title: LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized
    Text-Guided Image Editing
summary: Text-guided image editing aims to modify specific regions of an image
  according to natural language instructions while maintaining the general
  structure and the background fidelity. Existing methods utilize masks derived
  from cross-attention maps generated from diffusion models to identify the
  target regions for modification. However, since cross-attention mechanisms
  focus on semantic relevance, they struggle to maintain the image integrity. As
  a result, these methods often lack spatial consistency, leading to editing
  artifacts and distortions. In this work, we address these limitations and
  introduce LOCATEdit, which enhances cross-attention maps through a graph-based
  approach utilizing self-attention-derived patch relationships to maintain
  smooth, coherent attention across image regions, ensuring that alterations are
  limited to the designated items while retaining the surrounding structure.
  \method consistently and substantially outperforms existing baselines on
  PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on
  various editing tasks. Code can be found on
  https://github.com/LOCATEdit/LOCATEdit/
paper_authors._id: 67e68802327f995244ac2e45
paper_authors.name: Achint Soni
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T12:25:43.793Z
paper_authors._id: 6584bd1a5b94130d87c5f701
paper_authors.avatarUrl: /avatars/615e392f98e763144736c363f980a85a.svg
paper_authors.fullname: Achint Soni
paper_authors.type: user
paper_authors.user: Trickyjustice
paper_authors._id: 67e68802327f995244ac2e46
paper_authors.name: Meet Soni
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:21:44.872Z
paper_authors._id: 6731a9ea4d1ced57fa6b1edd
paper_authors.avatarUrl: /avatars/fd7123bff5517cd359dc6ec0a06bd81a.svg
paper_authors.fullname: Meet soni
paper_authors.type: user
paper_authors.user: Meet8494
paper_authors._id: 67e68802327f995244ac2e47
paper_authors.name: Sirisha Rambhatla
paper_title: LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized
    Text-Guided Image Editing
paper_summary: Text-guided image editing aims to modify specific regions of an image
  according to natural language instructions while maintaining the general
  structure and the background fidelity. Existing methods utilize masks derived
  from cross-attention maps generated from diffusion models to identify the
  target regions for modification. However, since cross-attention mechanisms
  focus on semantic relevance, they struggle to maintain the image integrity. As
  a result, these methods often lack spatial consistency, leading to editing
  artifacts and distortions. In this work, we address these limitations and
  introduce LOCATEdit, which enhances cross-attention maps through a graph-based
  approach utilizing self-attention-derived patch relationships to maintain
  smooth, coherent attention across image regions, ensuring that alterations are
  limited to the designated items while retaining the surrounding structure.
  \method consistently and substantially outperforms existing baselines on
  PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on
  various editing tasks. Code can be found on
  https://github.com/LOCATEdit/LOCATEdit/
paper_ai_summary: LOCATEdit enhances text-guided image editing by improving cross-attention maps with a graph-based approach to maintain spatial consistency and reduce editing artifacts.
paper_ai_keywords: text-guided image editing cross-attention maps diffusion models semantic relevance spatial consistency editing artifacts self-attention patch relationships PIE-Bench