README.md

---
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>


[![Paper](https://img.shields.io/badge/arXiv-2503.20318-b31b1b)](https://arxiv.org/abs/2503.20318)
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<!-- [📑 Paper](https://arxiv.org/abs/2503.20318) 
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[🐙 Github](https://github.com/QianWangX/EditCLIP) 
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</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.