Awesome
<h1 >L<sup>2</sup>DM: A Diffusion Model for Low-Light Image Enhancement</h1> <p> This paper presents L<sup>2</sup>DM, a novel framework for low-light image enhancement using diffusion models. <img src=https://github.com/Yore0/L2DM/blob/master/pic1.png> </p>Requirements
A suitable conda environment can be created and activated with:
conda env create -f environment.yaml
conda activate ldm
Data preparation
We used the LOL and LOL-v2 datasets, where the LOL-v2 dataset is divided into two parts: real and synthetic. The dataset and model weights are placed in Baidu Cloud for downloading.
Dataset files should be placed inside the data\
Pretrained Models
We need 3 network checkpoints, which are Auto-encoder checkpoints, COCO pre-training weights, and dataset-correlated weights.
Once downloaded, put the model.ckpt to ckpt/vq-f4/model.ckpt; epoch=000099.ckpt to ckpt/coco/epoch=000099.ckpt; <lol_>.ckpt to ckpt/<lol_>.ckpt
Training L<sup>2</sup>DM
In configs/latent-diffusion/ we provide configs for training L<sup>2</sup>DM on the LOL, LOL-real, LOL-synthetic datasets. Training can be started by running
CUDA_VISIBLE_DEVICES=<GPU_ID> python main_ll.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,
Testing our results
Runpython d2l_ori.py --id 0 --dataset v1 --steps 20 --nrun 10 --sample dpm ,<br>
dataset are available in v1, v2-real, and v2-syn.