HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned
  Guidance
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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Jiazi Bu
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Pengyang Ling
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Yujie Zhou
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Pan Zhang
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Tong Wu
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Xiaoyi Dong
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2025-04-09T16:45:53.394Z
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Xiaoyi Dong
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Yuhang Zang
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2025-04-09T16:45:47.624Z
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Yuhang Zang
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yuhangzang
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Yuhang Cao
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2025-04-09T16:45:41.556Z
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Cao Yuhang
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Dahua Lin
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2025-04-09T16:45:29.529Z
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Dahua Lin
user
lindahua
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Jiaqi Wang
admin_assigned
2025-04-09T16:45:22.687Z
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Jiaqi Wang
user
myownskyW7
HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned
  Guidance
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.
HiFlow is a training-free framework that improves high-resolution image synthesis of text-to-image models by aligning pre-trained flow models across various aspects.
diffusion/flow models high-resolution image synthesis pre-trained flow models virtual reference flow low-frequency consistency structure preservation detail fidelity