Improve: Models can now be loaded directly onto NVIDIA GPUs

style_bert_vits2/models/infer.py

@@ -86,10 +86,10 @@ def get_net_g(model_path: str, version: str, device: str, hps: HyperParameters):
     _ = net_g.eval()
     if model_path.endswith(".pth") or model_path.endswith(".pt"):
         _ = utils.checkpoints.load_checkpoint(
-            model_path, net_g, None, skip_optimizer=True
+            model_path, net_g, None, skip_optimizer=True, device=device
         )
     elif model_path.endswith(".safetensors"):
-        _ = utils.safetensors.load_safetensors(model_path, net_g, True)
+        _ = utils.safetensors.load_safetensors(model_path, net_g, True, device=device)
     else:
         raise ValueError(f"Unknown model format: {model_path}")
     return net_g

style_bert_vits2/models/utils/checkpoints.py

@@ -15,6 +15,7 @@ def load_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     skip_optimizer: bool = False,
     for_infer: bool = False,
+    device: Union[str, torch.device] = "cpu",
 ) -> tuple[torch.nn.Module, Optional[torch.optim.Optimizer], float, int]:
     """
     指定されたパスからチェックポイントを読み込み、モデルとオプティマイザーを更新する。
@@ -31,7 +32,7 @@ def load_checkpoint(
     """
 
     assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+    checkpoint_dict = torch.load(checkpoint_path, map_location=device)
     iteration = checkpoint_dict["iteration"]
     learning_rate = checkpoint_dict["learning_rate"]
     logger.info(

style_bert_vits2/models/utils/safetensors.py

@@ -12,6 +12,7 @@ def load_safetensors(
     checkpoint_path: Union[str, Path],
     model: torch.nn.Module,
     for_infer: bool = False,
+    device: Union[str, torch.device] = "cpu",
 ) -> tuple[torch.nn.Module, Optional[int]]:
     """
     指定されたパスから safetensors モデルを読み込み、モデルとイテレーションを返す。
@@ -27,7 +28,7 @@ def load_safetensors(
 
     tensors: dict[str, Any] = {}
     iteration: Optional[int] = None
-    with safe_open(str(checkpoint_path), framework="pt", device="cpu") as f:  # type: ignore
+    with safe_open(str(checkpoint_path), framework="pt", device=device) as f:  # type: ignore
         for key in f.keys():
             if key == "iteration":
                 iteration = f.get_tensor(key).item()