diff --git a/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py b/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
index ecb06e8e..b2517b01 100644
--- a/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
+++ b/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
@@ -250,28 +250,13 @@ class ControlNetForForgeOfficial(scripts.Script):
             input_image = np.stack(input_image, axis=2)
         return input_image
 
-    @staticmethod
-    def bound_check_params(unit: external_code.ControlNetUnit) -> None:
-        """
-        Checks and corrects negative parameters in ControlNetUnit 'unit'.
-        Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
-        their default values if negative.
-
-        Args:
-            unit (external_code.ControlNetUnit): The ControlNetUnit instance to check.
-        """
-        preprocessor = global_state.get_preprocessor(unit.module)
-
-        if unit.processor_res < 0:
-            unit.processor_res = int(preprocessor.slider_resolution.gradio_update_kwargs.get('value', 512))
-
-        if unit.threshold_a < 0:
-            unit.threshold_a = int(preprocessor.slider_1.gradio_update_kwargs.get('value', 1.0))
-
-        if unit.threshold_b < 0:
-            unit.threshold_b = int(preprocessor.slider_2.gradio_update_kwargs.get('value', 1.0))
-
-        return
+    def get_input_data(self, p, unit, preprocessor):
+        mask = None
+        input_image, resize_mode = self.choose_input_image(p, unit)
+        assert isinstance(input_image, np.ndarray), 'Invalid input image!'
+        input_image = self.try_crop_image_with_a1111_mask(p, unit, input_image, resize_mode, preprocessor)
+        input_image = np.ascontiguousarray(input_image.copy()).copy()  # safe numpy
+        return input_image, mask, resize_mode
 
     @staticmethod
     def get_target_dimensions(p: StableDiffusionProcessing) -> Tuple[int, int, int, int]:
@@ -297,14 +282,6 @@ class ControlNetForForgeOfficial(scripts.Script):
 
         return h, w, hr_y, hr_x
 
-    def get_input_data(self, p, unit, preprocessor):
-        mask = None
-        input_image, resize_mode = self.choose_input_image(p, unit)
-        assert isinstance(input_image, np.ndarray), 'Invalid input image!'
-        input_image = self.try_crop_image_with_a1111_mask(p, unit, input_image, resize_mode, preprocessor)
-        input_image = np.ascontiguousarray(input_image.copy()).copy()  # safe numpy
-        return input_image, mask, resize_mode
-
     @torch.no_grad()
     def process_unit_after_click_generate(self,
                                           p: StableDiffusionProcessing,
@@ -440,6 +417,29 @@ class ControlNetForForgeOfficial(scripts.Script):
         logger.info(f"ControlNet Method {params.preprocessor.name} patched.")
         return
 
+    @staticmethod
+    def bound_check_params(unit: external_code.ControlNetUnit) -> None:
+        """
+        Checks and corrects negative parameters in ControlNetUnit 'unit'.
+        Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
+        their default values if negative.
+
+        Args:
+            unit (external_code.ControlNetUnit): The ControlNetUnit instance to check.
+        """
+        preprocessor = global_state.get_preprocessor(unit.module)
+
+        if unit.processor_res < 0:
+            unit.processor_res = int(preprocessor.slider_resolution.gradio_update_kwargs.get('value', 512))
+
+        if unit.threshold_a < 0:
+            unit.threshold_a = int(preprocessor.slider_1.gradio_update_kwargs.get('value', 1.0))
+
+        if unit.threshold_b < 0:
+            unit.threshold_b = int(preprocessor.slider_2.gradio_update_kwargs.get('value', 1.0))
+
+        return
+
     @torch.no_grad()
     def process_unit_after_every_sampling(self,
                                           p: StableDiffusionProcessing,