title: InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
summary: 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.
paper_authors._id: 66319bf82a4742700e71ff8e
paper_authors.name: Chanran Kim
paper_authors.status: claimed_verified
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paper_authors.fullname: Chanran Kim
paper_authors.type: user
paper_authors.user: seriousran
paper_authors._id: 66319bf82a4742700e71ff8f
paper_authors.name: Jeongin Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-24T07:33:46.274Z
paper_authors._id: 6631f92872ff6ce0c0b6525b
paper_authors.avatarUrl: /avatars/818772be487d4d7a595d95e6b49e7a3b.svg
paper_authors.fullname: Jeongin Lee
paper_authors.type: user
paper_authors.user: timdalee
paper_authors._id: 66319bf82a4742700e71ff90
paper_authors.name: Shichang Joung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:03:27.070Z
paper_authors._id: 6412cafe027aea38bc90a807
paper_authors.avatarUrl: /avatars/9b90dc4ca494b2cdc71972268fc94ac4.svg
paper_authors.fullname: shichang joung
paper_authors.type: user
paper_authors.user: deiddog
paper_authors._id: 66319bf82a4742700e71ff91
paper_authors.name: Bongmo Kim
paper_authors._id: 66319bf82a4742700e71ff92
paper_authors.name: Yeul-Min Baek
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:03:39.676Z
paper_authors._id: 6375f6aada21add16247b346
paper_authors.avatarUrl: /avatars/100a9236ed6f3149e98c86873babae9a.svg
paper_authors.fullname: Yeulmin Baek
paper_authors.type: user
paper_authors.user: ymbaek100
paper_title: InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
paper_summary: 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.
paper_ai_keywords: masked cross-attention mechanism multimodal embedding stack zero-shot multi-ID image generation pre-trained face recognition model ID preservation global and local features multi-ID generation state-of-the-art performance scalability