RoLA: Rotary-Positioned Low-Rank Linear Attention for Efficient Diffusion Transformers
Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequence length and quickly becomes the dominant inference bottleneck. Sparse low-rank hybrids alleviate this cost by combining a local sparse branch with a global compressed branch. In video DiTs equipped with 3D Rotary Position Embeddings (RoPE), the global branch faces a structural compatibility issue: when RoPE is applied before a nonlinear feature map, the rotation and nonlinearity generally do not commute, making it difficult to keep a query-independent linear summary while preserving relative rotary geometry. Existing work often sidesteps th...
| Authors | Zekun Zhang, Yixiang Cai, Yuxi Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 September 2026 |
| Last updated at source | 21 September 2026 |
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