Improve model card: add pipeline tag, paper link, and fix usage snippet

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

I've opened this PR to improve the model card for this artifact:
- Added `pipeline_tag: image-to-image` to the YAML metadata to improve discoverability.
- Added a link to the Hugging Face paper page.
- Fixed the `diffusers` usage snippet to be syntactically correct and use the `LDMPipeline` as specified in your `model_index.json`.

This helps researchers find and cite your work more easily!

README.md

@@ -1,14 +1,15 @@
 ---
-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 – LoDoInd (DM4CT)
@@ -16,9 +17,9 @@ tags:
 This repository contains the pretrained **latent-space diffusion model** used in the  
 **DM4CT: Benchmarking Diffusion Models for CT Reconstruction (ICLR 2026)** benchmark.
 
-🔗 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)
 
 ---
 
@@ -26,60 +27,71 @@ This repository contains the pretrained **latent-space diffusion model** used in
 
 This model learns a **prior over CT reconstruction images in a compressed latent space** using a denoising diffusion probabilistic model (DDPM).
 
-Unlike the pixel diffusion model, diffusion is performed in the latent space of a pretrained autoencoder.
+Unlike pixel diffusion models, diffusion is performed in the latent space of a pretrained autoencoder (VQ-VAE).
 
 - **Architecture**:
   - 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**: Industry CT dataset (LoDoInd)
 - **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.
+This model is intended to be combined with data-consistency correction for CT reconstruction tasks.
 
 ---
 
 ## 📊 Dataset: LoDoInd
 
-Source:  
-https://zenodo.org/records/10391412
+The model was trained on the industrial CT dataset [LoDoInd](https://zenodo.org/records/10391412). 
 
-Preprocessing steps:
-- Train/test split
-- Rescale reconstructed slices to (-1, 1)
-- No geometry information is embedded in the model
-
-The model learns an unconditional latent prior over CT slices.
+- Reconstructed slices were rescaled to the range (-1, 1).
+- The model learns an unconditional latent prior over CT slices; no specific geometry information is embedded in the weights.
 
 ---
 
 ## 🧠 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**: Available in the [DM4CT GitHub repository](https://github.com/DM4CT/DM4CT/blob/main/train_latent.py).
 
 ---
 
 ## 🚀 Usage
 
+You can load and use this model with the `diffusers` library:
+
 ```python
 from diffusers import LDMPipeline
+import torch
 
-LDMPipeline = DiffusionPipeline.from_pretrained(
+pipeline = LDMPipeline.from_pretrained(
     "jiayangshi/lodoind_latent_diffusion"
 )
+pipeline.to("cuda")
+
+# Generate a sample (unconditional prior)
+image = pipeline().images[0]
+image.save("generated_ct_slice.png")
+```
+
+Note: For actual CT reconstruction, this prior is typically used with data-consistency guidance as described in the paper.
+
+---
 
-pipeline.to("cuda")
+## Citation
+
+```bibtex
+@inproceedings{
+shi2026dmct,
+title={{DM}4{CT}: Benchmarking Diffusion Models for Computed Tomography Reconstruction},
+author={Shi, Jiayang and Pelt, Dani{\in}l M and Batenburg, K Joost},
+booktitle={The Fourteenth International Conference on Learning Representations},
+year={2026},
+url={https://openreview.net/forum?id=YE5scJekg5}
+}
+```