Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Paper • 2607.12987 • Published
How to use hcarrion/tinea_pedis with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda")
pipe.load_textual_inversion("hcarrion/tinea_pedis")These are textual inversion adaptation weights for stabilityai/stable-diffusion-2-1-base representing the tinea pedis (athlete's foot) condition.
This model is part of the cgDDI (Controllable Generation of Diverse Dermatological Imagery) framework presented in the paper Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification.
@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}
}
Base model
stabilityai/stable-diffusion-2-1-base