---
title: VINS-120K
canonical_url: "https://www.modelscope.cn/datasets/vivo/VINS-120K"
md_url: "https://www.modelscope.cn/datasets/vivo/VINS-120K.md"
repository: vivo/VINS-120K
chinese_name: VINS-120K
last_updated: 2026-06-04
license: "Apache License 2.0"
storage_size: "4.8 TB"
domain:
  - image
tasks:
  - image-editing
size_scale:
  - 10k-1m
downloads: 163943
stars: 13
---

# VINS-120K

> VINS-120K - vivo 在 ModelScope 开源的数据集。VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset

vivo/VINS-120K 是 ModelScope 魔搭社区上的image-editing数据集，涉及 image 领域，存储大小 4.8 TB，采用 Apache License 2.0 许可。

- **Repository**: vivo/VINS-120K
- **License**: Apache License 2.0
- **Tasks**: image-editing
- **Domain**: image
- **Storage size**: 4.8 TB
- **Downloads**: 163943
- **Stars**: 13
- **Last updated**: 2026-06-04

Source: https://www.modelscope.cn/datasets/vivo/VINS-120K

---

# VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset

<p>
  <a href="https://nju-pcalab.github.io/projects/VINS-120K/">
    <img src="https://img.shields.io/badge/Homepage-VINS--120K-2ea44f?style=for-the-badge" alt="Homepage">
  </a>
  <a href="https://arxiv.org/abs/2605.23518">
    <img src="https://img.shields.io/badge/arXiv-2605.23518-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv">
  </a>
  <a href="https://modelscope.cn/datasets/vivo/VINS-120K">
    <img src="https://img.shields.io/badge/ModelScope-vivo%2FVINS--120K-624aff?style=for-the-badge" alt="ModelScope">
  </a>
  <a href="https://huggingface.co/datasets/openvivo/VINS-120K">
    <img src="https://img.shields.io/badge/Hugging%20Face-vivo%2FVINS--120K-yellow?style=for-the-badge&logo=huggingface" alt="Hugging Face">
  </a>
</p>

## Dataset Summary

**VINS-120K** is a large-scale dataset for instruction-based ultra-high-resolution image editing. It contains **120K carefully curated editing triplets**, where each sample consists of a source image, an edited target image, and an English editing instruction.

Each image is ultra-high-resolution, with resolution no lower than **4K**. The dataset covers **13 editing types** across local editing, global editing, camera movement, and personalized generation.

VINS-120K is built from real-world ultra-high-resolution videos and high-quality open-source editing data. The data are filtered through multiple stages to ensure visual quality, instruction alignment, and aesthetic fidelity.

<div align="center">
  <img src="./assets/vins120k_overview.jpg" width="95%">
</div>

<div align="center">

**Figure:** Overview and visualized examples of edited triplets in VINS-120K.

</div>

## Dataset Structure

Each annotation entry follows the format below:

```text
edit_type: Editing category
input: Source image relative path
output: Edited image relative path
instruction: English editing instruction
```

The training data are divided into three parts:

```text
nano-consistent/
ultravideo/
x2edit/
```

The corresponding annotation files are:

```text
nano-consistent.json
ultravideo.json
x2edit.json
```

## Data Files

The repository contains both image archives and JSON annotation files.

```text
.
├── nano-consistent/
│   ├── Image.tar.split.000
│   ├── Image.tar.split.001
│   ├── ...
│   └── Image.tar.split.012
├── ultravideo/
│   ├── clips_short_*.tar
│   └── ...
├── x2edit/
│   ├── 0.tar.split.*
│   ├── 1.tar.split.*
│   ├── ...
│   └── 7.tar.split.*
├── nano-consistent.json
├── ultravideo.json
├── x2edit.json
├── benchmark.tar
└── assets/
    └── vins120k_overview.jpg
```

## Extracting the Training Data

### Extract `nano-consistent`

`nano-consistent` contains one tar archive split into multiple parts:

```text
Image.tar.split.000
Image.tar.split.001
...
Image.tar.split.012
```

Run:

```bash
mkdir -p extracted/nano-consistent

cat nano-consistent/Image.tar.split.* > nano-consistent/Image.tar
tar -xf nano-consistent/Image.tar -C extracted/nano-consistent
```

### Extract `ultravideo`

`ultravideo` contains independent tar archives and does not need merging.

Run:

```bash
mkdir -p extracted/ultravideo

for tar_file in ultravideo/clips_short_*.tar; do
    echo "Extracting ${tar_file} ..."
    tar -xf "${tar_file}" -C extracted/ultravideo
done
```

### Extract `x2edit`

`x2edit` contains multiple split tar archives. Each group should be merged separately:

```text
0.tar.split.* -> 0.tar
1.tar.split.* -> 1.tar
...
7.tar.split.* -> 7.tar
```

Run:

```bash
mkdir -p extracted/x2edit

for i in 0 1 2 3 4 5 6 7; do
    echo "Merging x2edit/${i}.tar.split.* ..."
    cat x2edit/${i}.tar.split.* > x2edit/${i}.tar

    echo "Extracting x2edit/${i}.tar ..."
    tar -xf x2edit/${i}.tar -C extracted/x2edit
done
```

## VINS-4KEval Benchmark

We also provide `benchmark.tar`, which contains the **VINS-4KEval** benchmark introduced in our paper.

VINS-4KEval is designed for evaluating instruction-based ultra-high-resolution image editing models. It contains **509 carefully selected 4K/UHR test samples** and covers the same 13 editing types as VINS-120K, including local editing, global editing, camera movement, and personalized generation.

This benchmark is intended to evaluate model performance in ultra-high-resolution image editing, especially in terms of instruction following, structural preservation, and fine-detail quality.

### Extract VINS-4KEval

```bash
mkdir -p extracted/benchmark

tar -xf benchmark.tar -C extracted/benchmark
```

## License

This dataset is released under the **Apache License 2.0**.

## Citation

If you find this dataset useful, please cite:

```bibtex
@inproceedings{chen2026vins,
  title={VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset},
  author={Chen, Zhizhou and Guan, Shanyan and Gao, Zhanxin and Ci, En and Ge, Yanhao and Li, Wei and Zhang, Zhenyu and Yang, Jian and Tai, Ying},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15302--15312},
  year={2026}
}
```