78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
import os.path
|
|
import sys
|
|
import traceback
|
|
|
|
import PIL.Image
|
|
import numpy as np
|
|
import torch
|
|
from basicsr.utils.download_util import load_file_from_url
|
|
|
|
import modules.upscaler
|
|
from modules import shared, modelloader
|
|
from modules.bsrgan_model_arch import RRDBNet
|
|
from modules.paths import models_path
|
|
|
|
|
|
class UpscalerBSRGAN(modules.upscaler.Upscaler):
|
|
def __init__(self, dirname):
|
|
self.name = "BSRGAN"
|
|
self.model_path = os.path.join(models_path, self.name)
|
|
self.model_name = "BSRGAN 4x"
|
|
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
|
|
self.user_path = dirname
|
|
super().__init__()
|
|
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
|
|
scalers = []
|
|
if len(model_paths) == 0:
|
|
scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
|
|
scalers.append(scaler_data)
|
|
for file in model_paths:
|
|
if "http" in file:
|
|
name = self.model_name
|
|
else:
|
|
name = modelloader.friendly_name(file)
|
|
try:
|
|
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
|
|
scalers.append(scaler_data)
|
|
except Exception:
|
|
print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
self.scalers = scalers
|
|
|
|
def do_upscale(self, img: PIL.Image, selected_file):
|
|
torch.cuda.empty_cache()
|
|
model = self.load_model(selected_file)
|
|
if model is None:
|
|
return img
|
|
model.to(shared.device)
|
|
torch.cuda.empty_cache()
|
|
img = np.array(img)
|
|
img = img[:, :, ::-1]
|
|
img = np.moveaxis(img, 2, 0) / 255
|
|
img = torch.from_numpy(img).float()
|
|
img = img.unsqueeze(0).to(shared.device)
|
|
with torch.no_grad():
|
|
output = model(img)
|
|
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
|
|
output = 255. * np.moveaxis(output, 0, 2)
|
|
output = output.astype(np.uint8)
|
|
output = output[:, :, ::-1]
|
|
torch.cuda.empty_cache()
|
|
return PIL.Image.fromarray(output, 'RGB')
|
|
|
|
def load_model(self, path: str):
|
|
if "http" in path:
|
|
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
|
|
progress=True)
|
|
else:
|
|
filename = path
|
|
if not os.path.exists(filename) or filename is None:
|
|
print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
|
|
return None
|
|
model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2) # define network
|
|
model.load_state_dict(torch.load(filename), strict=True)
|
|
model.eval()
|
|
for k, v in model.named_parameters():
|
|
v.requires_grad = False
|
|
return model
|
|
|