# Anime2sketch # https://github.com/Mukosame/Anime2Sketch ''' MIT License Copyright (c) 2022 Caroline Chan Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' import numpy as np import torch import torch.nn as nn import functools import os import cv2 from einops import rearrange class UnetGenerator(nn.Module): """Create a Unet-based generator""" def __init__( self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, ): """Construct a Unet generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer We construct the U-Net from the innermost layer to the outermost layer. It is a recursive process. """ super(UnetGenerator, self).__init__() # construct unet structure unet_block = UnetSkipConnectionBlock( ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True, ) # add the innermost layer for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters unet_block = UnetSkipConnectionBlock( ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout, ) # gradually reduce the number of filters from ngf * 8 to ngf unet_block = UnetSkipConnectionBlock( ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer ) unet_block = UnetSkipConnectionBlock( ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer ) unet_block = UnetSkipConnectionBlock( ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer ) self.model = UnetSkipConnectionBlock( output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer, ) # add the outermost layer def forward(self, input): """Standard forward""" return self.model(input) class UnetSkipConnectionBlock(nn.Module): """Defines the Unet submodule with skip connection. X -------------------identity---------------------- |-- downsampling -- |submodule| -- upsampling --| """ def __init__( self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False, ): """Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer inner_nc (int) -- the number of filters in the inner conv layer input_nc (int) -- the number of channels in input images/features submodule (UnetSkipConnectionBlock) -- previously defined submodules outermost (bool) -- if this module is the outermost module innermost (bool) -- if this module is the innermost module norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. """ super(UnetSkipConnectionBlock, self).__init__() self.outermost = outermost if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d if input_nc is None: input_nc = outer_nc downconv = nn.Conv2d( input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias ) downrelu = nn.LeakyReLU(0.2, True) downnorm = norm_layer(inner_nc) uprelu = nn.ReLU(True) upnorm = norm_layer(outer_nc) if outermost: upconv = nn.ConvTranspose2d( inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1 ) down = [downconv] up = [uprelu, upconv, nn.Tanh()] model = down + [submodule] + up elif innermost: upconv = nn.ConvTranspose2d( inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias ) down = [downrelu, downconv] up = [uprelu, upconv, upnorm] model = down + up else: upconv = nn.ConvTranspose2d( inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias, ) down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] if use_dropout: model = down + [submodule] + up + [nn.Dropout(0.5)] else: model = down + [submodule] + up self.model = nn.Sequential(*model) def forward(self, x): if self.outermost: return self.model(x) else: # add skip connections return torch.cat([x, self.model(x)], 1) class LineartAnimeDetector: def __init__(self, model_path="hf_download"): remote_model_path = ( "https://huggingface.co/lllyasviel/Annotators/resolve/main/netG.pth" ) modelpath = os.path.join(model_path, "netG.pth") if not os.path.exists(modelpath): from .utils import load_file_from_url load_file_from_url(remote_model_path, model_dir=model_path) norm_layer = functools.partial( nn.InstanceNorm2d, affine=False, track_running_stats=False ) net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False) ckpt = torch.load(modelpath) for key in list(ckpt.keys()): if "module." in key: ckpt[key.replace("module.", "")] = ckpt[key] del ckpt[key] net.load_state_dict(ckpt) net = net.cuda() net.eval() self.model = net def __call__(self, input_image): H, W, C = input_image.shape Hn = 256 * int(np.ceil(float(H) / 256.0)) Wn = 256 * int(np.ceil(float(W) / 256.0)) img = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC) with torch.no_grad(): image_feed = torch.from_numpy(img).float().cuda() image_feed = image_feed / 127.5 - 1.0 image_feed = rearrange(image_feed, "h w c -> 1 c h w") line = self.model(image_feed)[0, 0] * 127.5 + 127.5 line = line.cpu().numpy() line = cv2.resize(line, (W, H), interpolation=cv2.INTER_CUBIC) line = line.clip(0, 255).astype(np.uint8) return line