| |
|
|
| import yaml |
| import torch |
| from omegaconf import OmegaConf |
| import numpy as np |
|
|
| from einops import rearrange |
| import os |
| from modules import devices |
| from annotator.annotator_path import models_path |
| from annotator.lama.saicinpainting.training.trainers import load_checkpoint |
|
|
|
|
| class LamaInpainting: |
| model_dir = os.path.join(models_path, "lama") |
|
|
| def __init__(self): |
| self.model = None |
| self.device = devices.get_device_for("controlnet") |
|
|
| def load_model(self): |
| remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetLama.pth" |
| modelpath = os.path.join(self.model_dir, "ControlNetLama.pth") |
| if not os.path.exists(modelpath): |
| from basicsr.utils.download_util import load_file_from_url |
| load_file_from_url(remote_model_path, model_dir=self.model_dir) |
| config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml') |
| cfg = yaml.safe_load(open(config_path, 'rt')) |
| cfg = OmegaConf.create(cfg) |
| cfg.training_model.predict_only = True |
| cfg.visualizer.kind = 'noop' |
| self.model = load_checkpoint(cfg, os.path.abspath(modelpath), strict=False, map_location='cpu') |
| self.model = self.model.to(self.device) |
| self.model.eval() |
|
|
| def unload_model(self): |
| if self.model is not None: |
| self.model.cpu() |
|
|
| def __call__(self, input_image): |
| if self.model is None: |
| self.load_model() |
| self.model.to(self.device) |
| color = np.ascontiguousarray(input_image[:, :, 0:3]).astype(np.float32) / 255.0 |
| mask = np.ascontiguousarray(input_image[:, :, 3:4]).astype(np.float32) / 255.0 |
| with torch.no_grad(): |
| color = torch.from_numpy(color).float().to(self.device) |
| mask = torch.from_numpy(mask).float().to(self.device) |
| mask = (mask > 0.5).float() |
| color = color * (1 - mask) |
| image_feed = torch.cat([color, mask], dim=2) |
| image_feed = rearrange(image_feed, 'h w c -> 1 c h w') |
| result = self.model(image_feed)[0] |
| result = rearrange(result, 'c h w -> h w c') |
| result = result * mask + color * (1 - mask) |
| result *= 255.0 |
| return result.detach().cpu().numpy().clip(0, 255).astype(np.uint8) |
|
|
