80 lines
2.0 KiB
Python
80 lines
2.0 KiB
Python
from PIL import Image
|
|
import numpy as np
|
|
import torch
|
|
from pathlib import Path
|
|
import sys
|
|
|
|
|
|
def add_path(path, prepend=False):
|
|
if isinstance(path, list):
|
|
for p in path:
|
|
add_path(p, prepend)
|
|
return
|
|
|
|
if isinstance(path, Path):
|
|
path = path.resolve().as_posix()
|
|
|
|
if path not in sys.path:
|
|
if prepend:
|
|
sys.path.insert(0, path)
|
|
else:
|
|
sys.path.append(path)
|
|
|
|
|
|
# Get the absolute path of the parent directory of the current script
|
|
here = Path(__file__).parent.resolve()
|
|
|
|
# Construct the absolute path to the ComfyUI directory
|
|
comfy_dir = here.parent.parent
|
|
|
|
# Construct the path to the font file
|
|
font_path = here / "font.ttf"
|
|
|
|
# Add extern folder to path
|
|
extern_root = here / "extern"
|
|
add_path(extern_root)
|
|
for pth in extern_root.iterdir():
|
|
if pth.is_dir():
|
|
add_path(pth)
|
|
|
|
|
|
# Add the ComfyUI directory and custom nodes path to the sys.path list
|
|
add_path(comfy_dir)
|
|
add_path((comfy_dir / "custom_nodes"))
|
|
|
|
|
|
# Tensor to PIL (grabbed from WAS Suite)
|
|
def tensor2pil(image: torch.Tensor) -> Image.Image:
|
|
return Image.fromarray(
|
|
np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
|
|
)
|
|
|
|
|
|
# TODO: write pil2tensor counterpart (batch support)
|
|
# def tensor2pil(image: torch.Tensor) -> Union[Image.Image, List[Image.Image]]:
|
|
# batch_count = 1
|
|
# if len(image.shape) > 3:
|
|
# batch_count = image.size(0)
|
|
|
|
# if batch_count == 1:
|
|
# return Image.fromarray(
|
|
# np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
|
|
# )
|
|
# return [tensor2pil(image[i]) for i in range(batch_count)]
|
|
|
|
|
|
# Convert PIL to Tensor (grabbed from WAS Suite)
|
|
def pil2tensor(image: Image.Image) -> torch.Tensor:
|
|
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
|
|
|
|
|
def np2tensor(img_np):
|
|
return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
|
|
|
|
|
|
def tensor2np(tensor: torch.Tensor) -> np.ndarray:
|
|
return np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
|
|
|
|
|
|
|