  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.
