README.md

---
license: apache-2.0
task_categories:
  - image-to-image
tags:
  - image-generation
  - benchmark
  - evaluation
---

# ConceptEdit: Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision

[![arXiv](https://img.shields.io/badge/ArXiv-2608.16812-b31b1b?logo=arxiv)](https://arxiv.org/abs/2608.16812)  [![GitHub](https://img.shields.io/badge/GitHub-ConceptEdit-181717?logo=github)](https://github.com/inclusionAI/ConceptEdit)  [![Training Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-ConceptEdit--12M-yellow)](https://huggingface.co/datasets/inclusionAI/ConceptEdit-12M)  [![Benchmark Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Benchmark-ConceptEdit--Bench-yellow)](https://huggingface.co/datasets/inclusionAI/ConceptEdit-Bench)

ConceptEdit-Bench is the evaluation benchmark for ConceptEdit. It contains 1,000 curated image editing test cases across 6 major editing categories. Each test case includes a source image and a JSON metadata file with the edit instruction, taxonomy information, and a relative path to the source image.

The benchmark evaluation code is provided in the ConceptEdit GitHub repository:

```text
https://github.com/inclusionAI/ConceptEdit
```

## Files

This Hugging Face dataset repository provides the benchmark data package:

```text
conceptbench_data.tar
```

The tar file contains one top-level directory:

```text
data/
```

## How to extract

Extract the benchmark package with:

```bash
mkdir -p ConceptEdit-Bench

tar -xf conceptbench_data.tar -C ConceptEdit-Bench
```

After extraction, the expected layout is:

```text
ConceptEdit-Bench/
└── data/
    ├── taxonomy.json
    ├── images/
    │   ├── <image_id>.jpg
    │   └── ...
    ├── advanced_domain_application/
    ├── general_object_editing/
    ├── generation_composition/
    ├── global_enhancement_atmosphere/
    ├── portrait_human_specialized/
    └── text_graphic_design/
```

Each benchmark case JSON is stored under the taxonomy hierarchy:

```text
data/<category>/<sub_category>/<task>/<detail>.json
```

The source images are stored under:

```text
data/images/
```

You can inspect the tar file without extracting it:

```bash
tar -tf conceptbench_data.tar | head
```

## Expected data size

| Item | Count |
|---|---:|
| Case JSON files | 1,000 |
| Source images | 979 |
| Top-level categories | 6 |

Some source images are shared by multiple benchmark cases, so the number of source images is smaller than the number of case JSON files.

## JSON format

Each case JSON keeps only the fields needed for benchmark inference and evaluation:

```json
{
  "caption": "source image caption",
  "edit_concept": {
    "category": "...",
    "sub_category": "...",
    "task": "...",
    "detail": "..."
  },
  "instruction_en": "short English edit instruction",
  "instruction_zh": "short Chinese edit instruction",
  "detailed_instruction_en": "detailed English edit instruction",
  "detailed_instruction_zh": "detailed Chinese edit instruction",
  "local_image_path": "images/example.jpg"
}
```

Field descriptions:

- `caption`: short English caption for the source image.
- `edit_concept`: taxonomy information for the editing case.
- `instruction_en` / `instruction_zh`: short English and Chinese edit instructions.
- `detailed_instruction_en` / `detailed_instruction_zh`: detailed English and Chinese edit instructions.
- `local_image_path`: relative path to the source image under the extracted `data/` directory.

For example, if a JSON file contains:

```json
{
  "local_image_path": "images/example.jpg"
}
```

then the corresponding source image should be located at:

```text
ConceptEdit-Bench/data/images/example.jpg
```

All paths inside the released JSON files are relative paths. No local absolute paths are included.

## Using with the evaluation code

Clone the ConceptEdit code repository and place the extracted `data/` directory under the benchmark code directory expected by the scripts:

```bash
git clone https://github.com/inclusionAI/ConceptEdit.git
cd ConceptEdit/benchmark

# place or symlink the extracted data directory here
# expected path: ConceptEdit/benchmark/data/
```

The benchmark scripts read cases from `./data` and use `local_image_path` to find source images.

## Source Image Acknowledgement

The source images in ConceptEdit-12M are based on images from [Fine-T2I](https://huggingface.co/datasets/ma-xu/fine-t2i).


## Citation

If you find this benchmark useful, please cite the ConceptEdit paper and refer to the project repository:

```bibtex
@article{cui2026unlocking,
  title={Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision},
  author={Cui, Long and Liu, Xiaoqian and Qin, Qi and Xin, Yi and Lin, Tao and Li, Jianguo and Zhang, Linfeng},
  journal={arXiv preprint arXiv:2608.16812},
  year={2026}
}
```