7 papers
Fast-ARDiff: An Entropy-informed Acceleration Framework for Continuous Space Autoregressive Generation
Zhen Zou, Xiaoxiao Ma, Jie Huang +2
Autoregressive(AR)-diffusion hybrid paradigms combine AR's structured modeling with diffusion's photorealistic synthesis, yet suffer from high latency due to sequential AR generati…
From Structure to Detail: Hierarchical Distillation for Efficient Diffusion Model
Hanbo Cheng, Peng Wang, Kaixiang Lei +4
The inference latency of diffusion models remains a critical barrier to their real-time application. While trajectory-based and distribution-based step distillation methods offer s…
FEB-Cache: Frequency-Guided Exposure Bias Reduction for Enhancing Diffusion Transformer Caching
Zhen Zou, Feng Zhao
Diffusion Transformer (DiT) has exhibited impressive generation capabilities but faces great challenges due to its high computational complexity. To address this issue, various met…
LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation
Yinglin Duan, Zhengxia Zou, Tongwei Gu +8
Recent research has been increasingly focusing on developing 3D world models that simulate complex real-world scenarios. World models have found broad applications across various d…
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution
Hang Xu, Wei Yu, Jiangtong Tan +2
Blind Super-Resolution (blind SR) aims to enhance the model's generalization ability with unknown degradation, yet it still encounters severe overfitting issues. Some previous meth…
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse
Zichao Yu, Zhen Zou, Guojiang Shao +6
Diffusion models have demonstrated remarkable success in generative tasks, yet their iterative denoising process results in slow inference, limiting their practicality. While exist…