Sampling Faster — DDIM and the Art of Skipping Steps
A thousand network evaluations per image is not a product. The first great act of cost reduction in diffusion was realizing the thousand-step walk could be shortened without retraining anything: the same network, asked smarter questions, gets there in fifty steps — or twenty. This post covers DDIM and the sampler zoo it opened, the step-count economics that made diffusion deployable, and why samplers are the rare component you can swap after training.
DDPM's thousand steps were a property of one sampler, not the trained network. DDIM's deterministic walk follows the probability flow ODE — 50 steps, zero retraining, plus invertible noise-space addresses — and the sampler zoo is just numerical analysis: a ~40× cost cut from math alone, down to the ~10-step curvature floor.