Diffusion models have achieved unprecedented performance in image generation, yet they suffer from slow inference due to their iterative sampling process. To address this, early-exiting has recently been proposed, where the depth of the denoising network is made adaptive based on the (estimated) difficulty of each sampling step. Here, we discover an interesting "phase transition" in the sampling process of current adaptive diffusion models: the denoising network consistently exits early during the initial sampling steps, until it suddenly switches to utilizing the full network. Based on this, we propose accelerating generation by employing a shallower denoising network in the initial sampling steps and a deeper network in the later steps. We demonstrate empirically that our dual-backbone approach, DuoDiff, outperforms existing early-exit diffusion methods in both inference speed and generation quality. Importantly, DuoDiff is easy to implement and complementary to existing approaches for accelerating diffusion.
Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Huawei Technologies（华为技术有限公司）；Dalian University of Technology（大连理工大学）；Tsinghua University（清华大学）
Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
University of Modena and Reggio Emilia（摩德纳和雷吉奥艾米利亚大学）；INSAIT, Sofia University ``St. Kliment Ohridski'', Sofia, Bulgaria（INSAIT，索菲亚大学『圣克莱门特·欧赫里迪斯』，索菲亚，保加利亚）
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer
Beijing Institute of Technology（北京理工大学）；JD Explore Academy（京东探索研究院）；Wuhan University（武汉大学）；China Academic of Electronics and Information Technology（中国电子信息技术研究院）；City, University of London（伦敦大学城市学院）
University of Chinese Academy of Sciences（中国科学院大学）；Columbia University（哥伦比亚大学）
Deep unsupervised domain adaptation (UDA) has recently received increasing attention from researchers. However, existing methods are computationally intensive due to the computation cost of Convolutional Neural Networks (CNN) adopted by most work. To date, there is no effective network compression method for accelerating these models. In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models. TCP is capable of compressing the deep UDA model by pruning less important channels while simultaneously learning transferable features by reducing the cross-…
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone
Sandia National Laboratories（桑迪亚国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View
School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Mogo Auto Intelligence and Telematics Information Technology Co., Ltd（摩国汽车智能与远程信息处理信息技术有限公司）；Faculty of Information Technology, Beijing University of Technology（北京工业大学信息学部）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）
Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs). Previous works lay an emphasis on the design of attention module for specific functionality, e.g., light-weighted or task-oriented attention. However, they ignore the importance of where to plug in the attention module since they connect the modules individually with each block of the entire CNN backbone for granted, leading to incremental computational cost and number of parameters with the growth of network d…
XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Shandong Inspur Database Technology Co.,Ltd（山东浪潮数据库技术有限公司）
