Improving Editability in Image Generation with Layer-wise Memory
Authors: name: Daneul Kim, name: Jaeah Lee, name: Jaesik Park

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.