---
license: mit
library_name: diffusers
tags:
  - computed-tomography
  - ct-reconstruction
  - diffusion-model
  - latent-diffusion
  - inverse-problems
  - dm4ct
  - sparse-view-ct
---

# Latent Diffusion Model – Synchrotron (DM4CT)

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  

---

## 🔬 Model Overview

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.

- **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)
- **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.

---

## 📊 Dataset: Synchrotron

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)

---

## 🚀 Usage

```python
from diffusers import LDMPipeline

LDMPipeline = DiffusionPipeline.from_pretrained(
    "jiayangshi/synchrotron_latent_diffusion"
)

pipeline.to("cuda")