InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
Authors: name: Chanran Kim, name: Jeongin Lee, name: Shichang Joung, name: Bongmo Kim, name: Yeul-Min Baek

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.