1463 lines
47 KiB
Python
1463 lines
47 KiB
Python
from inspect import cleandoc
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import os
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import glob
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try:
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from comfy.comfy_types.node_typing import IO, ComfyNodeABC
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except:
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class IO:
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BOOLEAN = "BOOLEAN"
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INT = "INT"
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FLOAT = "FLOAT"
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STRING = "STRING"
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NUMBER = "FLOAT,INT"
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IMAGE = "IMAGE"
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MASK = "MASK"
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ANY = "*"
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ComfyNodeABC = object
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try:
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from folder_paths import get_input_directory, get_output_directory
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except:
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def get_input_directory():
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return "./"
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get_output_directory = get_input_directory
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def _require_numpy():
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try:
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import numpy as np
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return np
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except ModuleNotFoundError as e:
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raise ModuleNotFoundError(
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"basic_data_handling: Missing dependency 'numpy'. It seems your ComfyUI installation is faulty."
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"Only for development purposes: Install it with `pip install .[dev] numpy torch pillow` or `pip install numpy`."
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) from e
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def _require_torch():
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try:
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import torch
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return torch
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except ModuleNotFoundError as e:
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raise ModuleNotFoundError(
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"basic_data_handling: Missing dependency 'torch'. It seems your ComfyUI installation is faulty."
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"Only for development purposes: Install it with `pip install .[dev] numpy torch pillow` or `pip install torch`."
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) from e
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def _require_pillow():
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try:
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from PIL import Image, ImageOps
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return Image, ImageOps
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except ModuleNotFoundError as e:
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raise ModuleNotFoundError(
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"basic_data_handling: Missing dependency 'pillow'. It seems your ComfyUI installation is faulty."
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"Only for development purposes: Install it with `pip install .[dev] numpy torch pillow` or `pip install pillow`."
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) from e
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# helper functions:
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def load_image_helper(path: str):
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"""Helper function to load an image from a path"""
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Image, ImageOps = _require_pillow()
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try:
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import pillow_jxl # noqa: F401 - imported but unused, kept for JPEG XL support
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except ModuleNotFoundError:
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pass
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if not os.path.exists(path):
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return None
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# Open and process the image
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try:
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img = Image.open(path)
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img = ImageOps.exif_transpose(img)
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return img
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except Exception:
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return None
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def extract_mask_from_alpha(img):
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"""Extract a mask from the alpha channel of an image"""
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np = _require_numpy()
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torch = _require_torch()
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if 'A' in img.getbands():
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alpha = np.array(img.getchannel('A')).astype(np.float32) / 255.0
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mask_tensor = 1.0 - torch.from_numpy(alpha)
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elif img.mode == 'P' and 'transparency' in img.info:
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alpha = np.array(img.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
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mask_tensor = 1.0 - torch.from_numpy(alpha)
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else:
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# Create a blank mask if no alpha channel
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mask_tensor = torch.zeros((img.height, img.width), dtype=torch.float32)
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# Add batch dimension to mask
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mask_tensor = mask_tensor.unsqueeze(0)
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return mask_tensor
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def extract_mask_from_greyscale(img):
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"""Extract a mask from a greyscale image or the red channel of an RGB image"""
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np = _require_numpy()
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torch = _require_torch()
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if img.mode == 'L':
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# Image is already greyscale
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gray = np.array(img).astype(np.float32) / 255.0
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elif img.mode == 'RGB' or img.mode == 'RGBA':
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# Use the red channel of RGB or RGBA
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gray = np.array(img.getchannel('R')).astype(np.float32) / 255.0
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else:
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# Convert to greyscale if it's another format
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gray_img = img.convert('L')
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gray = np.array(gray_img).astype(np.float32) / 255.0
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# Convert to tensor and invert (white pixels in image = transparent in mask)
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mask_tensor = 1.0 - torch.from_numpy(gray)
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# Add batch dimension
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mask_tensor = mask_tensor.unsqueeze(0)
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return mask_tensor
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# the nodes:
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class PathAbspath(ComfyNodeABC):
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"""
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Returns the absolute path of a file or directory.
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This node takes a path and returns its absolute (full) path
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by resolving any relative path components and symbolic links.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("absolute path",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_abspath"
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def get_abspath(self, path: str) -> tuple[str]:
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return (os.path.abspath(path),)
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class PathBasename(ComfyNodeABC):
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"""
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Returns the base name of a path.
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This node extracts the filename component from a path,
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removing any directory information.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("basename",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_basename"
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def get_basename(self, path: str) -> tuple[str]:
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return (os.path.basename(path),)
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class PathCommonPrefix(ComfyNodeABC):
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"""
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Finds the common prefix of multiple paths.
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This node returns the longest common leading component of the given paths.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path1": (IO.STRING, {"default": ""}),
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},
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"optional": {
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"path2": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("common prefix",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_common_prefix"
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def get_common_prefix(self, path1: str, path2: str = "") -> tuple[str]:
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paths = [p for p in [path1, path2] if p]
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return (os.path.commonprefix(paths),)
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class PathDirname(ComfyNodeABC):
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"""
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Returns the directory name of a path.
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This node extracts the directory component from a path,
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removing the filename.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("dirname",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_dirname"
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def get_dirname(self, path: str) -> tuple[str]:
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return (os.path.dirname(path),)
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class PathExists(ComfyNodeABC):
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"""
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Checks if a path exists in the filesystem.
