metadata

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

⭐️ 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. It delivers FLUX-comparable image quality without exhibiting attribute entanglement issues. Hope it will be helpful to researchers working on related topics.

Paper

dataset info

Construction details: The condition images are two subjects randomly selected from the 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

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}
}