Improve model card and metadata (#1)

- Improve model card and metadata (f29b7757e90b8f2584228a4b1937df7ee8ae8a8b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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 – Synchrotron (DM4CT)
@@ -16,8 +17,8 @@ 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  
+🔗 Paper: [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589)  
+🔗 Project Page: https://dm4ct.github.io/DM4CT/  
 🔗 Codebase: https://github.com/DM4CT/DM4CT  
 
 ---
@@ -26,60 +27,77 @@ 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-based diffusion models, diffusion is performed in the latent space of a pretrained autoencoder.
 
 - **Architecture**:
   - VQ-VAE (image encoder/decoder)
   - 2D UNet operating in latent space
 - **Input resolution (image space)**: 768 × 768
-- **Latent resolution**: (insert latent size, e.g., 192 × 192)
+- **Latent resolution**: 192 × 192
 - **Channels**: 1 (grayscale CT slice)
 - **Training objective**: ε-prediction (standard DDPM formulation)
 - **Noise schedule**: Linear beta schedule
 - **Training dataset**: Synchrotron dataset of rocks (Synchrotron)
 - **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 tasks.
 
 ---
 
 ## 📊 Dataset: Synchrotron
 
-Source:  
-https://zenodo.org/records/15420527
+The model was trained on a real-world high-resolution CT dataset acquired at a high-energy synchrotron facility.
+
+Source: https://zenodo.org/records/15420527
 
 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.
-
 ---
 
 ## 🧠 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 script**: [train_latent.py](https://github.com/DM4CT/DM4CT/blob/main/train_latent.py)
 
 ---
 
 ## 🚀 Usage
 
+You can load and use this model using the `diffusers` library:
+
 ```python
-from diffusers import LDMPipeline
+from diffusers import DiffusionPipeline
+import torch
 
-LDMPipeline = DiffusionPipeline.from_pretrained(
+pipeline = DiffusionPipeline.from_pretrained(
     "jiayangshi/synchrotron_latent_diffusion"
 )
+pipeline.to("cuda")
+
+# Generate an unconditional sample from the CT prior
+# Note: For reconstruction tasks, this model is typically used with 
+# a custom solver incorporating CT data consistency.
+output = pipeline()
+image = output.images[0]
+image.save("reconstruction_prior.png")
+```
+
+---
 
-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 d}l M and Batenburg, K Joost},
+booktitle={The Fourteenth International Conference on Learning Representations},
+year={2026},
+url={https://openreview.net/forum?id=YE5scJekg5}
+}
+```