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. 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 this 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 superiority in achieving superior high-resolution image quality over current state-of-the-art methods.
View on arXiv@article{bu2025_2504.06232,
title={ HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance },
author={ Jiazi Bu and Pengyang Ling and Yujie Zhou and Pan Zhang and Tong Wu and Xiaoyi Dong and Yuhang Zang and Yuhang Cao and Dahua Lin and Jiaqi Wang },
journal={arXiv preprint arXiv:2504.06232},
year={ 2025 }
}