LAMIC: Layout-Aware Multi-Image Composition via Scalability of
  Multimodal Diffusion Transformer
Authors: name: Yuzhuo Chen, name: Zehua Ma, name: Jianhua Wang, name: Kai Kang, name: Shunyu Yao, name: Weiming Zhang

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.