LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer

• URL: http://arxiv.org/abs/2508.00477v1
• Date: Fri, 01 Aug 2025 09:51:54 GMT
• Title: LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer
• Authors: Yuzhuo Chen, Zehua Ma, Jianhua Wang, Kai Kang, Shunyu Yao, Weiming Zhang,
• Abstract summary: LAMIC is a Layout-Aware Multi-Image Composition framework.<n>It extends single-reference diffusion models to multi-reference scenarios in a training-free manner.<n>It consistently outperforms existing multi-reference baselines in ID-S, BG-S, IN-R and AVG scores across all settings.
• Score: 32.9330637921386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In controllable image synthesis, generating coherent and consistent images from multiple references with spatial layout awareness remains an open challenge. We present LAMIC, a Layout-Aware Multi-Image Composition framework that, for the first time, extends single-reference diffusion models to multi-reference scenarios in a training-free manner. Built upon the MMDiT model, LAMIC introduces two plug-and-play attention mechanisms: 1) Group Isolation Attention (GIA) to enhance entity disentanglement; and 2) Region-Modulated Attention (RMA) to enable layout-aware generation. To comprehensively evaluate model capabilities, we further introduce three metrics: 1) Inclusion Ratio (IN-R) and Fill Ratio (FI-R) for assessing layout control; and 2) Background Similarity (BG-S) for measuring background consistency. Extensive experiments show that LAMIC achieves state-of-the-art performance across most major metrics: it consistently outperforms existing multi-reference baselines in ID-S, BG-S, IN-R and AVG scores across all settings, and achieves the best DPG in complex composition tasks. These results demonstrate LAMIC's superior abilities in identity keeping, background preservation, layout control, and prompt-following, all achieved without any training or fine-tuning, showcasing strong zero-shot generalization ability. By inheriting the strengths of advanced single-reference models and enabling seamless extension to multi-image scenarios, LAMIC establishes a new training-free paradigm for controllable multi-image composition. As foundation models continue to evolve, LAMIC's performance is expected to scale accordingly. Our implementation is available at: https://github.com/Suchenl/LAMIC.

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