StyleBrush: Style Extraction and Transfer from a Single Image
Wancheng Feng, Wanquan Feng, Dawei Huang, Jiaming Pei, Guangliang, Cheng, Lukun Wang

TL;DR
StyleBrush is a novel method for style transfer from a single reference image that effectively separates style from structure, enabling high-quality stylization with a new dataset and achieving state-of-the-art results.
Contribution
We introduce StyleBrush, a new architecture with dual branches for style extraction and structural guidance, and create a large high-quality style image dataset for training.
Findings
Achieves state-of-the-art stylization quality
Uses a new dataset of 100K style images
Demonstrates effective style-structure separation
Abstract
Stylization for visual content aims to add specific style patterns at the pixel level while preserving the original structural features. Compared with using predefined styles, stylization guided by reference style images is more challenging, where the main difficulty is to effectively separate style from structural elements. In this paper, we propose StyleBrush, a method that accurately captures styles from a reference image and ``brushes'' the extracted style onto other input visual content. Specifically, our architecture consists of two branches: ReferenceNet, which extracts style from the reference image, and Structure Guider, which extracts structural features from the input image, thus enabling image-guided stylization. We utilize LLM and T2I models to create a dataset comprising 100K high-quality style images, encompassing a diverse range of styles and contents with high aesthetic…
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Taxonomy
TopicsImage Retrieval and Classification Techniques
