add saving safety_checker

optimum/exporters/openvino/convert.py

@@ -715,6 +715,9 @@ def export_from_model(
         tokenizer_3 = getattr(model, "tokenizer_3", None)
         if tokenizer_3 is not None:
             tokenizer_3.save_pretrained(output.joinpath("tokenizer_3"))
+        safety_checker = getattr(model, "safety_checker", None)
+        if safety_checker is not None:
+            safety_checker.save_pretrained(output.joinpath("safety_checker"))
 
         model.save_config(output)
 

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)
 

tests/openvino/test_diffusion.py

@@ -13,6 +13,7 @@
 #  limitations under the License.
 
 import unittest
+from pathlib import Path
 
 import numpy as np
 import pytest
@@ -35,6 +36,7 @@
     OVPipelineForInpainting,
     OVPipelineForText2Image,
 )
+from optimum.intel.openvino.utils import TemporaryDirectory
 from optimum.intel.utils.import_utils import is_transformers_version
 from optimum.utils.testing_utils import require_diffusers
 
@@ -309,6 +311,31 @@ def test_safety_checker(self, model_arch: str):
 
         np.testing.assert_allclose(ov_images, diffusers_images, atol=1e-4, rtol=1e-2)
 
+    @require_diffusers
+    def test_load_and_save_pipeline_with_safety_checker(self):
+        model_id = "katuni4ka/tiny-random-stable-diffusion-with-safety-checker"
+        ov_pipeline = self.OVMODEL_CLASS.from_pretrained(model_id)
+        self.assertTrue(ov_pipeline.safety_checker is not None)
+        self.assertIsInstance(ov_pipeline.safety_checker, StableDiffusionSafetyChecker)
+        with TemporaryDirectory() as tmpdirname:
+            ov_pipeline.save_pretrained(tmpdirname)
+            for subdir in [
+                "text_encoder",
+                "tokenizer",
+                "unet",
+                "vae_encoder",
+                "vae_decoder",
+                "scheduler",
+                "feature_extractor",
+            ]:
+                subdir_path = Path(tmpdirname) / subdir
+                self.assertTrue(subdir_path.is_dir())
+            loaded_pipeline = self.OVMODEL_CLASS.from_pretrained(tmpdirname)
+            self.assertTrue(loaded_pipeline.safety_checker is not None)
+            self.assertIsInstance(loaded_pipeline.safety_checker, StableDiffusionSafetyChecker)
+            del loaded_pipeline
+        del ov_pipeline
+
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_height_width_properties(self, model_arch: str):
         batch_size, height, width, num_images_per_prompt = 2, 128, 64, 4