TetherCache: 基于门控召回与可信对齐的自回归长视频生成稳定性方法
TetherCache: Stabilizing Autoregressive Long-Form Video Generation with Gated Recall and Trusted Alignment
AI总结：
提出TetherCache，一种无需训练、即插即用的缓存管理策略，通过门控召回（GRAB）和可信对齐编辑（TAME）缓解自回归视频扩散模型中的上下文漂移，实现稳定长视频生成。
摘要：
自回归视频扩散模型通过将新生成帧的条件建立在先前生成内容上，为流式变长视频生成提供了自然框架。然而，将这些模型扩展到分钟级生成仍具挑战：有限的KV缓存预算使模型无法保留完整历史，而反复以自生成帧为条件会导致上下文分布偏移随时间累积，引发视觉伪影、质量下降和时间漂移。本文提出TetherCache，一种无需训练、即插即用的缓存管理策略，用于抗漂移长视频生成。TetherCache将缓存组织为sink、memory和recent区域，并引入两种互补机制。首先，GRAB（基于注意力多样性平衡的门控召回）使用结合注意力相关性与时间多样性的门控分数选择长程记忆帧，在固定缓存预算下保留信息丰富且多样化的历史上下文。其次，TAME（通过记忆编辑的可信对齐）通过将新召回的记忆令牌的统计量对齐到可信上下文分布来对其进行轻量编辑，减少漂移历史特征造成的污染。基于Self-Forcing，TetherCache在VBench-Long的30秒、60秒和240秒设置上持续提升长视频生成质量。特别地，在240秒生成中，它显著提高了整体和语义分数，同时将质量漂移从7.84降至1.33，证明了其在稳定长程自回归视频扩散中的有效性。
英文摘要：
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content. However, extending these models to minute-level generation remains challenging: the limited KV-cache budget prevents the model from retaining the full history, while repeatedly conditioning on self-generated frames induces a context distribution shift that accumulates over time, leading to visual artifacts, quality degradation, and temporal drift. In this paper, we propose TetherCache, a training-free and plug-and-play cache management strategy for drift-resistant long video generation. TetherCache organizes the cache into sink, memory, and recent regions, and introduces two complementary mechanisms. First, GRAB (Gated Recall with Attention-Diversity Balancing) selects long-range memory frames using a gated score that combines attention-based relevance with temporal diversity, preserving informative yet diverse historical context under a fixed cache budget. Second, TAME (Trusted Alignment via Memory Editing) lightly edits newly recalled memory tokens by aligning their statistics to a trusted context distribution, reducing the pollution caused by drifted historical features. Built on Self-Forcing, TetherCache consistently improves long-video generation quality on VBench-Long across 30s, 60s, and 240s settings. In particular, for 240s generation, it substantially improves overall and semantic scores while reducing quality drift from 7.84 to 1.33, demonstrating its effectiveness for stable long-horizon autoregressive video diffusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
