fix(backend): nsfw checker on non-rgb images

invokeai/backend/image_util/safety_checker.py

@@ -56,16 +56,25 @@ class SafetyChecker:
         features = cls.feature_extractor([image], return_tensors="pt")
         features.to(device)
         cls.safety_checker.to(device)
-        x_image = np.array(image).astype(np.float32) / 255.0
+
+        # Only RGB(A) images are supported, so to prevent an error when a NSFW concept is detect in an image with
+        # a different image mode, we _must_ convert it to RGB and then to a normalized, batched np array.
+        rgb_image = image.convert("RGB")
+        # Convert to normalized (0-1) np array
+        x_image = np.array(rgb_image).astype(np.float32) / 255.0
+        # Add batch dimension and transpose to NCHW
         x_image = x_image[None].transpose(0, 3, 1, 2)
+        # A warning is logged if a NSFW concept is detected - silence those, so we can handle it ourselves.
         with SilenceWarnings():
+            # `clip_input` (features) is used to check for NSFW concepts. `images` is required, but it isn't actually
+            # checked for NSFW concepts. If a NSFW concept is detected, the the image is replaced with a black image.
             checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
         return has_nsfw_concept[0]
 
     @classmethod
     def blur_if_nsfw(cls, image: Image.Image) -> Image.Image:
         if cls.has_nsfw_concept(image):
-            logger.info("A potentially NSFW image has been detected. Image will be blurred.")
+            logger.warning("A potentially NSFW image has been detected. Image will be blurred.")
             blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
             caution = cls._get_caution_img()
             # Center the caution image on the blurred image