133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
import os
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import cv2
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import torch
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import numpy as np
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import torch.nn as nn
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from einops import rearrange
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from modules import devices
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from annotator.annotator_path import models_path
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norm_layer = nn.InstanceNorm2d
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class ResidualBlock(nn.Module):
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def __init__(self, in_features):
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super(ResidualBlock, self).__init__()
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conv_block = [ nn.ReflectionPad2d(1),
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nn.Conv2d(in_features, in_features, 3),
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norm_layer(in_features),
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nn.ReLU(inplace=True),
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nn.ReflectionPad2d(1),
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nn.Conv2d(in_features, in_features, 3),
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norm_layer(in_features)
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]
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self.conv_block = nn.Sequential(*conv_block)
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def forward(self, x):
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return x + self.conv_block(x)
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class Generator(nn.Module):
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def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
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super(Generator, self).__init__()
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# Initial convolution block
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model0 = [ nn.ReflectionPad2d(3),
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nn.Conv2d(input_nc, 64, 7),
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norm_layer(64),
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nn.ReLU(inplace=True) ]
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self.model0 = nn.Sequential(*model0)
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# Downsampling
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model1 = []
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in_features = 64
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out_features = in_features*2
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for _ in range(2):
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model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
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norm_layer(out_features),
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nn.ReLU(inplace=True) ]
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in_features = out_features
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out_features = in_features*2
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self.model1 = nn.Sequential(*model1)
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model2 = []
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# Residual blocks
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for _ in range(n_residual_blocks):
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model2 += [ResidualBlock(in_features)]
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self.model2 = nn.Sequential(*model2)
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# Upsampling
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model3 = []
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out_features = in_features//2
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for _ in range(2):
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model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
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norm_layer(out_features),
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nn.ReLU(inplace=True) ]
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in_features = out_features
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out_features = in_features//2
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self.model3 = nn.Sequential(*model3)
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# Output layer
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model4 = [ nn.ReflectionPad2d(3),
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nn.Conv2d(64, output_nc, 7)]
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if sigmoid:
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model4 += [nn.Sigmoid()]
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self.model4 = nn.Sequential(*model4)
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def forward(self, x, cond=None):
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out = self.model0(x)
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out = self.model1(out)
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out = self.model2(out)
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out = self.model3(out)
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out = self.model4(out)
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return out
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class LineartDetector:
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model_dir = os.path.join(models_path, "lineart")
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model_default = 'sk_model.pth'
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model_coarse = 'sk_model2.pth'
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def __init__(self, model_name):
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self.model = None
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self.model_name = model_name
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self.device = devices.get_device_for("controlnet")
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def load_model(self, name):
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remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name
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model_path = os.path.join(self.model_dir, name)
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if not os.path.exists(model_path):
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from scripts.utils import load_file_from_url
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load_file_from_url(remote_model_path, model_dir=self.model_dir)
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model = Generator(3, 1, 3)
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model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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model.eval()
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self.model = model.to(self.device)
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def unload_model(self):
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if self.model is not None:
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self.model.cpu()
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def __call__(self, input_image):
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if self.model is None:
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self.load_model(self.model_name)
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self.model.to(self.device)
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assert input_image.ndim == 3
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image = input_image
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with torch.no_grad():
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image = torch.from_numpy(image).float().to(self.device)
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image = image / 255.0
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image = rearrange(image, 'h w c -> 1 c h w')
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line = self.model(image)[0][0]
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line = line.cpu().numpy()
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line = (line * 255.0).clip(0, 255).astype(np.uint8)
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return line |