Official PyTorch implementation of the NeurIPS 2023 paper Direct Diffusion Bridge using Data Consistency for Inverse Problems. Code modified from I2SB.
by Hyungjin Chung, Jeongsol Kim, and Jong Chul Ye
The pre-trained checkpoints and dependencies all follow I2SB. Please consult the original source. We list the steps to make the repository self-contained.
conda env create --file requirements.yaml python=3
conda activate cddb
One can download the checkpoints by simply running
bash scripts/download_checkpoint.sh $DEG_NAME
In this work, we consider $DEG_NAME
: sr4x-pool, sr4x-bicubic, blur-uni, blur-gauss, jpeg-10
, but others can be used. One can also manually download the model weights and place it under ./results/{$DEG_NAME}
Simply run
./scripts/sample.sh
Use use-cddb
or use-cddb-deep
flag to run either algorithm. When neither are used, DDB (I2SB) sampling will be performed.
Also, make sure that --dataset-dir
specified matches the paths specified in ./dataset/val_faster_imagefolder_10k_fn.txt
and ./dataset/val_Faster_imagefolder_10k_label.txt
. If not, modify the txt file.
If you find this work interesting, please consider citing
@article{chung2023direct,
title={Direct Diffusion Bridge using Data Consistency for Inverse Problems},
author={Chung, Hyungjin and Kim, Jeongsol and Ye, Jong Chul},
journal={Advances in Neural Information Processing Systems},
year={2023}
}