import torch import torch.nn as nn import torch.nn.functional as F from torch import autocast 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.lucidrains.x_transformers import Encoder from scripts.audio.gen.use_diffuse_tts import ceil_multiple from trainer.networks import register_model from utils.util import checkpoint class ResBlock(TimestepBlock): def __init__( self, channels, emb_channels, dropout, out_channels=None, dims=2, kernel_size=3, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels padding = 1 if kernel_size == 3 else 2 self.in_layers = nn.Sequential( normalization(channels), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 1, padding=0), ) self.emb_layers = nn.Sequential( nn.SiLU(), linear( emb_channels, 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, 1) 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] h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class DiffusionTts(nn.Module): def __init__( self, model_channels, in_channels=100, num_tokens=256, out_channels=200, # mean and variance dropout=0, # m 1, 2, 4, 8 block_channels= (512,640, 768,1024), num_res_blocks= (3, 3, 3, 3), token_conditioning_resolutions=(2,4,8), attention_resolutions=(2,4,8), conv_resample=True, dims=1, use_fp16=False, kernel_size=3, scale_factor=2, time_embed_dim_multiplier=4, nil_guidance_fwd_proportion=.15, ): super().__init__() self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.attention_resolutions = attention_resolutions self.dropout = dropout self.conv_resample = conv_resample self.dtype = torch.float16 if use_fp16 else torch.float32 self.dims = dims self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion self.mask_token_id = num_tokens num_heads = model_channels // 64 padding = 1 if kernel_size == 3 else 2 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.code_embedding = nn.Embedding(num_tokens+1, model_channels) self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2), nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2)) self.conditioning_encoder = Encoder( dim=model_channels, depth=4, heads=num_heads, ff_dropout=dropout, attn_dropout=dropout, use_rmsnorm=True, ff_glu=True, rotary_pos_emb=True, ) self.codes_encoder = Encoder( dim=model_channels, depth=8, heads=num_heads, ff_dropout=dropout, attn_dropout=dropout, use_rms_scaleshift_norm=True, ff_glu=True, rotary_pos_emb=True, zero_init_branch_output=True, ) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) ) ] ) token_conditioning_blocks = [] self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, (blk_chan, num_blocks) in enumerate(zip(block_channels, num_res_blocks)): if ds in token_conditioning_resolutions: token_conditioning_block = nn.Conv1d(model_channels, ch, 1) token_conditioning_block.weight.data *= .02 self.input_blocks.append(token_conditioning_block) token_conditioning_blocks.append(token_conditioning_block) for _ in range(num_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=blk_chan, dims=dims, kernel_size=kernel_size, ) ] ch = blk_chan if ds in attention_resolutions: layers.append( AttentionBlock( ch, num_heads=num_heads, ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(block_channels) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0 ) ) ) 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, ), AttentionBlock( ch, num_heads=num_heads, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, (blk_chan, num_blocks) in list(enumerate(zip(block_channels, 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=blk_chan, dims=dims, kernel_size=kernel_size, ) ] ch = blk_chan if ds in attention_resolutions: layers.append( AttentionBlock( ch, ) ) 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)), ) def forward(self, x, timesteps, tokens, conditioning_input=None): """ 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 tokens: an aligned text input. :return: an [N x C x ...] Tensor of outputs. """ with autocast(x.device.type): orig_x_shape = x.shape[-1] cm = ceil_multiple(x.shape[-1], 16) if cm != 0: pc = (cm-x.shape[-1])/x.shape[-1] x = F.pad(x, (0,cm-x.shape[-1])) tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1]))) hs = [] time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) # Mask out guidance tokens for un-guided diffusion. if self.training and self.nil_guidance_fwd_proportion > 0: token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion tokens = torch.where(token_mask, self.mask_token_id, tokens) code_emb = self.code_embedding(tokens).permute(0,2,1) cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1) cond_emb = self.conditioning_encoder(cond_emb)[:, 0] code_emb = self.codes_encoder(code_emb.permute(0,2,1), norm_scale_shift_inp=cond_emb).permute(0,2,1) first = True time_emb = time_emb.float() h = x for k, module in enumerate(self.input_blocks): if isinstance(module, nn.Conv1d): h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest') h = h + h_tok else: with autocast(x.device.type, enabled=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) first = False 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) return out[:, :, :orig_x_shape] @register_model def register_diffusion_tts10(opt_net, opt): return DiffusionTts(**opt_net['kwargs']) if __name__ == '__main__': clip = torch.randn(2, 100, 500).cuda() tok = torch.randint(0,256, (2,230)).cuda() cond = torch.randn(2, 100, 300).cuda() ts = torch.LongTensor([600, 600]).cuda() model = DiffusionTts(512).cuda() print(sum(p.numel() for p in model.parameters()) / 1000000) model(clip, ts, tok, cond)