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

TL;DR

AnyFlow introduces a novel flow map-based distillation framework for video diffusion that optimizes the entire ODE sampling trajectory, enabling effective any-step video generation with improved scalability and reduced errors.

Contribution

It is the first to optimize full ODE trajectories for any-step video diffusion, shifting from endpoint to flow-map transition learning for better test-time scaling.

Findings

AnyFlow matches or surpasses consistency-based methods in few-step regimes.

It scales effectively with increased sampling steps.

Experiments cover models from 1.3B to 14B parameters.

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.