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。