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Configuration error
Configuration error
| import os | |
| import warnings | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| from PIL import Image | |
| from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, HF_MODEL_NAME | |
| 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: | |
| def __init__(self, model, coarse_model): | |
| self.model = model | |
| self.model_coarse = coarse_model | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="sk_model.pth", coarse_filename="sk_model2.pth"): | |
| model_path = custom_hf_download(pretrained_model_or_path, filename) | |
| coarse_model_path = custom_hf_download(pretrained_model_or_path, coarse_filename) | |
| model = Generator(3, 1, 3) | |
| model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) | |
| model.eval() | |
| coarse_model = Generator(3, 1, 3) | |
| coarse_model.load_state_dict(torch.load(coarse_model_path, map_location=torch.device('cpu'))) | |
| coarse_model.eval() | |
| return cls(model, coarse_model) | |
| def to(self, device): | |
| self.model.to(device) | |
| self.model_coarse.to(device) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, coarse=False, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| model = self.model_coarse if coarse else self.model | |
| assert detected_map.ndim == 3 | |
| with torch.no_grad(): | |
| image = torch.from_numpy(detected_map).float().to(self.device) | |
| image = image / 255.0 | |
| image = rearrange(image, 'h w c -> 1 c h w') | |
| line = model(image)[0][0] | |
| line = line.cpu().numpy() | |
| line = (line * 255.0).clip(0, 255).astype(np.uint8) | |
| detected_map = HWC3(line) | |
| detected_map = remove_pad(255 - detected_map) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |