README.md

metadata

license: mit
library_name: diffusers
tags:
  - 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.

🔗 Paper: https://openreview.net/forum?id=YE5scJekg5
🔗 Arxiv: https://arxiv.org/abs/2602.18589
🔗 Codebase: https://github.com/DM4CT/DM4CT

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🔬 Model Overview

This model learns a prior over CT reconstruction images using a denoising diffusion probabilistic model (DDPM).
It operates directly in pixel space (not latent space).

• Architecture: 2D UNet (Diffusers UNet2DModel)
• Input resolution: 768 × 768
• Channels: 1 (grayscale CT slice)
• Training objective: ε-prediction (standard DDPM formulation)
• Noise schedule: Linear beta schedule
• Training dataset: Synchrotron Dataset of rocks
• Intensity normalization: Rescaled to (-1, 1)

This model is intended to be combined with data-consistency correction for CT reconstruction.

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📊 Dataset: Real-world Synchrotron Dataset

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 image prior over CT slices.

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🧠 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

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🚀 Usage

from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained("jiayangshi/synchrotron_pixel_diffusion")
)

model.eval()