HASTE delivers up to 1.93x speedup on Wan2.1 video DiTs via head-wise adaptive sparse attention using temporal mask reuse and error-guided per-head calibration while preserving video quality.
Rainfusion: Adaptive video generation acceleration via multi-dimensional visual redundancy
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Attention sparsity in video DiTs is an input-stable layer-wise property, enabling offline profiling and online bidirectional QK co-clustering for up to 1.93x speedup with PSNR up to 29 dB.
RainFusion2.0 delivers 80% sparse attention in DiT models for video and image generation, yielding 1.5-1.8x end-to-end speedup with no quality loss via hardware-efficient block-wise prediction.
citing papers explorer
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HASTE: Training-Free Video Diffusion Acceleration via Head-Wise Adaptive Sparse Attention
HASTE delivers up to 1.93x speedup on Wan2.1 video DiTs via head-wise adaptive sparse attention using temporal mask reuse and error-guided per-head calibration while preserving video quality.
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Attention Sparsity is Input-Stable: Training-Free Sparse Attention for Video Generation via Offline Sparsity Profiling and Online QK Co-Clustering
Attention sparsity in video DiTs is an input-stable layer-wise property, enabling offline profiling and online bidirectional QK co-clustering for up to 1.93x speedup with PSNR up to 29 dB.
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RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention
RainFusion2.0 delivers 80% sparse attention in DiT models for video and image generation, yielding 1.5-1.8x end-to-end speedup with no quality loss via hardware-efficient block-wise prediction.