FlowDIS

FlowDIS enables highly accurate foreground segmentation, optionally guided by a text prompt. When ambiguity prevents the model from producing the desired result, the user can specify which elements to retain in the foreground.

Usage

pip install "git+ssh://git@github.com/Picsart-AI-Research/FlowDIS.git"

from PIL import Image
from flowdis import flowdis_predict, load_models

models = load_models(device="cuda")

input_img_path = "path/to/input.jpg"     # Input image path
output_mask_path = "path/to/output.png"  # Path to save the output mask

image = Image.open(input_img_path).convert("RGB")

mask = flowdis_predict(
    image=image,
    prompt="",  # Text prompt
    models=models,
    resolution=1024,
    num_inference_steps=2,
    device="cuda",
)
mask.save(output_mask_path)

License

This model is licensed under the PicsArt Inc. FlowDIS Model License.

Acknowledgements

This project is built on top of FLUX.1 [schnell] and DIS5K.

BibTeX

If you use our work in your research, please cite our publication:

@article{sargsyan2026flowdis,
  title={{FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching}},
  author={Sargsyan, Andranik and Navasardyan, Shant},
  journal={arXiv preprint arXiv:2605.05077},
  year={2026},
  eprint={2605.05077},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.05077}
}