388 lines
16 KiB
Python
388 lines
16 KiB
Python
from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16
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from models.diffusion.gaussian_diffusion import get_named_beta_schedule
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.respace import SpacedDiffusion, space_timesteps
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from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
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Downsample, Upsample
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import torch
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import torch.nn as nn
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from models.gpt_voice.lucidrains_dvae import eval_decorator
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from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
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from models.vqvae.gumbel_quantizer import GumbelQuantizer
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from models.vqvae.vqvae import Quantize
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from trainer.networks import register_model
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from utils.util import get_mask_from_lengths
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import models.gpt_voice.mini_encoder as menc
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class DiscreteEncoder(nn.Module):
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def __init__(self,
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in_channels,
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model_channels,
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out_channels,
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dropout,
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scale):
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super().__init__()
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self.blocks = nn.Sequential(
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conv_nd(1, in_channels, model_channels, 3, padding=1),
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menc.ResBlock(model_channels, dropout, dims=1),
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Downsample(model_channels, use_conv=True, dims=1, out_channels=model_channels*2, factor=scale),
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menc.ResBlock(model_channels*2, dropout, dims=1),
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Downsample(model_channels*2, use_conv=True, dims=1, out_channels=model_channels*4, factor=scale),
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menc.ResBlock(model_channels*4, dropout, dims=1),
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AttentionBlock(model_channels*4, num_heads=4),
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menc.ResBlock(model_channels*4, dropout, out_channels=out_channels, dims=1),
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)
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def forward(self, spectrogram):
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return self.blocks(spectrogram)
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class DiscreteDecoder(nn.Module):
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def __init__(self, in_channels, level_channels, scale):
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super().__init__()
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# Just raw upsampling, return a dict with each layer.
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self.init = conv_nd(1, in_channels, level_channels[0], kernel_size=3, padding=1)
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layers = []
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for i, lvl in enumerate(level_channels[:-1]):
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layers.append(nn.Sequential(normalization(lvl),
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nn.SiLU(lvl),
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Upsample(lvl, use_conv=True, dims=1, out_channels=level_channels[i+1], factor=scale)))
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self.layers = nn.ModuleList(layers)
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def forward(self, x):
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y = self.init(x)
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outs = [y]
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for layer in self.layers:
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y = layer(y)
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outs.append(y)
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return outs
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class DiffusionDVAE(nn.Module):
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def __init__(
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self,
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model_channels,
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num_res_blocks,
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in_channels=1,
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out_channels=2, # mean and variance
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spectrogram_channels=80,
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spectrogram_conditioning_levels=[3,4,5], # Levels at which spectrogram conditioning is applied to the waveform.
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dropout=0,
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channel_mult=(1, 2, 4, 8, 16, 32, 64),
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attention_resolutions=(16,32,64),
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conv_resample=True,
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dims=1,
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use_fp16=False,
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num_heads=1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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use_new_attention_order=False,
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kernel_size=5,
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quantize_dim=1024,
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num_discrete_codes=8192,
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scale_steps=4,
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conditioning_inputs_provided=True,
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):
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super().__init__()
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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self.in_channels = in_channels
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self.spectrogram_channels = spectrogram_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.num_res_blocks = num_res_blocks
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self.attention_resolutions = attention_resolutions
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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.dtype = torch.float16 if use_fp16 else torch.float32
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.dims = dims
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self.spectrogram_conditioning_levels = spectrogram_conditioning_levels
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self.scale_steps = scale_steps
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self.encoder = DiscreteEncoder(spectrogram_channels, model_channels*4, quantize_dim, dropout, scale_steps)
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#self.quantizer = Quantize(quantize_dim, num_discrete_codes, balancing_heuristic=True)
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self.quantizer = GumbelQuantizer(quantize_dim, quantize_dim, num_discrete_codes)
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# For recording codebook usage.
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self.codes = torch.zeros((1228800,), dtype=torch.long)
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self.code_ind = 0
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self.internal_step = 0
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decoder_channels = [model_channels * channel_mult[s-1] for s in spectrogram_conditioning_levels]
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self.decoder = DiscreteDecoder(quantize_dim, decoder_channels[::-1], scale_steps)
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padding = 1 if kernel_size == 3 else 2
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.conditioning_enabled = conditioning_inputs_provided
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if conditioning_inputs_provided:
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self.contextual_embedder = AudioMiniEncoder(self.spectrogram_channels, time_embed_dim)
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self.query_gen = AudioMiniEncoder(decoder_channels[0], time_embed_dim)
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self.embedding_combiner = EmbeddingCombiner(time_embed_dim)
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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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)
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]
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)
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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self.convergence_convs = nn.ModuleList([])
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for level, mult in enumerate(channel_mult):
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if level in spectrogram_conditioning_levels:
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self.convergence_convs.append(conv_nd(dims, ch*2, ch, 1))
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for _ in range(num_res_blocks):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=mult * model_channels,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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layers.append(
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AttentionBlock(
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ch,
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num_heads=num_heads,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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out_ch = ch
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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_steps
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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ds *= 2
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self._feature_size += ch
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self.middle_block = TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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),
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AttentionBlock(
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ch,
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num_heads=num_heads,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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),
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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),
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)
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self._feature_size += ch
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self.output_blocks = nn.ModuleList([])
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for level, mult in list(enumerate(channel_mult))[::-1]:
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for i in range(num_res_blocks + 1):
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ich = input_block_chans.pop()
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layers = [
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ResBlock(
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ch + ich,
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time_embed_dim,
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dropout,
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out_channels=model_channels * mult,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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)
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]
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ch = model_channels * mult
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if ds in attention_resolutions:
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layers.append(
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AttentionBlock(
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ch,
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num_heads=num_heads_upsample,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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)
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)
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if level and i == num_res_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_steps)
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)
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ds //= 2
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self.output_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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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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)
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def get_debug_values(self, step, __):
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# Note: this is very poor design, but quantizer.get_temperature not only retrieves the temperature, it also updates the step and thus it is extremely important that this function get called regularly.