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This node returns True if the path exists (either as a file or a directory),
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and False otherwise.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.BOOLEAN,)
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RETURN_NAMES = ("exists",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "check_exists"
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def check_exists(self, path: str) -> tuple[bool]:
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return (os.path.exists(path),)
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class PathExpandVars(ComfyNodeABC):
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"""
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Expands environment variables in a path.
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This node replaces environment variables in a path with their values.
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For example, $HOME or ${HOME} on Unix, or %USERPROFILE% on Windows.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("expanded path",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "expand_vars"
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def expand_vars(self, path: str) -> tuple[str]:
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return (os.path.expandvars(path),)
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class PathGetCwd(ComfyNodeABC):
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"""
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Returns the current working directory.
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This node returns the current working directory as an absolute path.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {}}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("current directory",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_cwd"
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def get_cwd(self) -> tuple[str]:
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return (os.getcwd(),)
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class PathGetExtension(ComfyNodeABC):
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"""
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Returns the extension of a file.
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This node extracts the file extension from a path,
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including the dot (e.g., '.txt').
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("extension",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_extension"
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def get_extension(self, path: str) -> tuple[str]:
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return (os.path.splitext(path)[1],)
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class PathGetSize(ComfyNodeABC):
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"""
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Returns the size of a file in bytes.
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This node returns the size in bytes of the file at the given path.
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Raises an error if the path doesn't exist or isn't a file.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
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RETURN_TYPES = (IO.INT,)
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RETURN_NAMES = ("size (bytes)",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "get_size"
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def get_size(self, path: str) -> tuple[int]:
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if not os.path.exists(path):
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raise FileNotFoundError(f"Basic data handling: Path does not exist: {path}")
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if not os.path.isfile(path):
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raise ValueError(f"Basic data handling: Path is not a file: {path}")
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return (os.path.getsize(path),)
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|
|
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class PathGlob(ComfyNodeABC):
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"""
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Finds paths matching a pattern.
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This node returns a list of paths matching the given pattern.
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The pattern follows shell-style wildcard rules:
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* - matches any number of characters
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? - matches a single character
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[seq] - matches any character in seq
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[!seq] - matches any character not in seq
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"pattern": (IO.STRING, {"default": "*.txt"}),
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},
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"optional": {
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"recursive": (IO.BOOLEAN, {"default": False}),
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}
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}
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RETURN_TYPES = (IO.STRING,)
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RETURN_NAMES = ("matching paths",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "glob_paths"
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OUTPUT_IS_LIST = (True,)
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# Class variable to store the last matched paths
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_last_matched_paths = {}
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@classmethod
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def IS_CHANGED(s, pattern: str, recursive: bool = False):
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# Get current paths
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current_paths = glob.glob(pattern, recursive=recursive)
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# Create a key for this specific pattern and recursive setting
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key = f"{pattern}_{recursive}"
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# If we haven't seen this pattern before, store it and trigger recalculation
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if key not in s._last_matched_paths:
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s._last_matched_paths[key] = current_paths
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return float("NaN")
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# Compare with previous paths
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previous_paths = s._last_matched_paths[key]
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if previous_paths != current_paths:
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# Update stored paths and trigger recalculation
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s._last_matched_paths[key] = current_paths
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return float("NaN")
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|
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# No changes, return a consistent value
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import hashlib
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m = hashlib.md5()
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m.update(str(current_paths).encode())
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return m.hexdigest()
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|
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def glob_paths(self, pattern: str, recursive: bool = False) -> tuple[list[str]]:
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return (glob.glob(pattern, recursive=recursive),)
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|
|
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class PathIsAbsolute(ComfyNodeABC):
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"""
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Checks if a path is absolute.
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This node returns True if the path is absolute (begins at the root directory),
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and False if it's relative.
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"""
|
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": (IO.STRING, {"default": ""}),
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}
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}
|
|
|
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RETURN_TYPES = (IO.BOOLEAN,)
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RETURN_NAMES = ("is absolute",)
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CATEGORY = "Basic/Path"
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DESCRIPTION = cleandoc(__doc__ or "")
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FUNCTION = "check_is_absolute"
|
|
|
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def check_is_absolute(self, path: str) -> tuple[bool]:
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return (os.path.isabs(path),)
|
|
|
|
|
|
class PathIsDir(ComfyNodeABC):
|
|
"""
|
|
Checks if a path points to a directory.
|
|
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|
This node returns True if the path exists and is a directory,
|
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and False otherwise.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
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}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.BOOLEAN,)
|
|
RETURN_NAMES = ("is dir",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "check_is_dir"
|
|
|
|
def check_is_dir(self, path: str) -> tuple[bool]:
|
|
return (os.path.isdir(path),)
|
|
|
|
|
|
class PathIsFile(ComfyNodeABC):
|
|
"""
|
|
Checks if a path points to a file.
|
|
|
|
This node returns True if the path exists and is a regular file,
|
|
and False otherwise.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.BOOLEAN,)
|
|
RETURN_NAMES = ("is file",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "check_is_file"
|
|
|
|
def check_is_file(self, path: str) -> tuple[bool]:
|
|
return (os.path.isfile(path),)
|
|
|
|
|
|
class PathJoin(ComfyNodeABC):
|
|
"""
|
|
Joins multiple path components into a single path.
|
|
|
|
This node takes multiple path components and joins them intelligently
|
|
to form a single path. It handles directory separators correctly
|
|
for the operating system.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path1": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"path2": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("path",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "join_paths"
|
|
|
|
def join_paths(self, path1: str, path2: str = "") -> tuple[str]:
|
|
paths = [p for p in [path1, path2] if p]
|
|
return (str(os.path.join(*paths)),)
|
|
|
|
|
|
class PathListDir(ComfyNodeABC):
|
|
"""
|
|
Lists the contents of a directory.
