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

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dc.contributor.authorLiang, Qianen_US
dc.contributor.authorChen, Zichongen_US
dc.contributor.authorZhou, Yangen_US
dc.contributor.authorHuang, Huien_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.date.accessioned2025-10-07T05:02:30Z
dc.date.available2025-10-07T05:02:30Z
dc.date.issued2025
dc.description.abstractAlthough 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.en_US
dc.description.number7
dc.description.sectionheadersStylization
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70251
dc.identifier.issn1467-8659
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70251
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70251
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Image processing
dc.subjectComputing methodologies → Image processing
dc.titleSPG: Style-Prompting Guidance for Style-Specific Content Creationen_US
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