Use the top benchmark model
install
git clone https://github.com/XPixelGroup/HAT.git
cd HAT
pip install -r requirements.txt
python setup.py develop
Download pre-trained model
pip install — upgrade gdown
gdown https://drive.google.com/u/0/uc?id=1Ma12vCWT27P9M99-s2RXnynKN-OQsBrv
Pre-trained models provided in the repository can be found on Google Drive.
Rewriting the configuration file
Below are the steps to run it with an image you have prepared yourself.
The configuration file for the model in options/test
path:
pretrain_network_g: ./experiments/pretrained_models/Real_HAT_GAN_SRx4.pth
Replace with the path of the pre-trained model file you downloaded earlier.
dataset items
datasets:
test_1: # the 1st test dataset
name: custom
type: SingleImageDataset
dataroot_lq: input_dir
io_backend:
type: disk
and specify the path of the directory containing the image you want to super-resolve in dataroot.
Set the val items to only the following.
val:
save_img: true
suffix: ~ # add suffix to saved images, if None, use exp name
execution
Run the test by specifying the configuration file rewritten above.
python hat/test.py -opt options/test/HAT_GAN_Real_SRx4.yml
Results are saved in the results directory.
The background is now clear.
If you encounter CUDA out of memory, add the following to the configuration file.
Divide the input image into smaller pieces and execute.
tile: # use the tile mode for limited GPU memory when testing.
tile_size: 512 # the higher, the more utilized GPU memory and the less performance change against the full image. must be an integer multiple of the window size.
tile_pad: 32 # overlapping between adjacency patches.must be an integer multiple of the window size.
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