This repo is an ongoing educational journey into diffusion models. The goal is to provide a progressively expanding collection of PyTorch reference implementations of the most important diffusion model milestones. I prefer to keep training and inference runnable locally on a laptop, I will rely more on small popular datasets and occasional synthetic data. Then name implies that by the time I finish, these models will be fairly "retro" (some of them already are). I plan to also write some survey notes.
Papers (tentatively) covered:
| Name | Authors | ArXiv Link | Year | Note |
|---|---|---|---|---|
| "Diffusion Probabilistic Models" | Sohl-Dickstein et al | arXiv:1503.03585 | 2015 | First paper that introduced the idea of diffusion models. |
| "Denoising Diffusion Probabilistic Models" | Ho et al | arXiv:2006.11239 | 2020 | Added some crucial modifications of the original architecture. |
| "Score-Based Generative Modeling through Stochastic Differential Equations" | Song et al. | arxiv:2011.13456 | 2021 | |
| "Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models" | Komanduri et. al | arxiv:2404.17735 | 2024 |
To update package versions:
Start by updating the version requirements.in and then run
bazel run requirements.update()The result can be validated by
bazel test requirements_test