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undo latent converter change

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This commit is contained in:
James Betker 2022-04-01 20:46:27 -06:00
parent 55c86e02c7
commit 00767219fc
1 changed files with 5 additions and 9 deletions
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14
codes/models/audio/tts/unet_diffusion_tts_flat0.py
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@ -152,11 +152,7 @@ class DiffusionTtsFlat(nn.Module):
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
) )
self.code_norm = normalization(model_channels) self.code_norm = normalization(model_channels)
self.autoregressive_latent_converter = nn.Sequential(nn.Conv1d(in_latent_channels, model_channels, 1), self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2), self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2), nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
@ -183,7 +179,7 @@ class DiffusionTtsFlat(nn.Module):
) )
if freeze_everything_except_autoregressive_inputs: if freeze_everything_except_autoregressive_inputs:
for ap in list(self.autoregressive_latent_converter.parameters()): for ap in list(self.latent_converter.parameters()):
ap.ALLOWED_IN_FLAT = True ap.ALLOWED_IN_FLAT = True
for p in self.parameters(): for p in self.parameters():
if not hasattr(p, 'ALLOWED_IN_FLAT'): if not hasattr(p, 'ALLOWED_IN_FLAT'):
@ -194,7 +190,7 @@ class DiffusionTtsFlat(nn.Module):
groups = { groups = {
'minicoder': list(self.contextual_embedder.parameters()), 'minicoder': list(self.contextual_embedder.parameters()),
'layers': list(self.layers.parameters()), 'layers': list(self.layers.parameters()),
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.autoregressive_latent_converter.parameters()) + list(self.autoregressive_latent_converter.parameters()), 'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()),
'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()), 'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
'time_embed': list(self.time_embed.parameters()), 'time_embed': list(self.time_embed.parameters()),
} }
@ -215,7 +211,7 @@ class DiffusionTtsFlat(nn.Module):
cond_emb = conds.mean(dim=-1) cond_emb = conds.mean(dim=-1)
cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1) cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
if is_latent(aligned_conditioning): if is_latent(aligned_conditioning):
code_emb = self.autoregressive_latent_converter(aligned_conditioning) code_emb = self.latent_converter(aligned_conditioning)
else: else:
code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1) code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
code_emb = self.code_converter(code_emb) code_emb = self.code_converter(code_emb)
@ -258,7 +254,7 @@ class DiffusionTtsFlat(nn.Module):
if conditioning_free: if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1]) code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
unused_params.extend(list(self.autoregressive_latent_converter.parameters())) unused_params.extend(list(self.latent_converter.parameters()))
else: else:
if precomputed_aligned_embeddings is not None: if precomputed_aligned_embeddings is not None:
code_emb = precomputed_aligned_embeddings code_emb = precomputed_aligned_embeddings