diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index 77344d0..4caac5b 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -1676,7 +1676,8 @@ class LoadAndResizeImage:
                     "repeat": ("INT", { "default": 1, "min": 1, "max": 4096, "step": 1, }),
                     "keep_proportion": ("BOOLEAN", { "default": False }),
                     "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
-                    "mask_channel": (s._color_channels, ), 
+                    "mask_channel": (s._color_channels, {"tooltip": "Channel to use for the mask output"}), 
+                    "background_color": ("STRING", { "default": "white", "tooltip": "Color to fill the alpha channel with. Enter a comma-separated RGB value.  E.g. 255, 255, 255 for white."}),
                     },
                 }
 
@@ -1685,11 +1686,25 @@ class LoadAndResizeImage:
     RETURN_NAMES = ("image", "mask", "width", "height","image_path",)
     FUNCTION = "load_image"
 
-    def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel):
+    def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel, background_color):
+        from PIL import ImageColor
         image_path = folder_paths.get_annotated_filepath(image)
 
         import node_helpers
         img = node_helpers.pillow(Image.open, image_path)
+
+        # Process the background_color
+        try:
+            # Try to parse as RGB tuple
+            bg_color_rgba = tuple(int(x.strip()) for x in background_color.split(','))
+        except ValueError:
+            # If parsing fails, it might be a hex color or named color
+            if background_color.startswith('#') or background_color.lower() in ImageColor.colormap:
+                bg_color_rgba = ImageColor.getrgb(background_color)
+            else:
+                raise ValueError(f"Invalid background color: {background_color}")
+
+        bg_color_rgba += (255,)  # Add alpha channel
         
         output_images = []
         output_masks = []
@@ -1715,12 +1730,28 @@ class LoadAndResizeImage:
         else:
             width, height = W, H
 
-        for i in ImageSequence.Iterator(img):
-            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+        for frame in ImageSequence.Iterator(img):
+            frame = node_helpers.pillow(ImageOps.exif_transpose, frame)
 
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            image = i.convert("RGB")
+            if frame.mode == 'I':
+                frame = frame.point(lambda i: i * (1 / 255))
+            
+            if frame.mode == 'P':
+                frame = frame.convert("RGBA")
+            elif 'A' in frame.getbands():
+                frame = frame.convert("RGBA")
+            
+            # Extract alpha channel if it exists
+            if 'A' in frame.getbands():
+                alpha_mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
+                alpha_mask = 1. - torch.from_numpy(alpha_mask)
+                bg_image = Image.new("RGBA", frame.size, bg_color_rgba)
+                # Composite the frame onto the background
+                frame = Image.alpha_composite(bg_image, frame)
+            else:
+                alpha_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            
+            image = frame.convert("RGB")
 
             if len(output_images) == 0:
                 w = image.size[0]
@@ -1733,17 +1764,17 @@ class LoadAndResizeImage:
 
             image = np.array(image).astype(np.float32) / 255.0
             image = torch.from_numpy(image)[None,]
-            mask = None
+            
             c = mask_channel[0].upper()
-            if c in i.getbands():
+            if c in frame.getbands():
                 if resize:
-                    i = i.resize((width, height), Image.Resampling.BILINEAR)
-                mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
+                    frame = frame.resize((width, height), Image.Resampling.BILINEAR)
+                mask = np.array(frame.getchannel(c)).astype(np.float32) / 255.0
                 mask = torch.from_numpy(mask)
                 if c == 'A':
-                     mask = 1. - mask
+                    mask = alpha_mask
             else:
-                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
 
             output_images.append(image)
             output_masks.append(mask.unsqueeze(0))
@@ -1758,7 +1789,6 @@ class LoadAndResizeImage:
                 output_image = output_image.repeat(repeat, 1, 1, 1)
                 output_mask = output_mask.repeat(repeat, 1, 1)
 
-     
         return (output_image, output_mask, width, height, image_path)