|
|
--- |
|
|
license: apache-2.0 |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: data/train-* |
|
|
dataset_info: |
|
|
features: |
|
|
- name: tgt_img |
|
|
dtype: image |
|
|
- name: cond_img_0 |
|
|
dtype: image |
|
|
- name: cond_img_1 |
|
|
dtype: image |
|
|
- name: prompt |
|
|
dtype: string |
|
|
- name: cond_prompt_0 |
|
|
dtype: string |
|
|
- name: cond_prompt_1 |
|
|
dtype: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 27532187682.875 |
|
|
num_examples: 29859 |
|
|
download_size: 27509349685 |
|
|
dataset_size: 27532187682.875 |
|
|
task_categories: |
|
|
- text-to-image |
|
|
tags: |
|
|
- text-to-image |
|
|
- customization |
|
|
--- |
|
|
|
|
|
<img src='./MUSAR_Gen.png' width='100%' /> |
|
|
|
|
|
⭐️ Although MUSAR is trained solely on diptych data constructed from concatenated single-subject samples, we recognize that a high-quality multi-subject paired dataset is highly beneficial for the field of image customization. To accelerate progress in this field, we are releasing the high-quality multi-subject dataset generated by MUSAR: [MUSAR-Gen](https://huggingface.co/datasets/guozinan/MUSAR-Gen). It delivers FLUX-comparable image quality without exhibiting attribute entanglement issues. Hope it will be helpful to researchers working on related topics. |
|
|
|
|
|
[Paper](https://huggingface.co/papers/2505.02823) |
|
|
|
|
|
# dataset info |
|
|
Construction details: The condition images are two subjects randomly selected from the [subjects200k](https://huggingface.co/datasets/Yuanshi/Subjects200K) dataset (excluding the 111,761 subjects used during the model training process). The prompt format is: "An undivided, seamless, and harmonious picture with two objects. in the xxx scene, Subject A and Subject B are placed together." By collecting the outputs of the MUSAR model, we obtained approximately 30,000 samples. |
|
|
|
|
|
## Quick Start |
|
|
- Load dataset |
|
|
```python |
|
|
from datasets import load_dataset |
|
|
# Load dataset |
|
|
dataset = load_dataset('guozinan/MUSAR-Gen') |
|
|
|
|
|
## Data Format |
|
|
| Key name | Type | Description | |
|
|
| -------------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
|
|
| `cond_img_0` | `image` | Reference Image Information (first image). | |
|
|
| `cond_img_1` | `image` | Reference Image Information (second image). | |
|
|
| `tgt_img` | `image` | Multi-subject customized result generated by the MUSAR model. | |
|
|
| `cond_prompt_0` | `str` | Textual description of the corresponding subject in cond_img_0. | |
|
|
| `cond_prompt_1` | `str` | Textual description of the corresponding subject in cond_img_1. | |
|
|
| `prompt` | `str` | Textual description of the tgt_img content. | |
|
|
|
|
|
## Citation |
|
|
If you use MUSAR-Gen dataset, please cite our paper: |
|
|
``` |
|
|
@article{guo2025musar, |
|
|
title={MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing}, |
|
|
author={Guo, Zinan and Zhang, Pengze and Wu, Yanze and Mou, Chong and Zhao, Songtao and He, Qian}, |
|
|
journal={arXiv preprint arXiv:2505.02823}, |
|
|
year={2025} |
|
|
} |
|
|
``` |
