Improve model card metadata and fix usage snippet

Hi! I'm Niels from the community team at Hugging Face.

I've noticed this model is part of the DM4CT benchmark for CT reconstruction. This PR improves the model card by:
- Adding the `pipeline_tag: image-to-image` to improve discoverability.
- Fixing a syntax error in the Python usage snippet.
- Linking the model to the associated paper page on Hugging Face.

README.md

@@ -1,23 +1,23 @@
 ---
-license: mit
 library_name: diffusers
+license: mit
+pipeline_tag: image-to-image
 tags:
-  - computed-tomography
-  - ct-reconstruction
-  - diffusion-model
-  - inverse-problems
-  - dm4ct
-  - sparse-view-ct
+- computed-tomography
+- ct-reconstruction
+- diffusion-model
+- inverse-problems
+- dm4ct
+- sparse-view-ct
 ---
 
 # Pixel Diffusion UNet – Real-world Synchrotron Dataset (DM4CT)
 
-This repository contains the pretrained **pixel-space diffusion UNet** used in the  
-**DM4CT: Benchmarking Diffusion Models for CT Reconstruction (ICLR 2026)** benchmark.
+This repository contains the pretrained **pixel-space diffusion UNet** presented in the paper [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589).
 
-🔗 Paper: https://openreview.net/forum?id=YE5scJekg5  
-🔗 Arxiv: https://arxiv.org/abs/2602.18589  
-🔗 Codebase: https://github.com/DM4CT/DM4CT  
+🔗 **Project Page:** [https://dm4ct.github.io/DM4CT/](https://dm4ct.github.io/DM4CT/)  
+🔗 **Arxiv:** [https://arxiv.org/abs/2602.18589](https://arxiv.org/abs/2602.18589)  
+🔗 **Codebase:** [https://github.com/DM4CT/DM4CT](https://github.com/DM4CT/DM4CT)  
 
 ---
 
@@ -31,7 +31,7 @@ It operates directly in **pixel space** (not latent space).
 - **Channels**: 1 (grayscale CT slice)
 - **Training objective**: ε-prediction (standard DDPM formulation)
 - **Noise schedule**: Linear beta schedule
-- **Training dataset**: Synchrotron Dataset of rocks 
+- **Training dataset**: Real-world Synchrotron Dataset of rocks 
 - **Intensity normalization**: Rescaled to (-1, 1)
 
 This model is intended to be combined with data-consistency correction for CT reconstruction.
@@ -40,8 +40,7 @@ This model is intended to be combined with data-consistency correction for CT re
 
 ## 📊 Dataset: Real-world Synchrotron Dataset
 
-Source:
-https://zenodo.org/records/15420527
+Source: [Zenodo](https://zenodo.org/records/15420527)
 
 Preprocessing steps:
 - Train/test split
@@ -54,22 +53,39 @@ The model learns an unconditional image prior over CT slices.
 
 ## 🧠 Training Details
 
-- Optimizer: AdamW
-- Learning rate: 1e-4
-- Batch size: (insert your batch size)
-- Training steps: (insert number of steps)
-- Hardware: NVIDIA A100 GPU
-
-Training script:
-https://github.com/DM4CT/DM4CT/blob/main/train_pixel.py
+- **Optimizer**: AdamW
+- **Learning rate**: 1e-4
+- **Hardware**: NVIDIA A100 GPU
+- **Training script**: [train_pixel.py](https://github.com/DM4CT/DM4CT/blob/main/train_pixel.py)
 
 ---
 
 ## 🚀 Usage
 
+You can use this model with the `diffusers` library as follows:
+
 ```python
 from diffusers import DDPMPipeline
+
+# Load the pipeline
 pipeline = DDPMPipeline.from_pretrained("jiayangshi/synchrotron_pixel_diffusion")
-)
 
-model.eval()
+# Access the UNet model
+model = pipeline.unet
+model.eval()
+```
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{
+shi2026dmct,
+title={{DM}4{CT}: Benchmarking Diffusion Models for Computed Tomography Reconstruction},
+author={Shi, Jiayang and Pelt, Dani{\"e}l M and Batenburg, K Joost},
+booktitle={The Fourteenth International Conference on Learning Representations},
+year={2026},
+url={https://openreview.net/forum?id=YE5scJekg5}
+}
+```