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