forked from mrq/DL-Art-School
more cleanup
This commit is contained in:
James Betker
parent
fef1066687
commit
a5d2123daa
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@ -146,8 +146,6 @@ class DiffusionWaveformGen(nn.Module):
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:param num_res_blocks: number of residual blocks per downsample.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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@ -165,39 +163,24 @@ class DiffusionWaveformGen(nn.Module):
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num_res_blocks=(1,1,0),
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token_conditioning_resolutions=(1,4),
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mid_resnet_depth=10,
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conv_resample=True,
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dims=1,
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use_fp16=False,
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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=1,
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freeze_main_net=False,
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use_scale_shift_norm=True,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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# Parameters for super-sampling.
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super_sampling=False,
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super_sampling_max_noising_factor=.1,
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):
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super().__init__()
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if super_sampling:
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in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.dims = dims
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self.super_sampling_enabled = super_sampling
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self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.alignment_size = 2 ** (len(channel_mult)+1)
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self.freeze_main_net = freeze_main_net
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self.in_mel_channels = in_mel_channels
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padding = 1 if kernel_size == 3 else 2
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time_embed_dim = model_channels * time_embed_dim_multiplier
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self.time_embed = nn.Sequential(
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@ -217,7 +200,7 @@ class DiffusionWaveformGen(nn.Module):
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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@ -242,8 +225,8 @@ class DiffusionWaveformGen(nn.Module):
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dropout,
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out_channels=int(mult * model_channels),
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dims=dims,
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kernel_size=kernel_size,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=3,
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use_scale_shift_norm=True,
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)
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]
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ch = int(mult * model_channels)
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@ -255,7 +238,7 @@ class DiffusionWaveformGen(nn.Module):
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self.input_blocks.append(
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TimestepEmbedSequential(
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Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=3, pad=1
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ch, True, dims=dims, out_channels=out_ch, factor=2, ksize=3, pad=1
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)
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)
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)
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@ -279,15 +262,15 @@ class DiffusionWaveformGen(nn.Module):
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dropout,
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out_channels=int(model_channels * mult),
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dims=dims,
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kernel_size=kernel_size,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=3,
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use_scale_shift_norm=True,
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)
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]
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ch = int(model_channels * mult)
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if level and i == num_blocks:
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out_ch = ch
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layers.append(
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Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
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Upsample(ch, True, dims=dims, out_channels=out_ch, factor=2)
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)
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ds //= 2
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self.output_blocks.append(TimestepEmbedSequential(*layers))
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@ -296,20 +279,10 @@ class DiffusionWaveformGen(nn.Module):
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
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zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
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)
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if self.freeze_main_net:
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mains = [self.time_embed, self.contextual_embedder, self.unconditioned_embedding, self.conditioning_timestep_integrator,
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self.input_blocks, self.middle_block, self.output_blocks, self.out]
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for m in mains:
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for p in m.parameters():
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p.requires_grad = False
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p.DO_NOT_TRAIN = True
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def get_grad_norm_parameter_groups(self):
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if self.freeze_main_net:
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return {}
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groups = {
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'input_blocks': list(self.input_blocks.parameters()),
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'output_blocks': list(self.output_blocks.parameters()),
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@ -61,6 +61,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
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if mode == 'spec_decode':
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self.diffusion_fn = self.perform_diffusion_spec_decode
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self.squeeze_ratio = opt_eval['squeeze_ratio']
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elif 'from_codes' == mode:
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self.diffusion_fn = self.perform_diffusion_from_codes
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self.local_modules['codegen'] = get_music_codegen()
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@ -81,11 +82,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
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def perform_diffusion_spec_decode(self, audio, sample_rate=22050):
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real_resampled = audio
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audio = audio.unsqueeze(0)
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output_shape = (1, 256, audio.shape[-1] // 256)
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output_shape = (1, self.squeeze_ratio, audio.shape[-1] // self.squeeze_ratio)
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mel = self.spec_fn({'in': audio})['out']
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gen = self.diffuser.p_sample_loop(self.model, output_shape,
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model_kwargs={'codes': mel})
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gen = pixel_shuffle_1d(gen, 256)
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gen = pixel_shuffle_1d(gen, self.squeeze_ratio)
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return gen, real_resampled, normalize_mel(self.spec_fn({'in': gen})['out']), normalize_mel(mel), sample_rate
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