Pyramid Diffusion Models For Low-light Image Enhancement
Dewei Zhou, Zongxin Yang, Yi Yang
In IJCAI'2023
The evauluation results on LOL are as follows | Method | PSNR | SSIM | LPIPS |
---|---|---|---|---|
KIND | 20.87 | 0.80 | 0.17 | |
KIND++ | 21.30 | 0.82 | 0.16 | |
Bread | 22.96 | 0.84 | 0.16 | |
IAT | 23.38 | 0.81 | 0.26 | |
HWMNet | 24.24 | 0.85 | 0.12 | |
LLFLOW | 24.99 | 0.92 | 0.11 | |
PyDiff (Ours) | 27.09 | 0.93 | 0.10 |
git clone https://github.com/limuloo/PyDIff.git
cd PyDiff
conda create -n PyDiff python=3.7
conda activate PyDiff
conda install pytorch==1.7.0 torchvision torchaudio cudatoolkit=11.0 -c pytorch
cd BasicSR-light
pip install -r requirements.txt
BASICSR_EXT=True sudo $(which python) setup.py develop
cd ../PyDiff
pip install -r requirements.txt
BASICSR_EXT=True sudo $(which python) setup.py develop
You can refer to the following links to download the LOL dataset and put it in the following way:
PyDiff/
BasicSR-light/
PyDiff/
dataset/
LOLdataset/
our485/
eval15/
You can refer to the following links to download the pretrained model and put it in the following way:
PyDiff/
BasicSR-light/
PyDiff/
pretrained_models/
LOLweights.pth
cd PyDiff/
CUDA_VISIBLE_DEVICES=0 python pydiff/train.py -opt options/infer.yaml
NOTE: When testing on your own dataset, set 'use_kind_align' in 'infer.yaml' to false. For details, please refer to https://github.com/limuloo/PyDIff/issues/6.
For training purposes, the utilization of the following commands is advised if you possess 2 GPUs with a memory capacity of 24GB or higher, as outlined in the paper.
cd PyDiff/
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=22666 pydiff/train.py -opt options/train_v1.yaml --launcher pytorch
Otherwise, you can use the following commands for training, which requires 1 GPU with memory >=24GB.
cd PyDiff/
CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port=22666 pydiff/train.py -opt options/train_v2.yaml --launcher pytorch
Please update the following fields in the PyDiff/options/train_v3.yaml
file: YOUR_TRAIN_DATASET_GT_ROOT
, YOUR_TRAIN_DATASET_INPUT_ROOT
, YOUR_EVAL_DATASET_GT_ROOT
, and YOUR_EVAL_DATASET_INPUT_ROOT
. If required, please also update the PyDiff/pydiff/data/lol_dataset.py
. Finally, please employ the subsequent commands to initiate the training process:
cd PyDiff/
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=22666 pydiff/train.py -opt options/train_v3.yaml --launcher pytorch
Please feel free to customize additional parameters to meet your specific requirements. To enable PyDiff to train on a single GPU, the PyDiff/options/train_v2.yaml
file can be consulted.
If you find our work useful for your research, please cite our paper
@article{zhou2023pyramid,
title={Pyramid Diffusion Models For Low-light Image Enhancement},
author={Zhou, Dewei and Yang, Zongxin and Yang, Yi},
journal={arXiv preprint arXiv:2305.10028},
year={2023}
}
Our code is partly built upon BasicSR. Thanks to the contributors of their great work.