Style transfer is an artistic research topic within a series of generative tasks, and it is quite challenging to stably and reliably guide the generation of images with the expected content and style. Early methods followed a predefined style, defined by a reference image, and applied it to another image while preserving the latter’s content structure, but the generated effects lacked high fidelity. As diffusion models have evolved, the content guidance for style transfer has transitioned from images to text, potentially leading to instance alterations. Generating with multiple conditions constrained in the latent space is challenging. Within the EDM framework, we have utilized cross-attention to design style and content embedding modules, overcoming instance alterations without the need for paired datasets and achieving high-resolution, high-fidelity results. Our experimental findings demonstrate that our architecture has advanced to the forefront of image-to-image style transfer capabilities.

Transformer's recent integration into style transfer leverages its proficiency in establishing long-range dependencies, albeit at the expense of attenuated local modeling. This paper introduces Strips Window Attention Transformer (S2WAT), a novel hierarchical vision transformer designed for style transfer. S2WAT employs attention computation in diverse window shapes to capture both short- and long-range dependencies. The merged dependencies utilize the "Attn Merge" strategy, which adaptively determines spatial weights based on their relevance to the target. Extensive experiments on representative datasets show the proposed method's effectiveness compared to state-of-the-art (SOTA) transformer-based and other approaches. The code and pre-trained models are available at https://github.com/AlienZhang1996/S2WAT.

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged, and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT (Style Transfer via Transformers) for image style transfer, and an edge loss function that can enhance the content details and avoid generating blurred results due to the excessive rendering of style features. Extensive qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer approaches while alleviating the content leak problem.