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