fix: remove checkpoint wrapper on decode — direct call preserves grad chain

_unnorm_decode was wrapped in checkpoint(use_reentrant=False) to avoid saving
inference-mode weight tensors during backward. Since _strip_inference() now
cleans all params/buffers before any forward pass, the checkpoint is no longer
needed and was silently breaking the gradient chain from mel_gen back to x0.

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

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
+        # Direct call — inference flags were stripped from all model weights
-        # not try to save model weights for backward. The VAE / generator
+        # at the top of _do_optimize, so no checkpoint wrapper is needed.
-        # weights are inference-flagged tensors (loaded in the main thread);
+        x_un    = net_generator.unnormalize(x)
-        # saving them for backward would raise "Inference tensors cannot be
+        mel_gen = feature_utils.decode(x_un)
-        # 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
-        )
 
         # ── Style loss ───────────────────────────────────────────────────────
         loss = style_weight * _mel_style_loss(mel_gen, ref_mean, ref_gram)