Link model to paper and GitHub repository

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 +11,27 @@ tags:
 - textual_inversion
 inference: true
 ---
-    
+
 # Textual inversion text2image fine-tuning - hcarrion/morphea
-These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1-base. You can find some example images in the following. 
 
+These are textual inversion adaptation weights for stabilityai/stable-diffusion-2-1-base. 
+
+This model is a disease-conditioned checkpoint for **morphea** (scleroderma) trained as part of the **cgDDI** (Controllable Generation of Diverse Dermatological Imagery) framework, presented in the paper [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987).
+
+## Resources
+
+- **Repository:** [GitHub - hectorcarrion/ControllableGenDDI](https://github.com/hectorcarrion/ControllableGenDDI)
+- **Dataset:** [Hugging Face - hcarrion/ControllableGenDDI](https://huggingface.co/datasets/hcarrion/ControllableGenDDI)
+
+## 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}
+}
+```