title: FaceStudio: Put Your Face Everywhere in Seconds
summary: This study investigates identity-preserving image synthesis, an intriguing
  task in image generation that seeks to maintain a subject's identity while
  adding a personalized, stylistic touch. Traditional methods, such as Textual
  Inversion and DreamBooth, have made strides in custom image creation, but they
  come with significant drawbacks. These include the need for extensive resources
  and time for fine-tuning, as well as the requirement for multiple reference
  images. To overcome these challenges, our research introduces a novel approach
  to identity-preserving synthesis, with a particular focus on human images. Our
  model leverages a direct feed-forward mechanism, circumventing the need for
  intensive fine-tuning, thereby facilitating quick and efficient image
  generation. Central to our innovation is a hybrid guidance framework, which
  combines stylized images, facial images, and textual prompts to guide the image
  generation process. This unique combination enables our model to produce a
  variety of applications, such as artistic portraits and identity-blended
  images. Our experimental results, including both qualitative and quantitative
  evaluations, demonstrate the superiority of our method over existing baseline
  models and previous works, particularly in its remarkable efficiency and
  ability to preserve the subject's identity with high fidelity.
paper_authors._id: 656fd910ec366e93ca2e26b1
paper_authors.name: Yuxuan Yan
paper_authors._id: 656fd910ec366e93ca2e26b2
paper_authors.name: Chi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-06T12:00:36.414Z
paper_authors._id: 64196320ed725fef64419c2a
paper_authors.avatarUrl: /avatars/96feb22fb5e8931d6c9e0ea06148266f.svg
paper_authors.fullname: Chi Zhang
paper_authors.type: user
paper_authors.user: DrChiZhang
paper_authors._id: 656fd910ec366e93ca2e26b3
paper_authors.name: Rui Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-12T08:09:20.353Z
paper_authors._id: 630443483926de1f7ec6bda0
paper_authors.avatarUrl: /avatars/e8e95ab0d03f58c61846ccc1318502fe.svg
paper_authors.fullname: Rui Wang
paper_authors.type: user
paper_authors.user: budui
paper_authors._id: 656fd910ec366e93ca2e26b4
paper_authors.name: Pei Cheng
paper_authors._id: 656fd910ec366e93ca2e26b5
paper_authors.name: Gang Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-06T11:45:19.399Z
paper_authors._id: 63417332c5565a4b8d43a0d8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
paper_authors.fullname: Gang Yu
paper_authors.type: user
paper_authors.user: skicy
paper_authors._id: 656fd910ec366e93ca2e26b6
paper_authors.name: Bin Fu
paper_title: FaceStudio: Put Your Face Everywhere in Seconds
paper_summary: This study investigates identity-preserving image synthesis, an intriguing
  task in image generation that seeks to maintain a subject's identity while
  adding a personalized, stylistic touch. Traditional methods, such as Textual
  Inversion and DreamBooth, have made strides in custom image creation, but they
  come with significant drawbacks. These include the need for extensive resources
  and time for fine-tuning, as well as the requirement for multiple reference
  images. To overcome these challenges, our research introduces a novel approach
  to identity-preserving synthesis, with a particular focus on human images. Our
  model leverages a direct feed-forward mechanism, circumventing the need for
  intensive fine-tuning, thereby facilitating quick and efficient image
  generation. Central to our innovation is a hybrid guidance framework, which
  combines stylized images, facial images, and textual prompts to guide the image
  generation process. This unique combination enables our model to produce a
  variety of applications, such as artistic portraits and identity-blended
  images. Our experimental results, including both qualitative and quantitative
  evaluations, demonstrate the superiority of our method over existing baseline
  models and previous works, particularly in its remarkable efficiency and
  ability to preserve the subject's identity with high fidelity.
paper_ai_summary: A hybrid guidance framework for identity-preserving image synthesis efficiently generates stylistic portraits by combining stylized images, facial images, and textual prompts.
paper_ai_keywords: identity-preserving synthesis Textual Inversion DreamBooth direct feed-forward mechanism hybrid guidance framework artistic portraits identity-blended images qualitative evaluations quantitative evaluations