Learn more details about this course: https://online.stanford.edu/courses/cme296-diffusion-and-large-vision-models
To follow along with the course schedule and syllabus, visit: https://cme296.stanford.edu/syllabus/
Chapters:
00:00:00 Introduction
00:06:04 Class logistics
00:12:23 Outline of the class
00:17:47 Motivating example
00:23:31 Intuition behind diffusion
00:26:33 Image representation
00:46:29 Variational formulation
00:49:01 Joint probability distribution
00:55:58 Strategy to derive a tractable loss
00:57:39 ELBO derivation
01:05:34 KL divergence
01:10:10 Bayes' rule
01:27:18 DDPM training
01:28:11 Inference with DDPM
01:29:49 Faster sampling with DDIM
For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education
Afshine Amidi is an Adjunct Lecturer at Stanford University.
Shervine Amidi is an Adjunct Lecturer at Stanford University.