diff --git a/nodes.py b/nodes.py
index db3c10e..cfce1d8 100644
--- a/nodes.py
+++ b/nodes.py
@@ -726,7 +726,7 @@ class GrowMaskWithBlur:
             "required": {
                 "mask": ("MASK",),
                 "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
-                "incremental_expandrate": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
                 "tapered_corners": ("BOOLEAN", {"default": True}),
                 "flip_input": ("BOOLEAN", {"default": False}),
                 "blur_radius": ("FLOAT", {
@@ -739,7 +739,7 @@ class GrowMaskWithBlur:
                 "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
             },
         }
-    
+
     CATEGORY = "KJNodes/masking"
 
     RETURN_TYPES = ("MASK", "MASK",)
@@ -749,7 +749,7 @@ class GrowMaskWithBlur:
     def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor):
         alpha = lerp_alpha
         decay = decay_factor
-        if( flip_input ):
+        if flip_input:
             mask = 1.0 - mask
         c = 0 if tapered_corners else 1
         kernel = np.array([[c, 1, c],
@@ -758,44 +758,37 @@ class GrowMaskWithBlur:
         growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
         out = []
         previous_output = None
+        current_expand = expand
         for m in growmask:
             output = m.numpy()
-            for _ in range(abs(expand)):
-                if expand < 0:
+            for _ in range(abs(round(current_expand))):
+                if current_expand < 0:
                     output = scipy.ndimage.grey_erosion(output, footprint=kernel)
                 else:
                     output = scipy.ndimage.grey_dilation(output, footprint=kernel)
-            if expand < 0:
-                expand -= abs(incremental_expandrate)  # Use abs(growrate) to ensure positive change
+            if current_expand < 0:
+                current_expand -= abs(incremental_expandrate)
             else:
-                expand += abs(incremental_expandrate)  # Use abs(growrate) to ensure positive change
+                current_expand += abs(incremental_expandrate)
             output = torch.from_numpy(output)
             if alpha < 1.0 and previous_output is not None:
-                # Interpolate between the previous and current frame
                 output = alpha * output + (1 - alpha) * previous_output
             if decay < 1.0 and previous_output is not None:
-                # Add the decayed previous output to the current frame
                 output += decay * previous_output
                 output = output / output.max()
             previous_output = output
             out.append(output)
 
         if blur_radius != 0:
-            # Convert the tensor list to PIL images, apply blur, and convert back
             for idx, tensor in enumerate(out):
-                # Convert tensor to PIL image
                 pil_image = tensor2pil(tensor.cpu().detach())[0]
-                # Apply Gaussian blur
                 pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius))
-                # Convert back to tensor
                 out[idx] = pil2tensor(pil_image)
             blurred = torch.cat(out, dim=0)
             return (blurred, 1.0 - blurred)
         else:
             return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
-           
-        
-        
+
 class PlotNode:
     @classmethod
     def INPUT_TYPES(s):