diff --git a/IPAdapterPlus.py b/IPAdapterPlus.py
index 69c15cf..6496034 100644
--- a/IPAdapterPlus.py
+++ b/IPAdapterPlus.py
@@ -672,14 +672,16 @@ class IPAdapterTiled:
 
         del ipadapter
 
-        # 2. Extract the tiles
         tile_size = 256     # I'm using 256 instead of 224 as it is more likely divisible by the latent size, it will be downscaled to 224 by the clip vision encoder
         _, oh, ow, _ = image.shape
+        if attn_mask is None:
+            attn_mask = torch.ones([1, oh, ow], dtype=image.dtype, device=image.device)
+
+        # 2. Extract the tiles
         image = image.permute([0,3,1,2])
-        if attn_mask is not None:
-            attn_mask = attn_mask.unsqueeze(1)
-            # the mask should have the same proportions as the reference image and the latent
-            attn_mask = T.Resize((oh, ow), interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
+        attn_mask = attn_mask.unsqueeze(1)
+        # the mask should have the same proportions as the reference image and the latent
+        attn_mask = T.Resize((oh, ow), interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
 
         # if the image is almost a square, we crop it to a square
         if oh / ow > 0.75 and oh / ow < 1.33:
@@ -687,32 +689,30 @@ class IPAdapterTiled:
             image = T.CenterCrop(min(oh, ow))(image)
             resize = (tile_size*2, tile_size*2)
 
-            if attn_mask is not None:
-                attn_mask = T.CenterCrop(min(oh, ow))(attn_mask)
-        # otherwise resize the smallest side to 224 and the other proportionally
+            attn_mask = T.CenterCrop(min(oh, ow))(attn_mask)
+        # otherwise resize the smallest side and the other proportionally
         else:
             resize = (int(tile_size * ow / oh), tile_size) if oh < ow else (tile_size, int(tile_size * oh / ow))
 
+         # using PIL for better results
         imgs = []
         for img in image:
-            img = T.ToPILImage()(img) # using PIL for better results
+            img = T.ToPILImage()(img)
             img = img.resize(resize, resample=Image.Resampling['LANCZOS'])
             imgs.append(T.ToTensor()(img))
         image = torch.stack(imgs)
         del imgs, img
 
-        if attn_mask is not None:
-            attn_mask = T.Resize(resize[::-1], interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask) # we don't need a high quality resize for the mask
+        # we don't need a high quality resize for the mask
+        attn_mask = T.Resize(resize[::-1], interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
 
         # we allow a maximum of 4 tiles
         if oh / ow > 4 or oh / ow < 0.25:
             crop = (tile_size, tile_size*4) if oh < ow else (tile_size*4, tile_size)
             image = T.CenterCrop(crop)(image)
-            if attn_mask is not None:
-                attn_mask = T.CenterCrop(crop)(attn_mask)
+            attn_mask = T.CenterCrop(crop)(attn_mask)
 
-        if attn_mask is not None:
-            attn_mask = attn_mask.squeeze(1)
+        attn_mask = attn_mask.squeeze(1)
 
         if sharpening > 0:
             image = contrast_adaptive_sharpening(image, sharpening)
@@ -727,7 +727,7 @@ class IPAdapterTiled:
         overlap_x = max(0, (tiles_x * tile_size - ow) / (tiles_x - 1 if tiles_x > 1 else 1))
         overlap_y = max(0, (tiles_y * tile_size - oh) / (tiles_y - 1 if tiles_y > 1 else 1))
 
-        base_mask = torch.zeros([1, oh, ow], dtype=image.dtype, device=image.device)
+        base_mask = torch.zeros([attn_mask.shape[0], oh, ow], dtype=image.dtype, device=image.device)
 
         # extract all the tiles from the image and create the masks
         tiles = []
@@ -738,10 +738,7 @@ class IPAdapterTiled:
                 start_y = int(y * (tile_size - overlap_y))
                 tiles.append(image[:, start_y:start_y+tile_size, start_x:start_x+tile_size, :])
                 mask = base_mask.clone()
-                if attn_mask is not None:
-                    mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size] = attn_mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size]
-                else:
-                    mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size] = 1
+                mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size] = attn_mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size]
                 masks.append(mask)
         del mask