Improve model card with metadata and description

README.md

@@ -1,3 +1,85 @@
 ---
-license: apache-2.0
+license: mit
+pipeline_tag: text-to-image
+library_name: diffusers
 ---
+
+# DiffCoRe-Mix : Context-Guided Responsible Data Augmentation with Diffusion Models [ICLRw-2025]
+<p align="center">
+    <img src="assets/placeholder.png" alt="DiffCoRe-Mix Overview">
+</p>
+
+<p align="center">
+  <a href="https://arxiv.org/pdf/2503.10687"><img src="https://img.shields.io/badge/Paper-ICLR2025-blue" alt="arXiV"></a>&nbsp;&nbsp;
+  <a href="https://github.com/khawar-islam/DiffCoRe-Mix"><img src="https://img.shields.io/badge/Code-GitHub-brightgreen" alt="Code"></a>&nbsp;&nbsp;
+  <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellowgreen" alt="License"></a>
+</p>
+
+#### [Khawar Islam](mailto:khawar.islam@student.unimelb.edu.au), [Naveed Akhtar](mailto:naveed.akhtar1@unimelb.edu.au)
+#### **School of Computing and Information Systems, The University of Melbourne**
+
+
+### 📢 Latest Updates
+- **Mar-15-25**: Preprint is available.
+- **Mar-13-25**: Public release of the code and models.
+- **Mar-12-25**: Paper accepted at **ICLRw-2025**.
+
+
+---
+
+
+### Key Features
+
+- **Contextual & Negative Prompting:** Guides the diffusion process to generate domain-specific images while suppressing undesired content.
+- **Hard Cosine Similarity Filtration:** Uses CLIP embeddings to filter out generated samples that do not meet semantic alignment criteria.
+- **Composite Image Mixing:** Combines real and generative images using both pixel-wise and patch-wise strategies.
+
+---
+
+## Install
+1. Clone this repository and navigate to DiffCoRe-Mix folder
+```bash
+git clone https://github.com/khawar-islam/DiffCoRe-Mix.git
+cd DiffCoRe-Mix
+```
+
+2. Install Package
+```shell
+conda create -n DiffCoreMix python=3.9.19 -y
+conda activate DiffCoreMix
+```
+
+3. Download pre-trained CosXL model
+```
+https://huggingface.co/cocktailpeanut/c/blob/main/cosxl.safetensors
+```
+
+4. To run the augmentation process, use:
+```bash
+python main.py --dataset <DATASET_NAME> --output_folder <PATH_TO_OUTPUT_FOLDER> --aug_per <AUGMENTATION_PERCENTAGE>
+```
+
+5. For instance, to augment the CUB200 dataset with 30% augmentation
+```bash
+python main.py --dataset cub200 --output_folder /path/to/cub200/train --aug_per 0.3
+```
+
+---
+
+## Examples
+<p align="center">
+    <img src="assets/diversity.jpg" alt="DiffCoRe-Mix Overview">
+</p>
+
+---
+
+## Citation
+
+If you use DiffCoRe-Mix in your research, please cite our paper:
+```
+@inproceedings{islam2025context,
+  title={Context-Guided Responsible Data Augmentation with Diffusion Models},
+  author={Islam, Khawar and AKHTAR, NAVEED},
+  booktitle={ICLR 2025 Workshop on Navigating and Addressing Data Problems for Foundation Models}
+}
+```