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fix: sanitize all submodule buffers of mel_converter + guarantee target_mel output

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Previous fix only iterated mel_converter._buffers (direct buffers). Submodules
(e.g. Spectrogram.window) still held inference tensors. Switch to .modules()
to cover all nested buffers, matching the vocoder parameter sanitization.
Also add a zeros+copy_ safety net on target_mel output so conv can save it.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-04-09 02:14:12 +02:00
parent 51ac099073
commit df63b147e9
1 changed files with 20 additions and 17 deletions
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nodes/selva_bigvgan_trainer.py
+20 -17
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@@ -241,19 +241,17 @@ class SelvaBigvganTrainer:
fresh, requires_grad=True
)
# mel_converter buffers (mel_basis, hann_window, etc.) were loaded
# inside ComfyUI's outer inference_mode context, so they are inference
# tensors. Operations on inference tensors ALWAYS produce inference
# tensors, even inside inference_mode(False). torch.zeros() et al.
# create normal tensors in the current (non-inference) context, so
# we replace every buffer once via copy_() to break the chain.
for bname, buf in list(mel_converter._buffers.items()):
if buf is not None:
fresh = torch.zeros(
buf.shape, device=buf.device, dtype=buf.dtype
)
fresh.copy_(buf)
mel_converter._buffers[bname] = fresh
# mel_converter and its submodules (e.g. Spectrogram.window) have
# inference-tensor buffers loaded in ComfyUI's outer inference_mode.
# Must iterate .modules() — ._buffers only covers direct buffers.
for sub in mel_converter.modules():
for bname, buf in list(sub._buffers.items()):
if buf is not None:
fresh = torch.zeros(
buf.shape, device=buf.device, dtype=buf.dtype
)
fresh.copy_(buf)
sub._buffers[bname] = fresh
optimizer = torch.optim.AdamW(
vocoder.parameters(), lr=lr, betas=(0.8, 0.99)
@@ -280,11 +278,16 @@ class SelvaBigvganTrainer:
del _stacked
target_wav = target_flat.unsqueeze(1) # [B, 1, T]
# Fixed target mel — buffers are now normal tensors (sanitized
# above), so torch.no_grad() correctly produces a non-inference,
# no-grad leaf tensor that conv layers can save for backward.
# Compute target mel and guarantee it is not an inference tensor.
# Even with sanitized buffers a submodule we missed could still
# taint the output, so we always copy into a fresh tensor.
with torch.no_grad():
target_mel = mel_converter(target_flat) # [B, 80, T_mel]
_mel = mel_converter(target_flat)
target_mel = torch.empty(
_mel.shape, device=device, dtype=dtype
)
target_mel.copy_(_mel)
del _mel
# Vocoder forward: mel → waveform
pred_wav = vocoder(target_mel) # [B, 1, T_wav]