---
license: creativeml-openrail-m
configs:
- config_name: default
  data_files:
  - split: synth
    path: data/synth-*
  - split: real
    path: data/real-*
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: original_image
    dtype: image
  - name: partedit
    dtype: image
  - name: subject
    dtype: string
  - name: edit
    dtype: string
  - name: part
    dtype: string
  - name: gt_mask
    dtype: image
  - name: class_name
    dtype: string
  - name: prompt_original
    dtype: string
  - name: prompt_changed
    dtype: string
  - name: p2p_prompt
    dtype: string
  - name: p2p_template
    dtype: string
  - name: instructp2p_edit1
    dtype: string
  - name: instructp2p_edit2
    dtype: string
  - name: instructp2p_edit3
    dtype: string
  - name: seed
    dtype: int32
  splits:
  - name: synth
    num_bytes: 159677179
    num_examples: 60
  - name: real
    num_bytes: 9967718
    num_examples: 13
  download_size: 169623238
  dataset_size: 169644897
task_categories:
- text-to-image
- image-to-image
language:
- en
tags:
- Part Editing
- image
- Editing
size_categories:
- n<1K
arxiv: 2502.0405
pretty_name: PartEdit
---
<div align="center">

[![Paper](https://img.shields.io/badge/arXiv-2502.04050-b31b1b)](https://arxiv.org/abs/2502.04050)
[![Project Page](https://img.shields.io/badge/🌐-Project_Website-blue)](https://gorluxor.github.io/part-edit/)
[![🎨 SIGGRAPH 2025](https://img.shields.io/badge/🎨%20Accepted-SIGGRAPH%202025-blueviolet)](https://dl.acm.org/doi/10.1145/3721238.3730747)
</div>

# Dataset Card for Dataset Name

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This benchmark is part of [PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models](https://arxiv.org/abs/2502.04050) accepted in Siggraph 2025.

## Dataset Details

### Dataset Description

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Small benchmark of part editing. 


- **Curated by:** Authors
- **Funded by [optional]:** KAUST
- **Shared by [optional]:** Authors
- **Language(s) (NLP):** EN
- **License:** creativeml-openrail-m

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://gorluxor.github.io/part-edit/
- **Paper [optional]:** https://arxiv.org/abs/2502.04050
- **Demo [optional]:** https://gorluxor.github.io/part-edit/

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<!-- Motivation for the creation of this dataset. -->

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<!-- ### Source Data -->

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

<!-- #### Data Collection and Processing -->

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#### Annotation process

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Using https://www.makesense.ai/ to annotate ground truth regions.

<!-- #### Who are the annotators? -->

<!-- This section describes the people or systems who created the annotations. -->

<!-- Annotated GT regions by authors. -->

<!-- #### Personal and Sensitive Information -->

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

<!-- There are no personal identifiers, other than images of generated (synth) and few real samples.  -->

## Bias, Risks, and Limitations

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The generated images might contain biases from the underlying models.

### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@inproceedings{cvejic2025partedit,
  title={PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models},
  author={Cvejic, Aleksandar and Eldesokey, Abdelrahman and Wonka, Peter},
  booktitle={Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers},
  pages={1--11},
  year={2025}
}
```
**APA:**
```
Cvejic, A., Eldesokey, A., & Wonka, P. (2025, August). PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models. In Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers (pp. 1-11).
```
<!-- ## Glossary [optional] -->

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<!-- ## Dataset Card Authors [optional] -->

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