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import gc
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import numpy as np
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import PIL.Image
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import torch
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import torchvision
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from controlnet_aux import (
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CannyDetector,
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ContentShuffleDetector,
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HEDdetector,
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LineartAnimeDetector,
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LineartDetector,
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MidasDetector,
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MLSDdetector,
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NormalBaeDetector,
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OpenposeDetector,
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PidiNetDetector,
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)
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from controlnet_aux.util import HWC3
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from cv_utils import resize_image
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from depth_estimator import DepthEstimator
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from image_segmentor import ImageSegmentor
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from kornia.core import Tensor
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Midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
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MLSD = MLSDdetector.from_pretrained("lllyasviel/Annotators")
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Canny = CannyDetector()
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OPENPOSE = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
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class Preprocessor:
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MODEL_ID = "lllyasviel/Annotators"
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def __init__(self):
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self.model = None
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self.name = ""
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def load(self, name: str) -> None:
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if name == self.name:
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return
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if name == "Midas":
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self.model = Midas
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elif name == "MLSD":
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self.model =MLSD
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elif name == "Openpose":
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self.model = OPENPOSE
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elif name == "Canny":
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self.model = Canny
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else:
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raise ValueError
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torch.cuda.empty_cache()
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gc.collect()
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self.name = name
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def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
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if self.name == "Canny" or self.name == "MLSD":
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detect_resolution = kwargs.pop("detect_resolution")
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image_resolution = kwargs.pop("image_resolution", 512)
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image = np.array(image)
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image = HWC3(image)
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image = resize_image(image, resolution=detect_resolution)
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image = self.model(image, **kwargs)
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image = np.array(image)
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image = HWC3(image)
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image = resize_image(image, resolution=image_resolution)
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return PIL.Image.fromarray(image).convert('RGB')
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else:
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detect_resolution = kwargs.pop("detect_resolution", 512)
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image_resolution = kwargs.pop("image_resolution", 512)
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image = np.array(image)
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image = HWC3(image)
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image = resize_image(image, resolution=detect_resolution)
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image = self.model(image, **kwargs)
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image = np.array(image)
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image = HWC3(image)
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image = resize_image(image, resolution=image_resolution)
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return PIL.Image.fromarray(image)
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