fix: bypass @torch.inference_mode() on decode to preserve gradient chain

feature_utils.decode and autoencoder.decode are both decorated with
@torch.inference_mode(), which unconditionally destroys grad_fn on all
outputs — making loss.backward() fail with 'does not require grad'.

Fix: call feature_utils.tod.vae.decode() directly, which has no decorator
and is fully differentiable. Transpose matches the original wrapper signature.

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

nodes/selva_ditto_optimizer.py

@@ -399,10 +399,13 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
             x = x + dt * flow
 
         # ── Decode to mel (no vocoder — cheap) ──────────────────────────────
-        # Direct call — inference flags were stripped from all model weights
-        # at the top of _do_optimize, so no checkpoint wrapper is needed.
+        # feature_utils.decode and autoencoder.decode are both decorated with
+        # @torch.inference_mode(), which destroys the gradient chain.
+        # Bypass both wrappers and call vae.decode directly — it has no
+        # inference_mode decorator and is fully differentiable.
+        # The transpose matches feature_utils.decode: [B, T, C] → [B, C, T].
         x_un    = net_generator.unnormalize(x)
-        mel_gen = feature_utils.decode(x_un)
+        mel_gen = feature_utils.tod.vae.decode(x_un.transpose(1, 2))
 
         # ── Style loss ───────────────────────────────────────────────────────
         loss = style_weight * _mel_style_loss(mel_gen, ref_mean, ref_gram)