InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
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.
66319bf82a4742700e71ff8e
Chanran Kim
claimed_verified
2024-05-02T07:11:27.307Z
618a925429415f4b68c5ee78
https://cdn-avatars.huggingface.co/v1/production/uploads/1671626774917-618a925429415f4b68c5ee78.png
Chanran Kim
user
seriousran
66319bf82a4742700e71ff8f
Jeongin Lee
claimed_verified
2024-06-24T07:33:46.274Z
6631f92872ff6ce0c0b6525b
/avatars/818772be487d4d7a595d95e6b49e7a3b.svg
Jeongin Lee
user
timdalee
66319bf82a4742700e71ff90
Shichang Joung
admin_assigned
2024-05-02T08:03:27.070Z
6412cafe027aea38bc90a807
/avatars/9b90dc4ca494b2cdc71972268fc94ac4.svg
shichang joung
user
deiddog
66319bf82a4742700e71ff91
Bongmo Kim
66319bf82a4742700e71ff92
Yeul-Min Baek
admin_assigned
2024-05-02T08:03:39.676Z
6375f6aada21add16247b346
/avatars/100a9236ed6f3149e98c86873babae9a.svg
Yeulmin Baek
user
ymbaek100
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
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.
InstantFamily uses masked cross-attention and multimodal embeddings to generate images with preserved multi-IDs and high visual coherence.
masked cross-attention multimodal embedding stack pre-trained face recognition model multi-ID image generation single-ID preservation state-of-the-art performance scalability