HiAR

Hierarchical Autoregressive Video Generation with Pipelined Parallel Inference

arXiv | Website | Code | Model

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HiAR proposes hierarchical denoising for autoregressive video diffusion models, a paradigm shift from conventional block-first to step-first denoising order. By conditioning each block on context at a matched noise level, HiAR maximally attenuates error propagation while preserving temporal causality, achieving state-of-the-art long video generation (20s+) with significantly reduced quality drift.