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