HQ-Edit

# Dataset Card for HQ-EDIT

<!-- Provide a quick summary of the dataset. -->

HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3.
HQ-Edit’s high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models.

- **Homepage:** https://thefllood.github.io/HQEdit_web/
- **Repository:** https://github.com/UCSC-VLAA/HQ-Edit

If you would like to preview the data online using Dataset Viewer, please visit:
- **Dataset Demo:** https://huggingface.co/datasets/UCSC-VLAA/HQ-Edit-data-demo

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

"input" (str): description of input image.

"input_image" (image): the input image.

"edit" (str): edit instruction for transforming input images to output images.

"inverse_edit" (str): inverse-edit instructions for transforming output images back to input images.

"output" (str): description of output image.

"output_image" (image): the output image.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you find this dataset useful, please consider citing our paper:
```
@article{hui2024hq,
title = {HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing},
author = {Hui, Mude and Yang, Siwei and Zhao, Bingchen and Shi, Yichun and Wang, Heng and Wang, Peng and Zhou, Yuyin and Xie, Cihang},
journal = {arXiv preprint arXiv:2404.09990},
year = {2024}
}
```

# HQ-EDIT 数据集卡片

<!-- 快速概述该数据集。 -->

HQ-Edit是一款高质量的指令式图像编辑数据集，总计涵盖197,350条编辑样本。不同于以往依靠属性引导或人工反馈构建数据集的方案，我们设计了一套可扩展的数据采集流水线，依托GPT-4V与DALL-E 3两款先进的基础模型（foundation models）完成数据收集工作。HQ-Edit拥有高分辨率、细节饱满的图像资源，并配套全面的编辑指令提示，可有效增强现有图像编辑模型的能力上限。

- **项目主页：** https://thefllood.github.io/HQEdit_web/
- **代码仓库：** https://github.com/UCSC-VLAA/HQ-Edit

若您希望通过数据集查看器在线预览本数据集，请访问：
- **数据集演示：** https://huggingface.co/datasets/UCSC-VLAA/HQ-Edit-data-demo

## 数据集结构

<!-- 本节将介绍数据集的字段信息，以及数据集划分所用的标准、数据点间的关联关系等额外结构细节。 -->

"input"（字符串类型）：输入图像的描述文本。

"input_image"（图像类型）：待编辑的输入图像。

"edit"（字符串类型）：将输入图像转换为输出图像的编辑指令。

"inverse_edit"（字符串类型）：将输出图像还原为输入图像的反向编辑指令。

"output"（字符串类型）：编辑后输出图像的描述文本。

"output_image"（图像类型）：编辑完成的输出图像。

## 引用声明

<!-- 若该数据集配有相关论文或博客文章，请在此处附上APA与Bibtex格式的引用信息。 -->

若您认为本数据集对您的研究有所帮助，请考虑引用我们的论文：

@article{hui2024hq,
title = {HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing},
author = {Hui, Mude and Yang, Siwei and Zhao, Bingchen and Shi, Yichun and Wang, Heng and Wang, Peng and Zhou, Yuyin and Xie, Cihang},
journal = {arXiv preprint arXiv:2404.09990},
year = {2024}
}