In image processing, one of the most challenging tasks is to render an image's semantic meaning using a variety of artistic approaches. Existing techniques for arbitrary style transfer (AST) frequently experience mode-collapse, over-stylization, or under-stylization due to a disparity between the style and content images. We propose a novel framework called D$^2$Styler (Discrete Diffusion Styler) that leverages the discrete representational capability of VQ-GANs and the advantages of discrete diffusion, including stable training and avoidance of mode collapse. Our method uses Adaptive Instance Normalization (AdaIN) features as a context guide for the reverse diffusion process. This makes it easy to move features from the style image to the content image without bias. The proposed method substantially enhances the visual quality of style-transferred images, allowing the combination of content and style in a visually appealing manner. We take style images from the WikiArt dataset and content images from the COCO dataset. Experimental results demonstrate that D$^2$Styler produces high-quality style-transferred images and outperforms twelve existing methods on nearly all the metrics. The qualitative results and ablation studies provide further insights into the efficacy of our technique. The code is available at https://github.com/Onkarsus13/D2Styler.

本研究解决了目前任意风格迁移技术中存在的模式崩溃和风格化过度或不足的问题。提出的D²Styler框架利用VQ-GAN的离散表示能力和离散扩散的优势，通过自适应实例归一化(AdaIN)特征为反向扩散过程提供上下文指导，从而有效地将风格特征移植到内容图像中。实验结果显示，D²Styler在图像质量上显著优于现有的多个方法，具有广泛的应用潜力。

D2Styler：通过离散扩散方法推进任意风格迁移