diff --git a/src/brevitas/core/scaling/standalone.py b/src/brevitas/core/scaling/standalone.py
index 5126e4f83..6a14f86cb 100644
--- a/src/brevitas/core/scaling/standalone.py
+++ b/src/brevitas/core/scaling/standalone.py
@@ -80,8 +80,11 @@ def __init__(
     def forward(self, placeholder: Tensor, threshold: Optional[torch.Tensor] = None) -> Tensor:
         if threshold is None:
             threshold = torch.ones(1).type_as(placeholder)
-        value = self.value() / threshold
-        restricted_value = self.restrict_clamp_scaling(value)
+        # We first apply any restriction to scaling
+        # For IntQuant, this is no-op, retrocompatible.
+        threshold = self.restrict_clamp_scaling(self.restrict_init_module(threshold))
+        restricted_value = self.restrict_clamp_scaling(self.value())
+        restricted_value = restricted_value / threshold
         return restricted_value
 
 
@@ -145,6 +148,7 @@ def __init__(
             scaling_init = torch.tensor(scaling_init, dtype=dtype, device=device)
         if restrict_scaling_impl is not None:
             scaling_init = restrict_scaling_impl.restrict_init_tensor(scaling_init)
+            self.restrict_init_module = restrict_scaling_impl.restrict_init_module()
         if scaling_init.shape == SCALAR_SHAPE and scaling_shape is not None:
             scaling_init = torch.full(scaling_shape, scaling_init, dtype=dtype, device=device)
         self.value = Parameter(scaling_init)
@@ -154,8 +158,11 @@ def __init__(
     def forward(self, placeholder: Tensor, threshold: Optional[torch.Tensor] = None) -> Tensor:
         if threshold is None:
             threshold = torch.ones(1).type_as(placeholder)
-        value = abs_binary_sign_grad(self.restrict_clamp_scaling(self.value / threshold))
-        return value
+        # We first apply any restriction to scaling
+        # For IntQuant, this is no-op, retrocompatible.
+        threshold = self.restrict_clamp_scaling(self.restrict_init_module(threshold))
+        value = abs_binary_sign_grad(self.restrict_clamp_scaling(self.value))
+        return value / threshold
 
     def _load_from_state_dict(
             self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
@@ -363,7 +370,7 @@ def training_forward(self, stats_input: Tensor, threshold: torch.Tensor) -> Tens
             self.restrict_inplace_preprocess(self.buffer)
             inplace_tensor_mul(self.value.detach(), self.buffer)
             threshold = self.restrict_preprocess(threshold)
-            value = self.restrict_scaling_impl.combine_stats_threshold(value, threshold)
+            value = self.restrict_scaling_impl.combine_stats_threshold(self.value, threshold)
             self.counter = self.counter + 1
             return abs_binary_sign_grad(self.clamp_scaling(self.restrict_scaling(value)))
         else: