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Diffusion-based image translation guided by semantic texts or a single targetimage has enabled flexible style transfer which is not limited to the specificdomains. Unfortunately, due to the stochastic nature of diffusion models, it isoften difficult to maintain the original content of the image during thereverse diffusion. To address this, here we present a novel diffusion-basedunsupervised image translation method using disentangled style and contentrepresentation.  Specifically, inspired by the splicing Vision Transformer, we extractintermediate keys of multihead self attention layer from ViT model and usedthem as the content preservation loss. Then, an image guided style transfer isperformed by matching the [CLS] classification token from the denoised samplesand target image, whereas additional CLIP loss is used for the text-drivenstyle transfer. To further accelerate the semantic change during the reversediffusion, we also propose a novel semantic divergence loss and resamplingstrategy. Our experimental results show that the proposed method outperformsstate-of-the-art baseline models in both text-guided and image-guidedtranslation tasks.

Diffusion-based Image Translation using Disentangled Style and Content Representation