title: DreamMix: Decoupling Object Attributes for Enhanced Editability in
    Customized Image Inpainting
summary: Subject-driven image inpainting has emerged as a popular task in image
  editing alongside recent advancements in diffusion models. Previous methods
  primarily focus on identity preservation but struggle to maintain the
  editability of inserted objects. In response, this paper introduces DreamMix, a
  diffusion-based generative model adept at inserting target objects into given
  scenes at user-specified locations while concurrently enabling arbitrary
  text-driven modifications to their attributes. In particular, we leverage
  advanced foundational inpainting models and introduce a disentangled
  local-global inpainting framework to balance precise local object insertion
  with effective global visual coherence. Additionally, we propose an Attribute
  Decoupling Mechanism (ADM) and a Textual Attribute Substitution (TAS) module to
  improve the diversity and discriminative capability of the text-based attribute
  guidance, respectively. Extensive experiments demonstrate that DreamMix
  effectively balances identity preservation and attribute editability across
  various application scenarios, including object insertion, attribute editing,
  and small object inpainting. Our code is publicly available at
  https://github.com/mycfhs/DreamMix.
paper_authors._id: 6746801be0ff542317fa9902
paper_authors.name: Yicheng Yang
paper_authors._id: 6746801be0ff542317fa9903
paper_authors.name: Pengxiang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-09T10:07:39.672Z
paper_authors._id: 64245f2c089d5fae56b4549a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64245f2c089d5fae56b4549a/qUHFsL9Svwyj5BKpfMtaY.jpeg
paper_authors.fullname: Pengxiang Li
paper_authors.type: user
paper_authors.user: pengxiang
paper_authors._id: 6746801be0ff542317fa9904
paper_authors.name: Lu Zhang
paper_authors._id: 6746801be0ff542317fa9905
paper_authors.name: Liqian Ma
paper_authors._id: 6746801be0ff542317fa9906
paper_authors.name: Ping Hu
paper_authors._id: 6746801be0ff542317fa9907
paper_authors.name: Siyu Du
paper_authors._id: 6746801be0ff542317fa9908
paper_authors.name: Yunzhi Zhuge
paper_authors._id: 6746801be0ff542317fa9909
paper_authors.name: Xu Jia
paper_authors._id: 6746801be0ff542317fa990a
paper_authors.name: Huchuan Lu
paper_title: DreamMix: Decoupling Object Attributes for Enhanced Editability in
    Customized Image Inpainting
paper_summary: Subject-driven image inpainting has emerged as a popular task in image
  editing alongside recent advancements in diffusion models. Previous methods
  primarily focus on identity preservation but struggle to maintain the
  editability of inserted objects. In response, this paper introduces DreamMix, a
  diffusion-based generative model adept at inserting target objects into given
  scenes at user-specified locations while concurrently enabling arbitrary
  text-driven modifications to their attributes. In particular, we leverage
  advanced foundational inpainting models and introduce a disentangled
  local-global inpainting framework to balance precise local object insertion
  with effective global visual coherence. Additionally, we propose an Attribute
  Decoupling Mechanism (ADM) and a Textual Attribute Substitution (TAS) module to
  improve the diversity and discriminative capability of the text-based attribute
  guidance, respectively. Extensive experiments demonstrate that DreamMix
  effectively balances identity preservation and attribute editability across
  various application scenarios, including object insertion, attribute editing,
  and small object inpainting. Our code is publicly available at
  https://github.com/mycfhs/DreamMix.
paper_ai_keywords: diffusion-based generative model advanced foundational inpainting models disentangled local-global inpainting framework Attribute Decoupling Mechanism (ADM) Textual Attribute Substitution (TAS) module