diff --git a/comfy/ops.py b/comfy/ops.py
index 1cf22f0cc..9c0b54ff4 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -625,21 +625,29 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                         missing_keys.remove(key)
 
             def state_dict(self, *args, destination=None, prefix="", **kwargs):
-                sd = super().state_dict(*args, destination=destination, prefix=prefix, **kwargs)
-                if isinstance(self.weight, QuantizedTensor):
-                    layout_cls = self.weight._layout_cls
+                if destination is not None:
+                    sd = destination
+                else:
+                    sd = {}
 
-                    # Check if it's any FP8 variant (E4M3 or E5M2)
-                    if layout_cls in ("TensorCoreFP8E4M3Layout", "TensorCoreFP8E5M2Layout", "TensorCoreFP8Layout"):
-                        sd["{}weight_scale".format(prefix)] = self.weight._params.scale
-                    elif layout_cls == "TensorCoreNVFP4Layout":
-                        sd["{}weight_scale_2".format(prefix)] = self.weight._params.scale
-                        sd["{}weight_scale".format(prefix)] = self.weight._params.block_scale
+                if self.bias is not None:
+                    sd["{}bias".format(prefix)] = self.bias
+
+                if isinstance(self.weight, QuantizedTensor):
+                    sd_out = self.weight.state_dict("{}weight".format(prefix))
+                    for k in sd_out:
+                        sd[k] = sd_out[k]
 
                     quant_conf = {"format": self.quant_format}
                     if self._full_precision_mm_config:
                         quant_conf["full_precision_matrix_mult"] = True
                     sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
+
+                    input_scale = getattr(self, 'input_scale', None)
+                    if input_scale is not None:
+                        sd["{}input_scale".format(prefix)] = input_scale
+                else:
+                    sd["{}weight".format(prefix)] = self.weight
                 return sd
 
             def _forward(self, input, weight, bias):