88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
import os.path
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import sys
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import traceback
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import PIL.Image
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import numpy as np
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import torch
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from basicsr.utils.download_util import load_file_from_url
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import modules.upscaler
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from modules import devices, modelloader
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from modules.scunet_model_arch import SCUNet as net
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class UpscalerScuNET(modules.upscaler.Upscaler):
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def __init__(self, dirname):
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self.name = "ScuNET"
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self.model_name = "ScuNET GAN"
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self.model_name2 = "ScuNET PSNR"
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self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
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self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
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self.user_path = dirname
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super().__init__()
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model_paths = self.find_models(ext_filter=[".pth"])
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scalers = []
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add_model2 = True
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for file in model_paths:
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if "http" in file:
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name = self.model_name
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else:
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name = modelloader.friendly_name(file)
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if name == self.model_name2 or file == self.model_url2:
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add_model2 = False
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try:
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scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
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scalers.append(scaler_data)
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except Exception:
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print(f"Error loading ScuNET model: {file}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if add_model2:
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scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
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scalers.append(scaler_data2)
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self.scalers = scalers
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def do_upscale(self, img: PIL.Image, selected_file):
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torch.cuda.empty_cache()
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model = self.load_model(selected_file)
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if model is None:
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return img
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device = devices.device_scunet
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(device)
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img = img.to(device)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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output = 255. * np.moveaxis(output, 0, 2)
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output = output.astype(np.uint8)
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output = output[:, :, ::-1]
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torch.cuda.empty_cache()
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return PIL.Image.fromarray(output, 'RGB')
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def load_model(self, path: str):
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device = devices.device_scunet
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if "http" in path:
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filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
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progress=True)
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else:
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filename = path
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if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
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print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
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return None
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model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
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model.load_state_dict(torch.load(filename), strict=True)
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model.eval()
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for k, v in model.named_parameters():
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v.requires_grad = False
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model = model.to(device)
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return model
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