fix: add CFG to LoRA mel pre-generation to match inference conditions

Pre-generated mels were using a bare forward pass with no classifier-free
guidance, producing mels that don't match what the vocoder sees at inference
(where cfg_strength=4.5 is the default). Now uses ode_wrapper with
preprocess_conditions/get_empty_conditions, same as the sampler node.

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

nodes/selva_bigvgan_trainer.py

@@ -374,13 +374,14 @@ def _find_audio_for_npz(npz_path: Path):
 
 
 def _pregenerate_lora_mels(model, data_dir, lora_adapter_path, device, dtype,
-                           sample_rate, duration, seed=42, num_steps=25):
+                           sample_rate, duration, seed=42, num_steps=25,
+                           cfg_strength=4.5):
     """Generate LoRA mels for all clips with matching audio in data_dir.
 
-    Uses the LoRA adapter to run full ODE generation → VAE decode → mel for
+    Uses the LoRA adapter to run full ODE generation with CFG → VAE decode →
-    each clip's conditioning features. Returns (lora_mel, clean_audio) pairs
+    mel for each clip's conditioning features.  CFG matches the sampler's
-    that the vocoder trainer can use: vocoder learns to produce clean audio
+    default (4.5) so the degraded mels the vocoder trains on are representative
-    from LoRA-distorted mels.
+    of what it will see at inference time.
 
     Returns list of (mel [n_mels, T_mel], audio [L]) CPU tensors.
     """
@@ -496,13 +497,16 @@ def _pregenerate_lora_mels(model, data_dir, lora_adapter_path, device, dtype,
                     print(f"  [BigVGAN] Failed loading {audio_path.name}: {e}", flush=True)
                     continue
 
-                # Generate LoRA latent via ODE
+                # Generate LoRA latent via ODE with CFG (matches sampler)
+                conditions = generator.preprocess_conditions(clip_f, sync_f, text_clip)
+                empty_conditions = generator.get_empty_conditions(bs=1)
+
                 x0 = torch.randn(1, seq_cfg.latent_seq_len, generator.latent_dim,
                                  device=device, dtype=dtype, generator=rng)
 
-                def velocity_fn(t, x, _cf=clip_f, _sf=sync_f, _tc=text_clip):
+                def velocity_fn(t, x, _cond=conditions, _empty=empty_conditions,
-                    return generator.forward(x, _cf, _sf, _tc,
+                                _cfg=cfg_strength):
-                                             t.reshape(1).to(device, dtype))
+                    return generator.ode_wrapper(t, x, _cond, _empty, _cfg)
 
                 x1_pred = fm.to_data(velocity_fn, x0)
                 x1_unnorm = generator.unnormalize(x1_pred.clone())