fix(bigvgan-trainer): clone mel outputs to strip inference tensor flag from buffers

mel_converter buffers (mel_basis, hann_window) are inference tensors
because the model was loaded inside ComfyUI's torch.inference_mode().
Operations on them propagate the flag to outputs. Clone both target_mel
and pred_mel to get normal autograd-compatible tensors. .clone() is
differentiable so the grad graph to vocoder parameters is preserved.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/selva_bigvgan_trainer.py

@@ -235,9 +235,11 @@ class SelvaBigvganTrainer:
                         target_flat = torch.stack(batch).to(device, dtype).clone()  # [B, T]
                         target_wav  = target_flat.unsqueeze(1)                # [B, 1, T]
 
-                        # Fixed target mel (no grad needed here)
+                        # Fixed target mel (no grad needed here).
+                        # .clone() strips the inference-tensor flag inherited from
+                        # mel_converter's buffers (loaded inside ComfyUI's inference_mode).
                         with torch.no_grad():
-                            target_mel = mel_converter(target_flat)           # [B, 80, T_mel]
+                            target_mel = mel_converter(target_flat).clone()   # [B, 80, T_mel]
 
                         # Vocoder forward: mel → waveform
                         pred_wav = vocoder(target_mel)                        # [B, 1, T_wav]
@@ -248,7 +250,7 @@ class SelvaBigvganTrainer:
                         target_t = target_wav[...,  :T]
 
                         # Mel reconstruction loss: mel(pred) vs target_mel
-                        pred_mel = mel_converter(pred_t.squeeze(1))           # [B, 80, T_mel']
+                        pred_mel = mel_converter(pred_t.squeeze(1)).clone()   # [B, 80, T_mel']
                         T_mel    = min(pred_mel.shape[-1], target_mel.shape[-1])
                         mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])