Diffusion Models
Forward noising, denoising objectives, samplers, classifier-free guidance and latent diffusion.
7concepts
82flashcards
53minutes of reading
- 01 Consistency Models and Few-Step Sampling How consistency models learn a direct map from any point on a probability-flow trajectory to its origin, the difference between distilling that map from a diffusion teacher and training it from scratch, and how latent and adversarial variants reach one to four steps.
- 02 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.
- 03 Diffusion Transformers (DiT) Why replacing the diffusion U-Net with a plain vision transformer over latent patches made image quality a predictable function of forward-pass compute, and how adaLN-Zero conditioning and the MM-DiT joint-attention block made it work.
- 04 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.