Style transfer aims to render an image with the artistic features of a style image, while maintaining the original structure. Various methods have been put forward for this task, but some challenges still exist. For instance, it is difficult for CNN-based methods to handle global information and long-range dependencies between input images, for which transformer-based methods have been proposed. Although transformers can better model the relationship between content and style images, they require high-cost hardware and time-consuming inference. To address these issues, we design a novel transformer model that includes only the encoder, thus significantly reducing the computational cost. In addition, we also find that existing style transfer methods may lead to images under-stylied or missing content. In order to achieve better stylization, we design a content feature extractor and a style feature extractor, based on which pure content and style images can be fed to the transformer. Finally, we propose a novel network termed Puff-Net, i.e., pure content and style feature fusion network. Through qualitative and quantitative experiments, we demonstrate the advantages of our model compared to state-of-the-art ones in the literature.

我们提出了一种新颖的仅包含编码器的变换器模型，以显著降低计算成本，并设计了内容特征提取器和风格特征提取器，用于向变换器提供纯内容和风格图像。最后，我们提出了一种名为Puff-Net的新型网络，即纯内容和风格特征融合网络。通过定性和定量实验证明了我们模型相比文献中的最先进模型的优势。

Puff-Net: 纯内容和风格特征融合网络的高效样式迁移