diff --git a/modules_forge/controlnet.py b/modules_forge/controlnet.py
index 8ad88311..b0ca32d1 100644
--- a/modules_forge/controlnet.py
+++ b/modules_forge/controlnet.py
@@ -15,7 +15,8 @@ def apply_controlnet_advanced(
         positive_advanced_weighting=None,
         negative_advanced_weighting=None,
         advanced_frame_weighting=None,
-        advanced_sigma_weighting=None
+        advanced_sigma_weighting=None,
+        advanced_mask_weighting=None
 ):
     """
 
@@ -53,6 +54,12 @@ def apply_controlnet_advanced(
         sigma_min = unet.model.model_sampling.sigma_min
         advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
 
+    # advanced_mask_weighting
+
+    A mask can be applied to control signals.
+    This should be a tensor with shape B 1 H W where the H and W can be arbitrary.
+    This mask will be resized automatically to match the shape of all injection layers.
+
     """
 
     cnet = controlnet.copy().set_cond_hint(image_bchw, strength, (start_percent, end_percent))
@@ -61,6 +68,13 @@ def apply_controlnet_advanced(
     cnet.advanced_frame_weighting = advanced_frame_weighting
     cnet.advanced_sigma_weighting = advanced_sigma_weighting
 
+    if advanced_mask_weighting is not None:
+        assert isinstance(advanced_mask_weighting, torch.Tensor)
+        B, C, H, W = advanced_mask_weighting.shape
+        assert B > 0 and C == 1 and H > 0 and W > 0
+
+    cnet.advanced_mask_weighting = advanced_mask_weighting
+
     m = unet.clone()
     m.add_patched_controlnet(cnet)
     return m
@@ -72,10 +86,12 @@ def compute_controlnet_weighting(control, cnet):
     negative_advanced_weighting = cnet.negative_advanced_weighting
     advanced_frame_weighting = cnet.advanced_frame_weighting
     advanced_sigma_weighting = cnet.advanced_sigma_weighting
+    advanced_mask_weighting = cnet.advanced_mask_weighting
     transformer_options = cnet.transformer_options
 
     if positive_advanced_weighting is None and negative_advanced_weighting is None \
-            and advanced_frame_weighting is None and advanced_sigma_weighting is None:
+            and advanced_frame_weighting is None and advanced_sigma_weighting is None \
+            and advanced_mask_weighting is None:
         return control
 
     cond_or_uncond = transformer_options['cond_or_uncond']
@@ -92,6 +108,9 @@ def compute_controlnet_weighting(control, cnet):
 
     for k, v in control.items():
         for i in range(len(v)):
+            control_signal = control[k][i]
+            B, C, H, W = control_signal.shape
+
             positive_weight = 1.0
             negative_weight = 1.0
             sigma_weight = 1.0
@@ -112,6 +131,9 @@ def compute_controlnet_weighting(control, cnet):
             final_weight = positive_weight * (1.0 - cond_mark) + negative_weight * cond_mark
             final_weight = final_weight * sigma_weight * frame_weight
 
-            control[k][i] = control[k][i] * final_weight[:, None, None, None]
+            if isinstance(advanced_mask_weighting, torch.Tensor):
+                control_signal = control_signal * torch.nn.functional.interpolate(advanced_mask_weighting, size=(H, W), mode='bilinear')
+
+            control[k][i] = control_signal * final_weight[:, None, None, None]
 
     return control