import random import torch import torch.nn as nn import torch.nn.functional as F from torch import autocast from x_transformers import Encoder from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \ Downsample, Upsample, TimestepBlock from models.audio.tts.mini_encoder import AudioMiniEncoder from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder from scripts.audio.gen.use_diffuse_tts import ceil_multiple from trainer.networks import register_model from utils.util import checkpoint def is_sequence(t): return t.dtype == torch.long class ResBlock(TimestepBlock): def __init__( self, channels, emb_channels, dropout, out_channels=None, dims=2, kernel_size=3, efficient_config=True, use_scale_shift_norm=False, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_scale_shift_norm = use_scale_shift_norm 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, eff_kernel, padding=eff_padding), ) self.emb_layers = nn.Sequential( nn.SiLU(), linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() else: self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, x, emb ) def _forward(self, x, emb): h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = torch.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class ResBlockSimple(nn.Module): def __init__( self, channels, dropout, out_channels=None, dims=1, kernel_size=3, efficient_config=True, ): super().__init__() self.channels = channels 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, eff_kernel, padding=eff_padding), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() else: self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding) def forward(self, x): return checkpoint( self._forward, x ) def _forward(self, x): h = self.in_layers(x) h = self.out_layers(h) return self.skip_connection(x) + h class AudioVAE(nn.Module): def __init__(self, channels, dropout): super().__init__() # 1, 4, 16, 64, 256 level_resblocks = [1, 1, 2, 2, 2] level_ch_mult = [1, 2, 4, 6, 8] levels = [] for i, (resblks, chdiv) in enumerate(zip(level_resblocks, level_ch_mult)): blocks = [ResBlockSimple(channels*chdiv, dropout=dropout, kernel_size=5) for _ in range(resblks)] if i != len(level_ch_mult)-1: blocks.append(nn.Conv1d(channels*chdiv, channels*level_ch_mult[i+1], kernel_size=5, padding=2, stride=4)) levels.append(nn.Sequential(*blocks)) self.down_levels = nn.ModuleList(levels) levels = [] lastdiv = None for resblks, chdiv in reversed(list(zip(level_resblocks, level_ch_mult))): if lastdiv is not None: blocks = [nn.Conv1d(channels*lastdiv, channels*chdiv, kernel_size=5, padding=2)] else: blocks = [] blocks.extend([ResBlockSimple(channels*chdiv, dropout=dropout, kernel_size=5) for _ in range(resblks)]) levels.append(nn.Sequential(*blocks)) lastdiv = chdiv self.up_levels = nn.ModuleList(levels) def forward(self, x): h = x for level in self.down_levels: h = level(h) for k, level in enumerate(self.up_levels): h = level(h) if k != len(self.up_levels)-1: h = F.interpolate(h, scale_factor=4, mode='linear') return h class DiffusionTts(nn.Module): """ The full UNet model with attention and timestep embedding. Customized to be conditioned on an aligned prior derived from a autoregressive GPT-style model. :param in_channels: channels in the input Tensor. :param in_latent_channels: channels from the input latent. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, model_channels, in_channels=1, out_channels=2, # mean and variance dropout=0, # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48), num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2), # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0) # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 conv_resample=True, dims=1, use_fp16=False, 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. use_scale_shift_norm=True, freeze_main=False, # Parameters for regularization. unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. ): super().__init__() self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.dims = dims self.unconditioned_percentage = unconditioned_percentage self.enable_fp16 = use_fp16 self.alignment_size = max(2 ** (len(channel_mult)+1), 256) 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( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.structural_cond_input = nn.Conv1d(in_channels, model_channels, kernel_size=5, padding=2) self.aligned_latent_padding_embedding = nn.Parameter(torch.zeros(1,in_channels,1)) self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) self.structural_processor = AudioVAE(model_channels, dropout) self.surrogate_head = nn.Conv1d(model_channels, in_channels, 1) self.input_block = conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, model_channels*2, model_channels, 1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): for _ in range(num_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=int(mult * model_channels), dims=dims, kernel_size=kernel_size, efficient_config=efficient_convs, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = int(mult * model_channels) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1 ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, kernel_size=kernel_size, efficient_config=efficient_convs, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]: for i in range(num_blocks + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=int(model_channels * mult), dims=dims, kernel_size=kernel_size, efficient_config=efficient_convs, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = int(model_channels * mult) if level and i == num_blocks: out_ch = ch layers.append( Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)), ) if freeze_main: for p in self.parameters(): p.DO_NOT_TRAIN = True p.requires_grad = False for m in [self.structural_processor, self.structural_cond_input, self.surrogate_head]: for p in m.parameters(): del p.DO_NOT_TRAIN p.requires_grad = True def get_grad_norm_parameter_groups(self): groups = { 'input_blocks': list(self.input_blocks.parameters()), 'output_blocks': list(self.output_blocks.parameters()), 'middle_transformer': list(self.middle_block.parameters()), 'structural_processor': list(self.structural_processor.parameters()), } return groups def fix_alignment(self, x, aligned_conditioning): """ The UNet requires that the input is a certain multiple of 2, defined by the UNet depth. Enforce this by padding both and before forward propagation and removing the padding before returning. """ cm = ceil_multiple(x.shape[-1], self.alignment_size) if cm != 0: pc = (cm-x.shape[-1])/x.shape[-1] x = F.pad(x, (0,cm-x.shape[-1])) aligned_conditioning = F.pad(aligned_conditioning, (0,int(pc*aligned_conditioning.shape[-1]))) return x, aligned_conditioning def forward(self, x, timesteps, conditioning, conditioning_free=False): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param conditioning: should just be the truth value. produces a latent through an autoencoder, then uses diffusion to decode that latent. at inference, only the latent is passed in. :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered. :return: an [N x C x ...] Tensor of outputs. """ # Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net. orig_x_shape = x.shape[-1] x, aligned_conditioning = self.fix_alignment(x, conditioning) with autocast(x.device.type, enabled=self.enable_fp16): # Note: this block does not need to repeated on inference, since it is not timestep-dependent. if conditioning_free: code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1) surrogate = torch.zeros_like(x) else: code_emb = self.structural_cond_input(aligned_conditioning) code_emb = self.structural_processor(code_emb) code_emb = F.interpolate(code_emb, size=(x.shape[-1],), mode='linear') surrogate = self.surrogate_head(code_emb) x = self.input_block(x) x = torch.cat([x, code_emb], dim=1) # Everything after this comment is timestep dependent. hs = [] time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) time_emb = time_emb.float() h = x for k, module in enumerate(self.input_blocks): with autocast(x.device.type, enabled=self.enable_fp16 and not first): # First block has autocast disabled to allow a high precision signal to be properly vectorized. h = module(h, time_emb) hs.append(h) h = self.middle_block(h, time_emb) for module in self.output_blocks: h = torch.cat([h, hs.pop()], dim=1) h = module(h, time_emb) # Last block also has autocast disabled for high-precision outputs. h = h.float() out = self.out(h) # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors. extraneous_addition = 0 params = [self.aligned_latent_padding_embedding, self.unconditioned_embedding] for p in params: extraneous_addition = extraneous_addition + p.mean() out = out + extraneous_addition * 0 return out[:, :, :orig_x_shape], surrogate[:, :, :orig_x_shape] @register_model def register_unet_diffusion_waveform_gen2(opt_net, opt): return DiffusionTts(**opt_net['kwargs']) if __name__ == '__main__': clip = torch.randn(2, 1, 32868) aligned_sequence = torch.randn(2,1,32868) ts = torch.LongTensor([600, 600]) model = DiffusionTts(128, channel_mult=[1,1.5,2, 3, 4, 6, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 1], kernel_size=3, scale_factor=2, time_embed_dim_multiplier=4, efficient_convs=False) # Test with sequence aligned conditioning o = model(clip, ts, aligned_sequence)