Guide · Open source
How to use Awesome-Diffusion-Models: a guide to its resources
Updated · written with AI from the project's own documentation and checked against our catalogue data
Awesome-Diffusion-Models is a curated list of links about diffusion models, a family of machine learning methods used for generating images, audio and other data. It is a reading list rather than a software tool, so there is nothing to install or run. This guide shows you how to navigate it, which sections to start with, and how to find papers by application area.
Awesome-Diffusion-Modelsdiff-usion/Awesome-Diffusion-Models · ★ 12.4k · MIT Install options & downloads Quick start
- 1Open the homepage. Go to https://diff-usion.github.io/Awesome-Diffusion-Models/ for the full list of links.
- 2Read an introductory post. Start with a post in the Introductory Posts section to learn the basic ideas.
- 3Watch or attend a lecture. Pick an entry from Introductory Videos or Introductory Lectures to see the material explained.
- 4Study a tutorial notebook. Open an item from Tutorial and Jupyter Notebook and read its code alongside the explanation.
- 5Find papers by topic. Use the Papers section to locate research in your application area, such as Vision or Audio.
- 6Clone the repository if needed. Run git clone https://github.com/diff-usion/Awesome-Diffusion-Models to keep a local copy of the list.
What Awesome-Diffusion-Models is and is not
The repository contains a collection of resources and papers on diffusion models. It groups links into introductory material (posts, papers, videos, lectures and notebooks) and a longer list of research papers organised by topic.
It is a list of links, not an application or library. You cannot run it to generate images. Use it to find material to read, watch or try elsewhere.
Open the list and find your way around
The easiest route is the project homepage, https://diff-usion.github.io/Awesome-Diffusion-Models/, which the README recommends because the README itself may not contain all the information due to page constraints. The README also has a Contents section with links to each part.
The top-level Resources section holds introductory posts, introductory papers, introductory videos, introductory lectures, and tutorials and Jupyter notebooks. The Papers section holds a survey entry and topic groups.
- Resources: Introductory Posts, Introductory Papers, Introductory Videos, Introductory Lectures, Tutorial and Jupyter Notebook
- Papers: Survey, Vision, Audio, Natural Language, Tabular and Time Series, Graph, Reinforcement Learning, Theory, Applications
Start with the introductory material
If you are new to the field, begin with the Introductory Posts. Entries include Lilian Weng's 'What are Diffusion Models?' and Yang Song's 'Generative Modeling by Estimating Gradients of the Data Distribution'. Each entry lists an author, a link and a date.
The Introductory Videos and Introductory Lectures sections offer the same topics in video form, such as 'Diffusion models from scratch in PyTorch' and a CVPR 2022 tutorial page on denoising diffusion-based generative modeling.
Try the tutorial notebooks and minimal implementations
The Tutorial and Jupyter Notebook section links to code you can study. Examples include 'diffusion-for-beginners', 'Minimal implementation of diffusion models' and 'Diffusers - Hugging Face', which is linked as a Colab notebook.
The list does not state the requirements for these projects. Check each linked repository or notebook for its own setup instructions.
Download a local copy of the repository
If you want the list on your own machine, the README gives a clone command. This copies the repository, including the README, so you can browse the Markdown offline.
git clone https://github.com/diff-usion/Awesome-Diffusion-ModelsFind research papers by application area
The Papers section is organised by application. Vision is split into Generation, Classification, Segmentation, Image Translation, Inverse Problems, Medical Imaging, Multi-modal Learning, 3D Vision, Adversarial Attack and Miscellany. Audio includes Generation, Conversion, Enhancement, Separation and Text-to-Speech.
Other topics include Natural Language, Tabular and Time Series, Graph, Reinforcement Learning, Theory and Applications. Pick the area closest to your problem and scan its entries.
What people use it for
- A student building a reading plan from the introductory posts and papers
- A developer comparing minimal diffusion model implementations in the tutorial section
- A researcher listing papers on medical imaging or audio before a literature review
- Someone checking which topics diffusion research covers before choosing a deeper source
Frequently asked questions
Is Awesome-Diffusion-Models a software tool I can install?
No. The repository is a list of links to resources and papers on diffusion models. It is not an application or library, so there is nothing to install or run beyond optionally cloning the repository to read it locally.
What do I need to use Awesome-Diffusion-Models?
The source lists no prerequisites. You need a web browser to read the homepage or the README. Some linked notebooks run on Google Colab, but each one has its own requirements.
Is Awesome-Diffusion-Models free and what is its licence?
The repository is licensed under the MIT License, as shown by the badge in the README. The linked resources are independent projects and each has its own licence and terms, which you should check before reuse.
Is the list up to date?
Not necessarily. The analysis notes that the last push was on 2024-08-01 and no release exists, so newer work may be missing. Treat it as a starting point and check recent papers separately.
What are good alternatives to Awesome-Diffusion-Models?
The source does not name comparable projects, so this guide does not recommend specific alternatives. For a single structured course, look for one tutorial source, since this list spans many independent sources of varying depth.
Why does the README send me to the homepage?
The README says the homepage is the reference because the README may not contain all the information due to page constraints.
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Our verdict
Use this list if you want a broad map of diffusion model learning material and papers grouped by topic, and you are comfortable following links to independent sources. Skip it if you need a runnable tool, a single authoritative course, or coverage of the latest research, since the last push was 2024-08-01.
Install options & downloads