diff --git a/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py b/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
index 3ca5f927..5cc50697 100644
--- a/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
+++ b/extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
@@ -164,17 +164,21 @@ class ControlNetForForgeOfficial(scripts.Script):
         if unit.input_mode == external_code.InputMode.BATCH:
             image_list = []
             image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
-            for idx, filename in enumerate(os.listdir(unit.batch_image_dir)):
+            batch_image_files = shared.listfiles(unit.batch_image_dir)
+            for batch_modifier in getattr(unit, 'batch_modifiers', []):
+                batch_image_files = batch_modifier(batch_image_files, p)
+            for idx, filename in enumerate(batch_image_files):
                 if any(filename.lower().endswith(ext) for ext in image_extensions):
                     img_path = os.path.join(unit.batch_image_dir, filename)
                     logger.info(f'Try to read image: {img_path}')
                     img = np.ascontiguousarray(cv2.imread(img_path)[:, :, ::-1]).copy()
                     mask = None
-                    if len(unit.batch_mask_dir) > 0:
-                        if len(unit.batch_mask_dir) >= len(unit.batch_image_dir):
-                            mask_path = unit.batch_mask_dir[idx]
+                    if unit.batch_mask_dir:
+                        batch_mask_files = shared.listfiles(unit.batch_mask_dir)
+                        if len(batch_mask_files) >= len(batch_image_files):
+                            mask_path = batch_mask_files[idx]
                         else:
-                            mask_path = unit.batch_mask_dir[0]
+                            mask_path = batch_mask_files[0]
                         mask_path = os.path.join(unit.batch_mask_dir, mask_path)
                         mask = np.ascontiguousarray(cv2.imread(mask_path)[:, :, ::-1]).copy()
                     if img is not None:
diff --git a/ldm_patched/modules/controlnet.py b/ldm_patched/modules/controlnet.py
index 19234c1c..8dd29bab 100644
--- a/ldm_patched/modules/controlnet.py
+++ b/ldm_patched/modules/controlnet.py
@@ -92,9 +92,9 @@ def compute_controlnet_weighting(control, cnet):
 
             if isinstance(advanced_mask_weighting, torch.Tensor):
                 if advanced_mask_weighting.shape[0] != 1:
-                    k = int(control_signal.shape[0] // advanced_mask_weighting.shape[0])
-                    if control_signal.shape[0] == k * advanced_mask_weighting.shape[0]:
-                        advanced_mask_weighting = advanced_mask_weighting.repeat(k, 1, 1, 1)
+                    k_ = int(control_signal.shape[0] // advanced_mask_weighting.shape[0])
+                    if control_signal.shape[0] == k_ * advanced_mask_weighting.shape[0]:
+                        advanced_mask_weighting = advanced_mask_weighting.repeat(k_, 1, 1, 1)
                 control_signal = control_signal * torch.nn.functional.interpolate(advanced_mask_weighting.to(control_signal), size=(H, W), mode='bilinear')
 
             control[k][i] = control_signal * final_weight[:, None, None, None]
@@ -244,7 +244,7 @@ class ControlNet(ControlBase):
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
+            self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
@@ -264,7 +264,7 @@ class ControlNet(ControlBase):
             wrapper_args['inner_model'] = self.control_model
             control = controlnet_model_function_wrapper(**wrapper_args)
         else:
-            control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
+            control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint.to(self.device), timesteps=timestep.float(), context=context.to(dtype), y=y)
         return self.control_merge(None, control, control_prev, output_dtype)
 
     def copy(self):
@@ -549,7 +549,7 @@ class T2IAdapter(ControlBase):
             self.control_input = None
             self.cond_hint = None
             width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
-            self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
+            self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float()
             if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
                 self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
@@ -567,7 +567,7 @@ class T2IAdapter(ControlBase):
                 wrapper_args['inner_t2i_model'] = self.t2i_model
                 self.control_input = controlnet_model_function_wrapper(**wrapper_args)
             else:
-                self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
+                self.control_input = self.t2i_model(self.cond_hint.to(x_noisy))
 
             self.t2i_model.cpu()