diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index a08eec192..6fc34551e 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -19,14 +19,6 @@ def get_module_names(model: ModelMixin) -> list:
     return modules_names
 
 
-def unset_config_on_save(quantization_config: SDNQConfig) -> SDNQConfig:
-    quantization_config.quantization_device = None
-    quantization_config.return_device = None
-    quantization_config.non_blocking = False
-    quantization_config.add_skip_keys = False
-    return quantization_config
-
-
 def normalize_tied_weights_keys_for_save(model: ModelMixin, is_pipeline: bool = False) -> list[tuple[torch.nn.Module, object]]:
     normalized_modules = []
     modules_to_walk = []
@@ -53,19 +45,6 @@ def restore_tied_weights_keys_after_save(normalized_modules: list[tuple[torch.nn
 
 
 def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "5GB", is_pipeline: bool = False, sdnq_config: SDNQConfig | None = None) -> None:
-    if is_pipeline:
-        for module_name in get_module_names(model):
-            module = getattr(model, module_name, None)
-            if hasattr(module, "config") and hasattr(module.config, "quantization_config") and isinstance(module.config.quantization_config, SDNQConfig):
-                module.config.quantization_config = unset_config_on_save(module.config.quantization_config)
-            if hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
-                module.quantization_config = unset_config_on_save(module.quantization_config)
-    else:
-        if hasattr(model, "config") and hasattr(model.config, "quantization_config") and isinstance(model.config.quantization_config, SDNQConfig):
-            model.config.quantization_config = unset_config_on_save(model.config.quantization_config)
-        if hasattr(model, "quantization_config") and isinstance(model.quantization_config, SDNQConfig):
-            model.quantization_config = unset_config_on_save(model.quantization_config)
-
     normalized_modules = normalize_tied_weights_keys_for_save(model, is_pipeline=is_pipeline)
     try:
         model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
@@ -74,7 +53,6 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "5
 
     quantization_config_path = os.path.join(model_path, "quantization_config.json")
     if sdnq_config is not None: # if provided, save global config
-        sdnq_config = unset_config_on_save(sdnq_config)
         sdnq_config.to_json_file(quantization_config_path)
 
     if is_pipeline: