动态调整生成步数，让简单帧快跑、复杂帧精修。
DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation
- 用置信度头动态分配每帧的去噪步数，智能节省计算。
- 在H100上实现22.63 FPS，延迟低于1秒，质量不降反升。
- 无需额外数据，适配实时视频生成与交互式应用。
视频扩散变换器虽达顶尖视觉质量，但推理成本高，难以用于实时场景。现有蒸馏框架虽降低延迟，却对每帧固定使用去噪步数，导致简单帧浪费算力，复杂帧又不够精细。本文提出DSA——一种基于置信度的自回归视频扩散动态步数分配框架。DSA引入轻量级置信度头，在分布匹配蒸馏目标下与生成器联合训练，用于估计每帧去噪可靠性。推理时，该置信度信号动态调节扩散步数：简单帧提前终止以提速，复杂帧则增加精修。方法无需额外视频数据、无启发式规则、仅需少量结构修改。实验表明，DSA实现实时自回归视频生成，在H100上达到22.63 FPS且延迟低于1秒，视觉质量（VBench）优于或媲美近期自回归与双向模型，证明置信度引导的自适应采样是交互式视频生成的有效路径。
原文摘要 · Abstract (English)
Video diffusion transformers have achieved state-of-the-art visual quality, but their high inference cost remains a major bottleneck for real-time applications. Recent distillation frameworks produce autoregressive video diffusion models with reduced latency, yet these models still use a fixed number of denoising steps per frame, wasting computation on predictable frames and under-refining challenging ones. We present DSA, a confidence-guided adaptive computation framework for AR video diffusion. DSA introduces a lightweight confidence head, trained jointly with the generator under a distribution-matching distillation objective, to estimate per-frame denoising reliability. At inference, this confidence signal dynamically adjusts the number of diffusion steps: simple frames terminate early for speed, while complex frames receive additional refinement. Our method requires no extra video data, no heuristics, and little architectural modification. Experiments show that DSA achieves real-time autoregressive video generation, reaching 22.63 FPS with sub-second latency on H100 GPUs, while maintaining competitive or superior VBench quality compared to recent autoregressive and bidirectional video diffusion models. Our results demonstrate that confidence-guided adaptive sampling provides an effective and practical path toward interactive video generation.
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