用Mamba架构生成艺术风格图像，速度快且无伪影。
StyMam: A Mamba-Based Generator for Artistic Style Transfer
- 采用Mamba架构与双路径扫描机制，兼顾局部纹理与全局依赖。
- 在多种数据集上实现更高图像质量，推理速度优于主流方法。
- 适合需要快速生成高质量艺术风格图的创作者与应用开发。
图像风格迁移旨在将特定艺术风格的视觉特征融入内容图像，同时保持其结构。现有方法主要依赖生成对抗网络（GAN）或稳定扩散（SD）。基于CNN或Transformer的GAN方法难以同时捕捉局部与全局依赖，导致伪影和不协调模式；基于SD的方法虽缓解此类问题，但常无法保留内容结构且推理缓慢。为此，我们重新审视GAN，提出基于Mamba的生成器StyMam，以生成高质量风格化图像，且无伪影与不协调模式。具体地，引入基于Mamba的生成器，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，后者建模全局依赖。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优算法。
原文摘要 · Abstract (English)
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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