diff --git a/run.py b/run.py
index 322675d..e809494 100644
--- a/run.py
+++ b/run.py
@@ -13,7 +13,7 @@ import random
 from pathlib import Path
 import tkinter as tk
 from tkinter import filedialog
-from opennsfw2 import predict_image as dataset
+from opennsfw2 import predict_image as face_check
 from tkinter.filedialog import asksaveasfilename
 from core.processor import process_video, process_img
 from core.utils import is_img, detect_fps, set_fps, create_video, add_audio, extract_frames, rreplace
@@ -24,6 +24,9 @@ import cv2
 import threading
 from PIL import Image, ImageTk
 
+if 'ROCMExecutionProvider' not in core.globals.providers:
+    import torch
+
 pool = None
 args = {}
 
@@ -68,7 +71,6 @@ def pre_check():
     if '--gpu' in sys.argv:
         NVIDIA_PROVIDERS = ['CUDAExecutionProvider', 'TensorrtExecutionProvider']
         if len(list(set(core.globals.providers) - set(NVIDIA_PROVIDERS))) == 1:
-            import torch
             CUDA_VERSION = torch.version.cuda
             CUDNN_VERSION = torch.backends.cudnn.version()
             if not torch.cuda.is_available() or not CUDA_VERSION:
@@ -87,6 +89,10 @@ def pre_check():
 
 def start_processing():
     start_time = time.time()
+    threshold = len(['frame_args']) if len(args['frame_paths']) <= 10 else 10
+    for i in range(threshold):
+        if face_check(random.choice(args['frame_paths'])) > 0.7:
+            quit("[WARNING] Unable to determine location of the face in the target. Please make sure the target isn't wearing clothes matching to their skin.")
     if args['gpu']:
         process_video(args['source_img'], args["frame_paths"])
         end_time = time.time()
@@ -95,10 +101,6 @@ def start_processing():
         return
     frame_paths = args["frame_paths"]
     n = len(frame_paths)//(args['cores_count'])
-    for i in range(n):
-        continue
-        if dataset(random.choice(frame_paths)) > 0.7:
-            quit("[WARNING] Unable to determine location of the face in the target. Please make sure the target isn't wearing clothes matching to their skin.")
     processes = []
     for i in range(0, len(frame_paths), n):
         p = pool.apply_async(process_video, args=(args['source_img'], frame_paths[i:i+n],))
@@ -195,6 +197,8 @@ def start():
         print("\n[WARNING] No face detected in source image. Please try with another one.\n")
         return
     if is_img(target_path):
+        if face_check(target_path) > 0.7:
+            quit("[WARNING] Unable to determine location of the face in the target. Please make sure the target isn't wearing clothes matching to their skin.")
         process_img(args['source_img'], target_path, args['output_file'])
         status("swap successful!")
         return