Mel Massadian
512de6023e
- removed un-needed dependencies - added a ci to test comfy-embedded - fixed wheel order install
123 lines
3.0 KiB
Python
123 lines
3.0 KiB
Python
from PIL import Image
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import numpy as np
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import torch
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from pathlib import Path
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import sys
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from typing import List
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# region MISC Utilities
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def add_path(path, prepend=False):
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if isinstance(path, list):
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for p in path:
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add_path(p, prepend)
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return
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if isinstance(path, Path):
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path = path.resolve().as_posix()
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if path not in sys.path:
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if prepend:
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sys.path.insert(0, path)
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else:
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sys.path.append(path)
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# todo use the requirements library
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reqs_map = {
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"onnxruntime": "onnxruntime-gpu==1.15.1",
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"basicsr": "basicsr==1.4.2",
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"rembg": "rembg==2.0.50",
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"qrcode": "qrcode[pil]",
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}
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def import_install(package_name):
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from pip._internal import main as pip_main
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try:
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__import__(package_name)
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except ImportError:
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package_spec = reqs_map.get(package_name)
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if package_spec is None:
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print(f"Installing {package_name}")
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package_spec = package_name
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pip_main(["install", package_spec])
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__import__(package_name)
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# endregion
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# region GLOBAL VARIABLES
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# Get the absolute path of the parent directory of the current script
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here = Path(__file__).parent.resolve()
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# Construct the absolute path to the ComfyUI directory
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comfy_dir = here.parent.parent
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# Construct the path to the font file
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font_path = here / "font.ttf"
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# Add extern folder to path
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extern_root = here / "extern"
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add_path(extern_root)
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for pth in extern_root.iterdir():
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if pth.is_dir():
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add_path(pth)
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# Add the ComfyUI directory and custom nodes path to the sys.path list
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add_path(comfy_dir)
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add_path((comfy_dir / "custom_nodes"))
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# endregion
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# region TENSOR UTILITIES
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def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
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batch_count = 1
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if len(image.shape) > 3:
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batch_count = image.size(0)
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if batch_count > 1:
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out = []
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for i in range(batch_count):
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out.extend(tensor2pil(image[i]))
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return out
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return [
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Image.fromarray(
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np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
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)
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]
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def pil2tensor(image: Image.Image | List[Image.Image]) -> torch.Tensor:
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if isinstance(image, list):
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return torch.cat([pil2tensor(img) for img in image], dim=0)
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor:
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if isinstance(img_np, list):
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return torch.cat([np2tensor(img) for img in img_np], dim=0)
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return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
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def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
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batch_count = 1
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if len(tensor.shape) > 3:
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batch_count = tensor.size(0)
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if batch_count > 1:
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out = []
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for i in range(batch_count):
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out.extend(tensor2np(tensor[i]))
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return out
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return [np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)]
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# endregion
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