Multi-subject customization aims to synthesize multiple user-specified subjects into a coherent image. To address issues such as subjects missing or conflicts, recent works incorporate layout guidance to provide explicit spatial constraints. However, existing methods still struggle to balance three critical objectives: text alignment, subject identity preservation, and layout control, while the reliance on additional training further limits their scalability and efficiency. In this paper, we present AnyMS, a novel training-free framework for layout-guided multi-subject customization. AnyMS leverages three input conditions: text prompt, subject images, and layout constraints, and introduces a bottom-up dual-level attention decoupling mechanism to harmonize their integration during generation. Specifically, global decoupling separates cross-attention between textual and visual conditions to ensure text alignment. Local decoupling confines each subject's attention to its designated area, which prevents subject conflicts and thus guarantees identity preservation and layout control. Moreover, AnyMS employs pre-trained image adapters to extract subject-specific features aligned with the diffusion model, removing the need for subject learning or adapter tuning. Extensive experiments demonstrate that AnyMS achieves state-of-the-art performance, supporting complex compositions and scaling to a larger number of subjects.

翻译：多主体定制旨在将多个用户指定的主体合成到一幅连贯的图像中。为解决主体缺失或冲突等问题，近期研究引入了布局引导以提供显式的空间约束。然而，现有方法仍难以平衡三个关键目标：文本对齐、主体身份保持与布局控制，且对额外训练的依赖进一步限制了其可扩展性与效率。本文提出AnyMS，一种面向布局引导多主体定制的新型免训练框架。AnyMS利用三种输入条件：文本提示、主体图像与布局约束，并引入自底向上的双层级注意力解耦机制以协调生成过程中的多条件融合。具体而言，全局解耦分离文本条件与视觉条件间的交叉注意力以确保文本对齐；局部解耦将每个主体的注意力限制在其指定区域内，从而避免主体冲突并保障身份保持与布局控制。此外，AnyMS采用预训练图像适配器提取与扩散模型对齐的主体特征，无需进行主体学习或适配器微调。大量实验表明，AnyMS实现了最先进的性能，支持复杂构图并可扩展至更多数量的主体。