This course explores diffusion-based generative models for vision. You will study the foundations of diffusion, score matching and flow matching, modern architectures such as Diffusion Transformers, and methods for controllable image generation and evaluation. The course combines theory with practical insights into state-of-the-art generative models. Ideal for students with a background in linear algebra, probability, calculus, and machine learning. Learn more and enroll: https://online.stanford.edu/courses/cme296-diffusion-and-large-vision-models View the course syllabus: https://cme296.stanford.edu/syllabus/ Afshine Amidi is an Adjunct Lecturer at Stanford University. Shervine Amidi is an Adjunct Lecturer at Stanford University.