xiaotanhua/UnicEdit-10M

---
license: apache-2.0
task_categories:
- image-to-image
language:
- zh
- en
size_categories:
- 10M<n<100M
---
# UnicEdit-10M: Large-scale Image Editing Dataset

## Dataset Description

This is a large-scale image editing dataset.

### Dataset Structure

Each sample contains:
- `key`: Unique identifier (MD5 hash)
- `edit_task`: Main editing category
- `edit_subtask`: Sub editing category
- `src_image`: Source image
- `edit_image`: Edited image
- `prompt_cn`: Chinese editing instruction
- `prompt_en`: English editing instruction

### Usage

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("xiaotanhua/UnicEdit-10M")

# Streaming mode (recommended for large datasets)
dataset = load_dataset("xiaotanhua/UnicEdit-10M", streaming=True)

# Access samples
for sample in dataset['train']:
print(sample['key'])
print(sample['prompt_en'])
# sample['src_image'] and sample['edit_image'] are PIL Image objects
break
```

## 📜 Citation

If you find UnicBench useful for your research, please cite our paper:

```bibtex
@article{ye2025unicedit,
title={UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits},
author={Ye, Keming and Huang, Zhipeng and Fu, Canmiao and Liu, Qingyang and Cai, Jiani and Lv, Zheqi and Li, Chen and Lyu, Jing and Zhao, Zhou and Zhang, Shengyu},
journal={arXiv preprint arXiv:2512.02790},
year={2025}
}
```

### License

Apache 2.0

### Disclaimer

If you have any copyright concerns, please contact us immediately and we will remove the relevant content.