diff --git a/nodes.py b/nodes.py
index 40a2ce8..a8700a1 100644
--- a/nodes.py
+++ b/nodes.py
@@ -39,7 +39,7 @@
     StitchingRetargetingNetwork,
 )
 from .liveportrait.utils.camera import get_rotation_matrix
-from .liveportrait.utils.crop import _transform_img_kornia
+from .liveportrait.utils.crop import _transform_img_kornia, _transform_pts
 
 
 class InferenceConfig:
@@ -616,20 +616,22 @@ def INPUT_TYPES(s):
                      ],
                     ),
             "rotate": ("BOOLEAN", {"default": True}),
+            "output_masks": ("BOOLEAN", {"default": False}),
             },           
         }
 
-    RETURN_TYPES = ("IMAGE", "CROPINFO",)
-    RETURN_NAMES = ("cropped_image", "crop_info",)
+    RETURN_TYPES = ("IMAGE", "CROPINFO", "MASK")
+    RETURN_NAMES = ("cropped_image", "crop_info", "mask")
     FUNCTION = "process"
     CATEGORY = "LivePortrait"
 
-    def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ratio, face_index, face_index_order, rotate):
+    def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ratio, face_index, face_index_order, rotate, output_masks):
         source_image_np = (source_image.contiguous() * 255).byte().numpy()
 
         # Initialize lists
         crop_info_list = []
         cropped_images_list = []
+        masks_list = []
         source_info = []
         source_rot_list = []
         f_s_list = []
@@ -640,7 +642,8 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
         for i in tqdm(range(len(source_image_np)), desc='Detecting, cropping, and processing..', total=len(source_image_np)):
             # Cropping operation
             crop_info, cropped_image_256 = cropper.crop_single_image(source_image_np[i], dsize, scale, vy_ratio, vx_ratio, face_index, face_index_order, rotate)
-            
+            if output_masks:
+                mask = np.zeros(source_image_np[i].shape[:2],dtype=float) 
             # Processing source images
             if crop_info:
                 crop_info_list.append(crop_info)
@@ -661,6 +664,11 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
                 f_s = pipeline.live_portrait_wrapper.extract_feature_3d(I_s)
                 f_s_list.append(f_s)
 
+                if output_masks:
+                    pts = np.array([[0,0],[0,dsize],[dsize,dsize],[dsize,0]],dtype=float)
+                    pts = (_transform_pts(pts,crop_info["M_c2o"])+0.5).astype(np.int32)
+                    cv2.fillPoly(mask, pts=[pts], color=1.0)
+                
                 del I_s
                 
             else:
@@ -671,7 +679,10 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
                 x_s_list.append(None)
                 source_info.append(None)
                 source_rot_list.append(None)
-        
+            
+            if output_masks:
+                masks_list.append(torch.from_numpy(mask).unsqueeze(0))   
+                
             # Update progress bar
             pbar.update(1)
         
@@ -679,6 +690,8 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
             torch.stack([torch.from_numpy(np_array) for np_array in cropped_images_list])
             / 255
         )
+        if output_masks:
+            masks_list = torch.cat(masks_list,axis=0).float()
         
         crop_info_dict = {
             'crop_info_list': crop_info_list,
@@ -688,7 +701,7 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
             'source_info': source_info
         }
 
-        return (cropped_tensors_out, crop_info_dict)
+        return (cropped_tensors_out, crop_info_dict, masks_list)
 
 class LivePortraitRetargeting:
     @classmethod