hopefully this works lol

app.py

@@ -18,10 +18,11 @@ from diffusers import (
     StableDiffusionLatentUpscalePipeline,
     StableDiffusionImg2ImgPipeline,
     StableDiffusionControlNetImg2ImgPipeline,
+    StableDiffusionSafetyChecker,
     DPMSolverMultistepScheduler,
     EulerDiscreteScheduler
 )
-from transformers import AutoFeatureExtractor
+from transformers import AutoFeatureExtractor, CLIPFeatureExtractor
 import random
 import time
 import tempfile
@@ -50,6 +51,8 @@ main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
     BASE_MODEL,
     controlnet=controlnet,
     vae=vae,
+    safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
+    feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),                                                           
     torch_dtype=torch.float16,
 ).to("cuda")
 image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
@@ -180,7 +183,10 @@ def inference(
         logger.debug("Output Types: generated_image=%s", type(None))
         logger.debug("Content of out_image: %s", out_image)
         logger.debug("Structure of out_image: %s", dir(out_image))
-        return out_image["images"][0]
+        if not out_image.nsfw_content_detected[0]:
+            return out_image["images"][0]
+        else:
+            print("NSFW detected. Nice try.")
         
     
     except Exception as e: