  The rapid development of generative diffusion models has significantly
advanced the field of style transfer. However, most current style transfer
methods based on diffusion models typically involve a slow iterative
optimization process, e.g., model fine-tuning and textual inversion of style
concept. In this paper, we introduce FreeStyle, an innovative style transfer
method built upon a pre-trained large diffusion model, requiring no further
optimization. Besides, our method enables style transfer only through a text
description of the desired style, eliminating the necessity of style images.
Specifically, we propose a dual-stream encoder and single-stream decoder
architecture, replacing the conventional U-Net in diffusion models. In the
dual-stream encoder, two distinct branches take the content image and style
text prompt as inputs, achieving content and style decoupling. In the decoder,
we further modulate features from the dual streams based on a given content
image and the corresponding style text prompt for precise style transfer. Our
experimental results demonstrate high-quality synthesis and fidelity of our
method across various content images and style text prompts. Compared with
state-of-the-art methods that require training, our FreeStyle approach notably
reduces the computational burden by thousands of iterations, while achieving
comparable or superior performance across multiple evaluation metrics including
CLIP Aesthetic Score, CLIP Score, and Preference. We have released the code
anonymously at:
\href{https://anonymous.4open.science/r/FreeStyleAnonymous-0F9B}
