HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance

• URL: http://arxiv.org/abs/2504.06232v1
• Date: Tue, 08 Apr 2025 17:30:40 GMT
• Title: HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
• Authors: Jiazi Bu, Pengyang Ling, Yujie Zhou, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang,
• Abstract summary: HiFlow is a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models.<n>HiFlow substantially elevates the quality of high-resolution image synthesis of T2I models.
• Score: 70.69373563281324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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