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add efficient config to tts9

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This commit is contained in:
James Betker 2022-03-12 15:10:35 -07:00
parent 896accb71f
commit 0523777ff7
1 changed files with 14 additions and 4 deletions
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18
codes/models/gpt_voice/unet_diffusion_tts9.py
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@ -34,6 +34,7 @@ class ResBlock(TimestepBlock):
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
):
super().__init__()
self.channels = channels
@ -41,11 +42,13 @@ class ResBlock(TimestepBlock):
self.dropout = dropout
self.out_channels = out_channels or channels
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 1, padding=0),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.emb_layers = nn.Sequential(
@ -67,7 +70,7 @@ class ResBlock(TimestepBlock):
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x, emb):
"""
@ -146,6 +149,7 @@ class DiffusionTts(nn.Module):
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Parameters for super-sampling.
@ -178,6 +182,7 @@ class DiffusionTts(nn.Module):
self.jit_enabled = jit_enabled
self.jit_forward = None
padding = 1 if kernel_size == 3 else 2
down_kernel = 1 if efficient_convs else 3
time_embed_dim = model_channels * time_embed_dim_multiplier
self.time_embed = nn.Sequential(
@ -251,6 +256,7 @@ class DiffusionTts(nn.Module):
out_channels=int(mult * model_channels),
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs
)
]
ch = int(mult * model_channels)
@ -270,7 +276,7 @@ class DiffusionTts(nn.Module):
self.input_blocks.append(
TimestepEmbedSequential(
Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
)
)
)
@ -286,6 +292,7 @@ class DiffusionTts(nn.Module):
dropout,
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
),
AttentionBlock(
ch,
@ -298,6 +305,7 @@ class DiffusionTts(nn.Module):
dropout,
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
),
)
self._feature_size += ch
@ -314,6 +322,7 @@ class DiffusionTts(nn.Module):
out_channels=int(model_channels * mult),
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
)
]
ch = int(model_channels * mult)
@ -466,7 +475,8 @@ if __name__ == '__main__':
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
super_sampling=False)
super_sampling=False,
efficient_convs=False)
# Test with latent aligned conditioning
o = model(clip, ts, aligned_latent, cond)
# Test with sequence aligned conditioning