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DUET: A Diversity-Quality Duet of Distillation Experts for Two-Step Video Generation

Research Multimodal & Generative

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TL;DR - DUET is a two-step video diffusion distillation method that assigns a trajectory-level (sCM) expert to the high-noise step and a distribution-level (DMD) expert to the low-noise step, resolving the usual quality-versus-diversity trade-off in few-step distillation. It matters because it makes near-real-time video generation viable without collapsing output variety.

  • Diagnoses a paradigm split: sCM-style trajectory distillation preserves diversity, DMD-style distribution distillation yields higher quality; DUET assigns each to the noise level where it is strongest instead of blending losses.
  • Experts are trained independently under their native objectives, avoiding the optimization difficulties of loss-level combinations.
  • DUET+ adds RL-guided expert adaptation to fix the two identified bottlenecks: the relay interface between experts and the high-noise stage.
  • On a Wan2.1-T2V-1.3B backbone, two-step DUET approaches DMD quality while keeping roughly twice DMD's structural diversity; DUET+ raises quality further without losing that diversity edge.

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DUET: A Diversity-Quality Duet of Distillation Experts for Two-Step Video Generation

arXiv cs.CV Zian Li, Litong Gong, Borui Liao, Pengfei Liu, Xinyu Wang, Xinyuan Wei, Yifan Gao, Tiezheng Ge, Muhan Zhang 2026-08-10 arXiv:2608.09637
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-21 14:23:34.936409 UTC

TL;DR - DUET is a two-step video diffusion distillation method that assigns a trajectory-level (sCM) expert to the high-noise step and a distribution-level (DMD) expert to the low-noise step, resolving the usual quality-versus-diversity trade-off in few-step distillation. It matters because it makes near-real-time video generation viable without collapsing output variety.

  • Diagnoses a paradigm split: sCM-style trajectory distillation preserves diversity, DMD-style distribution distillation yields higher quality; DUET assigns each to the noise level where it is strongest instead of blending losses.
  • Experts are trained independently under their native objectives, avoiding the optimization difficulties of loss-level combinations.
  • DUET+ adds RL-guided expert adaptation to fix the two identified bottlenecks: the relay interface between experts and the high-noise stage.
  • On a Wan2.1-T2V-1.3B backbone, two-step DUET approaches DMD quality while keeping roughly twice DMD's structural diversity; DUET+ raises quality further without losing that diversity edge.
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