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.
2 units · Letter or Credit/No Credit
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.
Offered in Spring 2027 at Stanford University.