LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer
Yuzhuo Chen, Zehua Ma, Jianhua Wang, Kai Kang, Shunyu Yao, Weiming Zhang
Abstract
In controllable image synthesis, generating coherent and consistent images from multiple references with spatial layout awareness remains an open challenge. We propose 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
BibTeX
@inproceedings{aaai2026_lamiclayoutaware,
title = {LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer},
author = {Yuzhuo Chen and Zehua Ma and Jianhua Wang and Kai Kang and Shunyu Yao and Weiming Zhang},
booktitle = {AAAI 2026},
year = {2026}
}