diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 10ac1e7de..4c7cd5e3e 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -794,17 +794,28 @@ class ModelPatcher:
                     """Convert all parameters and buffers to memory-mapped tensors
                     
                     This method mimics PyTorch's Module.to() behavior but converts
-                    tensors to memory-mapped format instead.
+                    tensors to memory-mapped format instead, using _apply() method.
+                    
+                    Note: For Parameters, we modify .data in-place because 
+                    MemoryMappedTensor cannot be wrapped in torch.nn.Parameter.
+                    For buffers, _apply() will automatically update the reference.
                     """
-                    import pdb; pdb.set_trace()
                     logging.info(f"model {self.__class__.__name__} is calling to_mmap method")
+                    
                     def convert_fn(t):
-                        if isinstance(t, torch.Tensor) and not isinstance(t, torch.nn.Parameter):
+                        """Convert function for _apply()
+                        
+                        - For Parameters: modify .data and return the Parameter object
+                        - For buffers (plain Tensors): return new MemoryMappedTensor
+                        """
+                        if isinstance(t, torch.nn.Parameter):
+                            # For parameters, modify data in-place and return the parameter
+                            if isinstance(t.data, torch.Tensor):
+                                t.data = to_mmap(t.data)
+                            return t
+                        elif isinstance(t, torch.Tensor):
+                            # For buffers (plain tensors), return the converted tensor
                             return to_mmap(t)
-                        elif isinstance(t, torch.nn.Parameter):
-                            # For parameters, convert the data and wrap back in Parameter
-                            param_mmap = to_mmap(t.data)
-                            return torch.nn.Parameter(param_mmap, requires_grad=t.requires_grad)
                         return t
                     
                     return self._apply(convert_fn)