|
|
|
|
This node returns a list of files and directories in the specified path.
|
|
If 'files_only' is True, it only returns files.
|
|
If 'dirs_only' is True, it only returns directories.
|
|
If both are False, it returns all contents.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"files_only": (IO.BOOLEAN, {"default": False}),
|
|
"dirs_only": (IO.BOOLEAN, {"default": False}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("entries",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "list_directory"
|
|
OUTPUT_IS_LIST = (True,)
|
|
|
|
def list_directory(self, path: str, files_only: str = False, dirs_only: str = False) -> tuple[list[str]]:
|
|
if not path:
|
|
path = os.getcwd()
|
|
|
|
if not os.path.exists(path):
|
|
raise FileNotFoundError(f"Directory does not exist: {path}")
|
|
if not os.path.isdir(path):
|
|
raise NotADirectoryError(f"Basic data handling: Path is not a directory: {path}")
|
|
|
|
entries = os.listdir(path)
|
|
|
|
if files_only:
|
|
entries = [e for e in entries if os.path.isfile(os.path.join(path, e))]
|
|
elif dirs_only:
|
|
entries = [e for e in entries if os.path.isdir(os.path.join(path, e))]
|
|
|
|
return (entries,)
|
|
|
|
|
|
class PathNormalize(ComfyNodeABC):
|
|
"""
|
|
Normalizes a path.
|
|
|
|
This node normalizes a path by collapsing redundant separators,
|
|
resolving up-level references, and converting to the correct
|
|
separator for the operating system.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("normalized path",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "normalize_path"
|
|
|
|
def normalize_path(self, path: str) -> tuple[str]:
|
|
return (os.path.normpath(path),)
|
|
|
|
|
|
class PathSetExtension(ComfyNodeABC):
|
|
"""
|
|
Sets the file extension for a path.
|
|
|
|
This node replaces the current extension in a path with a new one.
|
|
The extension should include the dot (e.g., '.jpg').
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {}),
|
|
"extension": (IO.STRING, {"default": ".txt"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("path",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "set_extension"
|
|
|
|
def set_extension(self, path: str, extension: str) -> tuple[str]:
|
|
# Make sure extension starts with a dot
|
|
if not extension.startswith('.') and extension:
|
|
extension = '.' + extension
|
|
|
|
root, _ = os.path.splitext(path)
|
|
return (root + extension,)
|
|
|
|
|
|
class PathRelative(ComfyNodeABC):
|
|
"""
|
|
Returns a relative path.
|
|
|
|
This node computes a relative path from the 'start' path to the 'path'.
|
|
If 'start' is not provided, the current working directory is used.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"start": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("relative path",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "get_relative_path"
|
|
|
|
def get_relative_path(self, path: str, start: str = "") -> tuple[str]:
|
|
if not start:
|
|
start = os.getcwd()
|
|
return (os.path.relpath(path, start),)
|
|
|
|
|
|
class PathSplit(ComfyNodeABC):
|
|
"""
|
|
Splits a path into directory and filename components.
|
|
|
|
This node takes a path and returns a tuple containing the directory path
|
|
and the filename.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING, IO.STRING)
|
|
RETURN_NAMES = ("directory", "filename")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "split_path"
|
|
|
|
def split_path(self, path: str) -> tuple[str, str]:
|
|
return os.path.split(path)
|
|
|
|
|
|
class PathSplitExt(ComfyNodeABC):
|
|
"""
|
|
Splits a path into name and extension components.
|
|
|
|
This node takes a path and returns a tuple containing the path without
|
|
the extension and the extension (including the dot).
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING, IO.STRING)
|
|
RETURN_NAMES = ("path without ext", "extension")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "split_ext"
|
|
|
|
def split_ext(self, path: str) -> tuple[str, str]:
|
|
return os.path.splitext(path)
|
|
|
|
|
|
class PathLoadStringFile(ComfyNodeABC):
|
|
"""
|
|
Loads a text file in UTF-8 encoding and returns its content as a STRING
|
|
without any further processing.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = (IO.STRING, IO.BOOLEAN)
|
|
RETURN_NAMES = ("text", "exists")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "load_text"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, path):
|
|
try:
|
|
if os.path.exists(path):
|
|
return os.path.getmtime(path)
|
|
except Exception:
|
|
pass
|
|
return float("NaN") # Return NaN if file doesn't exist or can't access modification time
|
|
|
|
def load_text(self, path: str):
|
|
exists = os.path.exists(path)
|
|
|
|
if not exists:
|
|
return ("", False)
|
|
|
|
try:
|
|
with open(path, "r", encoding="utf-8") as f:
|
|
text = f.read()
|
|
return (text, True)
|
|
except Exception:
|
|
return ("", False)
|
|
|
|
|
|
class PathLoadImageRGB(ComfyNodeABC):
|
|
"""
|
|
Loads an image from a file path and returns only the RGB channels.
