Improve model card: add paper, code, dataset links and metadata

Hi!

This PR improves the model card for `hcarrion/keloid` by:
- Adding `library_name: diffusers` and `pipeline_tag: text-to-image` to the YAML metadata.
- Linking the model card to the corresponding paper ([Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987)).
- Linking to the official GitHub repository and Hugging Face dataset.
- Specifying the trigger token (`<keloid-class>`) to make it clear how to use the model.
- Providing the BibTeX citation.

README.md

@@ -1,17 +1,54 @@
-
 ---
-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
-- text-to-image
-- diffusers
 - textual_inversion
 inference: true
 ---
-    
-# Textual inversion text2image fine-tuning - hcarrion/keloid
-These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1-base. You can find some example images in the following. 
 
+# cgDDI: Keloid Textual Inversion Model
+
+This repository contains the textual inversion adaptation weights for `stabilityai/stable-diffusion-2-1-base` representing the **keloid** disease concept. 
+
+It is part of the **cgDDI** (Controllable Generation of Diverse Dermatological Imagery) framework introduced in the paper [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987) (MICCAI 2026).
+
+## Model Description
+
+cgDDI is a hybrid framework designed to address the systematic lack of expertly annotated dermatological images across diverse skin tones and rare diseases. This model learns the specific concept of **keloid** through textual inversion and fine-tuning, allowing the generation of diverse, realistic, and skin-tone-controlled synthetic samples.
+
+- **Disease Category:** Keloid
+- **Trigger Token/Concept:** `<keloid-class>`
+- **Base Model:** `stabilityai/stable-diffusion-2-1-base`
+
+## cgDDI Framework Overview
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/hectorcarrion/ControllableGenDDI/main/assets/cgddi_framework_colors_2.png" width="900" alt="cgDDI framework: healthy synthetics, lesion-mapped synthetics, semantic synthetics, and fair classification">
+</p>
+
+1. **Healthy Synthetics:** Synthesizes realistic healthy skin samples without disturbing other input properties.
+2. **Lesion Mapping:** Maps single-sample rare lesions onto novel skin-tones and locations non-parametrically.
+3. **Semantic Synthetics:** Allows for efficient parametric generation of targeted dermatological conditions using textual inversion and LoRA.
+
+## Resources
+
+- **Paper:** [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987)
+- **Code:** [GitHub Repository](https://github.com/hectorcarrion/ControllableGenDDI)
+- **Dataset:** [Hugging Face Dataset](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}
+}
+```