add safety_checker to save_pretrained

optimum/intel/openvino/modeling_diffusion.py

@@ -295,6 +295,8 @@ def _save_pretrained(self, save_directory: Union[str, Path]):
             self.tokenizer_3.save_pretrained(save_directory / "tokenizer_3")
         if self.feature_extractor is not None:
             self.feature_extractor.save_pretrained(save_directory / "feature_extractor")
+        if getattr(self, "safety_checker", None) is not None:
+            self.safety_checker.save_pretrained(save_directory / "safety_checker")
 
         self._save_openvino_config(save_directory)
 
@@ -422,12 +424,15 @@ def _from_pretrained(
                     module = getattr(pipelines, module_name)
                 else:
                     module = importlib.import_module(module_name)
+                logger.warn(module)
                 class_obj = getattr(module, module_class)
                 load_method = getattr(class_obj, "from_pretrained")
                 # Check if the module is in a subdirectory
                 if (model_save_path / name).is_dir():
+                    logger.warn(name)
                     submodels[name] = load_method(model_save_path / name)
                 else:
+                    logger.warn(name)
                     submodels[name] = load_method(model_save_path)
 
         models = {
@@ -449,8 +454,10 @@ def _from_pretrained(
         if (quantization_config is None or quantization_config.dataset is None) and not compile_only:
             for name, path in models.items():
                 if name in kwargs:
+                    logger.warn(name)
                     models[name] = kwargs.pop(name)
                 else:
+                    logger.warn(name)
                     models[name] = cls.load_model(path, quantization_config) if path.is_file() else None
         elif compile_only:
             ov_config = kwargs.get("ov_config", {})