# SynDiff

**Repository Path**: dogeblog/SynDiff

## Basic Information

- **Project Name**: SynDiff
- **Description**: diffusion model for t1 and t2
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-12
- **Last Updated**: 2024-06-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SynDiff

Official PyTorch implementation of SynDiff described in the [paper](https://arxiv.org/abs/2207.08208).

Muzaffer Özbey, Salman UH Dar, Hasan A Bedel, Onat Dalmaz, Şaban Özturk, Alper Güngör, Tolga Çukur, "Unsupervised Medical Image Translation with Adversarial Diffusion Models", arXiv 2022.

<img src="./figures/adv_diff v2.png" width="600px">

<img src="./figures/syndiff v2 quality.png" width="600px">

## Dependencies

```
python>=3.6.9
torch>=1.7.1
torchvision>=0.8.2
cuda=>11.2
ninja
python3.x-dev (apt install, x should match your python3 version, ex: 3.8)
```

## Installation
- Clone this repo:
```bash
git clone https://github.com/icon-lab/SynDiff
cd SynDiff
```

## Dataset
You should structure your aligned dataset in the following way:



```
input_path/
  ├── data_train_contrast1.mat
  ├── data_train_contrast2.mat
  ├── data_val_contrast1.mat
  ├── data_val_contrast2.mat
  ├── data_test_contrast1.mat
  ├── data_test_contrast2.mat
```

where .mat files has shape of (#images, width, height) and image values are between 0 and 1.0.

## Train

<br />

```
python3 train.py --image_size 256 --exp exp_syndiff --num_channels 2 --num_channels_dae 64 --ch_mult 1 1 2 2 4 4 --num_timesteps 4 --num_res_blocks 2 --batch_size 1 --contrast1 T1 --contrast2 T2 --num_epoch 500 --ngf 64 --embedding_type positional --use_ema --ema_decay 0.999 --r1_gamma 1. --z_emb_dim 256 --lr_d 1e-4 --lr_g 1.6e-4 --lazy_reg 10 --num_process_per_node 1 --save_content --local_rank 0 --input_path /input/path/for/data --output_path /output/for/results
```

<br />

## Test

<br />

```
python test.py --image_size 256 --exp exp_syndiff --num_channels 2 --num_channels_dae 64 --ch_mult 1 1 2 2 4 4 --num_timesteps 4 --num_res_blocks 2 --batch_size 1 --embedding_type positional  --z_emb_dim 256 --contrast1 T1  --contrast2 T2 --which_epoch 50 --gpu_chose 0 --input_path /input/path/for/data --output_path /output/for/results
```

<br />
<br />


# Citation
You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.
```
@article{ozbey2022unsupervised,
  title={Unsupervised Medical Image Translation with Adversarial Diffusion Models},
  author={{\"O}zbey, Muzaffer and Dar, Salman UH and Bedel, Hasan A and Dalmaz, Onat and {\"O}zturk, {\c{S}}aban and G{\"u}ng{\"o}r, Alper and {\c{C}}ukur, Tolga},
  journal={arXiv preprint arXiv:2207.08208},
  year={2022}
}

```
For any questions, comments and contributions, please contact Muzaffer Özbey (muzafferozbey94[at]gmail.com ) <br />

(c) ICON Lab 2022

<br />

# Acknowledgements

This code uses libraries from, [pGAN](https://github.com/icon-lab/pGAN-cGAN), [StyleGAN-2](https://github.com/NVlabs/stylegan2), and [DD-GAN](https://github.com/NVlabs/denoising-diffusion-gan) repositories.