Author

Ye Huang

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#artificial intelligence Preprint Oct 2026

MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers

Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resolution 3D asset generation. However, existing methods can degrade generation quality and fidelity at high sparsity levels. Through controlled oracle comparisons, we trac...

Jia-Rui Chen, Ze-Qiang Lai, Jiang-Shan Wang et al. · 0 citations

Preprint Sep 2026

DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence

High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff between reconstruction fidelity and generation efficiency: high compression image encoder always increases the learning difficulty of diffusion training, resulting in slow...

Xue-Zheng Huang, Ye Huang, Zi-Jun Liao et al. · 0 citations

Jul 2026

FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation

This work presents \method, a training-free sparse-attention system that improves the distributed execution efficiency of adaptive sparse attention under multi-GPU sequence parallelism.

Hao Liu, Chenghua Huang, Ye Huang et al. · arXiv.org · 0 citations