HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned
  Guidance
Authors: name: Jiazi Bu; name: Pengyang Ling; name: Yujie Zhou; name: Pan Zhang; name: Tong Wu; name: Xiaoyi Dong; name: Yuhang Zang; name: Yuhang Cao; name: Dahua Lin; name: Jiaqi Wang
Published: 2025-04-08 17:30:40

Abstract: 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.