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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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faed465a0b
Get ESRGAN, SCUNet, and SwinIR working correctly on MPS by ensuring memory is contiguous for tensor views before sending to MPS device.
88 lines
3.1 KiB
Python
88 lines
3.1 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 = devices.mps_contiguous_to(img.unsqueeze(0), 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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