Ride the Wave: Precision-Allocated Sparse Attention for Smooth Video Generation
Summary: arXiv:2604.12219v1 Announce Type: cross
Abstract
Video Diffusion Transformers have revolutionized high-fidelity video generation but suffer from the massive computational burden of self-attention. While sparse attention provides a promising acceleration solution, existing methods frequently provoke severe visual flickering caused by static sparsity patterns and deterministic block routing. To resolve these limitations, we propose Precision-Allocated Sparse Attention (PASA), a training-free framework designed for highly efficient and temporally smooth video generation.
Key Features of PASA
- Curvature-Aware Dynamic Budgeting: PASA implements a mechanism that profiles the generation trajectory acceleration across timesteps. This allows for the elastic allocation of computation budgets, ensuring high-precision processing during critical semantic transitions.
- Hardware-Aligned Grouped Approximations: Instead of relying on global homogenizing estimations, PASA captures fine-grained local variations with hardware-aligned grouped approximations, thereby maintaining peak compute throughput.
- Stochastic Selection Bias: By introducing a probabilistic approach into the attention routing mechanism, PASA softens rigid selection boundaries and eliminates selection oscillation. This effectively addresses the localized computational starvation that leads to temporal flickering.
Performance Evaluation
Extensive evaluations on leading video diffusion models demonstrate that PASA achieves substantial inference acceleration while consistently producing remarkably fluid and structurally stable video sequences. The results indicate a significant improvement in both the efficiency and quality of video generation compared to existing methods that utilize static sparsity patterns and deterministic routing.
Conclusion
The development of Precision-Allocated Sparse Attention marks a pivotal advancement in the field of video generation. By addressing the computational inefficiencies and visual inconsistencies associated with traditional attention mechanisms, PASA paves the way for more seamless and high-quality video outputs. As the demand for real-time video generation continues to grow in various applications, the integration of PASA could prove to be a game-changer for developers and researchers alike.
Future Work
Looking ahead, further research is needed to refine the PASA framework and explore its potential across a wider range of applications, including interactive media, virtual reality, and augmented reality. The ongoing evolution of AI in video generation promises exciting opportunities and challenges, and PASA is poised to play a crucial role in this dynamic landscape.