One Step and Sixty Frames — Distillation, Consistency Models, and Video Diffusion
The series opened with a trade: diffusion bought quality and stability by paying in steps. Every post since has haggled the price down — better solvers, smaller spaces, straighter paths. This closing post covers the two endgames: collapsing the walk to a single stride (distillation and consistency models), and spending the budget on the hardest canvas there is — video, where the pixel count explodes but the architecture barely changes. It ends with the mental model the whole series compresses into.
The step bill falls to one by learning destinations instead of following paths: progressive distillation halves step counts, adversarial losses restore sharpness, consistency models make point-to-endpoint the objective itself. Then video — the same stack over spacetime patches, where temporal coherence is just attention across time. Closes the series with the factored mental model.