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StyleComposer: Training-Free Multi-Reference Style Composition

2026-08-05 09:17:50
Sanghyeok Lee, Jihye Kang, Namhyuk Ahn

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

Abstract (translated)

URL

https://arxiv.org/abs/2608.05213

PDF

https://arxiv.org/pdf/2608.05213.pdf


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