Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network

Abstract

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 transformer 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 encoders 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. Then we can feed pure content and style images into the transformer. Finally we propose a network model termed Puff-Net i.e. efficient style transfer with 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. The code is availabel at https://github.com/ZszYmy9/Puff-Net.