StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image
  Generation
Zhouxia Wang Xintao Wang Liangbin Xie Zhongang Qi Ying Shan Wenping Wang Ping Luo
This paper presents a LoRA-free method for stylized image generation that
takes a text prompt and style reference images as inputs and produces an output
image in a single pass. Unlike existing methods that rely on training a
separate LoRA for each style, our method can adapt to various styles with a
unified model. However, this poses two challenges: 1) the prompt loses
controllability over the generated content, and 2) the output image inherits
both the semantic and style features of the style reference image, compromising
its content fidelity. To address these challenges, we introduce StyleAdapter, a
model that comprises two components: a two-path cross-attention module (TPCA)
and three decoupling strategies. These components enable our model to process
the prompt and style reference features separately and reduce the strong
coupling between the semantic and style information in the style references.
StyleAdapter can generate high-quality images that match the content of the
prompts and adopt the style of the references (even for unseen styles) in a
single pass, which is more flexible and efficient than previous methods.
Experiments have been conducted to demonstrate the superiority of our method
over previous works.