diff --git a/nodes/selva_bigvgan_trainer.py b/nodes/selva_bigvgan_trainer.py
index 3aa2d67..16ab5a1 100644
--- a/nodes/selva_bigvgan_trainer.py
+++ b/nodes/selva_bigvgan_trainer.py
@@ -35,6 +35,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torchaudio
 import comfy.utils
+import comfy.model_management
 import folder_paths
 
 from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
@@ -105,13 +106,13 @@ class _DiscriminatorR(nn.Module):
         from torch.nn.utils.parametrizations import weight_norm
         norm = weight_norm
         self.convs = nn.ModuleList([
-            norm(nn.Conv2d(1, 32,  (3, 9), padding=(1, 4))),
-            norm(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
-            norm(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
-            norm(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
-            norm(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))),
+            norm(nn.Conv2d(1,   128, (3, 9), padding=(1, 4))),
+            norm(nn.Conv2d(128, 128, (3, 9), stride=(1, 2), padding=(1, 4))),
+            norm(nn.Conv2d(128, 128, (3, 9), stride=(1, 2), padding=(1, 4))),
+            norm(nn.Conv2d(128, 128, (3, 9), stride=(1, 2), padding=(1, 4))),
+            norm(nn.Conv2d(128, 128, (3, 3), padding=(1, 1))),
         ])
-        self.conv_post = norm(nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
+        self.conv_post = norm(nn.Conv2d(128, 1, (3, 3), padding=(1, 1)))
 
     def spectrogram(self, x):
         """x: [B, 1, T] → [B, 1, freq, time]"""
@@ -408,6 +409,11 @@ class SelvaBigvganTrainer:
               f"segment={segment_seconds}s  steps={steps}  lr={lr}  "
               f"batch={batch_size}  lambda_l2sp={lambda_l2sp}\n", flush=True)
 
+        # Unload all other ComfyUI models (SelVA generator, etc.) to free VRAM
+        # before starting training. BigVGAN + discriminator need the headroom.
+        comfy.model_management.unload_all_models()
+        soft_empty_cache()
+
         if strategy == "offload_to_cpu":
             feature_utils.to(device)
             soft_empty_cache()