[N] MIT Flow Matching and Diffusion Lecture 2026
New MIT course provides the full stack for building modern AI generators, from theory to hands-on code.
MIT researchers Peter Holderrieth and Ezra Erives have launched their 2026 course, "MIT Flow Matching and Diffusion Lecture 2026," providing a comprehensive educational resource on the core technologies behind modern generative AI. The course is designed to teach the full stack, from foundational theory to practical implementation, for building state-of-the-art image, video, and protein generators. It builds upon last year's iteration with significant improvements and new content, positioning it as a definitive learning path for understanding cutting-edge diffusion models.
The curriculum is structured into three core components: lecture videos that introduce theory with step-by-step mathematical derivations, self-contained lecture notes for deep reference, and hands-on coding exercises that reinforce each component. Newly added topics for 2026 include working with latent spaces, the architecture of diffusion transformers (DiTs), and techniques for building language models using discrete diffusion models. The course materials, including lecture notes published on arXiv, are freely available online, supported by additional resources like a Flow Matching Guide and reference implementations from teams at Meta.
- Course covers the full theoretical and practical stack for modern AI generators (image, video, protein).
- Includes lecture videos with derivations, self-contained notes, and hands-on coding exercises for every component.
- Updated with new 2026 topics: latent spaces, diffusion transformers (DiTs), and language models via discrete diffusion.
Why It Matters
Democratizes advanced AI education, enabling more developers to build and understand the next generation of generative models.