arXiv Computer Vision
Sep 22

SparkDiffusion: Mitigating the High-Sparsity Trap --- A Unified Framework for up to $265\times$ Single-GPU Acceleration of Visual Generation

arXiv:2609.23153v1 Announce Type: new Abstract: Video diffusion transformers are expensive because attention dominates long spatiotemporal token sequences. We identify the \emph{high-sparsity trap}:...

By Yuxi Liu, Haoyu Li, Zekun Zhang, Tengxu Sun, Yixiang Cai, Jiayong Li, Yifei Xia, Tianle Liu, Baole Ai, Ang Wang, Jiamang Wang, Lin Qu, Kai Zhang, Kun Yuan, Bin Cui
arXiv Machine Learning
Jul 2

Mixture of Distributions Matters: Dynamic Sparse Attention for Efficient Video Diffusion Transformers

arXiv:2601. 11641v3 Announce Type: replace-cross Abstract: While Diffusion Transformers (DiTs) have achieved notable progress in video generation, this long-sequence generation task remains constrained by the quadratic complexity inherent to self-attention mechanisms, creating significant barriers to practical deployment.

By Yuxi Liu, Yipeng Hu, Zekun Zhang, Kunze Jiang, Kun Yuan
arXiv Computer Vision
Sep 18

Understanding and Exploiting Diagonal Attention Sparsity in Autoregressive Image Generation

The paper investigates how attention sparsity behaves in autoregressive image generation, finding a distinct diagonal sparsity pattern due to spatial locality of visual tokens. It introduces a diagonal‑aware sparse attention mechanism that skips KV entries along the diagonal within a recent window, achieving up to 3.1× higher throughput and 1.19× lower latency with less than 2% quality loss compared to dense inference.

By Daeun Kim, Junwha Hong, Changhun Oh, Yoonsung Kim, Yoonhyeong Lee, Jongse Park