diff --git a/image_preview.py b/image_preview.py
index b72af0f..e081452 100644
--- a/image_preview.py
+++ b/image_preview.py
@@ -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)",
 }