UCSC-VLAA/HQ-Edit-data-demo

---
dataset_info:
features:
- name: input
dtype: string
- name: input_image
dtype: image
- name: edit
dtype: string
- name: inverse_edit
dtype: string
- name: output
dtype: string
- name: output_image
dtype: image
language:
- en
size_categories:
- 100K<n<1M
license: cc-by-nc-4.0
---

# 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

## 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}
}
```

原始信息汇总

数据集概述

数据集名称

HQ-Edit

数据集描述

HQ-Edit是一个高质量的基于指令的图像编辑数据集，包含197,350次编辑。该数据集通过利用先进的基模型GPT-4V和DALL-E 3构建了一个可扩展的数据收集管道，与依赖属性指导或人类反馈的先前方法不同。

数据集特征

• input (字符串): 输入图像的描述。
• input_image (图像): 输入图像。
• edit (字符串): 将输入图像转换为输出图像的编辑指令。
• inverse_edit (字符串): 将输出图像转换回输入图像的逆向编辑指令。
• output (字符串): 输出图像的描述。
• output_image (图像): 输出图像。

数据集语言

• 英语

数据集大小

• 100K<n<1M

许可证

• cc-by-nc-4.0

引用信息

@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} }