Awesome
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
Hyungjin Chung and Jong Chul Ye
📢📢 The pre-trained model checkpoints and data are moved to a new location.
Official PyTorch implementation for Deep Diffusion Image Prior (DDIP), presented in the paper Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems, ECCV 2024.
Getting Started
Download pre-trained modei weights
# Download pre-trained model weights
mkdir -p './exp/vp'
wget -O './exp/vp/ellipses_ema.pth' 'https://www.dropbox.com/scl/fi/g2yd0ecpboz3vc8iwc530/ellipses_ema.pt?rlkey=i1yff5lj2ynk6of0uywqxnqyv&st=smhuffir&dl=1'
wget -O './exp/vp/fastmri_brain_320_complex_1m.pth' 'https://www.dropbox.com/scl/fi/febne0udjvq0cphbggrmy/fastmri_brain_320_complex_1m.pt?rlkey=k4e8tk21ueqjslsw17b05sbho&st=tain0kew&dl=1'
Download sample test data
# Download sample test data
mkdir -p './data'
wget -O './data/data.zip' 'https://www.dropbox.com/scl/fo/wqgpu59c1ge5gw6uwgldw/AHkRLVMkeyr-Odo4CbNtRYI?rlkey=mo6dcglz9pcsjinvgjvby5bm7&st=6n9okudo&dl=1'
# Extract zip file
unzip -q ./data/data.zip -d ./data
By default, the above scripts places the pre-trained model checkpoints under exp/vp
, and the sample data under data
.
Inverse Problem Solving
Each experiment in the paper can be reproduced by simply running the scripts in ./scripts
. All scripts will run by going through the main.py
file.
Citation
If you find our work interesting, please consider citing
@article{chung2024deep,
title={Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems},
author={Chung, Hyungjin and Ye, Jong Chul},
journal={arXiv preprint arXiv:2407.10641},
year={2024}
}