Pi-E-Flow: Uncertainty-Guided Flow Distillation for Autoregressive Video Generation

Few-step diffusion and flow distillation have made image generation much faster, but the same recipe is brittle for autoregressive (AR) video. We argue that the missing ingredient is not simply more computation, but better allocation of computation: in AR video, the student faces highly non-uniform uncertainty when imitating the teacher trajectory. Some denoising steps, especially near the $t=0$ endpoint, are fragile, and some spatial regions, such as motion, require substantially more refinement than static backgrounds. We introduce _Pi-E-Flow_, an uncertainty-guided flow distillation method for few-step AR video generation. Pi-E-Flow allocates generation computation along two axes. Along denoising time, it measures step uncertainty with a teacher-imitation error and chooses sampling schedules that balance the uncertainty of few-step sampling. Along space, it trains the model on heterogeneous patch timesteps, learns patch uncertainty, and assigns larger NFE budgets only to uncertain patches while promoting completed patches into the AR cache. This turns uniform few-step distillation into elastic compute allocation over the parts of the video trajectory where the student is most uncertain.