DynamicRad: Content-Adaptive Sparse Attention for Long Video Diffusion

TL;DR

DynamicRad introduces a content-adaptive sparse attention method for long video diffusion, significantly improving efficiency while maintaining high quality through a dual-mode strategy and offline optimization.

Contribution

It presents a novel adaptive sparse attention paradigm with a semantic motion router and offline Bayesian optimization, enhancing long video diffusion performance.

Findings

Achieves 1.7×–2.5× inference speedups over dense models.

Over 80% effective sparsity in attention mechanisms.

Matches or exceeds dense baseline quality in long-sequence settings.

Abstract

Leveraging the natural spatiotemporal energy decay in video diffusion offers a path to efficiency, yet relying solely on rigid static masks risks losing critical long-range information in complex dynamics. To address this issue, we propose \textbf{DynamicRad}, a unified sparse-attention paradigm that grounds adaptive selection within a radial locality prior. DynamicRad introduces a \textbf{dual-mode} strategy: \textit{static-ratio} for speed-optimized execution and \textit{dynamic-threshold} for quality-first filtering. To ensure robustness without online search overhead, we integrate an offline Bayesian Optimization (BO) pipeline coupled with a \textbf{semantic motion router}. This lightweight projection module maps prompt embeddings to optimal sparsity regimes with \textbf{minimal runtime overhead}. Unlike online profiling methods, our offline BO optimizes attention reconstruction error (MSE) on a physics-based proxy task, ensuring rapid convergence. Experiments on HunyuanVideo and Wan2.1-14B demonstrate that DynamicRad pushes the efficiency--quality Pareto frontier, achieving \textbf{1.7$\times$--2.5$\times$ inference speedups} with \textbf{over 80\% effective sparsity}. In some long-sequence settings, the dynamic mode even matches or exceeds the dense baseline, while mask-aware LoRA further improves long-horizon coherence. Code is available at https://github.com/Adamlong3/DynamicRad.