Add model card, metadata, and links to paper and repository (#1)

- Add model card, metadata, and links to paper and repository (47b9e071fd7bad11b73738f83a39718e4a81861b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,42 @@
+---
+license: mit
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- medical
+- lora
+- dermatology
+- textual-inversion
+---
+
+# cgDDI: Controllable Generation of Diverse Dermatological Imagery
+
+This repository contains a disease-conditioned LoRA checkpoint from **cgDDI** (**C**ontrollable **G**eneration of **D**iverse **D**ermatological **I**magery), a hybrid framework designed for fair and efficient malignancy classification. 
+
+For more details, please refer to the paper: [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987).
+
+## Resources
+- **Repository:** [GitHub - ControllableGenDDI](https://github.com/hectorcarrion/ControllableGenDDI)
+- **Dataset:** [🤗 hcarrion/ControllableGenDDI Dataset](https://huggingface.co/datasets/hcarrion/ControllableGenDDI)
+
+## Method Description
+
+cgDDI is a hybrid framework designed to address the systematic lack of expertly annotated images in dermatology, especially for underrepresented skin tones and rare diseases. It operates by:
+1. Synthesizing realistic healthy skin samples without disturbing other input properties.
+2. Mapping single-sample rare lesions onto novel skin-tones and locations non-parametrically.
+3. Allowing for efficient parametric generation with as few as 10 training samples via Textual Inversion and LoRA adapters.
+
+For more details on the training and generation pipeline, please refer to the official GitHub repository.
+
+## Citation
+
+```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}
+}
+```