Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
Instructions to use hcarrion/hyperpigmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hcarrion/hyperpigmentation 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/hyperpigmentation") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,402 Bytes
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base_model: stabilityai/stable-diffusion-2-1-base
library_name: diffusers
license: creativeml-openrail-m
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- textual_inversion
inference: true
---
# cgDDI: Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
This repository contains the textual inversion adaptation weights for `stabilityai/stable-diffusion-2-1-base` corresponding to the **hyperpigmentation** disease concept, as presented in the paper [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987).
* **Code:** [GitHub - ControllableGenDDI](https://github.com/hectorcarrion/ControllableGenDDI)
* **Dataset:** [hcarrion/ControllableGenDDI](https://huggingface.co/datasets/hcarrion/ControllableGenDDI)
## Citation
If you find this work useful in your research, 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}
}
``` |