StyleComposer: Training-Free Multi-Reference Style Composition
arXiv:2608.05213v1 Announce Type: new Abstract: The style of a painting is not monolithic: color, texture, and structure may come from different sources. Existing reference-guided methods transfer them as one style signal, leaving each attribute's source and strength outside the user's control. We ask where in a diffusion model one attribute can change while the others hold, and find that no single representation isolates all three. The proposed StyleComposer therefore routes each style attribute through the representation where it separates best and coordinates the routes over denoising time. Without training or inversion, it satisfies three references and the prompt jointly more closely than prior methods, and exposes one strength slider per attribute. Project page: https://lexxsh.github.io/StyleComposer
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[Submitted on 5 Aug 2026]
Title:StyleComposer: Training-Free Multi-Reference Style Composition
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Abstract:The style of a painting is not monolithic: color, texture, and structure may come from different sources. Existing reference-guided methods transfer them as one style signal, leaving each attribute's source and strength outside the user's control. We ask where in a diffusion model one attribute can change while the others hold, and find that no single representation isolates all three. The proposed StyleComposer therefore routes each style attribute through the representation where it separates best and coordinates the routes over denoising time. Without training or inversion, it satisfies three references and the prompt jointly more closely than prior methods, and exposes one strength slider per attribute. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.05213 [cs.CV]
(or arXiv:2608.05213v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.05213
arXiv-issued DOI via DataCite
Submission history
From: Namhyuk Ahn [view email] [v1] Wed, 5 Aug 2026 09:17:50 UTC (10,718 KB)
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