Diffusion Models
Forward noising, denoising objectives, samplers, classifier-free guidance and latent diffusion.
5concepts
68flashcards
36minutes of reading
- 01 Denoising Parameterisations: Epsilon, x-zero and v Three algebraically equivalent things a diffusion network can predict, why they train to completely different models, and how the choice interacts with the noise schedule and with distillation.
- 02 Samplers: DDPM, DDIM and Higher-Order Solvers Why sampling is numerical integration of an ODE or SDE rather than a fixed algorithm, how DDIM makes the process deterministic and skippable, and what higher-order solvers buy at the cost of stability.