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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.CV2026
CrossDistill: Balancing Quality and Diversity via Trajectory-Level Hybrid Few-Step Distillation
Yuxi Liu, Haoyu Li, Yixiang Cai +8
Few-step distillation accelerates diffusion models but must balance diversity and fidelity: trajectory-based distillation preserves mode coverage, while distribution matching sharp…
cs.CV2026
Mixture of Distributions Matters: Dynamic Sparse Attention for Efficient Video Diffusion Transformers
Yuxi Liu, Yipeng Hu, Zekun Zhang +2
While Diffusion Transformers (DiTs) have achieved notable progress in video generation, this long-sequence generation task remains constrained by the quadratic complexity inherent…