Link model to MICCAI 2026 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 +11,31 @@ tags:
 - textual_inversion
 inference: true
 ---
-    
-# Textual inversion text2image fine-tuning - hcarrion/acrochordon
-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 - acrochordon
+
+This repository contains the textual inversion adaptation weights for `stabilityai/stable-diffusion-2-1-base` corresponding to the **acrochordon** (skin tags) disease class.
+
+This model was introduced as part of the paper [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987).
+
+## About cgDDI
+**cgDDI** (**C**ontrollable **G**eneration of **D**iverse **D**ermatological **I**magery) is a hybrid framework designed to address the lack of equity in dermatological datasets by synthesizing realistic and diverse skin imagery. It allows learning disease-specific concept tokens using textual inversion on a base latent diffusion model, enabling the generation of synthetic, fair, and disease-conditioned skin samples.
+
+* **Paper**: [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987)
+* **Repository**: [hectorcarrion/ControllableGenDDI](https://github.com/hectorcarrion/ControllableGenDDI)
+* **Dataset**: [hcarrion/ControllableGenDDI](https://huggingface.co/datasets/hcarrion/ControllableGenDDI)
+
+For notebooks and scripts showing how to train, sample, and evaluate this textual inversion model, please refer to the [GitHub Repository](https://github.com/hectorcarrion/ControllableGenDDI).
 
+## Citation
+If you find these models useful, 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}
+}
+```