Add comprehensive model card for SV-DRR

This PR adds a comprehensive model card for SV-DRR, a high-fidelity novel view X-Ray synthesis model.

It includes:
- Relevant metadata: `pipeline_tag: image-to-image`, `library_name: diffusers`, and `license: apache-2.0`.
- A link to the paper: [SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model](https://huggingface.co/papers/2507.05148).
- A link to the code repository: https://github.com/xiechun298/SV-DRR.
- A detailed "Usage" section with Python/bash code snippets for inference, taken directly from the GitHub README.
- Visual demonstrations of the model's capabilities from the GitHub README.
- The official BibTeX citation.

This will improve discoverability and usability for researchers and practitioners on the Hugging Face Hub.

README.md

@@ -0,0 +1,112 @@
+---
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+# SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
+
+This model, presented in the paper [SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model](https://huggingface.co/papers/2507.05148), proposes a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Our approach leverages the Diffusion Transformer to preserve fine details and employs a weak-to-strong training strategy for stable high-resolution image generation, enabling higher-resolution outputs with improved control over viewing angles.
+
+Code: https://github.com/xiechun298/SV-DRR
+
+<p align="center">
+    <img src="https://github.com/xiechun298/SV-DRR/assets/demo2.gif" alt="demo2.gif" width="500"/>
+</p>
+
+## Visual Comparison with SOTA Methods
+![visulization](https://github.com/xiechun298/SV-DRR/assets/visulization.svg)
+
+## Usage
+
+### 🚀 Quick Start
+
+#### 🛠️ Environment Setup
+
+To ensure compatibility and reproducibility, follow these steps to set up the environment:
+
+1.  **Clone the Repository**:
+    ```bash
+    git clone https://github.com/xiechun-tsukuba/svdrr.git
+    cd svdrr
+    ```
+
+2.  **Create a Python Virtual Environment**:
+    ```bash
+    conda create -f environment.yaml
+    ```
+
+#### ⏬ Download Pretrained Models
+
+You can download the pretrained models by either:
+
+**Option 1: Automated Download (Recommended)**
+```bash
+python scripts/download_models.py
+```
+This will download all models into the `models/` directory. Shared components will be stored in the `shared/` folder, and symbolic links will be created in each model folder accordingly.
+
+**Option 2: Manual Download from Hugging Face**
+- 256 resolution: https://huggingface.co/xiechun-tsukuba/svdrr-dit-fb-256
+- 512 resolution: https://huggingface.co/xiechun-tsukuba/svdrr-dit-fb-512
+- 1024 resolution: https://huggingface.co/xiechun-tsukuba/svdrr-dit-fb-1024
+
+### 🔍 Inference
+
+**Important Note:** The coordinate system of LIDC-IDRI-DRR is opposite to the intuitive one — the polar angle increases downward, and the azimuth angle increases when rotating to the left. To invert the pose coordinate system, use the `--flip_pose` option.
+
+#### Single Image Inference
+
+**Default views (azimuth angles from -90° to 90° in 5° increments):**
+```bash
+python test_svdrr_DiT.py --model_path models/DiT-fb-512 \
+    --image_path demo/real_xray.jpg \
+    --log_dir outputs/ \
+    --image_size 512 \
+    --simple_pose
+```
+
+**User-specified views defined in camera_views.json:**
+```bash
+python test_svdrr_DiT.py --model_path models/DiT-fb-512 \
+    --image_path demo/real_xray.jpg \
+    --log_dir outputs/ \
+    --image_size 512 \
+    --poses demo/camera_views.json
+```
+
+#### Dataset Inference
+
+**Perform inference on the LIDC-IDRI-DRR dataset:**
+```bash
+python test_svdrr_DiT.py --model_path models/svdrr-DiT-fb-256 \
+    --dataset {path/to/dataset/} \
+    --log_dir outputs/ \
+    --image_size 256 
+```
+
+## BibTex
+If you find this work useful, a citation will be appreciated via:
+
+```bibtex
+@InProceedings{XieChu_SVDRR_MICCAI2025,
+        author = { Xie, Chun AND Yoshii, Yuichi AND Kitahara, Itaru},
+        title = { { SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model } },
+        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
+        year = {2025},
+        publisher = {Springer Nature Switzerland},
+        volume = {LNCS 15963},
+        month = {September},
+        page = {572 -- 582},
+        doi = {https://doi.org/10.1007/978-3-032-04965-0_54}
+}
+
+@misc{xie2025svdrr,
+        title = {SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model}, 
+        author = {Chun Xie and Yuichi Yoshii and Itaru Kitahara},
+        year = {2025},
+        eprint = {2507.05148},
+        archivePrefix = {arXiv},
+        doi = {https://doi.org/10.48550/arXiv.2507.05148}, 
+} 
+```