Two crash paths under "RuntimeError: Inference tensors cannot be
saved for backward":
1. clip_f / sync_f loaded from main-thread inference_mode carry the
inference flag. Clone them on entry to the worker thread so the
conditions built from them are clean non-inference tensors.
Also clone x after Phase 1 before the STE reconnection — Phase 1
runs under no_grad and produces outputs that may still carry the
flag through the conditions path.
2. net_generator.unnormalize + feature_utils.decode called outside
any checkpoint wrapper with requires_grad=True input. Backward
tried to save inference-flagged model weights. Wrapped both calls
in checkpoint(use_reentrant=False) so they recompute on backward
instead of storing activations.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
Ethanfel