Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Paper • 2607.12987 • Published
How to use hcarrion/trichilemmoma 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/trichilemmoma")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/trichilemmoma")These are textual inversion adaptation weights for stabilityai/stable-diffusion-2-1-base representing the trichilemmoma skin condition.
This model was introduced as part of the cgDDI (Controllable Generation of Diverse Dermatological Imagery) framework in the paper Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification.
If you find this model useful for your research, please cite:
@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