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”

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