| 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} | |
| } | |
| ``` |