diff --git a/scripts/cimage.py b/scripts/cimage.py
index 3892c51..fcf904e 100644
--- a/scripts/cimage.py
+++ b/scripts/cimage.py
@@ -1,57 +1,59 @@
-from typing import List, Union, Dict, Set, Tuple
-
-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
+from typing import List, Union, Dict, Set, Tuple
+
+# 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
+    return x_image, False
+
+
+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_checked_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