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| import os |
| import cv2 |
| import torch |
| import numpy as np |
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| from einops import rearrange |
| import os |
| from modules import devices |
| from annotator.annotator_path import models_path |
| from annotator.util import safe_step, nms |
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|
| class DoubleConvBlock(torch.nn.Module): |
| def __init__(self, input_channel, output_channel, layer_number): |
| super().__init__() |
| self.convs = torch.nn.Sequential() |
| self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) |
| for i in range(1, layer_number): |
| self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) |
| self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0) |
|
|
| def __call__(self, x, down_sampling=False): |
| h = x |
| if down_sampling: |
| h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2)) |
| for conv in self.convs: |
| h = conv(h) |
| h = torch.nn.functional.relu(h) |
| return h, self.projection(h) |
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|
|
| class ControlNetHED_Apache2(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1))) |
| self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2) |
| self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2) |
| self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3) |
| self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3) |
| self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3) |
|
|
| def __call__(self, x): |
| h = x - self.norm |
| h, projection1 = self.block1(h) |
| h, projection2 = self.block2(h, down_sampling=True) |
| h, projection3 = self.block3(h, down_sampling=True) |
| h, projection4 = self.block4(h, down_sampling=True) |
| h, projection5 = self.block5(h, down_sampling=True) |
| return projection1, projection2, projection3, projection4, projection5 |
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|
| netNetwork = None |
| remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth" |
| modeldir = os.path.join(models_path, "hed") |
| old_modeldir = os.path.dirname(os.path.realpath(__file__)) |
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| def apply_hed(input_image, is_safe=False): |
| global netNetwork |
| if netNetwork is None: |
| modelpath = os.path.join(modeldir, "ControlNetHED.pth") |
| old_modelpath = os.path.join(old_modeldir, "ControlNetHED.pth") |
| if os.path.exists(old_modelpath): |
| modelpath = old_modelpath |
| elif not os.path.exists(modelpath): |
| from basicsr.utils.download_util import load_file_from_url |
| load_file_from_url(remote_model_path, model_dir=modeldir) |
| netNetwork = ControlNetHED_Apache2().to(devices.get_device_for("controlnet")) |
| netNetwork.load_state_dict(torch.load(modelpath, map_location='cpu')) |
| netNetwork.to(devices.get_device_for("controlnet")).float().eval() |
|
|
| assert input_image.ndim == 3 |
| H, W, C = input_image.shape |
| with torch.no_grad(): |
| image_hed = torch.from_numpy(input_image.copy()).float().to(devices.get_device_for("controlnet")) |
| image_hed = rearrange(image_hed, 'h w c -> 1 c h w') |
| edges = netNetwork(image_hed) |
| edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] |
| edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] |
| edges = np.stack(edges, axis=2) |
| edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) |
| if is_safe: |
| edge = safe_step(edge) |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) |
| return edge |
|
|
| |
| def unload_hed_model(): |
| global netNetwork |
| if netNetwork is not None: |
| netNetwork.cpu() |
|
|
