Update Custom-Advanced-VACE-Node/nodes_utility.py

Custom-Advanced-VACE-Node/nodes_utility.py

@@ -121,21 +121,23 @@ class WanVideoVACEStartToEndFrame:
         device = start_image.device if start_image is not None else end_image.device
         B, H, W, C = start_image.shape if start_image is not None else end_image.shape
         
-        if control_strength < 1.0 and control_images is not None:
-            # strength happens at much smaller number
-            control_strength *= 2.0
-            control_strength = control_strength * control_strength / 8.0
-            control_images = torch.lerp(torch.ones((control_images.shape[0], control_images.shape[1], control_images.shape[2], control_images.shape[3])) * empty_frame_level, control_images, control_strength)
-            
-        # ease in control stuff?
-        if num_frames > control_ease and control_ease > 0:
-            empty_frame = torch.ones((1, control_images.shape[1], control_images.shape[2], control_images.shape[3])) * empty_frame_level
-            if start_image is not None:
-                for i in range(1, control_ease + 1):
-                    control_images[i] = torch.lerp(control_images[i], empty_frame, (control_ease - i) / (1 + control_ease))
-            else:
-                for i in range(num_frames - control_ease - 1, num_frames - 1):
-                    control_images[i] = torch.lerp(control_images[i], empty_frame, i / (1 + control_ease))
+        if control_images is not None:
+            # weaken the control images?
+            if control_strength < 1.0:
+                # strength happens at much smaller number
+                control_strength *= 2.0
+                control_strength = control_strength * control_strength / 8.0
+                control_images = torch.lerp(torch.ones((control_images.shape[0], control_images.shape[1], control_images.shape[2], control_images.shape[3])) * empty_frame_level, control_images, control_strength)
+                
+            # ease in control stuff?
+            if num_frames > control_ease and control_ease > 0:
+                empty_frame = torch.ones((1, control_images.shape[1], control_images.shape[2], control_images.shape[3])) * empty_frame_level
+                if start_image is not None:
+                    for i in range(1, control_ease + 1):
+                        control_images[i] = torch.lerp(control_images[i], empty_frame, (control_ease - i) / (1 + control_ease))
+                else:
+                    for i in range(num_frames - control_ease - 1, num_frames - 1):
+                        control_images[i] = torch.lerp(control_images[i], empty_frame, i / (1 + control_ease))
 
         if start_image is None and end_image is None and control_images is not None:
             if control_images.shape[0] >= num_frames: