diff --git a/nodes/selva_bigvgan_trainer.py b/nodes/selva_bigvgan_trainer.py
index 14756d1..158c514 100644
--- a/nodes/selva_bigvgan_trainer.py
+++ b/nodes/selva_bigvgan_trainer.py
@@ -258,6 +258,30 @@ def _multi_resolution_stft_loss(pred_wav, target_wav, device):
     return loss / len(_STFT_RESOLUTIONS)
 
 
+def _phase_aware_stft_loss(pred_wav, target_wav, device):
+    """FA-GAN complex STFT loss: L1 on real, imaginary, and magnitude components.
+
+    Penalizes phase smearing directly — plain magnitude loss cannot distinguish
+    a correct spectrum with wrong phase from a smeared spectrum with random phase.
+    Based on FA-GAN (arXiv:2407.04575), applied across three STFT resolutions.
+    inputs: [B, 1, T]
+    """
+    # cuFFT requires float32 regardless of model dtype
+    pred   = pred_wav.squeeze(1).float()    # [B, T]
+    target = target_wav.squeeze(1).float()
+    loss = torch.zeros(1, device=device)
+    for n_fft, hop, win in _STFT_RESOLUTIONS:
+        window = torch.hann_window(win, device=device)
+        ps = torch.stft(pred,   n_fft, hop, win, window, center=True, return_complex=True)
+        ts = torch.stft(target, n_fft, hop, win, window, center=True, return_complex=True)
+        T  = min(ps.shape[-1], ts.shape[-1])
+        ps, ts = ps[..., :T], ts[..., :T]
+        loss = loss + F.l1_loss(ps.real, ts.real)
+        loss = loss + F.l1_loss(ps.imag, ts.imag)
+        loss = loss + F.l1_loss(ps.abs(), ts.abs())
+    return loss / (len(_STFT_RESOLUTIONS) * 3)
+
+
 # ---------------------------------------------------------------------------
 # Node
 # ---------------------------------------------------------------------------
@@ -318,6 +342,14 @@ class SelvaBigvganTrainer:
                         "Increase to 1e-2 for all_params to prevent catastrophic forgetting."
                     ),
                 }),
+                "lambda_phase": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1,
+                    "tooltip": (
+                        "FA-GAN phase-aware loss weight. Adds L1 loss on real + imaginary + magnitude "
+                        "STFT components, penalizing phase smearing directly. 0 = disabled. "
+                        "1.0 is a good starting point alongside other losses."
+                    ),
+                }),
                 "save_every": ("INT", {"default": 500, "min": 50, "max": 10000}),
                 "seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFF}),
             },
@@ -335,7 +367,7 @@ class SelvaBigvganTrainer:
         }
 
     def train(self, model, data_dir, output_path, train_mode, steps, lr, batch_size,
-              segment_seconds, lambda_l2sp, save_every, seed, discriminator_path=""):
+              segment_seconds, lambda_l2sp, lambda_phase, save_every, seed, discriminator_path=""):
         import traceback
 
         device        = get_device()
@@ -443,7 +475,7 @@ class SelvaBigvganTrainer:
                     vocoder, mel_converter, clips,
                     device, dtype, strategy, feature_utils,
                     segment_samples, sample_rate,
-                    train_mode, steps, lr, batch_size, lambda_l2sp,
+                    train_mode, steps, lr, batch_size, lambda_l2sp, lambda_phase,
                     save_every, seed, out_path, disc_path, pbar,
                 )
             except Exception as e:
@@ -466,7 +498,7 @@ class SelvaBigvganTrainer:
 def _do_train(vocoder, mel_converter, clips,
               device, dtype, strategy, feature_utils,
               segment_samples, sample_rate,
-              train_mode, steps, lr, batch_size, lambda_l2sp,
+              train_mode, steps, lr, batch_size, lambda_l2sp, lambda_phase,
               save_every, seed, out_path, disc_path, pbar):
     """Execute training. Called in a fresh thread — no inference_mode active.
 
@@ -686,6 +718,12 @@ def _do_train(vocoder, mel_converter, clips,
                 primary_loss = mel_loss + stft_loss
                 loss_desc = f"mel={mel_loss.item():.4f}  stft={stft_loss.item():.4f}"
 
+            # ── FA-GAN phase-aware loss (real + imag + mag STFT) ────────────
+            if lambda_phase > 0.0:
+                phase_loss    = _phase_aware_stft_loss(pred_t, target_t, device)
+                primary_loss  = primary_loss + lambda_phase * phase_loss
+                loss_desc    += f"  phase={phase_loss.item():.4f}"
+
             # ── L2-SP regularization ─────────────────────────────────────────
             l2sp_loss = torch.zeros(1, device=device)
             if lambda_l2sp > 0.0 and ref_params: