diff --git a/__init__.py b/__init__.py
index a774176..315c169 100644
--- a/__init__.py
+++ b/__init__.py
@@ -20,13 +20,15 @@ else:
 		print("Can't find pypng! Please install to enable 16bit image support.")
 		pass
 	else:
-		from .highprec import SaveImageHighPrec, PreviewImageHighPrec
+		from .highprec import SaveImageHighPrec, PreviewImageHighPrec, LoadImageHighPrec
 		NODE_CLASS_MAPPINGS.update({
 			"SaveImageHighPrec": SaveImageHighPrec,
 			"PreviewImageHighPrec": PreviewImageHighPrec,
+			"LoadImageHighPrec": LoadImageHighPrec,
 		})
 		
 		NODE_DISPLAY_NAME_MAPPINGS.update({
 			"SaveImageHighPrec": SaveImageHighPrec.TITLE,
 			"PreviewImageHighPrec": PreviewImageHighPrec.TITLE,
+			"LoadImageHighPrec": LoadImageHighPrec.TITLE,
 		})
diff --git a/highprec.py b/highprec.py
index aba22f0..f15d2e5 100644
--- a/highprec.py
+++ b/highprec.py
@@ -1,6 +1,7 @@
 import os
 import png
 import json
+import torch
 import random
 import numpy as np
 from io import BytesIO
@@ -9,7 +10,7 @@ from PIL.PngImagePlugin import PngInfo
 
 import folder_paths
 from comfy.cli_args import args
-from nodes import SaveImage, PreviewImage
+from nodes import SaveImage, PreviewImage, LoadImage
 
 
 def get_PIL_tEXt(image, prompt, extra_pnginfo):
@@ -102,3 +103,32 @@ class PreviewImageHighPrec(SaveImageHighPrec):
 					{"images": ("IMAGE", ), },
 				"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
 				}
+
+class LoadImageHighPrec(LoadImage):
+	TITLE = "Load Image (16 bit)"
+	FUNCTION = "load_image_high_precision"
+
+	def load_image_high_precision(self, image):
+		if not image.endswith(".png"):
+			print("ColorMod: Only PNG files can be loaded in 16 bit color.")
+			return (self.load_image(image))
+
+		image_path = folder_paths.get_annotated_filepath(image)
+		reader = png.Reader(image_path)
+
+		raw = reader.read()
+		image = np.vstack(map(np.uint16, raw[2]))
+
+		dim_rgb = image.shape[1] // raw[0]
+		div_max = 1.0 if np.max(image) <= 255 else 256.0
+		
+		image = np.reshape(image,(raw[1], raw[0], dim_rgb))
+		image = np.array(image).astype(np.float32) / (255.0 * div_max )
+		image = torch.from_numpy(np.clip(image, 0.0, 1.0))[None,]
+
+		if image.shape[3] == 4:
+			mask = 1.0 - image[:,:,:,3]
+			image = torch.stack([image[:,:,:,x] for x in range(3)], dim = 3)
+		else:
+			mask = torch.zeros((64,64), dtype=torch.float32, device="cpu").unsqueeze(0)
+		return (image, mask)