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flat diffusion

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This commit is contained in:
James Betker 2022-03-17 17:45:27 -06:00
parent 428911cd4d
commit c14fc003ed
2 changed files with 28 additions and 31 deletions
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44
codes/models/audio/tts/unet_diffusion_tts_flat.py
View File

@ -101,21 +101,14 @@ class ResBlock(TimestepBlock):
class DiffusionLayer(nn.Module):
def __init__(self, model_channels, aligned_channels, cond_channels, dropout, num_heads):
def __init__(self, model_channels, dropout, num_heads):
super().__init__()
self.aligned_mutation = zero_module(conv_nd(1, aligned_channels, model_channels, 1))
self.cond_mutation = zero_module(conv_nd(1, cond_channels, model_channels, 1))
self.inp_mutation = conv_nd(1, model_channels, model_channels, 1)
self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
self.attn = AttentionBlock(model_channels, num_heads)
def forward(self, x, aligned, pointwise, time_emb):
a = self.aligned_mutation(aligned)
c = self.cond_mutation(pointwise.unsqueeze(-1))
f = self.inp_mutation(x)
y = self.resblk(f + c.repeat(1,1,f.shape[-1]) + F.interpolate(a, size=f.shape[-1], mode='nearest'), time_emb)
y = self.attn(y)
return y
def forward(self, x, time_emb):
y = self.resblk(x, time_emb)
return self.attn(y)
class DiffusionTtsFlat(nn.Module):
@ -147,8 +140,8 @@ class DiffusionTtsFlat(nn.Module):
self.enable_fp16 = use_fp16
self.layer_drop = layer_drop
self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
self.position_embed = nn.Embedding(max_positions, model_channels)
self.inp_block = conv_nd(1, in_channels, model_channels//2, 3, 1, 1)
self.position_embed = nn.Embedding(max_positions, model_channels//2)
self.time_embed = nn.Embedding(max_timesteps, model_channels)
# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
@ -171,7 +164,8 @@ class DiffusionTtsFlat(nn.Module):
ff_glu=True,
rotary_emb_dim=True,
)
))
)
)
self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
if in_channels > 60: # It's a spectrogram.
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
@ -196,14 +190,14 @@ class DiffusionTtsFlat(nn.Module):
self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
self.conditioning_timestep_integrator = TimestepEmbedSequential(
ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
AttentionBlock(model_channels, num_heads=num_heads),
ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
AttentionBlock(model_channels, num_heads=num_heads),
ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
AttentionBlock(model_channels, num_heads=num_heads),
ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
AttentionBlock(model_channels, num_heads=num_heads),
ResBlock(model_channels, model_channels, dropout, out_channels=model_channels//2, dims=1, kernel_size=1, use_scale_shift_norm=True),
)
self.layers = nn.ModuleList([DiffusionLayer(model_channels, model_channels, model_channels, dropout, num_heads) for _ in range(num_layers)])
self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)])
self.out = nn.Sequential(
normalization(model_channels),
@ -259,19 +253,19 @@ class DiffusionTtsFlat(nn.Module):
code_emb)
# Everything after this comment is timestep dependent.
x = self.inp_block(x)
pos_emb = self.position_embed(torch.arange(0, x.shape[-1], device=x.device)).unsqueeze(0).repeat(x.shape[0],1,1).permute(0,2,1)
x = x + pos_emb
time_emb = self.time_embed(timesteps)
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
pos_emb = self.position_embed(torch.arange(0, x.shape[-1], device=x.device)).unsqueeze(0).repeat(x.shape[0],1,1).permute(0,2,1)
x = self.inp_block(x) + pos_emb
x = torch.cat([x, F.interpolate(code_emb, size=x.shape[-1], mode='nearest')], dim=1)
for i, lyr in enumerate(self.layers):
# Do layer drop where applicable. Do not drop first and last layers.
if self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
unused_params.extend(list(lyr.parameters()))
else:
# First and last blocks will have autocast disabled for improved precision.
with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
x = lyr(x, code_emb, cond_emb, time_emb)
x = lyr(x, time_emb)
x = x.float()
out = self.out(x)

15
codes/trainer/eval/audio_diffusion_fid.py
View File

@ -129,12 +129,15 @@ class AudioDiffusionFid(evaluator.Evaluator):
output_size = univnet_mel.shape[-1]
aligned_codes_compression_factor = output_size // mel_codes.shape[-1]
padded_size = ceil_multiple(output_size, self.model.alignment_size)
padding_added = padded_size - output_size
padding_needed_for_codes = padding_added // aligned_codes_compression_factor
if padding_needed_for_codes > 0:
mel_codes = F.pad(mel_codes, (0, padding_needed_for_codes))
output_shape = (1, 100, padded_size)
if hasattr(self.model, 'alignment_size'):
padded_size = ceil_multiple(output_size, self.model.alignment_size)
padding_added = padded_size - output_size
padding_needed_for_codes = padding_added // aligned_codes_compression_factor
if padding_needed_for_codes > 0:
mel_codes = F.pad(mel_codes, (0, padding_needed_for_codes))
output_shape = (1, 100, padded_size)
else:
output_shape = univnet_mel.shape
gen_mel = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'aligned_conditioning': mel_codes,
'conditioning_input': univnet_mel})