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cs.CV2025

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…

cs.CV2025

BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation

Zeyu Zhang, Jinyuan Mao, Shuning Chang +5

Long video generation is a critical step toward building realistic world models, requiring both high visual fidelity and long-range interaction consistency. Recent autoregressive d…

cs.CV2025

Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation

Inferix Team, Tianyu Feng, Yizeng Han +13

World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic, and interactive high-quality videos.…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…