VisPrompt5M

This dataset is the official training dataset for FlowInOne. VisPrompt5M is a large-scale image editing and generation dataset.Each sample contains an input image and an output image pair, with metadata fields for subset and category.

Dataset subsets

This dataset currently includes the following subsets:

• class2image
• doodles_edit
• force_understand
• text_bbox_edit
• text_in_image_edit
• text2image
• trajectory_understand
• visual_marker_edit Each subset contains a train split stored in Parquet shards.

Dataset Features

Each record includes:

• pair_id (string): Stable unique ID for each image pair.
• subset (string): Subset name.
• category (string): Category or relative folder label.
• input_relpath (string): Relative path of the input image.
• output_relpath (string): Relative path of the output image.
• input_image (image): Input image bytes/path.
• output_image (image): Output image bytes/path.

Load the dataset

Load a specific subset

from datasets import load_dataset
ds = load_dataset(
    "CSU-JPG/VisPrompt5M",
    "doodles_edit",
    split="train",
)

Load all dataset

from datasets import load_dataset

subsets = [
    "class2image",
    "doodles_edit",
    "force_understand",
    "text_bbox_edit",
    "text_in_image_edit",
    "text2image",
    "trajectory_understand",
    "visual_marker_edit",
]

for subset in subsets:
    ds = load_dataset("CSU-JPG/VisPrompt5M", subset, split="train", streaming=True)

Citation

If you found our work useful, please consider citing:

@article{yi2026flowinoneunifyingmultimodalgenerationimagein,
      title={FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching}, 
      author={Junchao Yi and Rui Zhao and Jiahao Tang and Weixian Lei and Linjie Li and Qisheng Su and Zhengyuan Yang and Lijuan Wang and Xiaofeng Zhu and Alex Jinpeng Wang},    
      journal={arXiv preprint arXiv:2604.06757},
      year={2026}
}