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cs.CV2026
SparkDiffusion: Mitigating the High-Sparsity Trap --- A Unified Framework for up to Single-GPU Acceleration of Visual Generation
Yuxi Liu, Haoyu Li, Zekun Zhang +12
Video diffusion transformers are expensive because attention dominates long spatiotemporal token sequences. We identify the \emph{high-sparsity trap}: at extreme attention sparsity…
cs.CV2025
Training-free and Adaptive Sparse Attention for Efficient Long Video Generation
Yifei Xia, Suhan Ling, Fangcheng Fu +4
Generating high-fidelity long videos with Diffusion Transformers (DiTs) is often hindered by significant latency, primarily due to the computational demands of attention mechanisms…