Paper Detail
Hao Liu, Chenghuan Huang, Ye Huang, Zhiying Wen, Mohan Zhang, Chen Li, Ziyang Ma, Jing Lyu, Jiangsu Du
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 , a training-free sparse-attention system that improves the distributed execution efficiency of adaptive sparse attention under multi-GPU sequence parallelism. 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, reduces average load imbalance from 1.34 to 1.08 and delivers a 4.41times attention speedup over FlashAttention, while achieving a 2.02--2.11times DiT inference speedup with competitive video quality.
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@misc{liu2026fvattn,
title = {FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation},
author = {Hao Liu and Chenghuan Huang and Ye Huang and Zhiying Wen and Mohan Zhang and Chen Li and Ziyang Ma and Jing Lyu and Jiangsu Du},
year = {2026},
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 , a training-free sparse-attention system that improves the distributed execution efficiency of},
url = {https://huggingface.co/papers/2607.16190},
keywords = {huggingface daily},
eprint = {2607.16190},
archiveprefix = {arXiv},
}
{}