HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
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）；The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；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（香港创科旗下CPII）
Jiazi Bu, Pengyang Ling, Yujie Zhou, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang
Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality visuals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting intermediate states that are critical for preserving structure and synthesizing finer detail. To this end, we present HiFlow, a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models. Specifically, HiFlow establishes a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information, offering guidance for high-resolution generation through three key aspects: initialization alignment for low-frequency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. By leveraging such flow-aligned guidance, HiFlow substantially elevates the quality of high-resolution image synthesis of T2I models and demonstrates versatility across their personalized variants. Extensive experiments validate HiFlow's capability in achieving superior high-resolution image quality over state-of-the-art methods.
Distribution Aligned Multimodal and Multi-Domain Image Stylization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research Asia（微软亚洲研究院）；Kuaishou Technology（快手科技）
When Safety Collides: Resolving Multi-Category Harmful Conflicts in Text-to-Image Diffusion via Adaptive Safety Guidance
Sydney AI Centre, The University of Sydney（悉尼人工智能中心，悉尼大学）；City University of Hong Kong（香港城市大学）；TMLR Group, Hong Kong Baptist University（Baptist大学TMLR小组）
Training-free Dense-Aligned Diffusion Guidance for Modular Conditional Image Synthesis
Sichuan University（四川大学）；Singapore University of Technology and Design（新加坡科技设计大学）；The University of Adelaide（阿德莱德大学）；Australian National University（澳大利亚国立大学）
CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation
National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；Alibaba Group（阿里巴巴集团）
Follow-Your-MultiPose: Tuning-Free Multi-Character Text-to-Video Generation via Pose Guidance
School of Big Data and Software Engineering, Chongqing University（重庆大学大数据与软件学院）；The Hong Kong University of Science and Technology（香港科技大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shanghai Jiao Tong University（上海交通大学）
SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki（阿尔伯塔大学电气与计算机工程系）；Information Technologies Institute, CERTH（信息科技研究所）
Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy（人工智能与计算机系统单位，工程系，罗马生物医学大学，意大利罗马）；Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics, Umeå University, Umeå, Sweden（诊断与介入系，生物医学工程与放射物理，乌梅大学，瑞典乌梅）
SemanticControl: A Training-Free Approach for Handling Loosely Aligned Visual Conditions in ControlNet
Department of Computer Science and Engineering（计算机科学与工程系）；Korea University（韩国大学）；Electrical and Computer Engineering（电气与计算机工程系）；University of Michigan（密歇根大学）
