Improving Editability in Image Generation with Layer-wise Memory
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.
68183d9ae65ec5d5716c6d94
Daneul Kim
claimed_verified
2025-05-05T07:30:39.678Z
636b20591340f879a2eb98d0
/avatars/4fc5cb13f916bcbc842ccf387bd5f6c0.svg
Daneul Kim
user
carpedkm
68183d9ae65ec5d5716c6d95
Jaeah Lee
claimed_verified
2025-05-07T08:09:48.636Z
67cfbafeaa9404b59909fe59
/avatars/b30721e2c899fdd0921e686292390f8d.svg
Jaeah Lee
user
jaeah1ee
68183d9ae65ec5d5716c6d96
Jaesik Park
Improving Editability in Image Generation with Layer-wise Memory
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.
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.
layer-wise memory latent representations prompt embeddings Background Consistency Guidance Multi-Query Disentanglement cross-attention benchmark dataset semantic alignment interactive editing scenarios