title: FaceChain-SuDe: Building Derived Class to Inherit Category Attributes
    for One-shot Subject-Driven Generation
summary: Subject-driven generation has garnered significant interest recently due to
  its ability to personalize text-to-image generation. Typical works focus on
  learning the new subject's private attributes. However, an important fact has
  not been taken seriously that a subject is not an isolated new concept but
  should be a specialization of a certain category in the pre-trained model. This
  results in the subject failing to comprehensively inherit the attributes in its
  category, causing poor attribute-related generations. In this paper, motivated
  by object-oriented programming, we model the subject as a derived class whose
  base class is its semantic category. This modeling enables the subject to
  inherit public attributes from its category while learning its private
  attributes from the user-provided example. Specifically, we propose a
  plug-and-play method, Subject-Derived regularization (SuDe). It constructs the
  base-derived class modeling by constraining the subject-driven generated images
  to semantically belong to the subject's category. Extensive experiments under
  three baselines and two backbones on various subjects show that our SuDe
  enables imaginative attribute-related generations while maintaining subject
  fidelity. Codes will be open sourced soon at FaceChain
  (https://github.com/modelscope/facechain).
paper_authors._id: 65efc768424023c1047732ef
paper_authors.name: Pengchong Qiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-12T12:40:30.481Z
paper_authors._id: 64dc69f61855ce11cd799eab
paper_authors.avatarUrl: /avatars/511a490c7dd0bd5fa23d18eaf95881b3.svg
paper_authors.fullname: qiao
paper_authors.type: user
paper_authors.user: pengchonghercy
paper_authors._id: 65efc768424023c1047732f0
paper_authors.name: Lei Shang
paper_authors._id: 65efc768424023c1047732f1
paper_authors.name: Chang Liu
paper_authors._id: 65efc768424023c1047732f2
paper_authors.name: Baigui Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-12T12:43:08.153Z
paper_authors._id: 64d9f1409a6a7ae9846ab8d1
paper_authors.avatarUrl: /avatars/208b9d05b30f0094c3301936fabf4656.svg
paper_authors.fullname: Baigui Sun
paper_authors.type: user
paper_authors.user: sunbaigui
paper_authors._id: 65efc768424023c1047732f3
paper_authors.name: Xiangyang Ji
paper_authors._id: 65efc768424023c1047732f4
paper_authors.name: Jie Chen
paper_title: FaceChain-SuDe: Building Derived Class to Inherit Category Attributes
    for One-shot Subject-Driven Generation
paper_summary: Subject-driven generation has garnered significant interest recently due to
  its ability to personalize text-to-image generation. Typical works focus on
  learning the new subject's private attributes. However, an important fact has
  not been taken seriously that a subject is not an isolated new concept but
  should be a specialization of a certain category in the pre-trained model. This
  results in the subject failing to comprehensively inherit the attributes in its
  category, causing poor attribute-related generations. In this paper, motivated
  by object-oriented programming, we model the subject as a derived class whose
  base class is its semantic category. This modeling enables the subject to
  inherit public attributes from its category while learning its private
  attributes from the user-provided example. Specifically, we propose a
  plug-and-play method, Subject-Derived regularization (SuDe). It constructs the
  base-derived class modeling by constraining the subject-driven generated images
  to semantically belong to the subject's category. Extensive experiments under
  three baselines and two backbones on various subjects show that our SuDe
  enables imaginative attribute-related generations while maintaining subject
  fidelity. Codes will be open sourced soon at FaceChain
  (https://github.com/modelscope/facechain).
paper_ai_keywords: object-oriented programming derived class base class semantic category public attributes private attributes user-provided example plug-and-play method Subject-Derived regularization (SuDe) semantically belong imaginative attribute-related generations subject fidelity