Higher-Order Sampler

Key Insight

A trained diffusion model defines a smooth ODE whose solution carries pure noise to a clean image, so sampling is just numerically solving that ODE — and how many steps you need depends entirely on how accurate your solver is. The simplest solver, the Euler method, takes a straight-line step using the slope where it currently sits and accumulates error fast, so it needs many steps. Higher-order solvers cut the error per step: Heun's method is a predict-then-correct step that averages the slope at the start and end of each interval, and DPM-Solver++ is a multistep method tailored to the exact mathematical shape of the diffusion ODE — so 10–20 steps match what Euler needs 100+ for. This project swaps the slow many-step sampler of an existing DDPM for these and measures the quality-versus-steps trade-off.