handle unused encoder parameters
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@ -525,10 +525,16 @@ class TransformerDiffusionWithCheaterLatent(nn.Module):
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self.encoder = self.encoder.eval()
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def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
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unused_parameters = []
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encoder_grad_enabled = self.internal_step > self.freeze_encoder_until
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if not encoder_grad_enabled:
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unused_parameters.extend(list(self.encoder.parameters()))
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with torch.set_grad_enabled(encoder_grad_enabled):
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proj = self.encoder(truth_mel).permute(0,2,1)
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for p in unused_parameters:
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proj = proj + p.mean() * 0
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diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
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return diff
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