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arXiv · 2607.16190

FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation

Hao Liu·Chenghuan Huang·Ye Huang·Zhiying Wen·Mohan Zhang·Chen Li·Ziyang Ma·Jing Lyu·Jiangsu Du
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Abstract

Video Diffusion Transformers process long spatio-temporal sequences, making self-attention the main bottleneck in high-resolution video generation. Training-free sparse attention reduces this cost, but adaptive Top-$p$ routing creates uneven per-head workloads under multi-GPU sequence parallelism. The resulting workload heterogeneity turns sparse attention into a rank-level straggler problem. We present \method{}, a training-free sparse-attention system that improves the distributed execution efficiency of adaptive sparse attention under multi-GPU sequence parallelism. \method{} uses Top-$p$ routing, a Top-$k$ safety floor, and video-aware block organization as the sparse-routing frontend, then repairs the materialized mask at runtime. Runtime Load Balancing migrates a small number of heavy heads via P2P communication to shorten the current critical path. Slack-Aware Sparse Augmentation fills residual non-critical-rank slack with additional high-value blocks, while overlap hides scheduling and migration overhead behind existing computation. On step-distilled Wan2.2 I2V, \method{} reduces average load imbalance from 1.34 to 1.08 and delivers a $4.41\times$ attention speedup over FlashAttention, while achieving a $2.02$--$2.11\times$ DiT inference speedup with competitive video quality.

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Hao Liu, Chenghuan Huang, Ye Huang, Zhiying Wen, Mohan Zhang, Chen Li, Ziyang Ma, Jing Lyu, Jiangsu Du. 2026-07-17. FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation. https://arxiv.org/abs/2607.16190

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