mirror of https://github.com/vladmandic/automatic
56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
import os
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import time
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from PIL import Image
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from modules.upscaler import Upscaler, UpscalerData
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from modules import devices, paths
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from modules.shared import log
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MODELS = {
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"Spandrel 4x RealPLKSR NomosWebPhoto": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/4xNomosWebPhoto_RealPLKSR.safetensors",
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"Spandrel 2x RealPLKSR AnimeSharpV2": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/2x-AnimeSharpV2_RPLKSR_Sharp.pth",
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}
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class UpscalerSpandrel(Upscaler):
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def __init__(self, dirname=None): # pylint: disable=unused-argument
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super().__init__(False)
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self.name = "Spandrel"
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self.model_path = os.path.join(paths.models_path, 'Spandrel')
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self.user_path = os.path.join(paths.models_path, 'Spandrel')
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self.selected = None
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self.model = None
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self.scalers = []
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for model_name, model_path in MODELS.items():
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scaler = UpscalerData(name=model_name, path=model_path, upscaler=self)
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self.scalers.append(scaler)
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def process(self, img: Image.Image) -> Image.Image:
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from modules.image import convert
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tensor = convert.to_tensor(img).unsqueeze(0).to(devices.device)
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img = img.convert('RGB')
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t0 = time.time()
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with devices.inference_context():
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tensor = self.model(tensor)
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tensor = tensor.clamp(0, 1).squeeze(0).cpu()
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t1 = time.time()
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upscaled = convert.to_pil(tensor)
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log.debug(f'Upscale: name="{self.selected}" input={img.size} output={upscaled.size} time={t1 - t0:.2f}')
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return upscaled
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def do_upscale(self, img: Image, selected_model=None):
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from installer import install
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if selected_model is None:
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return img
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install('spandrel')
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try:
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import spandrel
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if (self.model is None) or (self.selected != selected_model):
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self.selected = selected_model
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model = self.find_model(selected_model)
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self.model = spandrel.ModelLoader().load_from_file(model.local_data_path)
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self.model.to(devices.device).eval()
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return self.process(img)
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except Exception as e:
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log.error(f'Spandrel: {e}')
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return img
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