Critic error accumulation is a fundamental bottleneck in distilling video diffusion models; filtering it via projection dramatically improves sample quality without architectural changes or extra computation.
This paper improves video diffusion model distillation by fixing a key problem: critic errors that accumulate during training and degrade sample quality. PDMD uses a simple mathematical projection to filter out these errors while preserving useful learning signals, achieving better video quality with fewer computational steps—all with just a one-line code change.