Edit Transfer: Learning Image Editing via Vision In-Context Relations
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.
67d8e00f0922c3dc8866520c
Lan Chen
admin_assigned
2025-03-18T09:21:18.982Z
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Lan Chen
user
Orannue
67d8e00f0922c3dc8866520d
Qi Mao
claimed_verified
2025-03-18T14:58:03.938Z
6388a7e98a5dbe2f3dc61faa
https://cdn-avatars.huggingface.co/v1/production/uploads/6388a7e98a5dbe2f3dc61faa/Zj8pR7BG_wOQjsYzosb5p.jpeg
Qi Mao
user
HelenMao
67d8e00f0922c3dc8866520e
Yuchao Gu
admin_assigned
2025-03-18T09:21:44.702Z
63021630a35b21bd8a53305a
/avatars/7a7e8b39749eda61e57d8a1908726558.svg
Gu Yuchao
user
guyuchao
67d8e00f0922c3dc8866520f
Mike Zheng Shou
admin_assigned
2025-03-18T09:21:29.754Z
661ab3da2b14565c7acccf5c
/avatars/fa4fc03664803e02aede4d4c3d50b393.svg
Mike Zheng Shou
user
AnalMom
Edit Transfer: Learning Image Editing via Vision In-Context Relations
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.
Edit Transfer, a visual relation in-context learning method, learns from a single example to apply transformations to new images, outperforming text-only and appearance-centric methods with few-shot learning.
Edit Transfer DiT-based text-to-image model LoRA fine-tuning visual relation in-context learning few-shot visual relation learning