无需训练即可自由组合多参考风格，精确控制色彩、纹理与结构。
StyleComposer: Training-Free Multi-Reference Style Composition
- 将不同风格属性分别通过最佳分离的表示路径传输
- 在不训练情况下同时满足三个参考和文本提示
- 每种属性提供独立强度调节滑块，适合创意设计
一幅画的风格并非单一整体：颜色、纹理和结构可能来自不同来源。现有参考引导方法将它们作为统一风格信号传递，导致用户无法控制各属性的来源与强度。我们探究在扩散模型中何种属性可独立变化而其他保持不变，发现没有单一表示能完全分离三者。为此，StyleComposer将每个风格属性路由至其分离效果最佳的表示，并在去噪过程中协调各路径。无需训练或反演，该方法能更准确地同时满足三个参考图像和文本提示，且为每种属性提供独立强度滑块。
原文摘要 · Abstract (English)
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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