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.

翻译：图像风格迁移旨在将特定艺术风格的视觉模式融入内容图像，同时保持其内容结构。现有方法主要依赖于生成对抗网络（GAN）或稳定扩散（SD）。基于GAN的方法使用CNN或Transformer难以同时捕获局部与全局依赖关系，导致伪影与不协调的图案。基于SD的方法虽减少了此类问题，但往往无法保持内容结构，且推理速度较慢。为解决这些问题，本文重新审视GAN并提出了一个基于Mamba的生成器，称为StyMam，以生成高质量的风格化图像，同时避免引入伪影与不协调的图案。具体而言，我们设计了一个基于Mamba的生成器，其包含残差双路径条带扫描机制与通道重加权空间注意力模块。前者高效捕获局部纹理特征，后者则建模全局依赖关系。最终，大量定性与定量实验表明，所提方法在质量与速度上均优于当前最先进的算法。