chore: remove debug VRAM logging

Training confirmed working — VRAM usage is normal backward-pass
activation memory, not a leak. Removed all debug _vram_log and _vram
calls. Kept the video_enc offload and torch.cuda.empty_cache fixes.

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

nodes/selva_bigvgan_trainer.py

@@ -772,37 +772,21 @@ class SelvaBigvganTrainer:
 
         # Unload all other ComfyUI models (SelVA generator, etc.) to free VRAM
         # before starting training. BigVGAN + discriminator need the headroom.
-        def _vram_log(label):
-            if device.type == "cuda":
-                alloc = torch.cuda.memory_allocated(device) / (1024**3)
-                resrv = torch.cuda.memory_reserved(device) / (1024**3)
-                free_cuda, total_cuda = torch.cuda.mem_get_info(device)
-                used_driver = (total_cuda - free_cuda) / (1024**3)
-                print(f"[BigVGAN VRAM] {label}: alloc={alloc:.2f} reserved={resrv:.2f} "
-                      f"driver_used={used_driver:.2f} GiB", flush=True)
-
-        _vram_log("before unload")
         comfy.model_management.unload_all_models()
-        _vram_log("after unload_all_models")
 
         # Move EVERYTHING to CPU first, then bring back only what we need.
         # ComfyUI may have loaded the full model to GPU; unload_all_models
         # doesn't always free model dicts passed between nodes.
         feature_utils.to("cpu")
-        _vram_log("after feature_utils.to(cpu)")
         if "generator" in model:
             model["generator"].to("cpu")
-            _vram_log("after generator.to(cpu)")
         if "video_enc" in model:
             model["video_enc"].to("cpu")
-            _vram_log("after video_enc.to(cpu)")
         soft_empty_cache()
-        _vram_log("after soft_empty_cache")
 
         # Only move mel_converter to GPU — it's tiny and needed for training.
         # _pregenerate_lora_mels handles its own device management for CLIP/tod.
         mel_converter.to(device)
-        _vram_log("after mel_converter.to(device)")
 
         # Pre-compute text CLIP embeddings in the main thread.
         # CLIP weights are inference tensors from ComfyUI loading — they only
@@ -1094,17 +1078,6 @@ def _do_train(vocoder, mel_converter, clips,
                   f"falling back to mel+STFT losses", flush=True)
             mpd = mrd = None
 
-    # VRAM snapshot before training loop
-    if device.type == "cuda":
-        alloc = torch.cuda.memory_allocated(device) / (1024**3)
-        resrv = torch.cuda.memory_reserved(device) / (1024**3)
-        free_cuda, total_cuda = torch.cuda.mem_get_info(device)
-        used_driver = (total_cuda - free_cuda) / (1024**3)
-        print(f"[BigVGAN VRAM] before training: "
-              f"pytorch_alloc={alloc:.2f} GiB, pytorch_reserved={resrv:.2f} GiB, "
-              f"driver_used={used_driver:.2f} GiB, driver_total={total_cuda/(1024**3):.2f} GiB",
-              flush=True)
-
     optimizer = torch.optim.AdamW(trainable_params, lr=lr, betas=(0.8, 0.99))
     vocoder.train()
 
@@ -1126,11 +1099,6 @@ def _do_train(vocoder, mel_converter, clips,
         print(f"[BigVGAN] LoRA mel cropping: {_mel_segment} mel frames "
               f"per {segment_samples} audio samples", flush=True)
 
-    def _vram(label):
-        if device.type == "cuda" and step < 1:
-            a = torch.cuda.memory_allocated(device) / (1024**3)
-            print(f"  [VRAM step0] {label}: {a:.2f} GiB", flush=True)
-
     try:
         for step in range(steps):
             if lora_mel_pairs:
@@ -1173,7 +1141,7 @@ def _do_train(vocoder, mel_converter, clips,
             # Clean target mel for mel loss (always from clean audio)
             with torch.no_grad():
                 target_mel = mel_converter(target_flat.float())       # [B, n_mels, T_mel]
-            _vram("after target_mel")
+
 
             # Gradient checkpointing: recompute BigVGAN activations during
             # backward instead of storing them. The 512x upsampling stack
@@ -1183,14 +1151,14 @@ def _do_train(vocoder, mel_converter, clips,
             pred_wav = torch.utils.checkpoint.checkpoint(
                 vocoder, input_mel.to(dtype), use_reentrant=False
             )                                                     # [B, 1, T_wav]
-            _vram("after vocoder forward")
+
 
             T = min(pred_wav.shape[-1], target_wav.shape[-1])
             pred_t   = pred_wav[...,  :T]
             target_t = target_wav[..., :T]
 
             # ── Compute loss ─────────────────────────────────────────────────
-            _vram("before loss")
+
             if mpd is not None and mrd is not None:
                 # Perceptual feature matching via frozen discriminators
                 with torch.no_grad():
@@ -1236,10 +1204,10 @@ def _do_train(vocoder, mel_converter, clips,
                 l2sp_loss = l2sp_loss * lambda_l2sp
 
             loss = primary_loss + l2sp_loss
-            _vram("after loss computation")
+
             optimizer.zero_grad()
             loss.backward()
-            _vram("after backward")
+
             torch.nn.utils.clip_grad_norm_(trainable_params, 1.0)
             optimizer.step()