ByteDance and UC San Diego · Video Diffusion Distillation

PDMD

Projected Distribution Matching Distillation, from ByteDance and UC San Diego (arXiv 2609.35768, 28 September), makes Distribution Matching Distillation more stable for few-step video diffusion by removing the part of the critic's error that lines up with the gap between student and critic predictions. The authors say it is a one-line change to DMD with no extra loss, network, data or training stage. They report 83.73 at 4 function evaluations on Wan2.1-T2V-1.3B (DMD2: 83.44) and 83.17 on MiniMax-H3 at 4 evaluations (DMD: 82.76), and two-step generation also works; results are self-reported. Code on GitHub (ZeamoxWang/pdmd) and a 4-step checkpoint on Hugging Face; the repo has no licence file.