This repository contains the textual inversion adaptation weights learned for pyogenic_granuloma based on the stabilityai/stable-diffusion-2-1-base architecture.

These weights are part of the cgDDI (Controllable Generation of Diverse Dermatological Imagery) framework introduced in the paper Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification.

Repository: GitHub - ControllableGenDDI
Dataset: cgDDI Synthetic Dataset

The model learns the disease-specific concept token <pyogenic_granuloma-class> to synthesize controllable, diverse dermatological imagery for fair and efficient malignancy classification.

Citation

If you find this work or the associated models useful, please cite:

bibtex

@inproceedings{carrion2026cgddi,
  title     = {Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification},
  author    = {Carri{\'o}n, H{\'e}ctor and Norouzi, Narges},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2026},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science}
}

hcarrion/pyogenic_granuloma

cgDDI: pyogenic_granuloma Textual Inversion

Citation

hcarrion/
pyogenic_granuloma