Peter Holderrieth and Ezra Erives just released their new MIT 2026 course on flow matching and diffusion models! It introduces the full stack of modern AI image, video, protein generators - theory & practice. It includes:
- Lecture Videos: Introducing theory & step-by-step derivations.
- Lecture Notes: Mathematically self-contained.
- Coding: Hands-on exercises for every component.
They improved upon last years' iteration and added new topics:
Latent spaces, diffusion transformers, building language models with discrete diffusion models.
Everything is available here: https://diffusion.csail.mit.edu
Original tweet by @peholderrieth: https://x.com/peholderrieth/status/2034274122763542953
Lecture notes: https://arxiv.org/abs/2506.02070
Additional resources:
- Flow Matching Guide and Code by Yaron Lipman, Marton Havasi, Peter Holderrieth, et al. https://arxiv.org/pdf/2412.06264
- Reference implementation by Meta https://github.com/facebookresearch/flow_matching
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