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import gc
import PIL.Image
import numpy as np
import torch
from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector, LineartAnimeDetector, LineartDetector,
MidasDetector, MLSDdetector, NormalBaeDetector, OpenposeDetector, PidiNetDetector)
from controlnet_aux.util import HWC3
from controlnet.cv_utils import resize_image
from controlnet.depth_estimator import DepthEstimator
from controlnet.image_segmentor import ImageSegmentor
class ControlNet_Preprocessor:
MODEL_ID = 'lllyasviel/Annotators'
def __init__(self):
self.model = None
self.name = ''
def load(self, name: str) -> None:
if name == self.name:
return
if name == 'HED':
self.model = HEDdetector.from_pretrained(self.MODEL_ID)
elif name == 'Midas':
self.model = MidasDetector.from_pretrained(self.MODEL_ID)
elif name == 'MLSD':
self.model = MLSDdetector.from_pretrained(self.MODEL_ID)
elif name == 'Openpose':
self.model = OpenposeDetector.from_pretrained(self.MODEL_ID)
elif name == 'PidiNet':
self.model = PidiNetDetector.from_pretrained(self.MODEL_ID)
elif name == 'NormalBae':
self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID)
elif name == 'Lineart':
self.model = LineartDetector.from_pretrained(self.MODEL_ID)
elif name == 'LineartAnime':
self.model = LineartAnimeDetector.from_pretrained(self.MODEL_ID)
elif name == 'Canny':
self.model = CannyDetector()
elif name == 'ContentShuffle':
self.model = ContentShuffleDetector()
elif name == 'DPT':
self.model = DepthEstimator()
elif name == 'UPerNet':
self.model = ImageSegmentor()
else:
raise ValueError
torch.cuda.empty_cache()
gc.collect()
self.name = name
def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
if self.name == 'Canny':
if 'detect_resolution' in kwargs:
detect_resolution = kwargs.pop('detect_resolution')
image = np.array(image)
image = HWC3(image)
image = resize_image(image, resolution=detect_resolution)
image = self.model(image, **kwargs)
return PIL.Image.fromarray(image)
elif self.name == 'Midas':
detect_resolution = kwargs.pop('detect_resolution', 512)
image_resolution = kwargs.pop('image_resolution', 512)
image = np.array(image)
image = HWC3(image)
image = resize_image(image, resolution=detect_resolution)
image = self.model(image, **kwargs)
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
return PIL.Image.fromarray(image)
else:
image = np.array(image)
return self.model(image, **kwargs)
@torch.inference_mode()
def preprocess_canny(self, image, image_resolution, low_threshold, high_threshold):
self.load('Canny')
control_image = self(
image=image,
low_threshold=low_threshold,
high_threshold=high_threshold,
detect_resolution=image_resolution
)
return control_image
@torch.inference_mode()
def preprocess_mlsd(self, image, image_resolution, preprocess_resolution, value_threshold, distance_threshold):
self.load('MLSD')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
thr_v=value_threshold,
thr_d=distance_threshold,
)
return control_image
@torch.inference_mode()
def preprocess_scribble(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name == 'HED':
self.load(preprocessor_name)
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
scribble=False,
)
elif preprocessor_name == 'PidiNet':
self.load(preprocessor_name)
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
safe=False,
)
else:
raise ValueError
return control_image
@torch.inference_mode()
def preprocess_scribble_interactive(self, image_and_mask, image_resolution):
image = image_and_mask['mask']
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
return control_image
@torch.inference_mode()
def preprocess_softedge(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name in ['HED', 'HED safe']:
safe = 'safe' in preprocessor_name
self.load('HED')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
scribble=safe,
)
elif preprocessor_name in ['PidiNet', 'PidiNet safe']:
safe = 'safe' in preprocessor_name
self.load('PidiNet')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
safe=safe,
)
else:
raise ValueError
return control_image
@torch.inference_mode()
def preprocess_openpose(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.load('Openpose')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
hand_and_face=True,
)
return control_image
@torch.inference_mode()
def preprocess_segmentation(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.load(preprocessor_name)
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
return control_image
@torch.inference_mode()
def preprocess_depth(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.load(preprocessor_name)
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
return control_image
@torch.inference_mode()
def preprocess_normal(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.load('NormalBae')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
return control_image
@torch.inference_mode()
def preprocess_lineart(self, image, image_resolution, preprocess_resolution, preprocessor_name):
if preprocessor_name in ['None', 'None (anime)']:
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name in ['Lineart', 'Lineart coarse']:
coarse = 'coarse' in preprocessor_name
self.load('Lineart')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
coarse=coarse,
)
elif preprocessor_name == 'Lineart (anime)':
self.load('LineartAnime')
control_image = self(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
else:
raise ValueError
return control_image
@torch.inference_mode()
def preprocess_shuffle(self, image, image_resolution, preprocessor_name):
if preprocessor_name == 'None':
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.load(preprocessor_name)
control_image = self(
image=image,
image_resolution=image_resolution,
)
return control_image
@torch.inference_mode()
def preprocess_ip2p(self, image, image_resolution):
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
return control_image
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