8 · Diffusion and score-based generation

Chapter status: outline

This is the planned scope of Chapter 8. The complete lesson, executable examples, and solutions will be developed later. Chapter 1 is available now.

Question. How can learning to remove noise support generation from noise?

• Learn: a forward noising process, learned reverse transitions, denoising objectives, score functions, and conditional generation; preview image and multimodal applications.

• Statistical lens: stochastic processes, denoising estimation, and the score \(\nabla_x\log p(x)\), which differs from a likelihood score with respect to model parameters.

• Python practice: simulate progressive Gaussian noising on a mixture distribution, then study a small learned denoiser and the effect of the noise schedule.

• Self-check: distinguish the known forward process from the fitted reverse process and separate training from sampling.

• Reading: Denoising Diffusion Probabilistic Models. Continuous-time formulations are an optional extension.