Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Paper • 2607.12987 • Published
How to use hcarrion/coccidioidomycosis 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", torch_dtype=torch.bfloat16, device_map="cuda")
pipe.load_textual_inversion("hcarrion/coccidioidomycosis")These are textual inversion adaptation weights for stabilityai/stable-diffusion-2-1-base to generate dermatological images depicting coccidioidomycosis.
This model was trained as part of the cgDDI framework presented in 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