import os from PIL import Image, ImageOps, ImageSequence import numpy as np import torch import hashlib import folder_paths class CachingImageLoader: @classmethod def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return { "required": { "image": ("STRING", {"default": 'enter path to file', "multiline": False}), } } CATEGORY = "image" RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "load_image" @classmethod def load_image(self, image): img = Image.open(image) output_images = [] output_masks = [] for i in ImageSequence.Iterator(img): i = ImageOps.exif_transpose(i) image = i.convert("RGB") 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) else: mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") output_images.append(image) output_masks.append(mask.unsqueeze(0)) if len(output_images) > 1: 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 @classmethod def IS_CHANGED(s, image): image_path = folder_paths.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) print(f'############### Digest {image} {image_path} {m.digest().hex()}') return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, image): if not folder_paths.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) return True @staticmethod def status(): return "OK"