diff --git a/nodes.py b/nodes.py
index dd09345..65d0094 100644
--- a/nodes.py
+++ b/nodes.py
@@ -63,14 +63,17 @@ class CreateGradientMask:
             offset_gradient = gradient - time  # Offset the gradient values based on time
             image_batch[i] = offset_gradient.reshape(1, -1)
         output = torch.from_numpy(image_batch)
-        out.append(output)
+        mask = output
+        print("gradientmaskshape")
+        print(mask.shape)
+        out.append(mask)
         if invert:
-            return (1.0 - torch.stack(out, dim=0),)
-        return (torch.stack(out, dim=0),)
+            return (1.0 - torch.cat(out, dim=0),)
+        return (torch.cat(out, dim=0),)
 
 class CreateTextMask:
     
-    RETURN_TYPES = ("MASK",)
+    RETURN_TYPES = ("IMAGE", "MASK",)
     FUNCTION = "createtextmask"
     CATEGORY = "KJNodes"
 
@@ -96,10 +99,10 @@ class CreateTextMask:
         # Define the number of images in the batch
         batch_size = frames
         out = []
+        masks = []
         rotation = start_rotation
-        rotation_increment = (end_rotation - start_rotation) / (batch_size - 1)
-        # Create an empty array to store the image batch
-        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
+        if frames > 1:
+            rotation_increment = (end_rotation - start_rotation) / (batch_size - 1)
         if font_path == "fonts\\TTNorms-Black.otf": #I don't know why relative path won't work otherwise...
             font_path = os.path.join(script_dir, font_path)
         # Generate the text
@@ -112,14 +115,15 @@ class CreateTextMask:
            text_center_y = text_y + text_height / 2
            draw.text((text_x, text_y), text, font=font, fill="white")
            image = image.rotate(rotation, center=(text_center_x, text_center_y))
+           image = np.array(image).astype(np.float32) / 255.0
+           image = torch.from_numpy(image)[None,]
+           mask = image[:, :, :, 0] 
+           masks.append(mask)
+           out.append(image)
            rotation += rotation_increment
-           image_batch[i] = np.array(image.convert("L"))
-        output = torch.from_numpy(image_batch)
-        rotation += 10
-        out.append(output)
         if invert:
-            return (1.0 - torch.stack(out, dim=0),)
-        return (torch.stack(out, dim=0),)
+            return (1.0 - torch.cat(out, dim=0),)
+        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
     
 class GrowMaskWithBlur:
     @classmethod
@@ -173,6 +177,7 @@ class GrowMaskWithBlur:
             else:
                 expand += abs(incremental_expandrate)  # Use abs(growrate) to ensure positive change
             output = torch.from_numpy(output)
+            print(output.shape)
             out.append(output)
         
         blurred = torch.stack(out, dim=0).reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)