|
|
|
|
This node loads an image from the specified path and processes it to
|
|
return only the RGB channels as a tensor, ignoring any alpha channel.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = (IO.IMAGE, IO.BOOLEAN)
|
|
RETURN_NAMES = ("image", "exists")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "load_image_rgb"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, path):
|
|
try:
|
|
if os.path.exists(path):
|
|
return os.path.getmtime(path)
|
|
except Exception:
|
|
pass
|
|
return float("NaN") # Return NaN if file doesn't exist or can't access modification time
|
|
|
|
def load_image_rgb(self, path: str):
|
|
import numpy as np
|
|
import torch
|
|
|
|
img = load_image_helper(path)
|
|
|
|
if img is None:
|
|
# Create an empty 1x1 image
|
|
empty_tensor = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
|
|
return (empty_tensor, False)
|
|
|
|
# Convert to RGB (removing alpha if present)
|
|
img_rgb = img.convert("RGB")
|
|
|
|
# Convert to tensor format expected by ComfyUI
|
|
image_tensor = np.array(img_rgb).astype(np.float32) / 255.0
|
|
image_tensor = torch.from_numpy(image_tensor)[None,]
|
|
|
|
return (image_tensor, True)
|
|
|
|
|
|
class PathLoadImageRGBA(ComfyNodeABC):
|
|
"""
|
|
Loads an image from a file path and returns RGB channels and Alpha as a mask.
|
|
|
|
This node loads an image from the specified path and processes it to
|
|
return the RGB channels as a tensor and the Alpha channel as a mask tensor.
|
|
If the image has no alpha channel, a blank mask is returned.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = (IO.IMAGE, IO.MASK, IO.BOOLEAN)
|
|
RETURN_NAMES = ("image", "mask", "exists")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "load_image_rgba"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, path):
|
|
try:
|
|
if os.path.exists(path):
|
|
return os.path.getmtime(path)
|
|
except Exception:
|
|
pass
|
|
return float("NaN") # Return NaN if file doesn't exist or can't access modification time
|
|
|
|
def load_image_rgba(self, path: str):
|
|
import numpy as np
|
|
import torch
|
|
|
|
img = load_image_helper(path)
|
|
|
|
if img is None:
|
|
# Create empty 1x1 image and mask
|
|
empty_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
|
|
empty_mask = torch.zeros((1, 1, 1), dtype=torch.float32)
|
|
return (empty_image, empty_mask, False)
|
|
|
|
# Convert to RGB for the image
|
|
img_rgb = img.convert("RGB")
|
|
|
|
# Convert to tensor format expected by ComfyUI
|
|
image_tensor = np.array(img_rgb).astype(np.float32) / 255.0
|
|
image_tensor = torch.from_numpy(image_tensor)[None,]
|
|
|
|
# Extract alpha channel as mask
|
|
mask_tensor = extract_mask_from_alpha(img)
|
|
|
|
return (image_tensor, mask_tensor, True)
|
|
|
|
|
|
class PathLoadMaskFromAlpha(ComfyNodeABC):
|
|
"""
|
|
Loads a mask from the alpha channel of an image.
|
|
|
|
This node loads an image from the specified path and extracts the alpha
|
|
channel to use as a mask. If the image has no alpha channel, a blank mask
|
|
is returned.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = (IO.MASK, IO.BOOLEAN)
|
|
RETURN_NAMES = ("mask", "exists")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "load_mask_from_alpha"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, path):
|
|
try:
|
|
if os.path.exists(path):
|
|
return os.path.getmtime(path)
|
|
except Exception:
|
|
pass
|
|
return float("NaN") # Return NaN if file doesn't exist or can't access modification time
|
|
|
|
def load_mask_from_alpha(self, path: str):
|
|
import torch
|
|
|
|
img = load_image_helper(path)
|
|
|
|
if img is None:
|
|
# Return empty 1x1 mask
|
|
empty_mask = torch.zeros((1, 1, 1), dtype=torch.float32)
|
|
return (empty_mask, False)
|
|
|
|
mask_tensor = extract_mask_from_alpha(img)
|
|
return (mask_tensor, True)
|
|
|
|
|
|
class PathLoadMaskFromGreyscale(ComfyNodeABC):
|
|
"""
|
|
Loads a mask from a greyscale image or the red channel of an RGB image.
|
|
|
|
This node loads an image from the specified path and creates a mask from it.
|
|
If the image is greyscale, the intensity is used directly.
|
|
If the image is RGB, the red channel is used.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"invert": (IO.BOOLEAN, {"default": False}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = (IO.MASK, IO.BOOLEAN)
|
|
RETURN_NAMES = ("mask", "exists")
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "load_mask_from_greyscale"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, path):
|
|
try:
|
|
if os.path.exists(path):
|
|
return os.path.getmtime(path)
|
|
except Exception:
|
|
pass
|
|
return float("NaN") # Return NaN if file doesn't exist or can't access modification time
|
|
|
|
def load_mask_from_greyscale(self, path: str, invert: bool = False):
|
|
import torch
|
|
|
|
img = load_image_helper(path)
|
|
|
|
if img is None:
|
|
# Return empty 1x1 mask
|
|
empty_mask = torch.zeros((1, 1, 1), dtype=torch.float32)
|
|
return (empty_mask, False)
|
|
|
|
mask_tensor = extract_mask_from_greyscale(img)
|
|
|
|
# Optionally invert the mask (1.0 - mask)
|
|
if invert:
|
|
mask_tensor = 1.0 - mask_tensor
|
|
|
|
return (mask_tensor, True)
|
|
|
|
|
|
class PathSaveStringFile(ComfyNodeABC):
|
|
"""
|
|
Saves a string to a text file.
|
|
|
|
This node takes a string and saves it to the specified path as a text file.
|
|
Optionally, you can choose to create the directory if it doesn't exist.
