title: Improving Editability in Image Generation with Layer-wise Memory
summary: Most real-world image editing tasks require multiple sequential edits to
  achieve desired results. Current editing approaches, primarily designed for
  single-object modifications, struggle with sequential editing: especially with
  maintaining previous edits along with adapting new objects naturally into the
  existing content. These limitations significantly hinder complex editing
  scenarios where multiple objects need to be modified while preserving their
  contextual relationships. We address this fundamental challenge through two key
  proposals: enabling rough mask inputs that preserve existing content while
  naturally integrating new elements and supporting consistent editing across
  multiple modifications. Our framework achieves this through layer-wise memory,
  which stores latent representations and prompt embeddings from previous edits.
  We propose Background Consistency Guidance that leverages memorized latents to
  maintain scene coherence and Multi-Query Disentanglement in cross-attention
  that ensures natural adaptation to existing content. To evaluate our method, we
  present a new benchmark dataset incorporating semantic alignment metrics and
  interactive editing scenarios. Through comprehensive experiments, we
  demonstrate superior performance in iterative image editing tasks with minimal
  user effort, requiring only rough masks while maintaining high-quality results
  throughout multiple editing steps.
paper_authors._id: 68183d9ae65ec5d5716c6d94
paper_authors.name: Daneul Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-05T07:30:39.678Z
paper_authors._id: 636b20591340f879a2eb98d0
paper_authors.avatarUrl: /avatars/4fc5cb13f916bcbc842ccf387bd5f6c0.svg
paper_authors.fullname: Daneul Kim
paper_authors.type: user
paper_authors.user: carpedkm
paper_authors._id: 68183d9ae65ec5d5716c6d95
paper_authors.name: Jaeah Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:09:48.636Z
paper_authors._id: 67cfbafeaa9404b59909fe59
paper_authors.avatarUrl: /avatars/b30721e2c899fdd0921e686292390f8d.svg
paper_authors.fullname: Jaeah Lee
paper_authors.type: user
paper_authors.user: jaeah1ee
paper_authors._id: 68183d9ae65ec5d5716c6d96
paper_authors.name: Jaesik Park
paper_title: Improving Editability in Image Generation with Layer-wise Memory
paper_summary: Most real-world image editing tasks require multiple sequential edits to
  achieve desired results. Current editing approaches, primarily designed for
  single-object modifications, struggle with sequential editing: especially with
  maintaining previous edits along with adapting new objects naturally into the
  existing content. These limitations significantly hinder complex editing
  scenarios where multiple objects need to be modified while preserving their
  contextual relationships. We address this fundamental challenge through two key
  proposals: enabling rough mask inputs that preserve existing content while
  naturally integrating new elements and supporting consistent editing across
  multiple modifications. Our framework achieves this through layer-wise memory,
  which stores latent representations and prompt embeddings from previous edits.
  We propose Background Consistency Guidance that leverages memorized latents to
  maintain scene coherence and Multi-Query Disentanglement in cross-attention
  that ensures natural adaptation to existing content. To evaluate our method, we
  present a new benchmark dataset incorporating semantic alignment metrics and
  interactive editing scenarios. Through comprehensive experiments, we
  demonstrate superior performance in iterative image editing tasks with minimal
  user effort, requiring only rough masks while maintaining high-quality results
  throughout multiple editing steps.
paper_ai_summary: A framework for iterative image editing uses layer-wise memory and background consistency guidance to maintain scene coherence and adapt new elements naturally across sequential edits.
paper_ai_keywords: layer-wise memory latent representations prompt embeddings Background Consistency Guidance Multi-Query Disentanglement cross-attention benchmark dataset semantic alignment interactive editing scenarios