ReplanFlow: Adaptive Flow-Map Scheduling for Few-Step Video Generation

Few-step video generation depends on both the accuracy of learned transitions and their composition under a limited inference budget. Finite-time flow maps allow flexible sampling endpoints, but preset endpoints during on-policy distillation and inference limit video quality when only a few sampling steps are available. We present ReplanFlow, which combines broader on-policy transition coverage with adaptive flow-map scheduling. During distillation, we sample nonuniform internal endpoints to prepare the generator for more varied transition compositions. Meanwhile, we train a lightweight endpoint predictor offline using quality scores of fully generated videos. At inference, it selects the next sampling endpoint based on the current video latent, text condition, and remaining step budget, using exactly the specified number of sampling steps. This requires neither candidate-video search nor online quality evaluation. On VBench, ReplanFlow outperforms the AnyFlow baseline at the same sampling budget under matched generator-training data. With only three sampling steps, it also surpasses the four-step baseline while achieving a measured 1.21× end-to-end speedup. Component ablations further show that broader training coverage increases the gains from adaptive scheduling.