|
|
If 'append' is True, the text is appended to an existing file instead of
|
|
overwriting it.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"text": (IO.STRING, {"default": ""}),
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"create_dirs": (IO.BOOLEAN, {"default": True}),
|
|
"append": (IO.BOOLEAN, {"default": False}),
|
|
"encoding": (IO.STRING, {"default": "utf-8"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.BOOLEAN,)
|
|
RETURN_NAMES = ("success",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "save_text"
|
|
OUTPUT_NODE = True
|
|
|
|
def save_text(self, text: str, path: str, create_dirs: bool = True, append: bool = False, encoding: str = "utf-8"):
|
|
if not path:
|
|
print("Basic data handling: Save failed - no path specified")
|
|
return (False,)
|
|
|
|
try:
|
|
# Create directories if needed
|
|
directory = os.path.dirname(path)
|
|
if directory and create_dirs and not os.path.exists(directory):
|
|
os.makedirs(directory)
|
|
|
|
mode = "a" if append else "w"
|
|
with open(path, mode, encoding=encoding) as f:
|
|
f.write(text)
|
|
|
|
action = "appended" if append else "saved"
|
|
print(f"Basic data handling: Successfully {action} text to {path}")
|
|
return (True,)
|
|
except Exception as e:
|
|
print(f"Basic data handling: Error saving text file: {e}")
|
|
return (False,)
|
|
|
|
|
|
def compose_prompt_text(prompt: str, negative_prompt: str) -> str:
|
|
"""
|
|
Build the generation-parameter text embedded into saved images.
|
|
|
|
Follows the Stable Diffusion WebUI convention: the (optional) positive
|
|
prompt is written first, followed by an optional ``Negative prompt:``
|
|
line::
|
|
|
|
<positive prompt>
|
|
Negative prompt: <negative prompt>
|
|
|
|
Returns an empty string when neither value is provided, in which case no
|
|
metadata is embedded into the file.
|
|
"""
|
|
lines = []
|
|
if prompt.strip():
|
|
lines.append(prompt.strip())
|
|
if negative_prompt.strip():
|
|
lines.append(f"Negative prompt: {negative_prompt.strip()}")
|
|
return "\n".join(lines)
|
|
|
|
|
|
def build_png_info(metadata_text: str):
|
|
"""
|
|
Wrap ``metadata_text`` in a Pillow ``PngInfo`` container under the standard
|
|
``parameters`` text-chunk key so it can be embedded in a PNG file.
|
|
|
|
Returns ``None`` when there is no text to embed (or Pillow's PNG metadata
|
|
support is unavailable), in which case the image should be saved without
|
|
extra metadata.
|
|
"""
|
|
if not metadata_text:
|
|
return None
|
|
try:
|
|
from PIL import PngImagePlugin
|
|
except ModuleNotFoundError:
|
|
return None
|
|
pnginfo = PngImagePlugin.PngInfo()
|
|
pnginfo.add_text("parameters", metadata_text)
|
|
return pnginfo
|
|
|
|
|
|
def build_image_exif(metadata_text: str, include_description: bool = True):
|
|
"""
|
|
Build an EXIF block that stores ``metadata_text`` for formats without a
|
|
native text chunk (JPEG, WEBP, JXL).
|
|
|
|
The payload is written into the EXIF ``UserComment`` field (tag 0x9286) of
|
|
the Exif IFD as ``UNICODE\0`` + UTF-16-BE, which matches what Stable
|
|
Diffusion WebUI / piexif based readers expect. When ``include_description``
|
|
is true, the payload is also written as UTF-8 into the EXIF
|
|
``ImageDescription`` field (tag 0x010E) of IFD0.
|
|
|
|
Returns the EXIF bytes (starting with the ``Exif\0\0`` marker), or ``None``
|
|
when there is no text to embed.
|
|
"""
|
|
if not metadata_text:
|
|
return None
|
|
try:
|
|
from PIL import ExifTags
|
|
except ModuleNotFoundError:
|
|
return None
|
|
Image, _ = _require_pillow()
|
|
exif = Image.Exif()
|
|
if include_description:
|
|
exif[0x010E] = metadata_text.encode("utf-8")
|
|
exif.get_ifd(ExifTags.IFD.Exif)[0x9286] = b"UNICODE\x00" + metadata_text.encode("utf-16-be")
|
|
return exif.tobytes()
|
|
|
|
|
|
def build_xmp_packet(metadata_text: str) -> bytes:
|
|
"""
|
|
Build an XMP packet storing ``metadata_text`` in the Dublin Core
|
|
``dc:description`` tag, as expected for JPEG XL ``xml `` boxes.
|
|
"""
|
|
from xml.sax.saxutils import escape
|
|
body = escape(metadata_text)
|
|
packet = (
|
|
'<?xpacket begin="\ufeff" id="W5M0MpCehiHzreSzNTczkc9d"?>\n'
|
|
'<x:xmpmeta xmlns:x="adobe:ns:meta/">\n'
|
|
'<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">\n'
|
|
'<rdf:Description xmlns:dc="http://purl.org/dc/elements/1.1/">\n'
|
|
'<dc:description><rdf:Alt><rdf:li xml:lang="x-default">' + body + '</rdf:li></rdf:Alt></dc:description>\n'
|
|
'</rdf:Description>\n'
|
|
'</rdf:RDF>\n'
|
|
'</x:xmpmeta>\n'
|
|
'<?xpacket end="w"?>'
|
|
)
|
|
return packet.encode("utf-8")
|
|
|
|
|
|
def metadata_save_kwargs(metadata_text: str, fmt: str) -> dict:
|
|
"""
|
|
Return the extra keyword arguments that embed ``metadata_text`` when saving
|
|
an image in the (lower-case) format ``fmt``.
