title: Concept Lancet: Image Editing with Compositional Representation
    Transplant
summary: Diffusion models are widely used for image editing tasks. Existing editing
  methods often design a representation manipulation procedure by curating an
  edit direction in the text embedding or score space. However, such a procedure
  faces a key challenge: overestimating the edit strength harms visual
  consistency while underestimating it fails the editing task. Notably, each
  source image may require a different editing strength, and it is costly to
  search for an appropriate strength via trial-and-error. To address this
  challenge, we propose Concept Lancet (CoLan), a zero-shot plug-and-play
  framework for principled representation manipulation in diffusion-based image
  editing. At inference time, we decompose the source input in the latent (text
  embedding or diffusion score) space as a sparse linear combination of the
  representations of the collected visual concepts. This allows us to accurately
  estimate the presence of concepts in each image, which informs the edit. Based
  on the editing task (replace/add/remove), we perform a customized concept
  transplant process to impose the corresponding editing direction. To
  sufficiently model the concept space, we curate a conceptual representation
  dataset, CoLan-150K, which contains diverse descriptions and scenarios of
  visual terms and phrases for the latent dictionary. Experiments on multiple
  diffusion-based image editing baselines show that methods equipped with CoLan
  achieve state-of-the-art performance in editing effectiveness and consistency
  preservation.
paper_authors._id: 67f01efe81f4f7a1b43f4930
paper_authors.name: Jinqi Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-06T08:11:28.131Z
paper_authors._id: 6279a4f6812ee439d9c72d3f
paper_authors.avatarUrl: /avatars/a35a5674d1168d345d9fc5018485283e.svg
paper_authors.fullname: Jinqi Luo
paper_authors.type: user
paper_authors.user: peterljq
paper_authors._id: 67f01efe81f4f7a1b43f4931
paper_authors.name: Tianjiao Ding
paper_authors._id: 67f01efe81f4f7a1b43f4932
paper_authors.name: Kwan Ho Ryan Chan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T09:13:58.769Z
paper_authors._id: 6627043551cedbbb0b352047
paper_authors.avatarUrl: /avatars/8c4f80a24f8ba8761d33692c4ed28c29.svg
paper_authors.fullname: Kwan Ho Ryan Chan
paper_authors.type: user
paper_authors.user: ryanckh
paper_authors._id: 67f01efe81f4f7a1b43f4933
paper_authors.name: Hancheng Min
paper_authors._id: 67f01efe81f4f7a1b43f4934
paper_authors.name: Chris Callison-Burch
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T10:33:20.017Z
paper_authors._id: 6303ce25fc783bfc744216af
paper_authors.avatarUrl: /avatars/09f5e87c1f56a1b7f6ef9c5037682285.svg
paper_authors.fullname: Chris Callison-Burch
paper_authors.type: user
paper_authors.user: CCB
paper_authors._id: 67f01efe81f4f7a1b43f4935
paper_authors.name: René Vidal
paper_title: Concept Lancet: Image Editing with Compositional Representation
    Transplant
paper_summary: Diffusion models are widely used for image editing tasks. Existing editing
  methods often design a representation manipulation procedure by curating an
  edit direction in the text embedding or score space. However, such a procedure
  faces a key challenge: overestimating the edit strength harms visual
  consistency while underestimating it fails the editing task. Notably, each
  source image may require a different editing strength, and it is costly to
  search for an appropriate strength via trial-and-error. To address this
  challenge, we propose Concept Lancet (CoLan), a zero-shot plug-and-play
  framework for principled representation manipulation in diffusion-based image
  editing. At inference time, we decompose the source input in the latent (text
  embedding or diffusion score) space as a sparse linear combination of the
  representations of the collected visual concepts. This allows us to accurately
  estimate the presence of concepts in each image, which informs the edit. Based
  on the editing task (replace/add/remove), we perform a customized concept
  transplant process to impose the corresponding editing direction. To
  sufficiently model the concept space, we curate a conceptual representation
  dataset, CoLan-150K, which contains diverse descriptions and scenarios of
  visual terms and phrases for the latent dictionary. Experiments on multiple
  diffusion-based image editing baselines show that methods equipped with CoLan
  achieve state-of-the-art performance in editing effectiveness and consistency
  preservation.
paper_ai_summary: Concept Lancet (CoLan) is a framework for diffusion-based image editing that improves performance and consistency by decomposing source images into latent concept representations.
paper_ai_keywords: diffusion models representation manipulation edit direction latent space text embedding diffusion score sparse linear combination visual concepts concept transplant CoLan-150K conceptual representation dataset