MIT 6.S184: Introduction to Flow Matching and Diffusion Models

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. Course Notes The course notes serve as the backbone of the course and provide a self-contained explanation of all material in the class. In contrast, lectures slides will generally not be sel ... Read full article.