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
00:07:45 Training lifecycle overview
00:10:39 Loss parameterization
00:16:08 Timestep sampling
00:22:27 Logit normal distribution
00:25:44 Sampling shift for different resolutions
00:43:56 Representation alignment (REPA)
00:49:21 Pre-training
00:52:46 Continued training (CT)
00:54:02 Supervised finetuning (SFT)
00:54:39 Preference tuning
00:56:48 Reward feedback learning (ReFL)
01:00:16 Flow-GRPO
01:03:23 Diffusion-DPO
01:05:06 Prompt enhancement (PE)
01:10:00 DreamBooth, low-rank adaptation (LoRA)
01:16:35 Distillation
01:21:42 Progressive distillation (PD)
01:25:14 InstaFlow
01:30:18 Consistency models (CM)
01:34:23 Distribution matching distillation (DMD)
01:39:16 Adversarial diffusion distillation (ADD)
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.
View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNdy8rt2rZ4T2xM0OjADnfu