diff --git a/src/brevitas/core/function_wrapper/shape.py b/src/brevitas/core/function_wrapper/shape.py
index f1dfc7796..e175e4445 100644
--- a/src/brevitas/core/function_wrapper/shape.py
+++ b/src/brevitas/core/function_wrapper/shape.py
@@ -165,6 +165,11 @@ def __init__(self, expanded_groupwise_shape, group_size, group_dim) -> None:
 
     @brevitas.jit.script_method
     def forward(self, x: torch.Tensor):
+        # This one is a bit tricky but we could end up here:
+        # - If we quantize the zero point, which will already have expanded shape matching the scale (although no padding, but we don't need the padding)
+        # - Groupwise HQO quantization, where weight will already have been padded and expanded
+        if len(x.shape) == len(self.expanded_groupwise_shape):
+            return x
         y = torch.nn.functional.pad(
             x, padding(x, self.group_size, self.group_dim), mode='constant', value=0.)
         y = y.view(self.expanded_groupwise_shape)
diff --git a/src/brevitas/core/stats/stats_op.py b/src/brevitas/core/stats/stats_op.py
index 461aeb3e6..29d4d06e8 100644
--- a/src/brevitas/core/stats/stats_op.py
+++ b/src/brevitas/core/stats/stats_op.py
@@ -692,7 +692,8 @@ def parameter_search(self, xl, x):
                 self.set_local_loss_mode(False)
                 qt_value = self.input_view_shape_impl(quant_tensor.value)
                 qt_scale = self.input_view_shape_impl(quant_tensor.scale)
-                qt_int = self.input_view_shape_impl(quant_tensor.int())
+                qt_zp = self.input_view_shape_impl(quant_tensor.zero_point)
+                qt_int = qt_value / qt_scale + qt_zp
                 loss = torch.abs(qt_value - x).mean()
                 best_candidate = torch.where(loss < best_loss, candidate, best_candidate)
                 if loss >= best_loss:
@@ -700,6 +701,9 @@ def parameter_search(self, xl, x):
                 best_loss = torch.min(loss, best_loss)
                 W_e = shrink_lp_op(x - qt_value, self.beta, self.lp_norm)
 
+                # Compared to the original formulation, the value we're looking for is:
+                # - scaled by qt_scale
+                # - opposite sign
                 val = self.input_view_shape_impl((x - W_e) - qt_int * qt_scale)
 
                 if self.stats_reduce_dim is None:
diff --git a/src/brevitas_examples/common/generative/quantize.py b/src/brevitas_examples/common/generative/quantize.py
index 10f7ce259..f74a91933 100644
--- a/src/brevitas_examples/common/generative/quantize.py
+++ b/src/brevitas_examples/common/generative/quantize.py
@@ -98,13 +98,16 @@
                     'sym': Int8WeightPerChannelFixedPoint},
                 'per_group': {
                     'sym': MXInt8Weight, 'asym': ShiftedMXUInt8Weight}},
+            'hqo': {
+                'per_group': {
+                    'asym': MXHQO}},
             'mse': {
                 'per_tensor': {
                     'sym': Int8WeightPerTensorFixedPointMSE},
                 'per_channel': {
-                    'sym': Int8WeightPerChannelFixedPointMSE}},
-            'per_group': {
-                'sym': MXInt8WeightMSE, 'asym': ShiftedMXUInt8WeightMSE}}},
+                    'sym': Int8WeightPerChannelFixedPointMSE},
+                'per_group': {
+                    'sym': MXInt8WeightMSE, 'asym': ShiftedMXUInt8WeightMSE}}}},
     'float': {
         'float_scale': {
             'stats': {