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 e138fb5c..9a41ddb5 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
@@ -105,9 +105,9 @@ class ControlNetExampleForge(scripts.Script):
             'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
         }
         negative_advanced_weighting = {
-            'input': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
-            'middle': [1.0],
-            'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
+            'input': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25],
+            'middle': [1.05],
+            'output': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25]
         }
 
         # The advanced_frame_weighting is a weight applied to each image in a batch.
@@ -125,10 +125,10 @@ class ControlNetExampleForge(scripts.Script):
         advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
 
         # 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
+        positive_advanced_weighting = None
+        negative_advanced_weighting = None
+        advanced_frame_weighting = None
+        advanced_sigma_weighting = None
 
         unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bhwc=control_image,
                                          strength=0.6, start_percent=0.0, end_percent=0.8,
diff --git a/modules_forge/controlnet.py b/modules_forge/controlnet.py
index 4fa50ed8..3d4959b6 100644
--- a/modules_forge/controlnet.py
+++ b/modules_forge/controlnet.py
@@ -1,3 +1,10 @@
+import torch
+
+
+def get_at(array, index, default=None):
+    return array[index] if 0 <= index < len(array) else default
+
+
 def apply_controlnet_advanced(
         unet,
         controlnet,
@@ -75,7 +82,36 @@ def compute_controlnet_weighting(
     sigmas = transformer_options['sigmas']
     cond_mark = transformer_options['cond_mark']
 
+    if advanced_frame_weighting is not None:
+        advanced_frame_weighting = torch.Tensor(advanced_frame_weighting * len(cond_or_uncond)).to(sigmas)
+        assert advanced_frame_weighting.shape[0] == cond_mark.shape[0], \
+            'Frame weighting list length is different from batch size!'
+
     if advanced_sigma_weighting is not None:
-        advanced_sigma_weighting = advanced_sigma_weighting(sigmas)
+        advanced_sigma_weighting = torch.cat([advanced_sigma_weighting(sigmas)] * len(cond_or_uncond))
+
+    for k, v in control.items():
+        for i in range(len(v)):
+            positive_weight = 1.0
+            negative_weight = 1.0
+            sigma_weight = 1.0
+            frame_weight = 1.0
+
+            if positive_advanced_weighting is not None:
+                positive_weight = get_at(positive_advanced_weighting.get(k, []), i, 1.0)
+
+            if negative_advanced_weighting is not None:
+                negative_weight = get_at(negative_advanced_weighting.get(k, []), i, 1.0)
+
+            if advanced_sigma_weighting is not None:
+                sigma_weight = advanced_sigma_weighting
+
+            if advanced_frame_weighting is not None:
+                frame_weight = advanced_frame_weighting
+
+            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]
 
     return control