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>
This commit is contained in:
2026-03-04 22:24:06 +01:00
parent 1b7f31ff73
commit 8d29a2ad86

View File

@@ -1,13 +1,17 @@
import hashlib
import json
import os
import random
import numpy as np
from PIL import Image
import torch
from PIL import Image, ImageOps, ImageSequence
from PIL.PngImagePlugin import PngInfo
import folder_paths
import node_helpers
from comfy.cli_args import args
from comfy_execution.graph_utils import ExecutionBlocker
class JDL_PreviewToLoad:
@@ -116,10 +120,100 @@ class JDL_PreviewToLoad:
return {"ui": {"images": results, "input_filename": [input_filename]}}
class JDL_LoadImage:
"""Load an image from the input directory with an active switch to skip downstream execution."""
@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))]
files = folder_paths.filter_files_content_types(files, ["image"])
return {
"required": {
"image": (sorted(files), {"image_upload": True}),
"active": ("BOOLEAN", {"default": True}),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
CATEGORY = "utils/image"
def load_image(self, image, active):
if not active:
return (ExecutionBlocker(None), ExecutionBlocker(None))
image_path = folder_paths.get_annotated_filepath(image)
img = node_helpers.pillow(Image.open, image_path)
output_images = []
output_masks = []
w, h = None, None
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))
frame = i.convert("RGB")
if len(output_images) == 0:
w = frame.size[0]
h = frame.size[1]
if frame.size[0] != w or frame.size[1] != h:
continue
frame_np = np.array(frame).astype(np.float32) / 255.0
frame_tensor = torch.from_numpy(frame_np)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
elif i.mode == 'P' and 'transparency' in i.info:
mask = np.array(i.convert('RGBA').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(frame_tensor)
output_masks.append(mask.unsqueeze(0))
if img.format == "MPO":
break
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, active):
if not active:
return "inactive"
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image, active):
if not active:
return True
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
return True
NODE_CLASS_MAPPINGS = {
"JDL_PreviewToLoad": JDL_PreviewToLoad,
"JDL_LoadImage": JDL_LoadImage,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"JDL_PreviewToLoad": "Preview to Load Image",
"JDL_LoadImage": "Load Image (Active Switch)",
}