added notes

invokeai/backend/model_manager/load/model_cache/model_locker.py

@@ -33,6 +33,20 @@ class ModelLocker(ModelLockerBase):
         """Return the model without moving it around."""
         return self._cache_entry.model
 
+    # ---------------------------- NOTE -----------------
+    # Ryan suggests keeping a copy of the model's state dict in CPU and copying it
+    # into the GPU with code like this:
+    #
+    # def state_dict_to(state_dict: dict[str, torch.Tensor], device: torch.device) -> dict[str, torch.Tensor]:
+    #    new_state_dict: dict[str, torch.Tensor] = {}
+    #    for k, v in state_dict.items():
+    #       new_state_dict[k] = v.to(device=device, copy=True, non_blocking=True)
+    #    return new_state_dict
+    #
+    # I believe we'd then use load_state_dict() to inject the state dict into the model.
+    # See: https://pytorch.org/tutorials/beginner/saving_loading_models.html
+    # ---------------------------- NOTE -----------------
+
     def lock(self) -> AnyModel:
         """Move the model into the execution device (GPU) and lock it."""
         if not hasattr(self.model, "to"):