LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer
Yuzhuo Chen, Zehua Ma, Jianhua Wang, Kai Kang, Shunyu Yao, Weiming Zhang
2025-08-06
Summary
This paper talks about LAMIC, a system that can combine multiple images into one picture in a smart way by understanding the layout and relationships between images using advanced AI techniques called multimodal diffusion transformers.
What's the problem?
The problem is that existing diffusion models usually work with just one image reference at a time, making it hard to blend several images together while controlling the final layout and appearance without extra training.
What's the solution?
LAMIC solves this by extending the model to handle many images at once with attention mechanisms that guide how different parts of each image should be composed together, allowing controllable and high-quality image synthesis without needing to train a new model.
Why it matters?
This matters because it makes it easier and faster to create complex, customized images from multiple sources, which can help artists, designers, and AI applications that require flexible image editing.
Abstract
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.