|
|
|
|
Returns an empty dict when there is no text to embed or when the format
|
|
cannot carry text metadata.
|
|
"""
|
|
if not metadata_text:
|
|
return {}
|
|
if fmt == "png":
|
|
return {"pnginfo": build_png_info(metadata_text)}
|
|
if fmt in ("jpg", "jpeg"):
|
|
exif = build_image_exif(metadata_text, include_description=True)
|
|
return {"exif": exif} if exif is not None else {}
|
|
if fmt in ("webp", "jxl"):
|
|
exif = build_image_exif(metadata_text, include_description=False)
|
|
kwargs = {"exif": exif} if exif is not None else {}
|
|
if fmt == "jxl":
|
|
# EXIF and XMP boxes are only available in the JXL container format
|
|
kwargs["use_container"] = True
|
|
kwargs["xmp"] = build_xmp_packet(metadata_text)
|
|
return kwargs
|
|
return {}
|
|
|
|
|
|
class PathSaveImageRGB(ComfyNodeABC):
|
|
"""
|
|
Saves an image to a file.
|
|
|
|
This node takes an image tensor and saves it to the specified path.
|
|
Supports various image formats like PNG, JPG, WEBP, JXL (if pillow-jxl is installed), etc.
|
|
|
|
When ``prompt`` and/or ``negative_prompt`` are provided, they are embedded
|
|
into the saved image as ``parameters`` metadata: in the PNG text chunk, in
|
|
the EXIF ``UserComment`` (and ``ImageDescription`` for JPEG) fields, and in
|
|
the EXIF + XMP boxes for JPEG XL. Formats that cannot carry text metadata
|
|
ignore the prompts.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"images": (IO.IMAGE,),
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"format": (IO.STRING, {"default": "png"}),
|
|
"quality": (IO.INT, {"default": 95, "min": 1, "max": 100}),
|
|
"create_dirs": (IO.BOOLEAN, {"default": True}),
|
|
"prompt": (IO.STRING, {"default": ""}),
|
|
"negative_prompt": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.BOOLEAN,)
|
|
RETURN_NAMES = ("success",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "save_image"
|
|
OUTPUT_NODE = True
|
|
|
|
def save_image(self, images, path: str, format: str = "png", quality: int = 95,
|
|
create_dirs: bool = True, prompt: str = "", negative_prompt: str = ""):
|
|
if not path:
|
|
print("Basic data handling: Save failed - no path specified")
|
|
return (False,)
|
|
|
|
# If the path doesn't have an extension or it doesn't match the format, add it
|
|
if not path.lower().endswith(f".{format.lower()}"):
|
|
path = f"{path}.{format.lower()}"
|
|
|
|
try:
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
# Check if pillow_jxl is available for JXL support
|
|
has_jxl_support = False
|
|
try:
|
|
import pillow_jxl # noqa: F401 - imported but unused, kept for JPEG XL support
|
|
has_jxl_support = True
|
|
except ModuleNotFoundError:
|
|
# pillow_jxl is not installed
|
|
if format.lower() == "jxl":
|
|
print("Basic data handling: JPEG XL format requested but pillow_jxl module is not installed. "
|
|
"Please install it with 'pip install pillow-jxl-plugin'.")
|
|
return (False,)
|
|
|
|
# Create directories if needed
|
|
directory = os.path.dirname(path)
|
|
if directory and create_dirs and not os.path.exists(directory):
|
|
os.makedirs(directory)
|
|
|
|
# Convert from tensor format back to PIL Image
|
|
# Extract the first image from the batch
|
|
i = 0
|
|
img_tensor = images[i].cpu().numpy()
|
|
|
|
# Convert to uint8 format for PIL
|
|
img_np = (img_tensor * 255).astype(np.uint8)
|
|
|
|
# Create PIL image
|
|
pil_img = Image.fromarray(img_np)
|
|
|
|
# Compose the prompt metadata to embed into the saved file
|
|
metadata_text = compose_prompt_text(prompt, negative_prompt)
|
|
fmt = format.lower()
|
|
|
|
# Save the image, embedding prompt metadata where the format supports it
|
|
if fmt == "jpg" or fmt == "jpeg":
|
|
pil_img.save(path, format="JPEG", quality=quality, **metadata_save_kwargs(metadata_text, fmt))
|
|
elif fmt == "webp":
|
|
pil_img.save(path, format="WEBP", quality=quality, **metadata_save_kwargs(metadata_text, fmt))
|
|
elif fmt == "jxl" and has_jxl_support:
|
|
# JPEG XL specific options
|
|
pil_img.save(path, format="JXL", quality=quality, **metadata_save_kwargs(metadata_text, fmt))
|
|
elif fmt == "png":
|
|
pil_img.save(path, format="PNG", **metadata_save_kwargs(metadata_text, fmt))
|
|
else:
|
|
if metadata_text:
|
|
print("Basic data handling: Prompt metadata is not supported for this format; skipping it.")
|
|
pil_img.save(path, format=format.upper())
|
|
|
|
print(f"Basic data handling: Successfully saved image to {path}")
|
|
return (True,)
|
|
except Exception as e:
|
|
print(f"Basic data handling: Error saving image: {e}")
|
|
return (False,)
|
|
|
|
|
|
class PathSaveImageRGBA(ComfyNodeABC):
|
|
"""
|
|
Saves an image with a mask to a file with transparency.
