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 dis
Velocity Adversarial Distillation and trajectory-aware supervision enable high-quality few-step diffusion and flow generation with state-of-the-art results.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
VA-TDM: A Unified View for Improving Few-Step Diffusion Models by Velocity Adversarial Distillation was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #14 of 30, reached on Nov 5, 15:28 UTC. It dropped off the list after Nov 6, 09:28 UTC.
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It was first seen on Nov 5, 2026 and has been on the list on 2 days (14 checks) since.