Improve model card metadata and fix usage snippet

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}
+}
+```