LAMIC: Layout-Aware Multi-Image Composition via Scalability of
  Multimodal Diffusion Transformer
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.
689300478da45ffb0a2b252a
Yuzhuo Chen
689300478da45ffb0a2b252b
Zehua Ma
689300478da45ffb0a2b252c
Jianhua Wang
689300478da45ffb0a2b252d
Kai Kang
689300478da45ffb0a2b252e
Shunyu Yao
689300478da45ffb0a2b252f
Weiming Zhang
LAMIC: Layout-Aware Multi-Image Composition via Scalability of
  Multimodal Diffusion Transformer
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.
LAMIC, a Layout-Aware Multi-Image Composition framework, extends single-reference diffusion models to multi-reference scenarios using attention mechanisms, achieving state-of-the-art performance in controllable image synthesis without training.
LAMIC Layout-Aware Multi-Image Composition diffusion models Group Isolation Attention Region-Modulated Attention Inclusion Ratio Fill Ratio Background Similarity ID-S BG-S IN-R AVG DPG zero-shot generalization