--- title: ConceptEdit-12M canonical_url: "https://www.modelscope.cn/datasets/inclusionAI/ConceptEdit-12M" md_url: "https://www.modelscope.cn/datasets/inclusionAI/ConceptEdit-12M.md" repository: inclusionAI/ConceptEdit-12M last_updated: 2026-09-02 license: apache-2.0 storage_size: "21 TB" downloads: 74 stars: 0 --- # ConceptEdit-12M > ConceptEdit-12M - inclusionAI 在 ModelScope 开源的数据集。ConceptEdit: Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision inclusionAI/ConceptEdit-12M 是 ModelScope 魔搭社区上的数据集，存储大小 21 TB，采用 apache-2.0 许可。 - **Repository**: inclusionAI/ConceptEdit-12M - **License**: apache-2.0 - **Storage size**: 21 TB - **Downloads**: 74 - **Stars**: 0 - **Last updated**: 2026-09-02 Source: https://www.modelscope.cn/datasets/inclusionAI/ConceptEdit-12M --- # 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) [![Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-ConceptEdit--12M-yellow)](https://huggingface.co/datasets/inclusionAI/ConceptEdit-12M) [![Benchmark](https://img.shields.io/badge/%F0%9F%A4%97%20Benchmark-ConceptEdit--Bench-yellow)](https://huggingface.co/datasets/inclusionAI/ConceptEdit-Bench) ConceptEdit-12M is a large-scale image editing dataset. Each sample is stored as a triplet: 1. a source image, 2. an edited image, 3. a JSON metadata file describing the edit instruction, edit category, relative image paths, and VQA-style quality checks. The dataset is packaged as multiple `.tar` shards. All paths inside the tar files and JSON files are relative paths; no local absolute paths are included. ## Data splits The released data contains four main splits: | Split | Number of tar shards | |---|---:| | `enhanced_prompt_square_resolution` | 154 | | `enhanced_prompt_random_resolution` | 162 | | `original_prompt_square_resolution` | 131 | | `original_prompt_random_resolution` | 169 | ## File structure After downloading, the dataset is organized as follows: ```text ConceptEdit-12M/ ├── enhanced_prompt_square_resolution/ │ ├── batch_0.tar │ ├── batch_1.tar │ └── ... ├── enhanced_prompt_random_resolution/ │ ├── batch_0.tar │ ├── batch_1.tar │ └── ... ├── original_prompt_square_resolution/ │ ├── batch_0.tar │ ├── batch_1.tar │ └── ... └── original_prompt_random_resolution/ ├── batch_0.tar ├── batch_1.tar └── ... ``` Each tar shard contains one batch directory. For example: ```text batch_0/ ├── 0_0_3.json ├── 0_0_3_source.jpg ├── 0_0_3_edit.png ├── 0_0_4.json ├── 0_0_4_source.jpg ├── 0_0_4_edit.png └── ... ``` For each sample ID, the three files are: ```text .json _source.jpg _edit.png ``` The JSON file contains relative paths to the corresponding source and edited images, for example: ```json { "id": "0_0_3", "images": { "source": "batch_0/0_0_3_source.jpg", "edited": "batch_0/0_0_3_edit.png" } } ``` ## How to extract The following commands are provided for format reference only. You may use faster extraction tools or multi-threaded extraction methods according to your own needs. Extract a single shard: ```bash mkdir -p ConceptEdit-12M/extracted/enhanced_prompt_square_resolution tar -xf ConceptEdit-12M/enhanced_prompt_square_resolution/batch_0.tar \ -C ConceptEdit-12M/extracted/enhanced_prompt_square_resolution ``` List the content of a shard without extracting: ```bash tar -tf ConceptEdit-12M/enhanced_prompt_square_resolution/batch_0.tar | head ``` Extract all shards in one split: ```bash SPLIT=enhanced_prompt_square_resolution mkdir -p ConceptEdit-12M/extracted/${SPLIT} for f in ConceptEdit-12M/${SPLIT}/batch_*.tar; do tar -xf "$f" -C ConceptEdit-12M/extracted/${SPLIT} done ``` Extract all four main splits: ```bash for SPLIT in \ enhanced_prompt_square_resolution \ enhanced_prompt_random_resolution \ original_prompt_square_resolution \ original_prompt_random_resolution; do mkdir -p ConceptEdit-12M/extracted/${SPLIT} for f in ConceptEdit-12M/${SPLIT}/batch_*.tar; do tar -xf "$f" -C ConceptEdit-12M/extracted/${SPLIT} done done ``` ## JSON format Each sample has one JSON file with the following top-level fields: ```text id images edit_concept instruction evaluation original_simple_caption ``` Field descriptions: - `id`: sample ID. The image and JSON filenames are derived from this ID. - `images`: relative paths to the source image and edited image. - `source`: source/original image path inside the extracted batch directory. - `edited`: edited image path inside the extracted batch directory. - `edit_concept`: taxonomy information for the edit. - `category` - `sub_category` - `task` - `detail` - `instruction`: natural-language edit instructions. - `short_en` - `short_zh` - `detailed_en` - `detailed_zh` - `evaluation`: VQA-style filtering and quality-control metadata. - `overall_vqa_score` - `keep` - `wrong_count` - `recaption_prompt_en` - `recaption_prompt_zh` - `vqa`: a list of VQA checks, each containing: - `dimension` - `question_en` - `question_zh` - `expected_answer` - `passed` - `original_simple_caption`: a short English caption for the source image. A simplified example: ```json { "id": "0_0_3", "images": { "source": "batch_0/0_0_3_source.jpg", "edited": "batch_0/0_0_3_edit.png" }, "edit_concept": { "category": "...", "sub_category": "...", "task": "...", "detail": "..." }, "instruction": { "short_en": "...", "short_zh": "...", "detailed_en": "...", "detailed_zh": "..." }, "evaluation": { "overall_vqa_score": 1.0, "keep": true, "wrong_count": 0, "recaption_prompt_en": "", "recaption_prompt_zh": "", "vqa": [ { "dimension": "1_primary_edit_success", "question_en": "...", "question_zh": "...", "expected_answer": "yes", "passed": true } ] }, "original_simple_caption": "A short English caption for the source image." } ``` ## 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 dataset useful, please cite: ```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} } ```