fix: DITTO gradient never reached x0, remove unused imports and dead code

DITTO critical bug: x was reassigned on every ODE step, so by the time
loss.backward() ran, x pointed to the final output tensor (grad_fn, not
a leaf) and x.grad was always None. The manual gradient transfer never
fired — x0 was never updated. The optimization was a no-op.

Fix: use a straight-through estimator after the no-grad prefix:
  x = x + (x0 - x0.detach())
This adds zero value but creates a grad_fn back to x0, so backward()
propagates ∂loss/∂x (at the Phase-1/2 boundary) directly to x0.grad.
Equivalent to truncated BPTT with ∂x_prefix/∂x0 ≈ I.

Also remove unused imports (SelvaSampler, _inject_tokens, random) that
caused cascade ImportError risk, and remove dead trainable_count variable
in BigVGAN trainer.

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

nodes/selva_bigvgan_trainer.py

@@ -404,11 +404,6 @@ class SelvaBigvganTrainer:
                 f"[BigVGAN] No usable clips found (need audio >= {segment_seconds}s)"
             )
 
-        trainable_count = sum(
-            1 for n, _ in vocoder.named_parameters() if "alpha" in n
-        ) if train_mode == "snake_alpha_only" else sum(
-            1 for _ in vocoder.parameters()
-        )
         print(f"[BigVGAN] {len(clips)} clips ready  mode={train_mode}  "
               f"segment={segment_seconds}s  steps={steps}  lr={lr}  "
               f"batch={batch_size}  lambda_l2sp={lambda_l2sp}\n", flush=True)