mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from pathlib import Path
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from typing import Any
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import numpy as np
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import torch
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from PIL import Image
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import invokeai.backend.util.logging as logger
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from invokeai.backend.model_manager.config import AnyModel
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def norm_img(np_img):
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if len(np_img.shape) == 2:
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np_img = np_img[:, :, np.newaxis]
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np_img = np.transpose(np_img, (2, 0, 1))
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np_img = np_img.astype("float32") / 255
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return np_img
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class LaMA:
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def __init__(self, model: AnyModel):
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self._model = model
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def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
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image = np.asarray(input_image.convert("RGB"))
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image = norm_img(image)
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mask = input_image.split()[-1]
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mask = np.asarray(mask)
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mask = np.invert(mask)
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mask = norm_img(mask)
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mask = (mask > 0) * 1
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device = next(self._model.buffers()).device
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image = torch.from_numpy(image).unsqueeze(0).to(device)
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mask = torch.from_numpy(mask).unsqueeze(0).to(device)
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with torch.inference_mode():
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infilled_image = self._model(image, mask)
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infilled_image = infilled_image[0].permute(1, 2, 0).detach().cpu().numpy()
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infilled_image = np.clip(infilled_image * 255, 0, 255).astype("uint8")
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infilled_image = Image.fromarray(infilled_image)
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return infilled_image
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@staticmethod
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def load_jit_model(url_or_path: str | Path, device: torch.device | str = "cpu") -> torch.nn.Module:
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model_path = url_or_path
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logger.info(f"Loading model from: {model_path}")
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model: torch.nn.Module = torch.jit.load(model_path, map_location="cpu").to(device) # type: ignore
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model.eval()
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return model
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