| 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__() |
|
|
| |
| model0 = [ nn.ReflectionPad2d(3), |
| nn.Conv2d(input_nc, 64, 7), |
| norm_layer(64), |
| nn.ReLU(inplace=True) ] |
| self.model0 = nn.Sequential(*model0) |
|
|
| |
| 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 = [] |
| |
| for _ in range(n_residual_blocks): |
| model2 += [ResidualBlock(in_features)] |
| self.model2 = nn.Sequential(*model2) |
|
|
| |
| 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) |
|
|
| |
| 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 |
