controlnet_aux/lineart_anime/__init__.py

import functools
import os
import warnings

import cv2
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image

from ..util import HWC3, resize_image


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):
 self.model = model

 @classmethod
 def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False):
 filename = filename or "netG.pth"

 if os.path.isdir(pretrained_model_or_path):
 model_path = os.path.join(pretrained_model_or_path, filename)
 else:
 model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)

 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(model_path)
 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.eval()

 return cls(net)

 def to(self, device):
 self.model.to(device)
 return self
 
 def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
 if "return_pil" in kwargs:
 warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
 output_type = "pil" if kwargs["return_pil"] else "np"
 if type(output_type) is bool:
 warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
 if output_type:
 output_type = "pil"

 device = next(iter(self.model.parameters())).device
 if not isinstance(input_image, np.ndarray):
 input_image = np.array(input_image, dtype=np.uint8)

 input_image = HWC3(input_image)
 input_image = resize_image(input_image, detect_resolution)

 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().to(device)
 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)

 detected_map = line

 detected_map = HWC3(detected_map)

 img = resize_image(input_image, image_resolution)
 H, W, C = img.shape

 detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
 detected_map = 255 - detected_map
 
 if output_type == "pil":
 detected_map = Image.fromarray(detected_map)

 return detected_map