HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912378590855168 |
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| author | Bu, Jiazi Ling, Pengyang Zhou, Yujie Zhang, Pan Wu, Tong Dong, Xiaoyi Zang, Yuhang Cao, Yuhang Lin, Dahua Wang, Jiaqi |
| author_facet | Bu, Jiazi Ling, Pengyang Zhou, Yujie Zhang, Pan Wu, Tong Dong, Xiaoyi Zang, Yuhang Cao, Yuhang Lin, Dahua Wang, Jiaqi |
| contents | 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. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality visuals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting intermediate states that are critical for preserving structure and synthesizing finer detail. 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 such 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 capability in achieving superior high-resolution image quality over state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance Bu, Jiazi Ling, Pengyang Zhou, Yujie Zhang, Pan Wu, Tong Dong, Xiaoyi Zang, Yuhang Cao, Yuhang Lin, Dahua Wang, Jiaqi Computer Vision and Pattern Recognition 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. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality visuals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting intermediate states that are critical for preserving structure and synthesizing finer detail. 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 such 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 capability in achieving superior high-resolution image quality over state-of-the-art methods. |
| title | HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.06232 |