Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by caching and reusing intermediate outputs based on feature distances between adjacent timesteps. However, existing caching strategies typically rely on raw feature differences that entangle content and noise. This design overlooks spectral evolution, where low-frequency structure appears early and high-frequency detail is refined later. We introduce Spectral-Evolution-Aware Cache (SeaCache), a training-free cache schedule that bases reuse decisions on a spectrally aligned representation. Through theoretical and empirical analysis, we derive a Spectral-Evolution-Aware (SEA) filter that preserves content-relevant components while suppressing noise. Employing SEA-filtered input features to estimate redundancy leads to dynamic schedules that adapt to content while respecting the spectral priors underlying the diffusion model. Extensive experiments on diverse visual generative models and the baselines show that SeaCache achieves state-of-the-art latency-quality trade-offs.
Not All Frames Deserve Full Computation: Accelerating Autoregressive Video Generation via Selective Computation and Predictive Extrapolation
Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Shenzhen University of Advanced Technology（深圳理工大学）
DiCache: Let Diffusion Model Determine Its Own Cache
Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；CPII under InnoHK（InnoHK下的CPII）
MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Data Science & Artificial Intelligence Research Institute, China Unicom（中国unicom数据科学与人工智能研究 institute）；Unicom Data Intelligence, China Unicom（中国unicom数据智能）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching
Shanghai Jiao Tong University（上海交通大学）；Shandong University（山东大学）；Terminal Intelligent Computing Division, Alibaba Cloud（阿里云终端智能计算事业部）；South China University of Technology（华南理工大学）；Jilin University（吉林大学）
LazyMAR: Accelerating Masked Autoregressive Models via Feature Caching
Beijing Institute of Technology（北京理工大学）；Central South University（中南大学）；China University of Mining and Technology（中国矿业大学）；University of Electronic Science and Technology of China（电子科技大学）；Tsinghua University（清华大学）；Shanghai Jiaotong University（上海交通大学）
针对 MAR 双向自注意力无法使用传统 KV 缓存的问题，本文提出训练免费、即插即用的 LazyMAR，通过令牌与条件两类特征缓存实现 2.83 倍加速且几乎不损失生成质量。
Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor
School of Computer Science, Wuhan University（武汉大学计算机学院）；PaddlePaddle Team, Baidu Inc（百度公司PaddlePaddle团队）；International Joint Innovation Center, The Electromagnetics Academy at Zhejiang University（浙江大学电磁学学院国际联合创新中心）；Yunnan Key Laboratory of Media Convergence（云南媒体融合重点实验室）
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China（人工智能学院，北京师范大学，北京）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China（人工智能与未来网络研究院，北京师范大学，珠海）
Shanghai Jiao Tong University（上海交通大学）；South China University of Technology（南方科技大学）；Beihang University（北航）；Huawei Technologies Ltd（华为技术有限公司）；Shanghai AI Lab（上海人工智能实验室）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Computer Science（计算机科学系）
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
School of information（信息学院）；Electronics, Beijing Institute of Technology, Beijing（电子学院，北京理工大学，北京）；Department of Computer Science, City University of Hong Kong, Hong Kong（计算机科学系，香港城市大学，香港）；School of Computer Science, Wuhan University, Wuhan（计算机科学学院，武汉大学，武汉）；School of Economics（经济学院）；Management, Communication University of China, Beijing（管理学院，中国传媒大学，北京）；School of Science（科学学院）；Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen（工程学院，香港中文大学（深圳），深圳）
