StyMam: A Mamba-Based Generator for Artistic Style Transfer

• URL: http://arxiv.org/abs/2601.12954v3
• Date: Sun, 25 Jan 2026 07:22:46 GMT
• Title: StyMam: A Mamba-Based Generator for Artistic Style Transfer
• Authors: Zhou Hong, Ning Dong, Yicheng Di, Xiaolong Xu, Rongsheng Hu, Yihua Shao, Run Ling, Yun Wang, Juqin Wang, Zhanjie Zhang, Ao Ma,
• Abstract summary: We propose a mamba-based generator to produce high-quality stylized images without introducing artifacts and disharmonious patterns.<n>Specifically, we introduce a mamba-based generator with a residual dual-path strip scanning mechanism and a channel-reweighted spatial attention module.<n>The proposed method outperforms state-of-the-art algorithms in both quality and speed.
• Score: 16.81948748572056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image style transfer aims to integrate the visual patterns of a specific artistic style into a content image while preserving its content structure. Existing methods mainly rely on the generative adversarial network (GAN) or stable diffusion (SD). GAN-based approaches using CNNs or Transformers struggle to jointly capture local and global dependencies, leading to artifacts and disharmonious patterns. SD-based methods reduce such issues but often fail to preserve content structures and suffer from slow inference. To address these issues, we revisit GAN and propose a mamba-based generator, termed as StyMam, to produce high-quality stylized images without introducing artifacts and disharmonious patterns. Specifically, we introduce a mamba-based generator with a residual dual-path strip scanning mechanism and a channel-reweighted spatial attention module. The former efficiently captures local texture features, while the latter models global dependencies. Finally, extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art algorithms in both quality and speed.

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