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return {'histogram_codes': self.codes, 'quantizer_temperature': self.quantizer.get_temperature(step)}
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@torch.no_grad()
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@eval_decorator
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def get_codebook_indices(self, images):
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img = self.norm(images)
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logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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sampled, commitment_loss, codes = self.codebook(logits)
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return codes
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def _decode_continouous(self, x, timesteps, embeddings, conditioning_inputs, num_conditioning_signals):
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if self.conditioning_enabled:
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assert conditioning_inputs is not None
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spec_hs = self.decoder(embeddings)[::-1]
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# Shape the spectrogram correctly. There is no guarantee it fits (though I probably should add an assertion here to make sure the resizing isn't too wacky.)
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spec_hs = [nn.functional.interpolate(sh, size=(x.shape[-1]//self.scale_steps**self.spectrogram_conditioning_levels[i],), mode='nearest') for i, sh in enumerate(spec_hs)]
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convergence_fns = list(self.convergence_convs)
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# Timestep embeddings and conditioning signals are combined using a small transformer.
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hs = []
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emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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if self.conditioning_enabled:
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mask = get_mask_from_lengths(num_conditioning_signals+1, conditioning_inputs.shape[1]+1) # +1 to account for the timestep embeddings we'll add.
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emb2 = torch.stack([self.contextual_embedder(ci.squeeze(1)) for ci in list(torch.chunk(conditioning_inputs, conditioning_inputs.shape[1], dim=1))], dim=1)
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emb = torch.cat([emb1.unsqueeze(1), emb2], dim=1)
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emb = self.embedding_combiner(emb, mask, self.query_gen(spec_hs[0]))
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else:
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emb = emb1
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# The rest is the diffusion vocoder, built as a standard U-net. spec_h is gradually fed into the encoder.
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next_spec = spec_hs.pop(0)
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next_convergence_fn = convergence_fns.pop(0)
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h = x.type(self.dtype)
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for k, module in enumerate(self.input_blocks):
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h = module(h, emb)
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if next_spec is not None and h.shape[-1] == next_spec.shape[-1]:
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h = torch.cat([h, next_spec], dim=1)
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h = next_convergence_fn(h)
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if len(spec_hs) > 0:
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next_spec = spec_hs.pop(0)
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next_convergence_fn = convergence_fns.pop(0)
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else:
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next_spec = None
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hs.append(h)
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assert len(spec_hs) == 0
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assert len(convergence_fns) == 0
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h = self.middle_block(h, emb)
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for module in self.output_blocks:
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h = torch.cat([h, hs.pop()], dim=1)
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h = module(h, emb)
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h = h.type(x.dtype)
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return self.out(h)
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def decode(self, x, timesteps, codes, conditioning_inputs=None, num_conditioning_signals=None):
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assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
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embeddings = self.quantizer.embed_code(codes).permute((0,2,1))
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return self._decode_continouous(x, timesteps, embeddings, conditioning_inputs, num_conditioning_signals)
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def forward(self, x, timesteps, spectrogram, conditioning_inputs=None, num_conditioning_signals=None):
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# Compute DVAE portion first.
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spec_logits = self.encoder(spectrogram).permute((0,2,1))
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sampled, commitment_loss, codes = self.quantizer(spec_logits)
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if self.training:
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# Compute from softmax outputs to preserve gradients.
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embeddings = sampled.permute((0,2,1))
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else:
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# Compute from codes only.
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embeddings = self.quantizer.embed_code(codes).permute((0,2,1))
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# This is so we can debug the distribution of codes being learned.
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if self.internal_step % 50 == 0:
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codes = codes.flatten()
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l = codes.shape[0]
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i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
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self.codes[i:i+l] = codes.cpu()
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self.code_ind = self.code_ind + l
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if self.code_ind >= self.codes.shape[0]:
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self.code_ind = 0
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self.internal_step += 1
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return self._decode_continouous(x, timesteps, embeddings, conditioning_inputs, num_conditioning_signals), commitment_loss
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@register_model
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def register_unet_diffusion_dvae(opt_net, opt):
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return DiffusionDVAE(**opt_net['kwargs'])
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'''
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class DiffusionDVAE(nn.Module):
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def __init__(
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self,
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model_channels,
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num_res_blocks,
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in_channels=1,
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out_channels=2, # mean and variance
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spectrogram_channels=80,
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spectrogram_conditioning_levels=[3,4,5], # Levels at which spectrogram conditioning is applied to the waveform.
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dropout=0,
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channel_mult=(1, 2, 4, 8, 16, 32, 64),
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attention_resolutions=(16,32,64),
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conv_resample=True,
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dims=1,
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use_fp16=False,
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num_heads=1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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use_new_attention_order=False,
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kernel_size=5,
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quantize_dim=1024,
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num_discrete_codes=8192,
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scale_steps=4,
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conditioning_inputs_provided=True,
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):
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'''
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# Test for ~4 second audio clip at 22050Hz
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if __name__ == '__main__':
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spec = torch.randn(4, 80, 160)
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ts = torch.LongTensor([432, 234, 100, 555])
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model = DiffusionDVAE(model_channels=128, num_res_blocks=1, in_channels=80, out_channels=160, spectrogram_conditioning_levels=[1,2],
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channel_mult=(1,2,4), attention_resolutions=[4], num_heads=4, kernel_size=3, scale_steps=2, conditioning_inputs_provided=False)
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print(model(torch.randn_like(spec), ts, spec)[0].shape)
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