diff --git a/nodes.py b/nodes.py
index 96fa2e0..d1d27f2 100644
--- a/nodes.py
+++ b/nodes.py
@@ -110,6 +110,9 @@ def INPUT_TYPES(cls):
                 'crop_size': ('INT', {'default': 512, 'min': 512, 'max': 1024, 'step': 128}),
                 'crop_factor': ('FLOAT', {'default': 1.5, 'min': 1.0, 'max': 3, 'step': 0.1}),
                 'mask_type': (mask_types,)
+            },
+            'optional': {
+                'image_override': ('IMAGE',),
             }
         }
     
@@ -118,7 +121,7 @@ def INPUT_TYPES(cls):
     FUNCTION = 'run'
     CATEGORY = 'facetools'
 
-    def run(self, faces, crop_size, crop_factor, mask_type):
+    def run(self, faces, crop_size, crop_factor, mask_type, image_override=None):
         if len(faces) == 0:
             empty_crop = torch.zeros((1,512,512,3))
             empty_mask = torch.zeros((1,512,512))
@@ -132,7 +135,7 @@ def run(self, faces, crop_size, crop_factor, mask_type):
         masks = []
         warps = []
         for face in faces:
-            M, crop = face.crop(crop_size, crop_factor)
+            M, crop = face.crop(crop_size, crop_factor, image_override)
             mask = mask_crop(face, M, crop, mask_type)
             crops.append(np.array(crop[0]))
             masks.append(np.array(mask[0]))
diff --git a/utils.py b/utils.py
index 363d177..cf9bdd4 100644
--- a/utils.py
+++ b/utils.py
@@ -109,7 +109,7 @@ def __init__(self, img, a, b, c, d) -> None:
         rot = cv2.getRotationMatrix2D((128*s,128*s), 90*i, 1)
         self.R = np.vstack((rot, np.array((0,0,1))))
     
-    def crop(self, size, crop_factor):
+    def crop(self, size, crop_factor, image_override=None):
         S = np.array([[1/crop_factor, 0, 0], [0, 1/crop_factor, 0], [0, 0, 1]])
         M = estimate_norm(self.kps, size)
         N = M @ self.R @ self.T2
@@ -117,7 +117,8 @@ def crop(self, size, crop_factor):
         T3 = np.array([[1, 0, -cx], [0, 1, -cy], [0, 0, 1]])
         T4 = np.array([[1, 0, cx], [0, 1, cy], [0, 0, 1]])
         N = N @ T4 @ S @ T3
-        crop = cv2.warpAffine(self.img.numpy(), N, (size, size))
+        img = self.img if image_override is None else image_override.squeeze()
+        crop = cv2.warpAffine(img.numpy(), N, (size, size))
         crop = torch.from_numpy(crop)[None]
         
         return N, crop