MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion

TL;DR

MUSE is a unified framework for multi-subject image synthesis that achieves precise spatial control and identity preservation by integrating layout and textual guidance through explicit semantic expansion.

Contribution

It introduces a novel concatenated cross-attention mechanism and a two-stage training strategy for improved layout-controllable multi-subject synthesis.

Findings

Superior spatial accuracy over existing methods

Enhanced identity consistency in generated images

Effective zero-shot multi-subject synthesis

Abstract

Existing text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality images guided by textual prompts. However, achieving multi-subject compositional synthesis with precise spatial control remains a significant challenge. In this work, we address the task of layout-controllable multi-subject synthesis (LMS), which requires both faithful reconstruction of reference subjects and their accurate placement in specified regions within a unified image. While recent advancements have separately improved layout control and subject synthesis, existing approaches struggle to simultaneously satisfy the dual requirements of spatial precision and identity preservation in this composite task. To bridge this gap, we propose MUSE, a unified synthesis framework that employs concatenated cross-attention (CCA) to seamlessly integrate layout specifications with textual guidance through explicit semantic space expansion. The proposed CCA mechanism enables bidirectional modality alignment between spatial constraints and textual descriptions without interference. Furthermore, we design a progressive two-stage training strategy that decomposes the LMS task into learnable sub-objectives for effective optimization. Extensive experiments demonstrate that MUSE achieves zero-shot end-to-end generation with superior spatial accuracy and identity consistency compared to existing solutions, advancing the frontier of controllable image synthesis. Our code and model are available at https://github.com/pf0607/MUSE.