Diffusion-based Image Translation using Disentangled Style and Content Representation

Our model can generate high-quality translation outputs using both text and image conditions. More results can be found in the experiment section. 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 (Tumanyan et al., 2022), 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. Image translation is a task in which the model receives an input image and converts it into a target domain. Early image translation approaches (Zhu et al., 2017; Park et al., 2020; Isola et al., 2017) were mainly designed for single domain translation, but soon extended to multi-domain translation (Choi et al., 2018; Lee et al., 2019).