5 papers
FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation
Hao Liu, Chenghuan Huang, Ye Huang +6
Video Diffusion Transformers process long spatio-temporal sequences, making self-attention the main bottleneck in high-resolution video generation. Training-free sparse attention r…
Chorus II: Cross-Request Sparsity Reuse for Efficient Image-to-Video Generation
Hao Liu, Chenghuan Huang, Xing Cai +5
Serving diffusion models for image-to-video generation is computationally expensive, posing significant challenges for large-scale deployment. Real I2V workloads often contain simi…
Beyond Few-Step Inference: Accelerating Video Diffusion Transformer Model Serving with Inter-Request Caching Reuse
Hao Liu, Ye Huang, Chenghuan Huang +5
Video Diffusion Transformer (DiT) models are a dominant approach for high-quality video generation but suffer from high inference cost due to iterative denoising. Existing caching…
Adaptive Hybrid Caching for Efficient Text-to-Video Diffusion Model Acceleration
Yuanxin Wei, Lansong Diao, Bujiao Chen +6
Efficient video generation models are increasingly vital for multimedia synthetic content generation. Leveraging the Transformer architecture and the diffusion process, video DiT m…
SRDiffusion: Accelerate Video Diffusion Inference via Sketching-Rendering Cooperation
Shenggan Cheng, Yuanxin Wei, Lansong Diao +8
Leveraging the diffusion transformer (DiT) architecture, models like Sora, CogVideoX and Wan have achieved remarkable progress in text-to-video, image-to-video, and video editing t…