| | |
| |
|
| | 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) |
| |
|
