controlnet_aux/lineart/__init__.py

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

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

 @classmethod
 def from_pretrained(cls, pretrained_model_or_path, filename=None, coarse_filename=None, cache_dir=None, local_files_only=False):
 filename = filename or "sk_model.pth"
 coarse_filename = coarse_filename or "sk_model2.pth"

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

 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)
 return self
 
 def __call__(self, input_image, coarse=False, 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)

 model = self.model_coarse if coarse else self.model
 assert input_image.ndim == 3
 image = input_image
 with torch.no_grad():
 image = torch.from_numpy(image).float().to(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 = 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