PyTorch Implementation of the Model and Training Process of Real-ESRGAN
- DIV2K
- Flickr2K
- OutdoorSceneTraining (OST)
I'll organize these three datasets, and after removing images with height or width less than 256, I'll compress them into a file called "train_HR.zip."
A weight file for a generator and discriminator model trained for 43500 iterations can be download from here and here(Google Drive Link).
python train.py
optional arguments:
-h, --help show this help message and exit
--crop_size CROP_SIZE
training images crop size
--upscale_factor UPSCALE_FACTOR
super resolution upscale factor
--batch_size BATCH_SIZE
batch size of train dataset
--warmup_batches WARMUP_BATCHES
number of batches with pixel-wise loss only
--n_batches N_BATCHES
number of batches of training
--residual_blocks RESIDUAL_BLOCKS
number of residual blocks in the generator
--batch BATCH batch to start training from
--lr LR adam: learning rate
--sample_interval SAMPLE_INTERVAL
interval between saving image samples
- patch size : 256
- batch size : 16
- optimizer : Adam
- First train generator for 10k iteration with learning rate
$2\times 10^{-4}$ - Then train generator and discriminator for 3.5k iteration with learning rate: 1e-4
- adopt exponential moving average(EMA) with beta = 0.999
- discriminator use Unet with SN(spectral normalization)
- Real Esrgan is trained with a combination loss of pixel-loss(L1 loss), perceptual loss and GAN loss
- use {conv1, ..., conv5} feature maps with weights {0.1, 0.1, 1, 1, 1} before activation in the pretrained VGG19 network as perceptual loss
python test_image.py
optional arguments:
-h, --help show this help message and exit
--upscale_factor UPSCALE_FACTOR
super resolution upscale factor
--test_mode {CPU,GPU}
using CPU or GPU
--image_name IMAGE_NAME
test low resolution image name
--model_name MODEL_NAME
generator model epoch name
Upscale Factor = 4
low quality image:
REAL-ESRGAN results:
low quality image:
REAL-ESRGAN results:
low quality image:
REAL-ESRGAN results:
Upscale Factor = 4
This is a batch of images saved every 100 batches.The leftmost column is the low-resolution image obtained by interpolation using the BICUBIC method. The middle column is the original high-resolution image, and the rightmost column is the super-resolution image reconstructed using the model.
