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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 custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, HF_MODEL_NAME | |
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 | |
self.device = "cpu" | |
def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="netG.pth"): | |
model_path = custom_hf_download(pretrained_model_or_path, filename) | |
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) | |
self.device = device | |
return self | |
def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): | |
input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
H, W, C = input_image.shape | |
Hn = 256 * int(np.ceil(float(H) / 256.0)) | |
Wn = 256 * int(np.ceil(float(W) / 256.0)) | |
input_image = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC) | |
with torch.no_grad(): | |
image_feed = torch.from_numpy(input_image).float().to(self.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 = line.clip(0, 255).astype(np.uint8) | |
#A1111 uses INTER AREA for downscaling so ig that is the best choice | |
detected_map = cv2.resize(HWC3(line), (W, H), interpolation=cv2.INTER_AREA) | |
detected_map = remove_pad(255 - detected_map) | |
if output_type == "pil": | |
detected_map = Image.fromarray(detected_map) | |
return detected_map | |