diff --git a/extensions-builtin/forge_preprocessor_clipvision/scripts/preprocessor_clipvision.py b/extensions-builtin/forge_preprocessor_clipvision/scripts/preprocessor_clipvision.py
index badd43e3..74f3de22 100644
--- a/extensions-builtin/forge_preprocessor_clipvision/scripts/preprocessor_clipvision.py
+++ b/extensions-builtin/forge_preprocessor_clipvision/scripts/preprocessor_clipvision.py
@@ -1,77 +1,11 @@
-# from modules_forge.supported_preprocessor import Preprocessor, PreprocessorParameter
-# from modules_forge.shared import preprocessor_dir, add_supported_preprocessor
-# from modules_forge.forge_util import resize_image_with_pad
-# from modules.modelloader import load_file_from_url
-#
-# import types
-# import torch
-# import numpy as np
-#
-# from einops import rearrange
-# from annotator.normalbae.models.NNET import NNET
-# from annotator.normalbae import load_checkpoint
-# from torchvision import transforms
-#
-#
-# class PreprocessorNormalBae(Preprocessor):
-#     def __init__(self):
-#         super().__init__()
-#         self.name = 'normalbae'
-#         self.tags = ['NormalMap']
-#         self.model_filename_filters = ['normal']
-#         self.slider_resolution = PreprocessorParameter(
-#             label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True)
-#         self.slider_1 = PreprocessorParameter(visible=False)
-#         self.slider_2 = PreprocessorParameter(visible=False)
-#         self.slider_3 = PreprocessorParameter(visible=False)
-#         self.show_control_mode = True
-#         self.do_not_need_model = False
-#         self.sorting_priority = 100  # higher goes to top in the list
-#
-#     def load_model(self):
-#         if self.model_patcher is not None:
-#             return
-#
-#         model_path = load_file_from_url(
-#             "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt",
-#             model_dir=preprocessor_dir)
-#
-#         args = types.SimpleNamespace()
-#         args.mode = 'client'
-#         args.architecture = 'BN'
-#         args.pretrained = 'scannet'
-#         args.sampling_ratio = 0.4
-#         args.importance_ratio = 0.7
-#         model = NNET(args)
-#         model = load_checkpoint(model_path, model)
-#         self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-#
-#         self.model_patcher = self.setup_model_patcher(model)
-#
-#     def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
-#         input_image, remove_pad = resize_image_with_pad(input_image, resolution)
-#
-#         self.load_model()
-#
-#         self.move_all_model_patchers_to_gpu()
-#
-#         assert input_image.ndim == 3
-#         image_normal = input_image
-#
-#         with torch.no_grad():
-#             image_normal = self.send_tensor_to_model_device(torch.from_numpy(image_normal))
-#             image_normal = image_normal / 255.0
-#             image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
-#             image_normal = self.norm(image_normal)
-#
-#             normal = self.model_patcher.model(image_normal)
-#             normal = normal[0][-1][:, :3]
-#             normal = ((normal + 1) * 0.5).clip(0, 1)
-#
-#             normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
-#             normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
-#
-#         return remove_pad(normal_image)
-#
-#
-# add_supported_preprocessor(PreprocessorNormalBae())
+from modules_forge.supported_preprocessor import Preprocessor, PreprocessorParameter
+from modules_forge.shared import preprocessor_dir, add_supported_preprocessor
+from modules.modelloader import load_file_from_url
+
+
+class PreprocessorClipVision(Preprocessor):
+    def __init__(self):
+        super().__init__()
+
+
+add_supported_preprocessor(PreprocessorClipVision())