Competition Domains Matter: Selective Head Globalization for Training-Free Sparse Attention in Video Diffusion Transformers

Key points

• We introduce EG-Sparse, a training-free framework that routes low-entropy heads to a grouped cross-head pool while retaining per-row local selection for high-entropy heads, using only scores already materialized by the sparse pipeline.1
• Across four configurations, EG-Sparse achieves higher video quality while reducing attention density and GPU denoising latency compared with strong training-free sparse-attention baselines.1
• Mechanism ablations further show that selective domain adaptation outperforms uniform, random, and fixed domain-assignment policies.1
• These results support competition-domain adaptation as a distinct design axis within centroid-guided training-free sparse attention.1

Sources (1)

• [1]Competition Domains Matter: Selective Head Globalization for Training-Free Sparse Attention in Video Diffusion Transformers
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 07:38 AM

  Training-free sparse attention is a practical route to efficient video diffusion transformer inference, yet existing methods typically fix the competition domain at each query row for every attention head.

  We introduce EG-Sparse, a training-free framework that routes low-entropy heads to a grouped cross-head pool while retaining per-row local selection for high-entropy heads, using only scores already materialized by the sparse pipeline.

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