load_image_from_path.py

import torch
import numpy as np
from PIL import Image, ImageOps, ImageSequence
import node_helpers

class LoadImageWithTransparencyFromPath:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_path": ("STRING", {"default": "", "multiline": False}),
            },
        }

    RETURN_TYPES = ("IMAGE", "MASK", "STRING")
    RETURN_NAMES = ("image", "mask", "image_path")
    FUNCTION = "load_image_alpha"
    CATEGORY = "Bjornulf"

    def load_image_alpha(self, image_path):
        # Validate that image_path is not None or empty
        if not image_path:
            raise ValueError("image_path cannot be None or empty")

        # Load the image using the provided path
        img = node_helpers.pillow(Image.open, image_path)

        output_images = []
        output_masks = []
        w, h = None, None
        excluded_formats = ['MPO']

        # Process each frame in the image sequence
        for i in ImageSequence.Iterator(img):
            i = node_helpers.pillow(ImageOps.exif_transpose, i)

            if i.mode == 'I':
                i = i.point(lambda i: i * (1 / 255))
            image = i.convert("RGBA")

            if len(output_images) == 0:
                w = image.size[0]
                h = image.size[1]

            if image.size[0] != w or image.size[1] != h:
                continue

            image = np.array(image).astype(np.float32) / 255.0
            image = torch.from_numpy(image)[None,]
            if 'A' in i.getbands():
                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
                mask = 1. - torch.from_numpy(mask)  # Invert mask as per ComfyUI convention
            else:
                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
            output_images.append(image)
            output_masks.append(mask.unsqueeze(0))

        # Handle multi-frame images
        if len(output_images) > 1 and img.format not in excluded_formats:
            output_image = torch.cat(output_images, dim=0)
            output_mask = torch.cat(output_masks, dim=0)
        else:
            output_image = output_images[0]
            output_mask = output_masks[0]

        return (output_image, output_mask, image_path)