diff --git a/modules_forge/unet_patcher.py b/modules_forge/unet_patcher.py
index 2e455859..ad52b093 100644
--- a/modules_forge/unet_patcher.py
+++ b/modules_forge/unet_patcher.py
@@ -1,4 +1,5 @@
 import copy
+import torch
 
 from ldm_patched.modules.model_patcher import ModelPatcher
 from ldm_patched.modules.sample import convert_cond
@@ -42,6 +43,23 @@ class UnetPatcher(ModelPatcher):
         self.extra_model_patchers_during_sampling.append(model_patcher)
         return
 
+    def add_extra_torch_module_during_sampling(self, m: torch.nn.Module, cast_to_unet_dtype: bool = True):
+        # Use this method to bind an extra torch.nn.Module to this UNet during sampling.
+        # This model `m` will be delegated to Forge memory management system.
+        # `m` will be loaded to GPU everytime when sampling starts.
+        # `m` will be unloaded if necessary.
+        # `m` will influence Forge's judgement about use GPU memory or
+        # capacity and decide whether to use module offload to make user's batch size larger.
+        # Use cast_to_unet_dtype if you want `m` to have same dtype with unet during sampling.
+
+        if cast_to_unet_dtype:
+            m.to(self.model.diffusion_model.dtype)
+
+        patcher = ModelPatcher(model=m, load_device=self.load_device, offload_device=self.offload_device)
+
+        self.add_extra_model_patcher_during_sampling(patcher)
+        return
+
     def add_patched_controlnet(self, cnet):
         cnet.set_previous_controlnet(self.controlnet_linked_list)
         self.controlnet_linked_list = cnet