| import os | |
| import torch | |
| import numpy as np | |
| from einops import rearrange | |
| from annotator.pidinet.model import pidinet | |
| from annotator.util import safe_step | |
| from modules import devices | |
| from annotator.annotator_path import models_path | |
| from scripts.utils import load_state_dict | |
| netNetwork = None | |
| remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/table5_pidinet.pth" | |
| modeldir = os.path.join(models_path, "pidinet") | |
| old_modeldir = os.path.dirname(os.path.realpath(__file__)) | |
| def apply_pidinet(input_image, is_safe=False, apply_fliter=False): | |
| global netNetwork | |
| if netNetwork is None: | |
| modelpath = os.path.join(modeldir, "table5_pidinet.pth") | |
| old_modelpath = os.path.join(old_modeldir, "table5_pidinet.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 = pidinet() | |
| ckp = load_state_dict(modelpath) | |
| netNetwork.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()}) | |
| netNetwork = netNetwork.to(devices.get_device_for("controlnet")) | |
| netNetwork.eval() | |
| assert input_image.ndim == 3 | |
| input_image = input_image[:, :, ::-1].copy() | |
| with torch.no_grad(): | |
| image_pidi = torch.from_numpy(input_image).float().to(devices.get_device_for("controlnet")) | |
| image_pidi = image_pidi / 255.0 | |
| image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w') | |
| edge = netNetwork(image_pidi)[-1] | |
| edge = edge.cpu().numpy() | |
| if apply_fliter: | |
| edge = edge > 0.5 | |
| if is_safe: | |
| edge = safe_step(edge) | |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) | |
| return edge[0][0] | |
| def unload_pid_model(): | |
| global netNetwork | |
| if netNetwork is not None: | |
| netNetwork.cpu() |