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

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.