1k
- Licence
- MIT
- Language
- HTML
- Last update
- 2.3 years ago
- Type
- Curated list
No commits in over a year — check for forks or alternatives before adopting.
What it is
This is a curated reading and resource list about diffusion models, a family of machine learning methods used for generating images, audio and other data. It groups links to introductory blog posts, papers, videos, lectures and Jupyter notebooks, and then a longer list of research papers by topic such as vision, audio, tabular data and graphs. People use it as a starting point for learning the field or for finding papers to read.
- Work through the introductory posts and papers in order to learn how diffusion models work
- Pick a tutorial notebook such as the Hugging Face Diffusers intro to try a small example
- Browse the papers section by topic, for example audio or medical imaging, to find relevant research
- Use the links as a reading list for a study group or course
Great for
- Students who want a structured set of introductory material on diffusion models
- Developers looking for tutorial notebooks and minimal implementations to study
- Researchers searching for papers organized by application area
- Anyone who wants to see which topics diffusion research covers before choosing a deeper source
Not ideal for
- People who want a runnable software tool; this repository is a list of links, not an application or library
- Readers who need up-to-date coverage, since the last push was 2024-08-01 and no release exists
- Those looking for a single authoritative tutorial, since the list spans many independent sources of varying depth
How to get it
- 1
Read it online
Clone the repository
git clone https://github.com/diff-usion/Awesome-Diffusion-Models - 2
Run it
- Open the project homepage at https://diff-usion.github.io/Awesome-Diffusion-Models/ for the full list
- Start with the Introductory Posts section and read one or two articles
- Watch one of the listed introductory videos for a visual explanation
- Open a tutorial notebook from the Tutorial and Jupyter Notebook section to try code
- 3
Get the most out of it
- Read the introductory posts first, then move to the papers section for your area of interest
- Use the table of contents in the README to jump directly to topics such as Audio or Medical Imaging
- Compare several tutorial notebooks, since they range from minimal implementations to Stable Diffusion examples
- Check the homepage, since the README says it may hold more information than the README itself
Downloads
Strengths
- Large collection of linked material, with about 12,000 GitHub stars indicating wide community use
- Organized into clear sections from beginner material to research papers by topic
- Licensed under MIT, so the list can be reused freely
Trade-offs
- It is a list of links, so quality and availability depend on each external source, and some links may be dead or outdated
- The README is truncated and the page notes it may not contain all information, so coverage is only partly visible here
- Last push was August 2024, so newer work in the field may be missing
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| Awesome-Diffusion-Models (this) | 12.4k | Curated list | beginner | 2.3 years ago | Students who want a structured set of introductory material on diffusion models |
| denoising-diffusion-pytorch | 10.7k | Library | intermediate | 1 months ago | Researchers and engineers who want a readable PyTorch reference for diffusion models |
| awesome-artificial-intelligence | 16.8k | Curated list | beginner | 3 months ago | Software developers who want to learn the foundations behind language models and agents |
| AI-For-Beginners | 70.5k | Learning resource | beginner | 13 days ago | Beginners who want a structured, self-paced introduction to AI |
| mit-deep-learning | 10.5k | Learning resource | beginner | 2.8 years ago | Students following MIT deep learning courses |
| Vane | 38k | App | intermediate | 2 months ago | People who want AI-written answers with links to sources |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.