Update modified_pipeline_semantic_stable_diffusion.py

modified_pipeline_semantic_stable_diffusion.py

@@ -721,37 +721,37 @@ class SemanticStableDiffusionPipeline(DiffusionPipeline):
                 callback(i, t, latents)
 
         
-        #  # 8. Post-processing
-        # image = self.decode_latents(latents)
+         # 8. Post-processing
+        image = self.decode_latents(latents)
 
-        # # 9. Run safety checker
-        # image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+        # 9. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
 
-        # # 10. Convert to PIL
-        # if output_type == "pil":
-        #     image = self.numpy_to_pil(image)
+        # 10. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
 
-        # if not return_dict:
-        #     return (image, has_nsfw_concept)
+        if not return_dict:
+            return (image, has_nsfw_concept)
 
-        #return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
         
-        # 8. Post-processing
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
+        # # 8. Post-processing
+        # if not output_type == "latent":
+        #     image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        #     image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
+        # else:
+        #     image = latents
+        #     has_nsfw_concept = None
 
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+        # if has_nsfw_concept is None:
+        #     do_denormalize = [True] * image.shape[0]
+        # else:
+        #     do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
 
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+        # image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
 
-        if not return_dict:
-            return (image, has_nsfw_concept)
+        # if not return_dict:
+        #     return (image, has_nsfw_concept)
 
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+        # return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)