added safety checking test

tests/openvino/test_stable_diffusion.py

@@ -555,3 +555,51 @@ def test_num_images_per_prompt_static_model(self, model_arch: str):
             inputs = _generate_inputs(batch_size)
             outputs = pipeline(**inputs, num_images_per_prompt=num_images, height=_height, width=width).images
             self.assertEqual(outputs.shape, (batch_size * num_images, height, width, 3))
+
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    @unittest.skipIf(is_diffusers_version("<=", "0.21.4"), "not supported with this diffusers version")
+    def test_safety_checker(self, model_arch: str):
+        ov_pipeline = self.MODEL_CLASS.from_pretrained(MODEL_NAMES[model_arch], export=True, ov_config=F32_CONFIG)
+        self.assertIsInstance(ov_pipeline.text_encoder, OVModelTextEncoder)
+        self.assertIsInstance(ov_pipeline.vae_encoder, OVModelVaeEncoder)
+        self.assertIsInstance(ov_pipeline.vae_decoder, OVModelVaeDecoder)
+        self.assertIsInstance(ov_pipeline.unet, OVModelUnet)
+        self.assertIsInstance(ov_pipeline.config, Dict)
+
+        from diffusers import LatentConsistencyModelPipeline
+        from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+
+        safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
+        pipeline = LatentConsistencyModelPipeline.from_pretrained(
+            MODEL_NAMES[model_arch], safety_checker=safety_checker
+        )
+
+        batch_size, num_images_per_prompt, height, width = 2, 3, 64, 128
+        latents = ov_pipeline.prepare_latents(
+            batch_size * num_images_per_prompt,
+            ov_pipeline.unet.config["in_channels"],
+            height,
+            width,
+            dtype=np.float32,
+            generator=np.random.RandomState(0),
+        )
+
+        kwargs = {
+            "prompt": ["sailing ship in storm by Leonardo da Vinci"] * batch_size,
+            "num_inference_steps": 1,
+            "num_images_per_prompt": num_images_per_prompt,
+            "height": height,
+            "width": width,
+            "guidance_scale": 8.5,
+        }
+
+        for output_type in ["latent", "np"]:
+            ov_outputs = ov_pipeline(latents=latents, output_type=output_type, **kwargs).images
+            self.assertIsInstance(ov_outputs, np.ndarray)
+            with torch.no_grad():
+                outputs = pipeline(latents=torch.from_numpy(latents), output_type=output_type, **kwargs).images
+
+            # Compare model outputs
+            self.assertTrue(np.allclose(ov_outputs, outputs, atol=1e-4))
+        # Compare model devices
+        self.assertEqual(pipeline.device.type, ov_pipeline.device)