Add util function for loading state_dicts from disk.

invokeai/backend/util/serialization.py

@@ -0,0 +1,37 @@
+from pathlib import Path
+from typing import Any, Optional, Union
+
+import torch
+from safetensors.torch import load_file
+
+
+def state_dict_to(
+    state_dict: dict[str, torch.Tensor], device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None
+) -> 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, dtype=dtype, non_blocking=True)
+    return new_state_dict
+
+
+def load_state_dict(file_path: Union[str, Path], device: str = "cpu") -> Any:
+    """Load a state_dict from a file that may be in either PyTorch or safetensors format. The file format is inferred
+    from the file extension.
+    """
+    file_path = Path(file_path)
+
+    if file_path.suffix == ".safetensors":
+        state_dict = load_file(
+            file_path,
+            device=device,
+        )
+    else:
+        # weights_only=True is used to address a security vulnerability that allows arbitrary code execution.
+        # This option was first introduced in https://github.com/pytorch/pytorch/pull/86812.
+        #
+        # mmap=True is used to both reduce memory usage and speed up loading. This setting causes torch.load() to more
+        # closely mirror the behaviour of safetensors.torch.load_file().  This option was first introduced in
+        # https://github.com/pytorch/pytorch/pull/102549. The discussion on that PR provides helpful context.
+        state_dict = torch.load(file_path, map_location=device, weights_only=True, mmap=True)
+
+    return state_dict