4 papers
Few-Step Distillation for Text-to-Image Generation: A Practical Guide
Yifan Pu, Yizeng Han, Zhiwei Tang +4
Diffusion distillation has dramatically accelerated class-conditional image synthesis, but its applicability to open-ended text-to-image (T2I) generation is still unclear. We prese…
RAPID^3: Tri-Level Reinforced Acceleration Policies for Diffusion Transformer
Wangbo Zhao, Yizeng Han, Zhiwei Tang +7
Diffusion Transformers (DiTs) excel at visual generation yet remain hampered by slow sampling. Existing training-free accelerators - step reduction, feature caching, and sparse att…
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion
Akide Liu, Zeyu Zhang, Zhexin Li +12
Diffusion generative models have become the standard for producing high-quality, coherent video content, yet their slow inference speeds and high computational demands hinder pract…
DyDiT++: Diffusion Transformers with Timestep and Spatial Dynamics for Efficient Visual Generation
Wangbo Zhao, Yizeng Han, Jiasheng Tang +6
Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior performance but suffers from substantial computational costs. Our investig…