diff --git a/__init__.py b/__init__.py
index dfbf668..0c435a1 100644
--- a/__init__.py
+++ b/__init__.py
@@ -56,6 +56,8 @@ NODE_CLASS_MAPPINGS = {
     "ImagePass": ImagePass,
     "ImagePadForOutpaintMasked": ImagePadForOutpaintMasked,
     "ImageAndMaskPreview": ImageAndMaskPreview,
+    "SplitImageChannels": SplitImageChannels,
+    "MergeImageChannels": MergeImageChannels,
     #batch cropping
     "BatchCropFromMask": BatchCropFromMask,
     "BatchCropFromMaskAdvanced": BatchCropFromMaskAdvanced,
@@ -194,6 +196,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "FloatToSigmas": "Float To Sigmas",
     "CustomSigmas": "Custom Sigmas",
     "ImagePass": "ImagePass",
+    "SplitImageChannels": "Split Image Channels",
+    "MergeImageChannels": "Merge Image Channels",
     #curve nodes
     "SplineEditor": "Spline Editor",
     "CreateShapeMaskOnPath": "Create Shape Mask On Path",
diff --git a/nodes/nodes.py b/nodes/nodes.py
index 1d1e7e7..9c2bab9 100644
--- a/nodes/nodes.py
+++ b/nodes/nodes.py
@@ -3558,7 +3558,70 @@ Remaps the image values to the specified range.
         if clamp:
             image = torch.clamp(image, min=0.0, max=1.0)
         return (image, )
+
+class SplitImageChannels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "image": ("IMAGE",),
+            },
+            }
     
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK")
+    RETURN_NAMES = ("red", "green", "blue", "mask")
+    FUNCTION = "split"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Splits image channels into images where the selected channel  
+is repeated for all channels, and the alpha as a mask. 
+"""
+        
+    def split(self, image):
+        red = image[:, :, :, 0:1] # Red channel
+        green = image[:, :, :, 1:2] # Green channel
+        blue = image[:, :, :, 2:3] # Blue channel
+        alpha = image[:, :, :, 3:4] # Alpha channel
+        alpha = alpha.squeeze(-1)
+
+        # Repeat the selected channel for all channels
+        red = torch.cat([red, red, red], dim=3)
+        green = torch.cat([green, green, green], dim=3)
+        blue = torch.cat([blue, blue, blue], dim=3)
+        return (red, green, blue, alpha)
+    
+class MergeImageChannels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "red": ("IMAGE",),
+            "green": ("IMAGE",),
+            "blue": ("IMAGE",),
+            
+            },
+            "optional": {
+                "mask": ("MASK", {"default": None}),
+                },
+            }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "merge"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Merges channel data into an image.  
+"""
+        
+    def merge(self, red, green, blue, alpha=None):
+        image = torch.stack([
+        red[..., 0, None], # Red channel
+        green[..., 1, None], # Green channel
+        blue[..., 2, None]   # Blue channel
+        ], dim=-1)
+        image = image.squeeze(-2)
+        if alpha is not None:
+            image = torch.cat([image, alpha], dim=-1)
+        return (image,)
+        
 class CameraPoseVisualizer:
                 
     @classmethod