Add Load Image node with active switch for branch skipping
New JDL_LoadImage node loads images from input/ (with upload support) and outputs IMAGE + MASK like the built-in LoadImage. An "active" toggle returns ExecutionBlocker(None) when off, silently skipping the entire downstream branch. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -1,13 +1,17 @@
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import hashlib
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import json
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import os
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import random
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import numpy as np
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from PIL import Image
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import torch
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from PIL import Image, ImageOps, ImageSequence
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from PIL.PngImagePlugin import PngInfo
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import folder_paths
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import node_helpers
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from comfy.cli_args import args
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from comfy_execution.graph_utils import ExecutionBlocker
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class JDL_PreviewToLoad:
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@@ -116,10 +120,100 @@ class JDL_PreviewToLoad:
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return {"ui": {"images": results, "input_filename": [input_filename]}}
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class JDL_LoadImage:
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"""Load an image from the input directory with an active switch to skip downstream execution."""
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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files = folder_paths.filter_files_content_types(files, ["image"])
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return {
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"required": {
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"image": (sorted(files), {"image_upload": True}),
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"active": ("BOOLEAN", {"default": True}),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "load_image"
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CATEGORY = "utils/image"
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def load_image(self, image, active):
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if not active:
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return (ExecutionBlocker(None), ExecutionBlocker(None))
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image_path = folder_paths.get_annotated_filepath(image)
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img = node_helpers.pillow(Image.open, image_path)
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output_images = []
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output_masks = []
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w, h = None, None
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for i in ImageSequence.Iterator(img):
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i = node_helpers.pillow(ImageOps.exif_transpose, i)
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if i.mode == 'I':
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i = i.point(lambda i: i * (1 / 255))
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frame = i.convert("RGB")
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if len(output_images) == 0:
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w = frame.size[0]
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h = frame.size[1]
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if frame.size[0] != w or frame.size[1] != h:
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continue
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frame_np = np.array(frame).astype(np.float32) / 255.0
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frame_tensor = torch.from_numpy(frame_np)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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elif i.mode == 'P' and 'transparency' in i.info:
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mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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output_images.append(frame_tensor)
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output_masks.append(mask.unsqueeze(0))
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if img.format == "MPO":
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break
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if len(output_images) > 1:
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output_image = torch.cat(output_images, dim=0)
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output_mask = torch.cat(output_masks, dim=0)
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else:
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output_image = output_images[0]
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output_mask = output_masks[0]
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return (output_image, output_mask)
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@classmethod
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def IS_CHANGED(s, image, active):
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if not active:
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return "inactive"
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(s, image, active):
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if not active:
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return True
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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return True
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NODE_CLASS_MAPPINGS = {
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"JDL_PreviewToLoad": JDL_PreviewToLoad,
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"JDL_LoadImage": JDL_LoadImage,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"JDL_PreviewToLoad": "Preview to Load Image",
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"JDL_LoadImage": "Load Image (Active Switch)",
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}
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