title: Editable Image Elements for Controllable Synthesis
summary: Diffusion models have made significant advances in text-guided synthesis
  tasks. However, editing user-provided images remains challenging, as the high
  dimensional noise input space of diffusion models is not naturally suited for
  image inversion or spatial editing. In this work, we propose an image
  representation that promotes spatial editing of input images using a diffusion
  model. Concretely, we learn to encode an input into "image elements" that can
  faithfully reconstruct an input image. These elements can be intuitively edited
  by a user, and are decoded by a diffusion model into realistic images. We show
  the effectiveness of our representation on various image editing tasks, such as
  object resizing, rearrangement, dragging, de-occlusion, removal, variation, and
  image composition. Project page:
  https://jitengmu.github.io/Editable_Image_Elements/
paper_authors._id: 662a967ce58d277c81d53e53
paper_authors.name: Jiteng Mu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-26T12:38:28.988Z
paper_authors._id: 63a38a4a2262903957e12f14
paper_authors.avatarUrl: /avatars/e1f7a90fad8792e2eb8d88ef03cea826.svg
paper_authors.fullname: JitengMu
paper_authors.type: user
paper_authors.user: JitengMu
paper_authors._id: 662a967ce58d277c81d53e54
paper_authors.name: Michaël Gharbi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-26T12:38:39.879Z
paper_authors._id: 658161ad208369a9403ca9a1
paper_authors.avatarUrl: /avatars/e2324ab2a712100681b0b8127305212b.svg
paper_authors.fullname: Michael Gharbi
paper_authors.type: user
paper_authors.user: mgharbi
paper_authors._id: 662a967ce58d277c81d53e55
paper_authors.name: Richard Zhang
paper_authors._id: 662a967ce58d277c81d53e56
paper_authors.name: Eli Shechtman
paper_authors._id: 662a967ce58d277c81d53e57
paper_authors.name: Nuno Vasconcelos
paper_authors._id: 662a967ce58d277c81d53e58
paper_authors.name: Xiaolong Wang
paper_authors._id: 662a967ce58d277c81d53e59
paper_authors.name: Taesung Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-26T12:50:00.655Z
paper_authors._id: 637563c59ef4e3b87a60d503
paper_authors.avatarUrl: /avatars/8264de115e6305e4720c78fc7dce7f8d.svg
paper_authors.fullname: Taesung Park
paper_authors.type: user
paper_authors.user: taesungp
paper_title: Editable Image Elements for Controllable Synthesis
paper_summary: Diffusion models have made significant advances in text-guided synthesis
  tasks. However, editing user-provided images remains challenging, as the high
  dimensional noise input space of diffusion models is not naturally suited for
  image inversion or spatial editing. In this work, we propose an image
  representation that promotes spatial editing of input images using a diffusion
  model. Concretely, we learn to encode an input into "image elements" that can
  faithfully reconstruct an input image. These elements can be intuitively edited
  by a user, and are decoded by a diffusion model into realistic images. We show
  the effectiveness of our representation on various image editing tasks, such as
  object resizing, rearrangement, dragging, de-occlusion, removal, variation, and
  image composition. Project page:
  https://jitengmu.github.io/Editable_Image_Elements/
paper_ai_keywords: diffusion models text-guided synthesis high dimensional noise input space image inversion spatial editing image elements realistic images object resizing rearrangement dragging de-occlusion removal variation image composition