DiffuseFIST: A Fast Image-guided Style Transfer Method for Adapting Large-scale Diffusion Models

Abstract

Pre-trained text-to-image (T2I) synthesis diffusion models (DM) have shown remarkable capabilities in generating diverse images. However, they struggle to satisfy the user’s requirements due to (i) text’s inherent imprecision in expressing specific styles and (ii) generation is time-consuming due to many iterations in reverse process of diffusion models. To address these issues, we propose a fast style transfer method adopting pre-trained large-scale diffusion models, dubbed as DiffuseFIST, which adds T-small (300) noise to accelerate reverse process and solely requires real-world images and artistic images as input. Specifically, to preserve content and prevent style leakage, we introduce Content Injection (CI) strategy to achieve fine-grained control over the generated structure by manipulating spatial features and self-attention inside the model. Furthermore, we design Iterative Style Guidance (ISG) strategy which allows explicit user guidance and control of stylization tradeoffs. Finally, we initialize latent variable with Whitening and Coloring Transform (WCT) to deal with the disharmonious color. Qualitative and quantitative experiments demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer methods.