Title: Diffusion-based Image Translation using disentangled style and content representation. Abstract: 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, dubbed as DiffuseIT, using disentangled style and content representation.
 Specifically, inspired by the  slicing 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. 