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ARXIV:2605.13724 · GENERATIVE VIDEO · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13724GENERATIVE VIDEOSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHYuchao Gu · Guian Fang · Yuxin Jiang · Weijia Mao · Song Han · Han Cai · +1 at arXiv
An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation.
Opportunity summary
Pain An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time,…
Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses…
Generative Video moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation.
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10.48550/arXiv.2605.13724An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation.
Abstract
Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This limitation arises because consistency distillation replaces the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behavior of ODE sampling. To address this limitation, we introduce AnyFlow, the first any-step video diffusion distillation framework based on flow maps. Instead of distilling a model for only a few fixed sampling steps, AnyFlow optimizes the full ODE sampling trajectory. To this end, we shift the distillation target from endpoint consistency mapping $(z_{t}\rightarrow z_{0})$ to flow-map transition learning $(z_{t}\rightarrow z_{r})$ over arbitrary time intervals. We further propose Flow Map Backward Simulation, which decomposes a full Euler rollout into shortcut flow-map transitions, enabling efficient on-policy distillation that reduces test-time errors (i.e., discretization error in few-step sampling and exposure bias in causal generation). Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses consistency-based counterparts in the few-step regime, while scaling with sampling step budgets.
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PROBLEM
An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectivene...
METHOD
Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses consistency-base...
WHY NOW
Generative Video moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses consistency-based counterparts in the few-step regime, while scaling with sampling step budgets. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative Video moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An any-step video diffusion distillation framework that optimizes the full ODE sampling trajectory for scalable video generation.
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Generative Video
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