VA-TDM: A Unified View for Improving Few-Step Diffusion Models by Velocity Adversarial Distillation

Yihong Luo, Tianyu Pang, Tianyang Hu

VA-TDM: A Unified View for Improving Few-Step Diffusion Models by Velocity Adversarial Distillation: 10 upvotes on Hugging Face Daily Papers, #20 of 38 papers on 2026-11-05. Day-by-day upvote history.

Upvotes over time

It had 11 upvotes by its first day, 12 upvotes by the next day, 12 upvotes by day 3, 10 upvotes by day 7, and has 10 now. It ranked #109 of 207 papers in its week and #165 of 340 in its month. Its code repository has 30 GitHub stars.

Few-step distillation offers efficient generation with diffusion and flow models, but preserving quality with only a few sampling steps remains challenging. Distribution-matching methods via score distillation provide a strong foundation, yet they rely on accurately tracking the student's changing distribution. At intermediate generation steps in few-step sampling, student-score estimation can be difficult, and commonly used estimators may describe a different distribution from the one used to update the student. Flow matching allows a more flexible approach to supervision through velocity fields constructed from the student's sampling process. Building on this perspective, we introduce Velocity Adversarial Distillation (VAD), which uses a learned critic to compare student and teacher fields and turn their disagreement into feedback for learning. VAD unifies score distillation, Fisher-based matching, and moment matching, also explaining existing heuristic MMD updates and delivering new training objectives. We further develop VA-TDM, which follows trajectory distribution matching: providing supervision for partial denoising along the student trajectory. Beyond this, VA-TDM combines transition-aware online velocity learning and dual-form VAD supervision across intermediate student transitions. Extensive results show that VA-TDM is powerful for learning few-step generators, achieving state-of-the-art four-step performance in both text-to-image and text-to-video generation across various backbones and scaling effectively to 33B MiniMax-H3. In particular, our VA-TDM-H3 notably outperforms the few-step Fast-H3 and is closer to the original many-step H3 according to human preference.

Paper page on Hugging Face · arXiv

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