forked from mrq/DL-Art-School
Make it even better
This commit is contained in:
James Betker
parent
e735d8e1fa
commit
723f324eda
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@ -76,28 +76,26 @@ class DiffusionTtsFlat(nn.Module):
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)
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)
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self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
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if in_channels > 60: # It's a spectrogram.
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self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
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CheckpointedXTransformerEncoder(
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needs_permute=True,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=model_channels,
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depth=4,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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))
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else:
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self.contextual_embedder = AudioMiniEncoder(1, model_channels, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
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# The contextual embedder processes a sample MEL that the output should be "like".
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self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
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CheckpointedXTransformerEncoder(
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needs_permute=True,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=model_channels,
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depth=4,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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))
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self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
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# This is a further encoder extension that integrates a timestep signal into the conditioning signal.
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self.conditioning_timestep_integrator = CheckpointedXTransformerEncoder(
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needs_permute=True,
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max_seq_len=-1,
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@ -117,6 +115,7 @@ class DiffusionTtsFlat(nn.Module):
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)
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self.integrate_conditioning = nn.Conv1d(model_channels*2, model_channels, 1)
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# This is the main processing module.
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self.layers = CheckpointedXTransformerEncoder(
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needs_permute=True,
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max_seq_len=-1,
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@ -151,18 +150,7 @@ class DiffusionTtsFlat(nn.Module):
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}
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return groups
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def forward(self, x, timesteps, aligned_conditioning, conditioning_input, conditioning_free=False):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
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:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
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:param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
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:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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def get_conditioning_encodings(self, aligned_conditioning, conditioning_input, conditioning_free, return_unused=False):
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# Shuffle aligned_latent to BxCxS format
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if is_latent(aligned_conditioning):
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aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
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@ -184,14 +172,31 @@ class DiffusionTtsFlat(nn.Module):
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unused_params.extend(list(self.latent_converter.parameters()))
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cond_emb_spread = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
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code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1))
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1),
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(conditioning_input.shape[0], 1, 1),
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code_emb)
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# Everything after this comment is timestep dependent.
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if return_unused:
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return code_emb, unused_params
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return code_emb
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def forward(self, x, timesteps, aligned_conditioning, conditioning_input, conditioning_free=False):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
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:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
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:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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code_emb, unused_params = self.get_conditioning_encodings(aligned_conditioning, conditioning_input, conditioning_free, return_unused=True)
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# Everything after this comment is timestep-dependent.
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb=time_emb)
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x = self.inp_block(x)
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