diff --git a/src/qonnx/transformation/qcdq_to_qonnx.py b/src/qonnx/transformation/qcdq_to_qonnx.py
index b7e35c0d..04f8342a 100644
--- a/src/qonnx/transformation/qcdq_to_qonnx.py
+++ b/src/qonnx/transformation/qcdq_to_qonnx.py
@@ -109,6 +109,7 @@ def apply(self, model: ModelWrapper) -> Tuple[ModelWrapper, bool]:
                 dq_init = model.get_initializer(dq_inp)
                 dq_scale_v = model.get_initializer(dq_scale)
                 dq_zeropt_v = model.get_initializer(dq_zeropt)
+                axis = get_by_name(dq_node.attribute, "axis")
                 if quant_candidates is None and dq_init is None:
                     continue
                 if any([x is None for x in [dq_scale_v, dq_zeropt_v]]):
@@ -123,13 +124,24 @@ def apply(self, model: ModelWrapper) -> Tuple[ModelWrapper, bool]:
                     # read quantized weight dtype for standalone deqnt
                     q_vi = model.get_tensor_valueinfo(dq_inp)
                     (bitwidth, signed, narrow) = extract_elem_type(q_vi.type.tensor_type.elem_type)
+                    scale_factor, zeropt = dq_scale, dq_zeropt
+                    # fix scale factor for Quant (different shape expectations wrt broadcasting)
+                    if not (axis is None):
+                        axis_i = axis.i
+                        ishape = model.get_tensor_shape(dq_inp)
+                        desired_shp = [1] * len(ishape)
+                        desired_shp[axis_i] = dq_scale_v.shape[0]
+                        dq_scale_v = dq_scale_v.reshape(desired_shp)
+                        dq_zeropt_v = dq_zeropt_v.reshape(desired_shp)
+                        model.set_initializer(scale_factor, dq_scale_v)
+                        model.set_initializer(zeropt, dq_zeropt_v)
                     # overwrite DQ initializer with scaled version
                     scaled_qnt_t = (dq_init - dq_zeropt_v) * dq_scale_v
                     scaled_qnt_t = scaled_qnt_t.astype(np.float32)
                     model.set_initializer(dq_inp, scaled_qnt_t)
                     q_inp = dq_inp
                     final_out = dq_node.output[0]
-                    scale_factor, zeropt = dq_scale, dq_zeropt
+                    
                     nodes_to_remove.append(dq_node)
                 elif quant_candidates[0].op_type in ["QuantizeLinear", "Clip"]:
                     clip_range = None
@@ -167,20 +179,19 @@ def apply(self, model: ModelWrapper) -> Tuple[ModelWrapper, bool]:
                     value_info = model.get_tensor_valueinfo(quant_node.output[0])
                     (bitwidth, signed, narrow) = extract_elem_type(value_info.type.tensor_type.elem_type, clip_range)
                     scale_factor, zeropt = q_scale, q_zeropt
+                    if not (axis is None):
+                        axis_i = axis.i
+                        ishape = model.get_tensor_shape(dq_inp)
+                        desired_shp = [1] * len(ishape)
+                        desired_shp[axis_i] = dq_scale_v.shape[0]
+                        dq_scale_v = dq_scale_v.reshape(desired_shp)
+                        dq_zeropt_v = dq_zeropt_v.reshape(desired_shp)
+                        model.set_initializer(scale_factor, dq_scale_v)
+                        model.set_initializer(zeropt, dq_zeropt_v)
                 else:
                     # handle all other cases, skip
                     continue
-                axis = get_by_name(dq_node.attribute, "axis")
-                # fix scale factor for Quant (different shape expectations wrt broadcasting)
-                if not (axis is None):
-                    axis_i = axis.i
-                    ishape = model.get_tensor_shape(dq_inp)
-                    desired_shp = [1] * len(ishape)
-                    desired_shp[axis_i] = dq_scale_v.shape[0]
-                    dq_scale_v = dq_scale_v.reshape(desired_shp)
-                    dq_zeropt_v = dq_zeropt_v.reshape(desired_shp)
-                    model.set_initializer(scale_factor, dq_scale_v)
-                    model.set_initializer(zeropt, dq_zeropt_v)
+                
                 # create new Quant node for suitable cases
                 new_q_node_name = "Quant_" + q_inp
                 bw_tensor_name = f"{new_q_node_name}_bitwidth"