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Arbitrary Style Transfer (AST) aims to transform images by adopting the stylefrom any selected artwork. Nonetheless, the need to accommodate diverse andsubjective user preferences poses a significant challenge. While some userswish to preserve distinct content structures, others might favor a morepronounced stylization. Despite advances in feed-forward AST methods, theirlimited customizability hinders their practical application. We propose a newapproach, ArtFusion, which provides a flexible balance between content andstyle. In contrast to traditional methods reliant on biased similarity losses,ArtFusion utilizes our innovative Dual Conditional Latent DiffusionProbabilistic Models (Dual-cLDM). This approach mitigates repetitive patternsand enhances subtle artistic aspects like brush strokes and genre-specificfeatures. Despite the promising results of conditional diffusion probabilisticmodels (cDM) in various generative tasks, their introduction to style transferis challenging due to the requirement for paired training data. ArtFusionsuccessfully navigates this issue, offering more practical and controllablestylization. A key element of our approach involves using a single image forboth content and style during model training, all the while maintainingeffective stylization during inference. ArtFusion outperforms existingapproaches on outstanding controllability and faithful presentation of artisticdetails, providing evidence of its superior style transfer capabilities.Furthermore, the Dual-cLDM utilized in ArtFusion carries the potential for avariety of complex multi-condition generative tasks, thus greatly broadeningthe impact of our research.

ArtFusion: Controllable Arbitrary Style Transfer using Dual Conditional Latent Diffusion Models