487 lines
21 KiB
Python
487 lines
21 KiB
Python
import functools
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import random
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import autocast
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from x_transformers import Encoder
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
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Downsample, Upsample, TimestepBlock
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from models.gpt_voice.mini_encoder import AudioMiniEncoder
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from models.gpt_voice.unet_diffusion_tts7 import CheckpointedXTransformerEncoder
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from scripts.audio.gen.use_diffuse_tts import ceil_multiple
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class ResBlock(TimestepBlock):
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def __init__(
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self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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dims=2,
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kernel_size=3,
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efficient_config=True,
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use_scale_shift_norm=False,
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_scale_shift_norm = use_scale_shift_norm
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padding = {1: 0, 3: 1, 5: 2}[kernel_size]
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eff_kernel = 1 if efficient_config else 3
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eff_padding = 0 if efficient_config else 1
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
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)
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
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),
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)
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
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def forward(self, x, emb):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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return checkpoint(
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self._forward, x, emb
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)
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def _forward(self, x, emb):
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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return self.skip_connection(x) + h
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class DiffusionTts(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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Customized to be conditioned on an aligned prior derived from a autoregressive
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GPT-style model.
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:param in_channels: channels in the input Tensor.
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:param in_latent_channels: channels from the input latent.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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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 num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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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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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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def __init__(
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self,
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model_channels,
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in_channels=1,
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in_latent_channels=1024,
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in_tokens=8193,
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out_channels=2, # mean and variance
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dropout=0,
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# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
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channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
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num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
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# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
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# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
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token_conditioning_resolutions=(1,16,),
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attention_resolutions=(512,1024,2048),
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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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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=4,
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efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
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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 num_heads_upsample == -1:
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num_heads_upsample = num_heads
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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.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.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.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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padding = 1 if kernel_size == 3 else 2
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down_kernel = 1 if efficient_convs else 3
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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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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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conditioning_dim = model_channels * 8
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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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self.code_converter = nn.Sequential(
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nn.Embedding(in_tokens, conditioning_dim),
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CheckpointedXTransformerEncoder(
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needs_permute=False,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=conditioning_dim,
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depth=3,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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))
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self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
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self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
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self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
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self.conditioning_timestep_integrator = TimestepEmbedSequential(
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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)
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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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token_conditioning_blocks = []
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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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for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
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if ds in token_conditioning_resolutions:
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token_conditioning_block = nn.Conv1d(conditioning_dim, ch, 1)
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token_conditioning_block.weight.data *= .02
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self.input_blocks.append(token_conditioning_block)
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token_conditioning_blocks.append(token_conditioning_block)
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for _ in range(num_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=int(mult * model_channels),
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dims=dims,
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kernel_size=kernel_size,
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efficient_config=efficient_convs,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = int(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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)
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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_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
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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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kernel_size=kernel_size,
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efficient_config=efficient_convs,
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use_scale_shift_norm=use_scale_shift_norm,
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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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),
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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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kernel_size=kernel_size,
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efficient_config=efficient_convs,
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use_scale_shift_norm=use_scale_shift_norm,
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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, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
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for i in range(num_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=int(model_channels * mult),
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dims=dims,
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kernel_size=kernel_size,
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efficient_config=efficient_convs,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = int(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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)
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)
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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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)
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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_grad_norm_parameter_groups(self):
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groups = {
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'minicoder': list(self.contextual_embedder.parameters()),
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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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'middle_transformer': list(self.middle_block.parameters()),
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}
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return groups
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def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_input=None, conditioning_free=False):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
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:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
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:param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
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:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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assert conditioning_input is not None
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if self.super_sampling_enabled:
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assert lr_input is not None
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if self.training and self.super_sampling_max_noising_factor > 0:
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noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
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lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
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lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
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x = torch.cat([x, lr_input], dim=1)
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# Shuffle aligned_latent to BxCxS format
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if is_latent(aligned_conditioning):
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aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
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# Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
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orig_x_shape = x.shape[-1]
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cm = ceil_multiple(x.shape[-1], 2048)
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if cm != 0:
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pc = (cm-x.shape[-1])/x.shape[-1]
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x = F.pad(x, (0,cm-x.shape[-1]))
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# Also fix aligned_latent, which is aligned to x.
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if is_latent(aligned_conditioning):
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aligned_conditioning = torch.cat([aligned_conditioning,
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self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1)
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else:
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aligned_conditioning = F.pad(aligned_conditioning, (0,int(pc*aligned_conditioning.shape[-1])))
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with autocast(x.device.type, enabled=self.enable_fp16):
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hs = []
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
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else:
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cond_emb = self.contextual_embedder(conditioning_input)
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if is_latent(aligned_conditioning):
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code_emb = self.latent_converter(aligned_conditioning)
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else:
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code_emb = self.code_converter(aligned_conditioning)
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cond_emb = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
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code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1),
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code_emb)
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# Everything after this comment is timestep dependent.
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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first = True
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time_emb = time_emb.float()
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h = x
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, nn.Conv1d):
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h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
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h = h + h_tok
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else:
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with autocast(x.device.type, enabled=self.enable_fp16 and not first):
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# First block has autocast disabled to allow a high precision signal to be properly vectorized.
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h = module(h, time_emb)
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hs.append(h)
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first = False
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h = self.middle_block(h, time_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, time_emb)
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# Last block also has autocast disabled for high-precision outputs.
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h = h.float()
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out = self.out(h)
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return out[:, :, :orig_x_shape]
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|
|
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@register_model
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def register_diffusion_tts9(opt_net, opt):
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return DiffusionTts(**opt_net['kwargs'])
|
|
|
|
|
|
if __name__ == '__main__':
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clip = torch.randn(2, 1, 32868)
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|
aligned_latent = torch.randn(2,388,1024)
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|
aligned_sequence = torch.randint(0,8192,(2,388))
|
|
cond = torch.randn(2, 1, 44000)
|
|
ts = torch.LongTensor([600, 600])
|
|
model = DiffusionTts(128,
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|
channel_mult=[1,1.5,2, 3, 4, 6, 8],
|
|
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
|
|
token_conditioning_resolutions=[1,4,16,64],
|
|
attention_resolutions=[],
|
|
num_heads=8,
|
|
kernel_size=3,
|
|
scale_factor=2,
|
|
time_embed_dim_multiplier=4,
|
|
super_sampling=False,
|
|
efficient_convs=False)
|
|
# Test with latent aligned conditioning
|
|
o = model(clip, ts, aligned_latent, cond)
|
|
# Test with sequence aligned conditioning
|
|
o = model(clip, ts, aligned_sequence, cond)
|
|
|