Some additional context regularization in tfd
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@ -215,6 +215,15 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
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left_pt = -1
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left_pt = -1
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cond_right = conditioning_input[:,:,cond_start+N:]
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cond_right = conditioning_input[:,:,cond_start+N:]
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right_pt = 0
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right_pt = 0
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if self.training:
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# Arbitrarily restrict the context given. We should support short contexts and without this they are never encountered.
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arb_context_cap = random.randint(50, 100)
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if cond_left.shape[-1] > arb_context_cap and random() > .5:
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cond_left = cond_left[:,:,-arb_context_cap:]
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if cond_right.shape[-1] > arb_context_cap and random() > .5:
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cond_right = cond_right[:,:,:arb_context_cap]
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elif cond_left is None:
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elif cond_left is None:
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assert conditioning_input.shape[-1] - cond_start - N >= 0, f'Some sort of conditioning misalignment, {conditioning_input.shape[-1], cond_start, N}'
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assert conditioning_input.shape[-1] - cond_start - N >= 0, f'Some sort of conditioning misalignment, {conditioning_input.shape[-1], cond_start, N}'
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cond_pre = conditioning_input[:,:,:cond_start]
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cond_pre = conditioning_input[:,:,:cond_start]
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@ -230,10 +230,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
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# 1. Generate the cheater latent using the input as a reference.
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# 1. Generate the cheater latent using the input as a reference.
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sampler = self.diffuser.ddim_sample_loop if self.ddim else self.diffuser.p_sample_loop
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sampler = self.diffuser.ddim_sample_loop if self.ddim else self.diffuser.p_sample_loop
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output_shape = (1, 256, cheater.shape[-1]-80)
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# center-pad the conditioning input (the center isn't actually used). this is hack for giving tfdpc5 a bigger working context.
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gen_cheater = sampler(self.model, output_shape, progress=True,
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cheater_padded = torch.cat([cheater[:,:,cheater.shape[-1]//2:], torch.zeros(1,256,160, device=cheater.device), cheater[:,:,:cheater.shape[-1]//2]], dim=-1)
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gen_cheater = sampler(self.model, cheater.shape, progress=True,
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causal=self.causal, causal_slope=self.causal_slope,
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causal=self.causal, causal_slope=self.causal_slope,
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model_kwargs={'conditioning_input': cheater, 'cond_start': 40})
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model_kwargs={'conditioning_input': cheater_padded, 'cond_start': 80})
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# 2. Decode the cheater into a MEL
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# 2. Decode the cheater into a MEL
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gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,gen_cheater.shape[-1]*16), progress=True,
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gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,gen_cheater.shape[-1]*16), progress=True,
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