diff --git a/nodes/selva_ditto_optimizer.py b/nodes/selva_ditto_optimizer.py
index 85916f2..1a93399 100644
--- a/nodes/selva_ditto_optimizer.py
+++ b/nodes/selva_ditto_optimizer.py
@@ -399,19 +399,10 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
             x = x + dt * flow
 
         # ── Decode to mel (no vocoder — cheap) ──────────────────────────────
-        # Wrap unnormalize + decode in gradient checkpointing so PyTorch does
-        # not try to save model weights for backward. The VAE / generator
-        # weights are inference-flagged tensors (loaded in the main thread);
-        # saving them for backward would raise "Inference tensors cannot be
-        # saved for backward". checkpoint(use_reentrant=False) recomputes the
-        # forward during backward instead of storing activations.
-        def _unnorm_decode(x_in):
-            x_un = net_generator.unnormalize(x_in)
-            return feature_utils.decode(x_un)
-
-        mel_gen = torch.utils.checkpoint.checkpoint(
-            _unnorm_decode, x, use_reentrant=False
-        )
+        # Direct call — inference flags were stripped from all model weights
+        # at the top of _do_optimize, so no checkpoint wrapper is needed.
+        x_un    = net_generator.unnormalize(x)
+        mel_gen = feature_utils.decode(x_un)
 
         # ── Style loss ───────────────────────────────────────────────────────
         loss = style_weight * _mel_style_loss(mel_gen, ref_mean, ref_gram)