Fast Image Super-Resolution via Consistency Rectified Flow
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917554722701312 |
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| author | Xu, Jiaqi Li, Wenbo Sun, Haoze Li, Fan Wang, Zhixin Peng, Long Ren, Jingjing Yang, Haoran Hu, Xiaowei Pei, Renjing Heng, Pheng-Ann |
| author_facet | Xu, Jiaqi Li, Wenbo Sun, Haoze Li, Fan Wang, Zhixin Peng, Long Ren, Jingjing Yang, Haoran Hu, Xiaowei Pei, Renjing Heng, Pheng-Ann |
| contents | Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step sampling largely hinders their practical applications. While recent efforts have introduced few- or single-step solutions, existing methods either inefficiently model the process from noisy input or fail to fully exploit iterative generative priors, compromising the fidelity and quality of the reconstructed images. To address this issue, we propose FlowSR, a novel approach that reformulates the SR problem as a rectified flow from low-resolution (LR) to high-resolution (HR) images. Our method leverages an improved consistency learning strategy to enable high-quality SR in a single step. Specifically, we refine the original consistency distillation process by incorporating HR regularization, ensuring that the learned SR flow not only enforces self-consistency but also converges precisely to the ground-truth HR target. Furthermore, we introduce a fast-slow scheduling strategy, where adjacent timesteps for consistency learning are sampled from two distinct schedulers: a fast scheduler with fewer timesteps to improve efficiency, and a slow scheduler with more timesteps to capture fine-grained texture details. Extensive experiments demonstrate that FlowSR achieves outstanding performance in both efficiency and image quality. Code: \href{https://github.com/jiaqixuac/FlowSR}{this https URL}. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_12377 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Image Super-Resolution via Consistency Rectified Flow Xu, Jiaqi Li, Wenbo Sun, Haoze Li, Fan Wang, Zhixin Peng, Long Ren, Jingjing Yang, Haoran Hu, Xiaowei Pei, Renjing Heng, Pheng-Ann Computer Vision and Pattern Recognition Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step sampling largely hinders their practical applications. While recent efforts have introduced few- or single-step solutions, existing methods either inefficiently model the process from noisy input or fail to fully exploit iterative generative priors, compromising the fidelity and quality of the reconstructed images. To address this issue, we propose FlowSR, a novel approach that reformulates the SR problem as a rectified flow from low-resolution (LR) to high-resolution (HR) images. Our method leverages an improved consistency learning strategy to enable high-quality SR in a single step. Specifically, we refine the original consistency distillation process by incorporating HR regularization, ensuring that the learned SR flow not only enforces self-consistency but also converges precisely to the ground-truth HR target. Furthermore, we introduce a fast-slow scheduling strategy, where adjacent timesteps for consistency learning are sampled from two distinct schedulers: a fast scheduler with fewer timesteps to improve efficiency, and a slow scheduler with more timesteps to capture fine-grained texture details. Extensive experiments demonstrate that FlowSR achieves outstanding performance in both efficiency and image quality. Code: \href{https://github.com/jiaqixuac/FlowSR}{this https URL}. |
| title | Fast Image Super-Resolution via Consistency Rectified Flow |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.12377 |