title: HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned
    Guidance
summary: 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.
paper_authors._id: 67f6406e49525c856f4705c4
paper_authors.name: Jiazi Bu
paper_authors._id: 67f6406e49525c856f4705c5
paper_authors.name: Pengyang Ling
paper_authors._id: 67f6406e49525c856f4705c6
paper_authors.name: Yujie Zhou
paper_authors._id: 67f6406e49525c856f4705c7
paper_authors.name: Pan Zhang
paper_authors._id: 67f6406e49525c856f4705c8
paper_authors.name: Tong Wu
paper_authors._id: 67f6406e49525c856f4705c9
paper_authors.name: Xiaoyi Dong
paper_authors.status: admin_assigned
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paper_authors._id: 67c0849ee08c178ef8d4e05c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/mQ6VdnjZnRhb0H_waPclo.png
paper_authors.fullname: Xiaoyi Dong
paper_authors.type: user
paper_authors.user: sweetFruit
paper_authors._id: 67f6406e49525c856f4705ca
paper_authors.name: Yuhang Zang
paper_authors.status: admin_assigned
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paper_authors.fullname: Yuhang Zang
paper_authors.type: user
paper_authors.user: yuhangzang
paper_authors._id: 67f6406e49525c856f4705cb
paper_authors.name: Yuhang Cao
paper_authors.status: admin_assigned
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paper_authors._id: 65000bef18830fabea469fdd
paper_authors.avatarUrl: /avatars/b320c77dfad039d9f9c54127f610d44f.svg
paper_authors.fullname: Cao Yuhang
paper_authors.type: user
paper_authors.user: yhcao
paper_authors._id: 67f6406e49525c856f4705cc
paper_authors.name: Dahua Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-09T16:45:29.529Z
paper_authors._id: 636317ed80c1a705a6eff396
paper_authors.avatarUrl: /avatars/3db090e101b916d9256d0d3e043db71d.svg
paper_authors.fullname: Dahua Lin
paper_authors.type: user
paper_authors.user: lindahua
paper_authors._id: 67f6406e49525c856f4705cd
paper_authors.name: Jiaqi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-09T16:45:22.687Z
paper_authors._id: 64b4eec4faa3181a5eab9c46
paper_authors.avatarUrl: /avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
paper_authors.fullname: Jiaqi Wang
paper_authors.type: user
paper_authors.user: myownskyW7
paper_title: HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned
    Guidance
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: diffusion/flow models high-resolution image synthesis pre-trained flow models virtual reference flow low-frequency consistency structure preservation detail fidelity