Add dataset card and metadata

This PR adds a structured dataset card with metadata to improve discoverability and usability. It organizes the existing README content into a clearer format.

README.md

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+---
+license: cc-by-nc-4.0 # Assuming a Creative Commons license, adjust if needed.
+task_categories:
+- image-to-image
+tags:
+- image-editing
+- artistic-style-transfer
+- few-shot-learning
+---
+
+# PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
+
+[Paper](https://arxiv.org/abs/2502.14397) | [Hugging Face Model](https://huggingface.co/nicolaus-huang/PhotoDoodle)
+
+
+Authors: [Huang Shijie](https://scholar.google.com/citations?user=HmqYYosAAAAJ), [Yiren Song](https://scholar.google.com.hk/citations?user=L2YS0jgAAAAJ), [Yuxuan Zhang](https://xiaojiu-z.github.io/YuxuanZhang.github.io/), [Hailong Guo](https://github.com/logn-2024), Xueyin Wang, [Mike Zheng Shou](https://sites.google.com/view/showlab), [Liu Jiaming](https://scholar.google.com/citations?user=SmL7oMQAAAAJ&hl=en)
+[Show Lab](https://sites.google.com/view/showlab), National University of Singapore
+
+
+<img src='./assets/teaser.png' width='100%' />
+
+
+## Dataset
+
+This dataset provides paired image data for artistic image editing. Each entry contains:
+
+* **source:** Path to the original image.
+* **target:** Path to the artistically edited image.
+* **caption:** A description of the edits applied.
+
+
+The dataset is available on [Hugging Face Datasets](https://huggingface.co/datasets/nicolaus-huang/PhotoDoodle).  See the [dataset README](./data/README.md) for further details (add if a separate README exists).
+
+
+## Quick Start (Model Usage)
+
+### Configuration
+1. **Environment setup:**
+   ```bash
+   git clone git@github.com:showlab/PhotoDoodle.git
+   cd PhotoDoodle
+   conda create -n doodle python=3.11.10
+   conda activate doodle
+   ```
+2. **Requirements installation:**
+   ```bash
+   pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
+   pip install --upgrade -r requirements.txt
+   ```
+
+### Inference
+The provided code integrates the `diffusers` pipeline with the PhotoDoodle model.  You can run inference using the script:
+
+```bash
+python inference.py
+```
+
+or directly use the pipeline:  (Code example from original README remains here)
+
+```python
+from src.pipeline_pe_clone import FluxPipeline
+import torch
+from PIL import Image
+
+# ... (rest of the inference code from original README)
+```
+
+### Model Weights
+[Hugging Face Model](https://huggingface.co/nicolaus-huang/PhotoDoodle) contains the following weights:
+
+| Model Name                     | Description                                    | Resolution |
+|---------------------------------|------------------------------------------------|------------|
+| `pretrain.safetensors`         | Base PhotoDoodle model                         | 768, 768    |
+| `sksmonstercalledlulu.safetensors` | Style model for Cartoon monster                  | 768, 512    |
+| `sksmagiceffects.safetensors`   | Style model for 3D effects                     | 768, 512    |
+| `skspaintingeffects.safetensors` | Style model for Flowing color blocks            | 768, 512    |
+| `sksedgeeffect.safetensors`     | Style model for Hand-drawn outline             | 768, 512    |
+
+**(Note: You need to load and fuse the `pretrained` checkpoint to use the style models.)**
+
+
+### Results
+![R-F](./assets/R-F.jpg)
+
+## Citation
+```
+@misc{huang2025photodoodlelearningartisticimage,
+      title={PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data}, 
+      author={Shijie Huang and Yiren Song and Yuxuan Zhang and Hailong Guo and Xueyin Wang and Mike Zheng Shou and Jiaming Liu},
+      year={2025},
+      eprint={2502.14397},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.14397}, 
+}
+```