# InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation

**Authors:** Chanran Kim, Jeongin Lee, Shichang Joung, Bongmo Kim, Yeul-Min Baek  
**arXiv:** [2404.19427](https://arxiv.org/abs/2404.19427)  
**Date:** 2024-04-30  
**Summary page:** https://www.summarizepaper.com/fr/paper/2404.19427v1/

## Abstract

In the field of personalized image generation, the ability to create images preserving concepts has significantly improved. Creating an image that naturally integrates multiple concepts in a cohesive and visually appealing composition can indeed be challenging. This paper introduces "InstantFamily," an approach that employs a novel masked cross-attention mechanism and a multimodal embedding stack to achieve zero-shot multi-ID image generation. Our method effectively preserves ID as it utilizes global and local features from a pre-trained face recognition model integrated with text conditions. Additionally, our masked cross-attention mechanism enables the precise control of multi-ID and composition in the generated images. We demonstrate the effectiveness of InstantFamily through experiments showing its dominance in generating images with multi-ID, while resolving well-known multi-ID generation problems. Additionally, our model achieves state-of-the-art performance in both single-ID and multi-ID preservation. Furthermore, our model exhibits remarkable scalability with a greater number of ID preservation than it was originally trained with.