SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based
  Image Editing
Due to the challenges of manually collecting accurate editing data, existing
datasets are typically constructed using various automated methods, leading to
noisy supervision signals caused by the mismatch between editing instructions
and original-edited image pairs. Recent efforts attempt to improve editing
models through generating higher-quality edited images, pre-training on
recognition tasks, or introducing vision-language models (VLMs) but fail to
resolve this fundamental issue. In this paper, we offer a novel solution by
constructing more effective editing instructions for given image pairs. This
includes rectifying the editing instructions to better align with the
original-edited image pairs and using contrastive editing instructions to
further enhance their effectiveness. Specifically, we find that editing models
exhibit specific generation attributes at different inference steps,
independent of the text. Based on these prior attributes, we define a unified
guide for VLMs to rectify editing instructions. However, there are some
challenging editing scenarios that cannot be resolved solely with rectified
instructions. To this end, we further construct contrastive supervision signals
with positive and negative instructions and introduce them into the model
training using triplet loss, thereby further facilitating supervision
effectiveness. Our method does not require the VLM modules or pre-training
tasks used in previous work, offering a more direct and efficient way to
provide better supervision signals, and providing a novel, simple, and
effective solution for instruction-based image editing. Results on multiple
benchmarks demonstrate that our method significantly outperforms existing
approaches. Compared with previous SOTA SmartEdit, we achieve 9.19%
improvements on the Real-Edit benchmark with 30x less training data and 13x
smaller model size.
68197c200e4203d6bc84cdfb
Ming Li
claimed_verified
2025-05-06T08:33:18.243Z
637f0eb22438d7485b8ef5d7
https://cdn-avatars.huggingface.co/v1/production/uploads/637f0eb22438d7485b8ef5d7/70h7dekqj7LuBobOXckmJ.jpeg
Ming Li
user
limingcv
68197c200e4203d6bc84cdfc
Xin Gu
68197c200e4203d6bc84cdfd
Fan Chen
68197c200e4203d6bc84cdfe
Xiaoying Xing
admin_assigned
2025-05-06T09:06:58.770Z
64ca92f738837b12d5f63729
/avatars/a361be3a5ccf9368717980d1faf69df0.svg
Xiaoying Xing
user
xiaoying0505
68197c200e4203d6bc84cdff
Longyin Wen
admin_assigned
2025-05-06T09:07:05.196Z
644df7eacfb40c94eae71186
/avatars/1daa4967efd34d54c59aa95970093dbd.svg
Longyin Wen
user
lionwen
68197c200e4203d6bc84ce00
Chen Chen
68197c200e4203d6bc84ce01
Sijie Zhu
extracted_pending
2025-05-06T03:04:04.536Z
65cbdea6d6c974694f09249a
/avatars/a317a1f545117e0699e1c56258980fd8.svg
Sijie Zhu
user
Zilence006
SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based
  Image Editing
Due to the challenges of manually collecting accurate editing data, existing
datasets are typically constructed using various automated methods, leading to
noisy supervision signals caused by the mismatch between editing instructions
and original-edited image pairs. Recent efforts attempt to improve editing
models through generating higher-quality edited images, pre-training on
recognition tasks, or introducing vision-language models (VLMs) but fail to
resolve this fundamental issue. In this paper, we offer a novel solution by
constructing more effective editing instructions for given image pairs. This
includes rectifying the editing instructions to better align with the
original-edited image pairs and using contrastive editing instructions to
further enhance their effectiveness. Specifically, we find that editing models
exhibit specific generation attributes at different inference steps,
independent of the text. Based on these prior attributes, we define a unified
guide for VLMs to rectify editing instructions. However, there are some
challenging editing scenarios that cannot be resolved solely with rectified
instructions. To this end, we further construct contrastive supervision signals
with positive and negative instructions and introduce them into the model
training using triplet loss, thereby further facilitating supervision
effectiveness. Our method does not require the VLM modules or pre-training
tasks used in previous work, offering a more direct and efficient way to
provide better supervision signals, and providing a novel, simple, and
effective solution for instruction-based image editing. Results on multiple
benchmarks demonstrate that our method significantly outperforms existing
approaches. Compared with previous SOTA SmartEdit, we achieve 9.19%
improvements on the Real-Edit benchmark with 30x less training data and 13x
smaller model size.
The paper presents a novel solution for improving instruction-based image editing by rectifying and enhancing editing instructions through contrastive supervision, thereby outperforming existing methods on benchmarks.
contrastive supervision signals triplet loss unified guide vision-language models (VLMs) instruction-based image editing editing instructions rectification inference steps