SPG: Style-Prompting Guidance for Style-Specific Content Creation

Liang, Qian; Chen, Zichong; Zhou, Yang; Huang, Hui

SPG: Style-Prompting Guidance for Style-Specific Content Creation

Files

cgf70251.pdf (19.07 MB)

Date

2025

Authors

Liang, Qian
Chen, Zichong
Zhou, Yang
Huang, Hui

Publisher

The Eurographics Association and John Wiley & Sons Ltd.

Abstract

Although recent text-to-image (T2I) diffusion models excel at aligning generated images with textual prompts, controlling the visual style of the output remains a challenging task. In this work, we propose Style-Prompting Guidance (SPG), a novel sampling strategy for style-specific image generation. SPG constructs a style noise vector and leverages its directional deviation from unconditional noise to guide the diffusion process toward the target style distribution. By integrating SPG with Classifier- Free Guidance (CFG), our method achieves both semantic fidelity and style consistency. SPG is simple, robust, and compatible with controllable frameworks like ControlNet and IPAdapter, making it practical and widely applicable. Extensive experiments demonstrate the effectiveness and generality of our approach compared to state-of-the-art methods. Code is available at https://github.com/Rumbling281441/SPG.

CCS Concepts: Computing methodologies → Image processing

        @article{10.1111:cgf.70251
,
journal = {Computer Graphics Forum},
title = {{SPG: Style-Prompting Guidance for Style-Specific Content Creation
}},
author = {Liang, Qian and 
Chen, Zichong and 
Zhou, Yang and 
Huang, Hui
},
year = {2025
},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.
},
ISSN = {1467-8659
},
DOI = {10.1111/cgf.70251
}
}