|
|
|
|
This node takes an image tensor and a mask tensor and saves them to the
|
|
specified path as an image with transparency, where the mask defines the
|
|
alpha channel.
|
|
|
|
When ``prompt`` and/or ``negative_prompt`` are provided, they are embedded
|
|
into the saved image as ``parameters`` metadata: in the PNG text chunk, in
|
|
the EXIF ``UserComment`` (and ``ImageDescription`` for JPEG) fields, and in
|
|
the EXIF + XMP boxes for JPEG XL. Formats that cannot carry text metadata
|
|
ignore the prompts.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"images": (IO.IMAGE,),
|
|
"mask": (IO.MASK,),
|
|
"path": (IO.STRING, {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"format": (IO.STRING, {"default": "png"}),
|
|
"quality": (IO.INT, {"default": 95, "min": 1, "max": 100}),
|
|
"invert_mask": (IO.BOOLEAN, {"default": False}),
|
|
"create_dirs": (IO.BOOLEAN, {"default": True}),
|
|
"prompt": (IO.STRING, {"default": ""}),
|
|
"negative_prompt": (IO.STRING, {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = (IO.BOOLEAN,)
|
|
RETURN_NAMES = ("success",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "save_image_with_mask"
|
|
OUTPUT_NODE = True
|
|
|
|
def save_image_with_mask(self, images, mask, path: str, format: str = "png",
|
|
quality: int = 95, invert_mask: bool = False,
|
|
create_dirs: bool = True, prompt: str = "",
|
|
negative_prompt: str = ""):
|
|
if not path:
|
|
print("Basic data handling: Save failed - no path specified")
|
|
return (False,)
|
|
|
|
# Check format compatibility - needs to support alpha channel
|
|
if format.lower() in ["jpg", "jpeg"]:
|
|
print("Basic data handling: JPEG format doesn't support transparency. Using PNG instead.")
|
|
format = "png"
|
|
|
|
# If the path doesn't have an extension or it doesn't match the format, add it
|
|
if not path.lower().endswith(f".{format.lower()}"):
|
|
path = f"{path}.{format.lower()}"
|
|
|
|
try:
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
# Check if pillow_jxl is available for JXL support
|
|
has_jxl_support = False
|
|
try:
|
|
import pillow_jxl # noqa: F401 - imported but unused, kept for JPEG XL support
|
|
has_jxl_support = True
|
|
except ModuleNotFoundError:
|
|
# pillow_jxl is not installed
|
|
if format.lower() == "jxl":
|
|
print("Basic data handling: JPEG XL format requested but pillow_jxl module is not installed. "
|
|
"Please install it with 'pip install pillow-jxl-plugin'.")
|
|
return (False,)
|
|
|
|
# Create directories if needed
|
|
directory = os.path.dirname(path)
|
|
if directory and create_dirs and not os.path.exists(directory):
|
|
os.makedirs(directory)
|
|
|
|
# Convert from tensor format back to PIL Image
|
|
# Extract the first image from the batch
|
|
i = 0
|
|
img_tensor = images[i].cpu().numpy()
|
|
mask_tensor = mask[i].cpu()
|
|
|
|
# Invert the mask if needed (1.0 becomes transparent, 0.0 becomes opaque)
|
|
if invert_mask:
|
|
mask_tensor = 1.0 - mask_tensor
|
|
|
|
# Convert to alpha channel (0-255)
|
|
alpha_np = (255.0 * (1.0 - mask_tensor.numpy())).astype(np.uint8)
|
|
|
|
# Convert to uint8 format for PIL
|
|
img_np = (img_tensor * 255).astype(np.uint8)
|
|
|
|
# Create PIL image (RGB)
|
|
pil_img = Image.fromarray(img_np)
|
|
|
|
# Create alpha channel image (avoid deprecated 'mode' kwarg in Pillow 13+)
|
|
alpha_img = Image.fromarray(alpha_np).convert("L")
|
|
|
|
# Convert to RGBA and add alpha channel
|
|
pil_img_rgba = pil_img.convert("RGBA")
|
|
pil_img_rgba.putalpha(alpha_img)
|
|
|
|
# Compose the prompt metadata to embed into the saved file
|
|
metadata_text = compose_prompt_text(prompt, negative_prompt)
|
|
fmt = format.lower()
|
|
|
|
# Save the image, embedding prompt metadata where the format supports it
|
|
if fmt == "webp":
|
|
pil_img_rgba.save(path, format="WEBP", quality=quality, **metadata_save_kwargs(metadata_text, fmt))
|
|
elif fmt == "jxl" and has_jxl_support:
|
|
# JPEG XL supports alpha channel
|
|
pil_img_rgba.save(path, format="JXL", quality=quality, **metadata_save_kwargs(metadata_text, fmt))
|
|
elif fmt == "png":
|
|
pil_img_rgba.save(path, format="PNG", **metadata_save_kwargs(metadata_text, fmt))
|
|
else:
|
|
if metadata_text:
|
|
print("Basic data handling: Prompt metadata is not supported for this format; skipping it.")
|
|
pil_img_rgba.save(path, format=format.upper())
|
|
|
|
print(f"Basic data handling: Successfully saved image with mask to {path}")
|
|
return (True,)
|
|
except Exception as e:
|
|
print(f"Basic data handling: Error saving image with mask: {e}")
|
|
return (False,)
|
|
|
|
|
|
class PathInputDir(ComfyNodeABC):
|
|
"""
|
|
Returns the ComfyUI input path.
