In total, we’re releasing four videos, with around 5.5 hours of content, covering the following topics (the lesson numbers start at “9”, since this is a continuation of Practical Deep Learning for Coders part 1, which had 8 lessons):
- Lesson 9 by Jeremy Howard: How to use Diffusers pipelines; What are the conceptual parts of Stable Diffusion
- Lesson 9A by Jonathan Whitaker: A deep dive into Stable Diffusion concepts and code
- Lesson 9B by Wasim Lorgat and Tanishq Abraham: The math of diffusion
- Lesson 10 by Jeremy Howard: Creating a custom diffusion pipeline; Starting “from the foundations”