Improve model card, add paper and GitHub links

This PR improves the model card for this cgDDI checkpoint by:
- Linking it to the MICCAI 2026 paper: [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987).
- Adding a link to the official GitHub repository: https://github.com/hectorcarrion/ControllableGenDDI.
- Defining `library_name: diffusers` and `pipeline_tag: text-to-image` in the YAML metadata to improve model discoverability.
- Describing the cgDDI framework and how this specific disease-conditioned adapter (atopic dermatitis) can be used.

README.md

@@ -1,7 +1,8 @@
-
 ---
-license: creativeml-openrail-m
 base_model: stabilityai/stable-diffusion-2-1-base
+license: creativeml-openrail-m
+library_name: diffusers
+pipeline_tag: text-to-image
 tags:
 - stable-diffusion
 - stable-diffusion-diffusers
@@ -10,8 +11,37 @@ tags:
 - textual_inversion
 inference: true
 ---
-    
-# Textual inversion text2image fine-tuning - hcarrion/atopic_dermatitis
-These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1-base. You can find some example images in the following. 
 
+# cgDDI: Textual Inversion for Atopic Dermatitis
+
+This repository contains the textual inversion adaptation weights for `atopic_dermatitis` (eczema), built on top of [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). 
+
+These weights were developed as part of the paper:
+**Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification (MICCAI 2026)**.
+
+* **Paper:** [Hugging Face Paper Page](https://huggingface.co/papers/2607.12987)
+* **Repository:** [GitHub - ControllableGenDDI](https://github.com/hectorcarrion/ControllableGenDDI)
+* **Dataset:** [hcarrion/ControllableGenDDI Dataset](https://huggingface.co/datasets/hcarrion/ControllableGenDDI)
+
+## About cgDDI
+cgDDI (**C**ontrollable **G**eneration of **D**iverse **D**ermatological **I**magery) is a hybrid framework designed to address the equitable performance bottleneck in dermatological AI. It enables:
+1. Synthesis of realistic healthy skin samples.
+2. Non-parametric mapping of rare lesions onto novel skin-tones and locations.
+3. Efficient parametric generation of diverse dermatological imagery using textual inversion.
+
+This specific checkpoint can be used to generate synthetic images of **atopic dermatitis** for fair and robust machine learning model training.
+
+## Citation
+
+If you find cgDDI helpful 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}
+}
+```