This repository contains the Textual Inversion adaptation weights (<lipoma-class>) for stabilityai/stable-diffusion-2-1-base representing lipoma lesions.

This model is part of the disease-conditioned checkpoints released under the cgDDI framework, presented in the paper Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification.

Resources

Paper: Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Repository: GitHub - ControllableGenDDI
Dataset: hcarrion/ControllableGenDDI

About cgDDI

We introduce cgDDI (Controllable Generation of Diverse Dermatological Imagery), a hybrid framework that:

Synthesizes realistic healthy skin samples without disturbing other input properties.
Maps single-sample rare lesions onto novel skin-tones and locations non-parametrically.
Allows for efficient parametric generation with as few as 10 training samples.

Citation

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/lipoma

cgDDI: Textual Inversion - lipoma

Resources

About cgDDI

Citation

hcarrion/
lipoma