fix: cast ref_mean/ref_gram to model dtype before loss computation

ref_mean and ref_gram are float32 (mel computed via cuFFT which requires
float32). mel_gen is bfloat16. F.l1_loss(bfloat16, float32) promotes to
float32, producing a float32 loss. loss.backward() then pushes float32
gradients through bfloat16 ops → 'Found dtype Float but expected BFloat16'.

Fix: clone().detach().to(dtype) at the start of _do_optimize.

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

nodes/selva_ditto_optimizer.py

@@ -274,9 +274,12 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
                  normalize, target_lufs, pbar):
     """Optimization loop — runs in a fresh thread (no inference_mode active)."""
 
-    # Strip inference flags from ref stats (came from main thread)
-    ref_mean = ref_mean.clone().detach()
-    ref_gram = ref_gram.clone().detach()
+    # Strip inference flags from ref stats (came from main thread) and cast to
+    # model dtype. ref_mean/ref_gram are float32 (computed via cuFFT mel path);
+    # mel_gen is model dtype (bfloat16). Mixed-dtype loss → float32 gradient →
+    # "Found dtype Float but expected BFloat16" in backward through bfloat16 ops.
+    ref_mean = ref_mean.clone().detach().to(dtype)
+    ref_gram = ref_gram.clone().detach().to(dtype)
 
     torch.manual_seed(seed)