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Sink-Sparse Hybrid Attention for Efficient Inference in T2I/T2V Diffusion Transformers
Diffusion Transformers (DiTs) have become a common backbone for high-quality text-to-image and text-to-video generation, but repeated dense self-attention remains a major inference bottleneck as token sequences grow and the same operator is invoked across many denoising steps. Our study suggests tha
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