8 papers
Block3D: Efficient Text-to-3D Generation via Block-Wise Diffusion
Bowen Cui, Weijie Wang, Zeyu Zhang +7
While text-to-3D generation has advanced rapidly, achieving high geometric fidelity at low inference cost remains challenging. Existing text-to-3D methods either decode discrete sh…
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…
K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling
Zhiwei Tang, Yuanyu He, Yizheng Han +4
Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-bound and inefficient. Existin…
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.…
OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs
Feng Chen, Yefei He, Shaoxuan He +9
Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge t…
ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality
Yefei He, Feng Chen, Yuanyu He +4
In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework for accelerating auto-regressive (AR) visual generation. The motivation stems from the o…