APRstyler: Adaptive Patch and Reversible Text-Guided Single Image Style Transfer

In recent years, text-guided image style transfer techniques have received ex-tensive attention. CLIPstyler is the first model to achieve this goal and has demonstrated impressive stylization results. However, related models represented by CLIPstyler suffer from problems such as content distortion and uneven style during the transformation process. To address these issues, taking CLIPstyler as the baseline model, we propose a new CLIP-based Text-Guided Single Image Style Transfer with Adaptive Patch Selection and Re-verse Generative Network APRStyler . APRStyler designs an end-to-end adaptive adjustment patch scheme to dynamically adjust the weight of each patch. This reduces the negative impact of poorly sampled patches on image quality, enabling the model to better align with the target style. In addition, we introduced a reverse generative network to regenerate the style image from the original image. Through the reverse generative network, the closer the generated original image is to the real original image, the less structural information is lost during the style transfer process. These can ensure that the image after style transfer not only maintains style consistency but also maximally preserves the content structure information of the original image. Experimental results show that APRStyler can not only generate natural and delicate artistic images that conform to human visual perception, but also significantly improve the effect and stability of style transfer.