Datasets:
File size: 4,767 Bytes
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dataset_info:
features:
- name: input_test
dtype: image
- name: input_gt
dtype: image
- name: exemplar_input
dtype: image
- name: exemplar_edit
dtype: image
- name: instruction
dtype: string
- name: og_description
dtype: string
- name: edit_description
dtype: string
- name: input_test_path
dtype: string
- name: input_gt_path
dtype: string
- name: exemplar_input_path
dtype: string
- name: exemplar_edit_path
dtype: string
- name: edit
dtype: string
- name: invert
dtype: string
- name: local
dtype: bool
- name: id
dtype: int32
splits:
- name: test
num_bytes: 4106538055.5
num_examples: 1277
download_size: 703956134
dataset_size: 4106538055.5
configs:
- config_name: default
data_files:
- split: test
path: data/train-*
task_categories:
- image-to-image
language:
- en
tags:
- Exemplar
- Editing
- Image2Image
- Diffusion
pretty_name: Top-Bench-X
size_categories:
- 1K<n<10K
---
# EditCLIP: Representation Learning for Image Editing
<div>
[](https://arxiv.org/abs/2503.20318)
[](https://qianwangx.github.io/EditCLIP/)
[](https://github.com/QianWangX/EditCLIP)
[](https://iccv2025.thecvf.com/)
<!-- [](https://iccv2025.thecvf.com/)
[](https://iccv2025.thecvf.com/) -->
<!-- [📑 Paper](https://arxiv.org/abs/2503.20318)
[💻 Project Page](https://qianwangx.github.io/EditCLIP/)
[🐙 Github](https://github.com/QianWangX/EditCLIP)
[](https://iccv2025.thecvf.com/) -->
</div>
## 📚 Introduction
The **TOP-Bench-X** dataset offers **Query** and **Exemplar** image pairs tailored for exemplar-based image editing. We built it by adapting the TOP-Bench dataset from [InstructBrush](https://royzhao926.github.io/InstructBrush/) (also uploaded huggingface version at [Aleksandar/InstructBrush-Bench](https://huggingface.co/datasets/Aleksandar/InstructBrush-Bench)). Specifically, we use the original training split to generate exemplar images and the test split to supply their corresponding queries. In total, TOP-Bench-X comprises **1,277** samples, including **257** distinct exemplars and **124** unique queries.
<img src="assets/teaser_editclip.png" alt="Teaser figure of EditCLIP" width="100%">
## 💡 Abstract
We introduce EditCLIP, a novel representation-learning approach for image editing. Our method learns a unified representation of edits by jointly encoding an input image and its edited counterpart, effectively capturing their transformation. To evaluate its effectiveness, we employ EditCLIP to solve two tasks: exemplar-based image editing and automated edit evaluation. In exemplar-based image editing, we replace text-based instructions in InstructPix2Pix with EditCLIP embeddings computed from a reference exemplar image pair. Experiments demonstrate that our approach outperforms state-of-the-art methods while being more efficient and versatile. For automated evaluation, EditCLIP assesses image edits by measuring the similarity between the EditCLIP embedding of a given image pair and either a textual editing instruction or the EditCLIP embedding of another reference image pair. Experiments show that EditCLIP aligns more closely with human judgments than existing CLIP-based metrics, providing a reliable measure of edit quality and structural preservation.
## 🧠 Data explained
Each sample consists of 4 images (2 pairs of images) and metadata, specifically:
1. *input_test* – the query image \(I_q\) from the test split (“before” image you want to edit)
2. *input_gt* – the ground-truth edited version of that query image (“after” image for the test)
3. *exemplar_input* – the exemplar’s input image \(I_i\) from the training split (“before” image of the exemplar)
4. *exemplar_edit* – the exemplar’s edited image \(I_e\) from the training split (“after” image of the exemplar)
## 🌟 Citation
```bibtex
@article{wang2025editclip,
title={EditCLIP: Representation Learning for Image Editing},
author={Wang, Qian and Cvejic, Aleksandar and Eldesokey, Abdelrahman and Wonka, Peter},
journal={arXiv preprint arXiv:2503.20318},
year={2025}
}
```
## 💳 License
This dataset is mainly a variation of TOP-Bench, confirm the license from the original authors.
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