InstantID: Zero-shot Identity-Preserving Generation in Seconds
There has been significant progress in personalized image synthesis with
methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world
applicability is hindered by high storage demands, lengthy fine-tuning
processes, and the need for multiple reference images. Conversely, existing ID
embedding-based methods, while requiring only a single forward inference, face
challenges: they either necessitate extensive fine-tuning across numerous model
parameters, lack compatibility with community pre-trained models, or fail to
maintain high face fidelity. Addressing these limitations, we introduce
InstantID, a powerful diffusion model-based solution. Our plug-and-play module
adeptly handles image personalization in various styles using just a single
facial image, while ensuring high fidelity. To achieve this, we design a novel
IdentityNet by imposing strong semantic and weak spatial conditions,
integrating facial and landmark images with textual prompts to steer the image
generation. InstantID demonstrates exceptional performance and efficiency,
proving highly beneficial in real-world applications where identity
preservation is paramount. Moreover, our work seamlessly integrates with
popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving
as an adaptable plugin. Our codes and pre-trained checkpoints will be available
at https://github.com/InstantID/InstantID.
65a76b903d3c83940823ebbe
Qixun Wang
admin_assigned
2024-01-17T09:14:57.341Z
643665d33193f279361cc292
https://cdn-avatars.huggingface.co/v1/production/uploads/643665d33193f279361cc292/k85RbhgQHp_gr2lLLOcgK.png
wangqixun
user
wangqixun
65a76b903d3c83940823ebbf
Xu Bai
65a76b903d3c83940823ebc0
Haofan Wang
extracted_confirmed
2024-04-25T02:59:36.296Z
637745113a63a2983ffbde13
https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
user
wanghaofan
65a76b903d3c83940823ebc1
Zekui Qin
65a76b903d3c83940823ebc2
Anthony Chen
claimed_verified
2024-11-05T07:59:11.348Z
6311d9ee04f842f79916158c
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PvOYgDEpe0x5ExLyj1BK-.png
chen
user
antonio-c
InstantID: Zero-shot Identity-Preserving Generation in Seconds
There has been significant progress in personalized image synthesis with
methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world
applicability is hindered by high storage demands, lengthy fine-tuning
processes, and the need for multiple reference images. Conversely, existing ID
embedding-based methods, while requiring only a single forward inference, face
challenges: they either necessitate extensive fine-tuning across numerous model
parameters, lack compatibility with community pre-trained models, or fail to
maintain high face fidelity. Addressing these limitations, we introduce
InstantID, a powerful diffusion model-based solution. Our plug-and-play module
adeptly handles image personalization in various styles using just a single
facial image, while ensuring high fidelity. To achieve this, we design a novel
IdentityNet by imposing strong semantic and weak spatial conditions,
integrating facial and landmark images with textual prompts to steer the image
generation. InstantID demonstrates exceptional performance and efficiency,
proving highly beneficial in real-world applications where identity
preservation is paramount. Moreover, our work seamlessly integrates with
popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving
as an adaptable plugin. Our codes and pre-trained checkpoints will be available
at https://github.com/InstantID/InstantID.
InstantID is a diffusion model-based solution for personalized image synthesis that uses a single facial image and integrates with pre-trained models while maintaining high face fidelity and efficiency.
Textual Inversion DreamBooth LoRA ID embedding-based methods diffusion model plug-and-play module IdentityNet semantic conditions spatial conditions facial images landmark images textual prompts identity preservation SD1.5 SDXL