The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance between style and content, whereas ignoring the significant demand for flexible and customized stylization results and thereby limiting their practical application. To address this critical issue, a novel AST approach namely HiCAST is proposed, which is capable of explicitly customizing the stylization results according to various source of semantic clues. In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM. It is characterized by introducing of \textit{Style Adapter}, which allows user to flexibly manipulate the output results by aligning multi-level style information and intrinsic knowledge in LDM. Lastly, we further extend our model to perform video AST. A novel learning objective is leveraged for video diffusion model training, which significantly improve cross-frame temporal consistency in the premise of maintaining stylization strength. Qualitative and quantitative comparisons as well as comprehensive user studies demonstrate that our HiCAST outperforms the existing SoTA methods in generating visually plausible stylization results.

提出了一种名为HiCAST的新的任意风格迁移方法，能够根据各种语义线索显式地定制风格化结果，包括了Latent Diffusion Model的条件式构建、引入Style Adapter进行风格灵活操作以及视频AST的拓展，经定性和定量比较以及综合用户研究表明，我们的HiCAST在生成视觉上合理的风格化结果方面优于现有方法。

HiCAST：基于适配器增强扩散模型的高度定制任意风格转换