title: SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based
    Image Editing
summary: 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.
paper_authors._id: 68197c200e4203d6bc84cdfb
paper_authors.name: Ming Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-06T08:33:18.243Z
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paper_authors.fullname: Ming Li
paper_authors.type: user
paper_authors.user: limingcv
paper_authors._id: 68197c200e4203d6bc84cdfc
paper_authors.name: Xin Gu
paper_authors._id: 68197c200e4203d6bc84cdfd
paper_authors.name: Fan Chen
paper_authors._id: 68197c200e4203d6bc84cdfe
paper_authors.name: Xiaoying Xing
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:06:58.770Z
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paper_authors.avatarUrl: /avatars/a361be3a5ccf9368717980d1faf69df0.svg
paper_authors.fullname: Xiaoying Xing
paper_authors.type: user
paper_authors.user: xiaoying0505
paper_authors._id: 68197c200e4203d6bc84cdff
paper_authors.name: Longyin Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:07:05.196Z
paper_authors._id: 644df7eacfb40c94eae71186
paper_authors.avatarUrl: /avatars/1daa4967efd34d54c59aa95970093dbd.svg
paper_authors.fullname: Longyin Wen
paper_authors.type: user
paper_authors.user: lionwen
paper_authors._id: 68197c200e4203d6bc84ce00
paper_authors.name: Chen Chen
paper_authors._id: 68197c200e4203d6bc84ce01
paper_authors.name: Sijie Zhu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-06T03:04:04.536Z
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paper_authors.avatarUrl: /avatars/a317a1f545117e0699e1c56258980fd8.svg
paper_authors.fullname: Sijie Zhu
paper_authors.type: user
paper_authors.user: Zilence006
paper_title: SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based
    Image Editing
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: contrastive supervision signals triplet loss unified guide vision-language models (VLMs) instruction-based image editing editing instructions rectification inference steps