Introduction to Flow Matching and Diffusion Models

MIT Computer Science Class 6.S184: Generative AI with Stochastic Differential Equations

Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more! This course aims to build up the mathematical framework underlying these models from first principles. At the end of the class, students will have built a toy image diffusion model from scratch, and along the way, will have gained hands-on experience with the mathematical toolbox of stochastic differential equations that is useful in many other fields. This course is ideal for students who want to develop a principled understanding of the theory and practice of generative AI.

The course notes serve as the backbone of the course and provide a self-contained explanation of all material in the class. In contrast, lecture slides will generally not be self-contained and are intended to provide accompanying visualizations during the lecture. You may view the notes by clicking on the colored link below.

The full video course and slides/course materials can be found here:

Course web page