run_safety_checker

latent_consistency_img2img.py

@@ -171,21 +171,8 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
         return prompt_embeds
 
     def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
             has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(
-                    image, output_type="pil"
-                )
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(
-                feature_extractor_input, return_tensors="pt"
-            ).to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
+
         return image, has_nsfw_concept
 
     def prepare_latents(
@@ -425,7 +412,9 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
             image = self.vae.decode(
                 denoised / self.vae.config.scaling_factor, return_dict=False
             )[0]
-            image, has_nsfw_concept = None
+            image, has_nsfw_concept = self.run_safety_checker(
+                image, device, prompt_embeds.dtype
+            )
         else:
             image = denoised
             has_nsfw_concept = None