AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation

Few-step video generation has been significantly advancedby consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocatedat test time, limiting their effectiveness for any-step video diffusion.We argue that this limitation arises because consistency distillation re-places the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behaviorof ODE sampling. To address this limitation, we introduce AnyFlow,the first any-step video diffusion distillation framework based on flowmaps. Instead of distilling a model for only a few fixed sampling steps,AnyFlow optimizes the full ODE sampling trajectory. To this end, weshift the distillation target from endpoint consistency mapping (zt → z0 )to flow-map transition learning (zt → zr ) over arbitrary time inter-vals. We further propose Flow Map Backward Simulation, whichdecomposes a full Euler rollout into shortcut flow-map transitions, en-abling efficient on-policy distillation that reduces test-time errors (i.e.,discretization error in few-step sampling and exposure bias in causalgeneration). Extensive experiments across both bidirectional and causalarchitectures, at scales ranging from 1.3B to 14B parameters, demon-strate that AnyFlow achieves performance comparable to or better thanconsistency-based counterparts in the few-step regime, while support-ing flexible and scalable sampling under varying step budgets. Code isreleased at https://github.com/NVLabs/AnyFlow.