RotateAttention: RoPE-Aware Rotation and Range Rectification for INT4 Quantized Attention in Video Generation
In $\textbf{DiT-based video generation models equipped with 3D Rotary Position Embeddings (3D RoPE)}$, the attention mechanism remains a primary computational bottleneck due to its quadratic complexity with respect to sequence length. While quantized $\textbf{FlashAttention}$ offers a promising path toward hardware acceleration, existing low-bit quantization methods overlook two critical challenges in this setting: $\textbf{1)}$ applying online rotation matrices -- a widely used technique for mitigating outliers in Queries ($Q$) and Keys ($K$) -- is difficult to reconcile with $\textbf{RoPE}$; and $\textbf{2)}$ the non-negative attention matrix $P = \exp(QK - \max(QK))$ makes symmetric qu...
| Authors | Yaofu Liu, Wanli Lan, Jinxi Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 July 2026 |
| Last updated at source | 11 July 2026 |
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