FrameFold: Response-Guided KV Cache Consolidation for Autoregressive Long Video Generation
In autoregressive video generation, retaining all historical key-value (KV) states increases memory usage and per-step attention costs as videos grow longer.
ProofPaper ↗
Key points
- A fixed cache budget bounds these costs but raises the challenge of preserving historical content that supports consistent, high-quality generation.
- Our analysis shows that spatial queries depend on different historical content and that higher current attention coverage does not necessarily yield better long-video quality.
- We introduce FrameFold, a training-free KV cache consolidation method that compares the content retrieved by the same recent historical queries before and after merging to guide both representative construction and merge selection.
- Under matched KV cache budgets, experiments on Self Forcing and Causal Forcing at 30 and 60 seconds and on LongLive at 120 seconds demonstrate improved consistency and visual quality.
Sources (1)
- [1]FrameFold: Response-Guided KV Cache Consolidation for Autoregressive Long Video GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 04:38 AM
In autoregressive video generation, retaining all historical key-value (KV) states increases memory usage and per-step attention costs as videos grow longer.
A fixed cache budget bounds these costs but raises the challenge of preserving historical content that supports consistent, high-quality generation.
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