--- 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 [![Paper](https://img.shields.io/badge/arXiv-2503.20318-b31b1b)](https://arxiv.org/abs/2503.20318) [![Project Page](https://img.shields.io/badge/🌐-Project_Page-blue)](https://qianwangx.github.io/EditCLIP/) [![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/QianWangX/EditCLIP) [![ICCV 2025](https://img.shields.io/badge/📷-Published_at_ICCV_2025-blue)](https://iccv2025.thecvf.com/) ## 📚 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. Teaser figure of EditCLIP

## 💡 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.