diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py
index f39daa00..f8901eca 100644
--- a/comfy_extras/nodes_compositing.py
+++ b/comfy_extras/nodes_compositing.py
@@ -102,7 +102,7 @@ class PorterDuffImageComposite:
 
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "composite"
-    CATEGORY = "compositing"
+    CATEGORY = "mask/compositing"
 
     def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):
         batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
@@ -149,7 +149,7 @@ class SplitImageWithAlpha:
                 }
         }
 
-    CATEGORY = "compositing"
+    CATEGORY = "mask/compositing"
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "split_image_with_alpha"
 
@@ -170,7 +170,7 @@ class JoinImageWithAlpha:
                 }
         }
 
-    CATEGORY = "compositing"
+    CATEGORY = "mask/compositing"
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "join_image_with_alpha"
 
diff --git a/nodes.py b/nodes.py
index 9f8e58d0..16bf07cc 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1372,31 +1372,6 @@ class LoadImage:
 
         return True
 
-class LoadImageWithAlpha(LoadImage):
-    @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))]
-        return {"required":
-                    {"image": (sorted(files), {"image_upload": True})},
-                }
-
-    CATEGORY = "compositing"
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-
-    FUNCTION = "load_image"
-    def load_image(self, image):
-        image_path = folder_paths.get_annotated_filepath(image)
-        i = Image.open(image_path)
-        i = ImageOps.exif_transpose(i)
-        image = i.convert("RGBA")
-        alpha = np.array(image.getchannel("A")).astype(np.float32) / 255.0
-        alpha = torch.from_numpy(alpha)[None,]
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        return (image, alpha)
-
 class LoadImageMask:
     _color_channels = ["alpha", "red", "green", "blue"]
     @classmethod
@@ -1631,7 +1606,6 @@ NODE_CLASS_MAPPINGS = {
     "SaveImage": SaveImage,
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
-    "LoadImageWithAlpha": LoadImageWithAlpha,
     "LoadImageMask": LoadImageMask,
     "ImageScale": ImageScale,
     "ImageScaleBy": ImageScaleBy,
@@ -1728,7 +1702,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SaveImage": "Save Image",
     "PreviewImage": "Preview Image",
     "LoadImage": "Load Image",
-    "LoadImageWithAlpha": "Load Image with Alpha",
     "LoadImageMask": "Load Image (as Mask)",
     "ImageScale": "Upscale Image",
     "ImageScaleBy": "Upscale Image By",