| 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() |
