diff --git a/scripts/cimage.py b/scripts/cimage.py
index 3892c51..35f09a7 100644
--- a/scripts/cimage.py
+++ b/scripts/cimage.py
@@ -1,57 +1,9 @@
-from typing import List, Union, Dict, Set, Tuple
+import tempfile
+from ifnude import detect
 
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from transformers import AutoFeatureExtractor
-import torch
-from PIL import Image, ImageFilter
-import numpy as np
-
-safety_model_id: str = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor: AutoFeatureExtractor = None
-safety_checker: StableDiffusionSafetyChecker = None
-
-
-def numpy_to_pil(images: np.ndarray) -> List[Image.Image]:
-    if images.ndim == 3:
-        images = images[None, ...]
-    images = (images * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-
-    return pil_images
-
-
-def check_image(x_image: np.ndarray) -> Tuple[np.ndarray, List[bool]]:
-    global safety_feature_extractor, safety_checker
-
-    if safety_feature_extractor is None:
-        safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
-        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-
-    safety_checker_input = safety_feature_extractor(
-        images=numpy_to_pil(x_image), return_tensors="pt"
-    )
-    x_checked_image, hs = safety_checker(
-        images=x_image, clip_input=safety_checker_input.pixel_values
-    )
-
-    return x_checked_image, hs
-
-
-def check_batch(x: torch.Tensor) -> torch.Tensor:
-    x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
-    x_checked_image, _ = check_image(x_samples_ddim_numpy)
-    x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
-    return x
-
-
-def convert_to_sd(img: Image) -> Image:
-    _, hs = check_image(np.array(img))
-    if any(hs):
-        img = (
-            img.resize((int(img.width * 0.1), int(img.height * 0.1)))
-            .resize(img.size, Image.BOX)
-            .filter(ImageFilter.BLUR)
-        )
-    return img
+def convert_to_sd(img):
+    shapes = []
+    chunks = detect(img)
+    for chunk in chunks:
+        shapes.append(chunk["score"] > 0.7)
+    return [any(shapes), tempfile.NamedTemporaryFile(delete=False, suffix=".png")]
diff --git a/scripts/faceswap.py b/scripts/faceswap.py
index e79eb9c..6d5bf22 100644
--- a/scripts/faceswap.py
+++ b/scripts/faceswap.py
@@ -13,7 +13,6 @@ from modules.face_restoration import FaceRestoration
 
 from scripts.roop_logging import logger
 from scripts.swapper import UpscaleOptions, swap_face, ImageResult
-from scripts.cimage import check_batch
 from scripts.roop_version import version_flag
 import os
 
@@ -179,8 +178,7 @@ class FaceSwapScript(scripts.Script):
 
     def postprocess_batch(self, p, *args, **kwargs):
         if self.enable:
-            images = kwargs["images"]
-            images[:] = check_batch(images)[:]
+            return images
 
     def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
         if self.enable and self.swap_in_generated:
diff --git a/scripts/swapper.py b/scripts/swapper.py
index 4a2f4b5..2a3e79e 100644
--- a/scripts/swapper.py
+++ b/scripts/swapper.py
@@ -28,40 +28,7 @@ class UpscaleOptions:
     face_restorer: FaceRestoration = None
     restorer_visibility: float = 0.5
 
-
-def save_image(img: Image, filename: str):
-    convert_to_sd(img).save(filename)
-
-
-def cosine_distance(vector1: np.ndarray, vector2: np.ndarray) -> float:
-    vec1 = vector1.flatten()
-    vec2 = vector2.flatten()
-
-    dot_product = np.dot(vec1, vec2)
-    norm1 = np.linalg.norm(vec1)
-    norm2 = np.linalg.norm(vec2)
-
-    cosine_distance = 1 - (dot_product / (norm1 * norm2))
-    return cosine_distance
-
-
-def cosine_similarity(test_vec: np.ndarray, source_vecs: List[np.ndarray]) -> float:
-    cos_dist = sum(cosine_distance(test_vec, source_vec) for source_vec in source_vecs)
-    average_cos_dist = cos_dist / len(source_vecs)
-    return average_cos_dist
-
-
-ANALYSIS_MODEL = None
-
-
-def getAnalysisModel():
-    global ANALYSIS_MODEL
-    if ANALYSIS_MODEL is None:
-        ANALYSIS_MODEL = insightface.app.FaceAnalysis(
-            name="buffalo_l", providers=providers
-        )
-    return ANALYSIS_MODEL
-
+ANALYSIS_MODEL = insightface.app.FaceAnalysis(name="buffalo_l", providers=providers)
 
 FS_MODEL = None
 CURRENT_FS_MODEL_PATH = None
@@ -108,7 +75,7 @@ def upscale_image(image: Image, upscale_options: UpscaleOptions):
 
 
 def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640)):
-    face_analyser = copy.deepcopy(getAnalysisModel())
+    face_analyser = copy.deepcopy(ANALYSIS_MODEL)
     face_analyser.prepare(ctx_id=0, det_size=det_size)
     face = face_analyser.get(img_data)
 
@@ -140,8 +107,10 @@ def swap_face(
     faces_index: Set[int] = {0},
     upscale_options: Union[UpscaleOptions, None] = None,
 ) -> ImageResult:
-    fn = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-    if model is not None:
+    result_image = target_img
+    converted = convert_to_sd(target_img)
+    scale, fn = converted[0], converted[1]
+    if model is not None and not scale:
         source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
         target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
         source_face = get_face_single(source_img, face_index=0)
@@ -160,8 +129,7 @@ def swap_face(
             result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
             if upscale_options is not None:
                 result_image = upscale_image(result_image, upscale_options)
-
-            save_image(result_image, fn.name)
         else:
             logger.info("No source face found")
+    result_image.save(fn.name)
     return ImageResult(path=fn.name)