# 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/en/paper/2404.19427v1/

## Key points

- Personalized image generation has advanced in preserving multiple concepts in visually appealing compositions.
- Generating images that integrate various concepts seamlessly can be challenging.
- The paper "InstantFamily" introduces a novel approach using masked cross-attention and multimodal embedding for zero-shot multi-ID image generation.
- InstantFamily effectively preserves identity by utilizing global and local features from a pre-trained face recognition model, along with text conditions.
- The method allows precise control over multi-ID preservation and composition in generated images.
- InstantFamily demonstrates effectiveness in generating images with multiple IDs and addresses common challenges in multi-ID generation.
- The model shows state-of-the-art performance in both multi-ID and single-ID preservation.
- Notable scalability of InstantFamily enables it to preserve a greater number of IDs than originally trained with, showcasing robustness and versatility.

## Summary

The field of personalized image generation has seen significant advancements in the ability to create images that preserve multiple concepts in a visually appealing composition. However, generating images that seamlessly integrate various concepts can be a challenging task. In response to this challenge, the paper titled "InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation" introduces a novel approach that leverages a masked cross-attention mechanism and a multimodal embedding stack to achieve zero-shot multi-ID image generation. The key innovation of the InstantFamily method lies in its ability to effectively preserve identity (ID) by utilizing both global and local features from a pre-trained face recognition model, integrated with text conditions. This unique combination allows for precise control over multi-ID preservation and composition in the generated images. Through experiments, the authors demonstrate the effectiveness of InstantFamily in generating images with multiple IDs while addressing common challenges associated with multi-ID generation. Furthermore, the model showcased state-of-the-art performance not only in multi-ID preservation but also in single-ID preservation. One notable aspect of InstantFamily is its remarkable scalability, as it can preserve a greater number of IDs than it was originally trained with. This scalability highlights the robustness and versatility of the model in handling diverse image generation tasks. Authored by Chanran Kim, Jeongin Lee, Shichang Joung, Bongmo Kim, and Yeul-Min Baek, "InstantFamily" represents a significant contribution to the field of personalized image generation. Its innovative approach and impressive results position it as a promising tool for generating high-quality images that seamlessly incorporate multiple identities while maintaining visual appeal and coherence.