FAST: Flexibly Controllable Arbitrary Style Transfer via Latent Diffusion Models

Abstract

The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually pursue the balance between style and content by adjusting general coarse-level stylized strength, thereby leading to unsatisfactory results and hindering their practical application. To address this critical issue, a novel AST approach namely Flexibly Controllable Arbitrary Style Transfer (FAST) is proposed, which is capable of explicitly customizing the stylization results according to various sources of semantic clues. In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instances as conditions of LDM. It is characterized by introducing Style-Adapter, which allows users to flexibly manipulate the stylization results via aligning multi-level style control information and intrinsic knowledge in LDM, meanwhile enhancing the model with improved capacity to harmonize content detail retention and stylization strength. Lastly, our model is extended to handle video AST task. A novel learning objective is leveraged for video diffusion model training, which considerably improves cross-frame temporal consistency on the premise of maintaining stylization strength. Qualitative and quantitative comparisons as well as user studies demonstrate our presented approach outperforms the existing SoTA methods in generating visually plausible stylization results.