diff --git a/nodes/selva_bigvgan_trainer.py b/nodes/selva_bigvgan_trainer.py
index eed1a0b..0819d2f 100644
--- a/nodes/selva_bigvgan_trainer.py
+++ b/nodes/selva_bigvgan_trainer.py
@@ -187,8 +187,6 @@ class SelvaBigvganTrainer:
             soft_empty_cache()
 
         mel_converter.to(device)
-        vocoder.requires_grad_(True)
-        optimizer = torch.optim.AdamW(vocoder.parameters(), lr=lr, betas=(0.8, 0.99))
 
         torch.manual_seed(seed)
         random.seed(seed)
@@ -222,12 +220,22 @@ class SelvaBigvganTrainer:
         try:
             with torch.inference_mode(False):
                 with torch.enable_grad():
-                    # Vocoder parameters were loaded inside ComfyUI's inference_mode()
-                    # and are inference tensors. Autograd cannot save them for backward.
-                    # Clone inside inference_mode(False) to get normal tensors.
-                    for param in vocoder.parameters():
-                        param.data = param.data.clone()
+                    # Vocoder parameters are inference tensors (loaded inside ComfyUI's
+                    # inference_mode). param.data = clone() only changes storage — the
+                    # nn.Parameter object itself still carries the inference flag.
+                    # Replace each parameter with a fresh nn.Parameter created here
+                    # (inside inference_mode(False)) so the object itself is normal.
+                    import torch.nn as nn_mod
+                    for module in vocoder.modules():
+                        for pname, param in list(module._parameters.items()):
+                            if param is not None:
+                                module._parameters[pname] = nn_mod.Parameter(
+                                    param.data.clone(), requires_grad=True
+                                )
 
+                    optimizer = torch.optim.AdamW(
+                        vocoder.parameters(), lr=lr, betas=(0.8, 0.99)
+                    )
                     vocoder.train()
 
                     for step in range(steps):