Improve model card metadata and content

This PR updates the model card with the correct `pipeline_tag` to improve discoverability. It also adds a link to the project page and completes the sample usage snippet using the `diffusers` library.

README.md

@@ -1,24 +1,24 @@
 ---
-license: mit
 library_name: diffusers
+license: mit
+pipeline_tag: image-to-image
 tags:
-  - computed-tomography
-  - ct-reconstruction
-  - diffusion-model
-  - latent-diffusion
-  - inverse-problems
-  - dm4ct
-  - sparse-view-ct
+- computed-tomography
+- ct-reconstruction
+- diffusion-model
+- latent-diffusion
+- inverse-problems
+- dm4ct
+- sparse-view-ct
 ---
 
 # Latent Diffusion Model – LoDoChallenge (DM4CT)
 
-This repository contains the pretrained **latent-space diffusion model** used in the  
-**DM4CT: Benchmarking Diffusion Models for CT Reconstruction (ICLR 2026)** benchmark.
+This repository contains the pretrained **latent-space diffusion model** used in the benchmark **DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction (ICLR 2026)**.
 
-🔗 Paper: https://openreview.net/forum?id=YE5scJekg5  
-🔗 Arxiv: https://arxiv.org/abs/2602.18589  
-🔗 Codebase: https://github.com/DM4CT/DM4CT  
+- **Paper:** [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589)
+- **Project Page:** [https://dm4ct.github.io/DM4CT/](https://dm4ct.github.io/DM4CT/)
+- **Codebase:** [https://github.com/DM4CT/DM4CT](https://github.com/DM4CT/DM4CT)
 
 ---
 
@@ -32,23 +32,19 @@ Unlike the pixel diffusion model, diffusion is performed in the latent space of
   - VQ-VAE (image encoder/decoder)
   - 2D UNet operating in latent space
 - **Input resolution (image space)**: 512 × 512
-- **Latent resolution**: (insert latent size, e.g., 64 × 64)
 - **Channels**: 1 (grayscale CT slice)
 - **Training objective**: ε-prediction (standard DDPM formulation)
 - **Noise schedule**: Linear beta schedule
 - **Training dataset**: Low Dose Grand Challenge (LoDoChallenge)
 - **Intensity normalization**: Rescaled to (-1, 1)
 
-The diffusion model operates purely in latent space and relies on the autoencoder for encoding and decoding.
-
-This model is intended to be combined with data-consistency correction for CT reconstruction.
+The diffusion model operates purely in latent space and relies on the autoencoder for encoding and decoding. This model is intended to be combined with data-consistency correction for CT reconstruction.
 
 ---
 
 ## 📊 Dataset: Low Dose Grand Challenge
 
-Source:  
-https://www.aapm.org/grandchallenge/lowdosect/
+Source: [https://www.aapm.org/grandchallenge/lowdosect/](https://www.aapm.org/grandchallenge/lowdosect/)
 
 Preprocessing steps:
 - Train/test split
@@ -61,25 +57,40 @@ The model learns an unconditional latent 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 scripts:
-- Latent diffusion: https://github.com/DM4CT/DM4CT/blob/main/train_latent.py
-- Autoencoder training: (insert if separate)
+- **Optimizer**: AdamW
+- **Learning rate**: 1e-4
+- **Hardware**: NVIDIA A100 GPU
+- **Training scripts**: [train_latent.py](https://github.com/DM4CT/DM4CT/blob/main/train_latent.py)
 
 ---
 
 ## 🚀 Usage
 
 ```python
-from diffusers import LDMPipeline
+from diffusers import DiffusionPipeline
+import torch
 
-LDMPipeline = DiffusionPipeline.from_pretrained(
+pipeline = DiffusionPipeline.from_pretrained(
     "jiayangshi/lodochallenge_latent_diffusion"
 )
 
-pipeline.to("cuda")
+pipeline.to("cuda")
+
+# Generate an unconditional CT slice prior
+image = pipeline(batch_size=1).images[0]
+image.save("reconstructed_slice.png")
+```
+
+---
+
+## 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}
+}
+```