diff --git a/invokeai/backend/image_util/invisible_watermark.py b/invokeai/backend/image_util/invisible_watermark.py
index 3e8604f9c3..37b3ca918c 100644
--- a/invokeai/backend/image_util/invisible_watermark.py
+++ b/invokeai/backend/image_util/invisible_watermark.py
@@ -20,12 +20,12 @@ class InvisibleWatermark:
     """
 
     @classmethod
-    def invisible_watermark_available(self) -> bool:
+    def invisible_watermark_available(cls) -> bool:
         return config.invisible_watermark
 
     @classmethod
-    def add_watermark(self, image: Image, watermark_text: str) -> Image:
-        if not self.invisible_watermark_available():
+    def add_watermark(cls, image: Image.Image, watermark_text: str) -> Image.Image:
+        if not cls.invisible_watermark_available():
             return image
         logger.debug(f'Applying invisible watermark "{watermark_text}"')
         bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
diff --git a/invokeai/backend/image_util/safety_checker.py b/invokeai/backend/image_util/safety_checker.py
index fd1f05f10e..b9649925e1 100644
--- a/invokeai/backend/image_util/safety_checker.py
+++ b/invokeai/backend/image_util/safety_checker.py
@@ -26,8 +26,8 @@ class SafetyChecker:
     tried_load: bool = False
 
     @classmethod
-    def _load_safety_checker(self):
-        if self.tried_load:
+    def _load_safety_checker(cls):
+        if cls.tried_load:
             return
 
         if config.nsfw_checker:
@@ -35,31 +35,31 @@ class SafetyChecker:
                 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
                 from transformers import AutoFeatureExtractor
 
-                self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
-                self.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
+                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
+                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
                 logger.info("NSFW checker initialized")
             except Exception as e:
                 logger.warning(f"Could not load NSFW checker: {str(e)}")
         else:
             logger.info("NSFW checker loading disabled")
-        self.tried_load = True
+        cls.tried_load = True
 
     @classmethod
-    def safety_checker_available(self) -> bool:
-        self._load_safety_checker()
-        return self.safety_checker is not None
+    def safety_checker_available(cls) -> bool:
+        cls._load_safety_checker()
+        return cls.safety_checker is not None
 
     @classmethod
-    def has_nsfw_concept(self, image: Image) -> bool:
-        if not self.safety_checker_available():
+    def has_nsfw_concept(cls, image: Image.Image) -> bool:
+        if not cls.safety_checker_available():
             return False
 
         device = choose_torch_device()
-        features = self.feature_extractor([image], return_tensors="pt")
+        features = cls.feature_extractor([image], return_tensors="pt")
         features.to(device)
-        self.safety_checker.to(device)
+        cls.safety_checker.to(device)
         x_image = np.array(image).astype(np.float32) / 255.0
         x_image = x_image[None].transpose(0, 3, 1, 2)
         with SilenceWarnings():
-            checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values)
+            checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
         return has_nsfw_concept[0]