Edit Transfer: Learning Image Editing via Vision In-Context Relations
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
We introduce a new setting, Edit Transfer, where a model learns a
transformation from just a single source-target example and applies it to a new
query image. While text-based methods excel at semantic manipulations through
textual prompts, they often struggle with precise geometric details (e.g.,
poses and viewpoint changes). Reference-based editing, on the other hand,
typically focuses on style or appearance and fails at non-rigid
transformations. By explicitly learning the editing transformation from a
source-target pair, Edit Transfer mitigates the limitations of both text-only
and appearance-centric references. Drawing inspiration from in-context learning
in large language models, we propose a visual relation in-context learning
paradigm, building upon a DiT-based text-to-image model. We arrange the edited
example and the query image into a unified four-panel composite, then apply
lightweight LoRA fine-tuning to capture complex spatial transformations from
minimal examples. Despite using only 42 training samples, Edit Transfer
substantially outperforms state-of-the-art TIE and RIE methods on diverse
non-rigid scenarios, demonstrating the effectiveness of few-shot visual
relation learning.
