Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains. Unfortunately, due to the stochastic nature of diffusion models, it is often difficult to maintain the original content of the image during the reverse diffusion. To address this, here we present a novel diffusion-based unsupervised image translation method using disentangled style and content representation. Specifically, inspired by the splicing Vision Transformer, we extract intermediate keys of multihead self attention layer from ViT model and used them as the content preservation loss. Then, an image guided style transfer is performed by matching the [CLS] classification token from the denoised samples and target image, whereas additional CLIP loss is used for the text-driven style transfer. To further accelerate the semantic change during the reverse diffusion, we also propose a novel semantic divergence loss and resampling strategy. Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks.

本文提出了一种基于扩散的非监督图像转换方法，使用分离的风格和内容表征，并使用ViT模型中的中间键提取多头自注意层作为内容保存损失，并匹配文本驱动风格转移的[CLS]分类令牌，同时使用额外的CLIP损失，实验结果表明，该方法在文本引导和图像引导的转换任务中优于现有的基准模型。

基于扩散的图像翻译：使用解缕分离的风格和内容表征