title: InstantID: Zero-shot Identity-Preserving Generation in Seconds
summary: 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.
paper_authors._id: 65a76b903d3c83940823ebbe
paper_authors.name: Qixun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-17T09:14:57.341Z
paper_authors._id: 643665d33193f279361cc292
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/643665d33193f279361cc292/k85RbhgQHp_gr2lLLOcgK.png
paper_authors.fullname: wangqixun
paper_authors.type: user
paper_authors.user: wangqixun
paper_authors._id: 65a76b903d3c83940823ebbf
paper_authors.name: Xu Bai
paper_authors._id: 65a76b903d3c83940823ebc0
paper_authors.name: Haofan Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-04-25T02:59:36.296Z
paper_authors._id: 637745113a63a2983ffbde13
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 65a76b903d3c83940823ebc1
paper_authors.name: Zekui Qin
paper_authors._id: 65a76b903d3c83940823ebc2
paper_authors.name: Anthony Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:59:11.348Z
paper_authors._id: 6311d9ee04f842f79916158c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PvOYgDEpe0x5ExLyj1BK-.png
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: antonio-c
paper_title: InstantID: Zero-shot Identity-Preserving Generation in Seconds
paper_summary: 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.
paper_ai_keywords: Textual Inversion DreamBooth LoRA storage demands fine-tuning reference images ID embedding-based methods forward inference model parameters pre-trained models face fidelity InstantID diffusion model plug-and-play module IdentityNet semantic conditions spatial conditions facial images landmark images textual prompts image generation SD1.5 SDXL pre-trained checkpoints