diff --git a/README.md b/README.md
index 61b916c0..b21b6cf6 100644
--- a/README.md
+++ b/README.md
@@ -352,6 +352,7 @@ The memory optimization in this example is fully automatic. You do not need to c
 
 import cv2
 import gradio as gr
+import torch
 
 from modules import scripts
 from modules.shared_cmd_options import cmd_opts
@@ -472,18 +473,26 @@ class ControlNetExampleForge(scripts.Script):
         sigma_min = unet.model.model_sampling.sigma_min
         advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
 
+        # You can even input a tensor to mask all control injections
+        # The mask will be automatically resized during inference in UNet.
+        # The size should be B 1 H W and the H and W are not important
+        # because they will be resized automatically
+        advanced_mask_weighting = torch.ones(size=(1, 1, 512, 512))
+
         # But in this simple example we do not use them
         positive_advanced_weighting = None
         negative_advanced_weighting = None
         advanced_frame_weighting = None
         advanced_sigma_weighting = None
+        advanced_mask_weighting = None
 
         unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image_bchw,
                                          strength=0.6, start_percent=0.0, end_percent=0.8,
                                          positive_advanced_weighting=positive_advanced_weighting,
                                          negative_advanced_weighting=negative_advanced_weighting,
                                          advanced_frame_weighting=advanced_frame_weighting,
-                                         advanced_sigma_weighting=advanced_sigma_weighting)
+                                         advanced_sigma_weighting=advanced_sigma_weighting,
+                                         advanced_mask_weighting=advanced_mask_weighting)
 
         p.sd_model.forge_objects.unet = unet
 
diff --git a/extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py b/extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
index 2f9a168c..9c10cb23 100644
--- a/extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
+++ b/extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
@@ -2,6 +2,7 @@
 
 import cv2
 import gradio as gr
+import torch
 
 from modules import scripts
 from modules.shared_cmd_options import cmd_opts
@@ -122,18 +123,26 @@ class ControlNetExampleForge(scripts.Script):
         sigma_min = unet.model.model_sampling.sigma_min
         advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
 
+        # You can even input a tensor to mask all control injections
+        # The mask will be automatically resized during inference in UNet.
+        # The size should be B 1 H W and the H and W are not important
+        # because they will be resized automatically
+        advanced_mask_weighting = torch.ones(size=(1, 1, 512, 512))
+
         # But in this simple example we do not use them
         positive_advanced_weighting = None
         negative_advanced_weighting = None
         advanced_frame_weighting = None
         advanced_sigma_weighting = None
+        advanced_mask_weighting = None
 
         unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image_bchw,
                                          strength=0.6, start_percent=0.0, end_percent=0.8,
                                          positive_advanced_weighting=positive_advanced_weighting,
                                          negative_advanced_weighting=negative_advanced_weighting,
                                          advanced_frame_weighting=advanced_frame_weighting,
-                                         advanced_sigma_weighting=advanced_sigma_weighting)
+                                         advanced_sigma_weighting=advanced_sigma_weighting,
+                                         advanced_mask_weighting=advanced_mask_weighting)
 
         p.sd_model.forge_objects.unet = unet
 
diff --git a/ldm_patched/modules/controlnet.py b/ldm_patched/modules/controlnet.py
index b9879235..9ddd3c6b 100644
--- a/ldm_patched/modules/controlnet.py
+++ b/ldm_patched/modules/controlnet.py
@@ -87,7 +87,7 @@ def compute_controlnet_weighting(control, cnet):
             final_weight = final_weight * sigma_weight * frame_weight
 
             if isinstance(advanced_mask_weighting, torch.Tensor):
-                control_signal = control_signal * torch.nn.functional.interpolate(advanced_mask_weighting, size=(H, W), mode='bilinear')
+                control_signal = control_signal * torch.nn.functional.interpolate(advanced_mask_weighting.to(control_signal), size=(H, W), mode='bilinear')
 
             control[k][i] = control_signal * final_weight[:, None, None, None]