Spaces:
Paused
Paused
| # https://github.com/advimman/lama | |
| 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) | |