|
|
|
|
This is where input images are usually stored when using ComfyUI
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("input_path",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "execute"
|
|
DISPLAY_NAME = "Get ComfyUI Input Path"
|
|
|
|
def execute(self) -> tuple[str]:
|
|
return (get_input_directory(),)
|
|
|
|
|
|
class PathOutputDir(ComfyNodeABC):
|
|
"""
|
|
Returns the ComfyUI output path.
|
|
|
|
This is where output images are usually stored when using ComfyUI
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {}
|
|
|
|
RETURN_TYPES = (IO.STRING,)
|
|
RETURN_NAMES = ("output_path",)
|
|
CATEGORY = "Basic/Path"
|
|
DESCRIPTION = cleandoc(__doc__ or "")
|
|
FUNCTION = "execute"
|
|
DISPLAY_NAME = "Get ComfyUI Output Path"
|
|
|
|
def execute(self) -> tuple[str]:
|
|
return (get_output_directory(),)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Basic data handling: PathAbspath": PathAbspath,
|
|
"Basic data handling: PathBasename": PathBasename,
|
|
"Basic data handling: PathCommonPrefix": PathCommonPrefix,
|
|
"Basic data handling: PathDirname": PathDirname,
|
|
"Basic data handling: PathExists": PathExists,
|
|
"Basic data handling: PathExpandVars": PathExpandVars,
|
|
"Basic data handling: PathGetCwd": PathGetCwd,
|
|
"Basic data handling: PathGetExtension": PathGetExtension,
|
|
"Basic data handling: PathSetExtension": PathSetExtension,
|
|
"Basic data handling: PathGetSize": PathGetSize,
|
|
"Basic data handling: PathGlob": PathGlob,
|
|
"Basic data handling: PathInputDir": PathInputDir,
|
|
"Basic data handling: PathIsAbsolute": PathIsAbsolute,
|
|
"Basic data handling: PathIsDir": PathIsDir,
|
|
"Basic data handling: PathIsFile": PathIsFile,
|
|
"Basic data handling: PathJoin": PathJoin,
|
|
"Basic data handling: PathListDir": PathListDir,
|
|
"Basic data handling: PathNormalize": PathNormalize,
|
|
"Basic data handling: PathOutputDir": PathOutputDir,
|
|
"Basic data handling: PathRelative": PathRelative,
|
|
"Basic data handling: PathSplit": PathSplit,
|
|
"Basic data handling: PathSplitExt": PathSplitExt,
|
|
"Basic data handling: PathLoadStringFile": PathLoadStringFile,
|
|
"Basic data handling: PathLoadImageRGB": PathLoadImageRGB,
|
|
"Basic data handling: PathLoadImageRGBA": PathLoadImageRGBA,
|
|
"Basic data handling: PathLoadMaskFromAlpha": PathLoadMaskFromAlpha,
|
|
"Basic data handling: PathLoadMaskFromGreyscale": PathLoadMaskFromGreyscale,
|
|
"Basic data handling: PathSaveStringFile": PathSaveStringFile,
|
|
"Basic data handling: PathSaveImageRGB": PathSaveImageRGB,
|
|
"Basic data handling: PathSaveImageRGBA": PathSaveImageRGBA,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"Basic data handling: PathAbspath": "abspath",
|
|
"Basic data handling: PathBasename": "basename",
|
|
"Basic data handling: PathCommonPrefix": "common prefix",
|
|
"Basic data handling: PathDirname": "dirname",
|
|
"Basic data handling: PathExists": "exists",
|
|
"Basic data handling: PathExpandVars": "expand vars",
|
|
"Basic data handling: PathGetCwd": "get current working directory",
|
|
"Basic data handling: PathGetExtension": "get extension",
|
|
"Basic data handling: PathSetExtension": "set extension",
|
|
"Basic data handling: PathGetSize": "get size",
|
|
"Basic data handling: PathGlob": "glob",
|
|
"Basic data handling: PathInputDir": "input dir",
|
|
"Basic data handling: PathIsAbsolute": "is absolute",
|
|
"Basic data handling: PathIsDir": "is dir",
|
|
"Basic data handling: PathIsFile": "is file",
|
|
"Basic data handling: PathJoin": "join",
|
|
"Basic data handling: PathListDir": "list dir",
|
|
"Basic data handling: PathNormalize": "normalize",
|
|
"Basic data handling: PathOutputDir": "output dir",
|
|
"Basic data handling: PathRelative": "relative",
|
|
"Basic data handling: PathSplit": "split",
|
|
"Basic data handling: PathSplitExt": "splitext",
|
|
"Basic data handling: PathLoadStringFile": "load STRING from file",
|
|
"Basic data handling: PathLoadImageRGB": "load IMAGE from file (RGB)",
|
|
"Basic data handling: PathLoadImageRGBA": "load IMAGE+MASK from file (RGBA)",
|
|
"Basic data handling: PathLoadMaskFromAlpha": "load MASK from alpha channel",
|
|
"Basic data handling: PathLoadMaskFromGreyscale": "load MASK from greyscale/red",
|
|
"Basic data handling: PathSaveStringFile": "save STRING to file",
|
|
"Basic data handling: PathSaveImageRGB": "save IMAGE to file",
|
|
"Basic data handling: PathSaveImageRGBA": "save IMAGE+MASK to file",
|
|
}
|