forked from mrq/DL-Art-School
clean up
This commit is contained in:
James Betker
parent
6fc4f49e86
commit
71b73db044
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@ -1,191 +0,0 @@
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import json
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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 x_transformers import Encoder, TransformerWrapper
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from models.audio.tts.unet_diffusion_tts6 import CheckpointedLayer
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from models.audio.tts.unified_voice2 import ConditioningEncoder
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from models.audio.tts.tacotron2.text.cleaners import english_cleaners
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from trainer.networks import register_model
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from utils.util import opt_get
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def clustered_mask(probability, shape, dev, lateral_expansion_radius_max=3):
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"""
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Produces a masking vector of the specified shape where each element has probability to be zero.
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lateral_expansion_radius_max neighbors of any element that is zero also have a 50% chance to be zero.
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Effectively, this produces clusters of masks tending to be lateral_expansion_radius_max wide.
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Note: This means the algorithm has a far higher output probability for zeros then <probability>.
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"""
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mask = torch.rand(shape, device=dev)
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mask = (mask < probability).float()
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kernel = torch.tensor([.5 for _ in range(lateral_expansion_radius_max)] + [1] + [.5 for _ in range(lateral_expansion_radius_max)], device=dev)
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mask = F.conv1d(mask.unsqueeze(1), kernel.view(1,1,2*lateral_expansion_radius_max+1), padding=lateral_expansion_radius_max).squeeze(1)
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return torch.bernoulli(torch.clamp(mask, 0, 1)) == 0 # ==0 logically inverts the mask.
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class CheckpointedTransformerWrapper(nn.Module):
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"""
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Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
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to channels-last that XTransformer expects.
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"""
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def __init__(self, **xtransformer_kwargs):
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super().__init__()
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self.transformer = TransformerWrapper(**xtransformer_kwargs)
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for i in range(len(self.transformer.transformer.attn_layers.layers)):
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n, b, r = self.transformer.transformer.attn_layers.layers[i]
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self.transformer.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, *args, **kwargs):
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return self.transformer(*args, **kwargs)
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class CtcCodeGenerator(nn.Module):
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def __init__(self, model_dim=512, layers=10, num_heads=8, dropout=.1, ctc_codes=36, max_pad=121, max_repeat=30, mask_probability=.1):
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super().__init__()
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self.max_pad = max_pad
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self.max_repeat = max_repeat
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self.mask_probability = mask_probability
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self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=num_heads, mean=True)
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self.initial_embedding = nn.Embedding(ctc_codes, model_dim)
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self.combiner = nn.Linear(model_dim*2, model_dim)
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self.transformer = TransformerWrapper(
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num_tokens=max_pad*max_repeat+1,
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max_seq_len=-1, # Unneeded for rotary embeddings.
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attn_layers=Encoder(
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dim=model_dim,
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depth=layers,
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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_pos_emb=True
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)
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)
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self.transformer.token_emb = nn.Identity() # This class handles the initial embeddings.
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self.transformer.to_logits = nn.Identity()
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self.ctc_head = nn.Linear(model_dim, max_pad*max_repeat+1)
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self.inp_head = nn.Linear(model_dim, ctc_codes)
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def forward(self, conditioning_input, codes, separators, repeats, unpadded_lengths):
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max_len = unpadded_lengths.max()
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codes = codes[:, :max_len]
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loss_mask = torch.ones_like(codes)
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for i, l in enumerate(unpadded_lengths):
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loss_mask[i, l:] = 0
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if self.training:
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codes = clustered_mask(self.mask_probability, codes.shape, codes.device) * codes
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if separators.max() > self.max_pad:
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print(f"Got unexpectedly long separators. Max: {separators.max()}, {separators}")
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separators = torch.clip(separators, 0, self.max_pad)
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separators = separators[:, :max_len]
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if repeats.max() > self.max_repeat:
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print(f"Got unexpectedly long repeats. Max: {repeats.max()}, {repeats}")
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repeats = torch.clip(repeats, 1, self.max_repeat)
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repeats = repeats[:, :max_len]
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repeats = repeats - 1 # min(repeats) is 1; make it 0 to avoid wasting a prediction slot.
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labels = separators + repeats * self.max_pad
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# Perform conditioning encoder in FP32, with the transformer in FP16
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cond = self.conditioning_encoder(conditioning_input).unsqueeze(1).repeat(1,codes.shape[1],1)
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h = torch.cat([cond, self.initial_embedding(codes)], dim=-1)
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h = self.combiner(h)
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with torch.autocast(codes.device.type):
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logits = self.transformer(h)
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ctc_pred = self.ctc_head(logits)
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code_pred = self.inp_head(logits)
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ctcloss = F.cross_entropy(ctc_pred.float().permute(0,2,1), labels, reduction='none')
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ctcloss = torch.mean(ctcloss * loss_mask)
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codeloss = F.cross_entropy(code_pred.float().permute(0,2,1), codes, reduction='none')
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codeloss = torch.mean(codeloss * loss_mask)
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return ctcloss, codeloss
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def generate(self, speech_conditioning_input, texts):
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codes = []
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max_seq = 50
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for text in texts:
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# First, generate CTC codes from the given texts.
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vocab = json.loads('{" ": 4, "E": 5, "T": 6, "A": 7, "O": 8, "N": 9, "I": 10, "H": 11, "S": 12, "R": 13, "D": 14, "L": 15, "U": 16, "M": 17, "W": 18, "C": 19, "F": 20, "G": 21, "Y": 22, "P": 23, "B": 24, "V": 25, "K": 26, "\'": 27, "X": 28, "J": 29, "Q": 30, "Z": 31}')
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text = english_cleaners(text)
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text = text.strip().upper()
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cd = []
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for c in text:
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if c not in vocab.keys():
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continue
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cd.append(vocab[c])
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codes.append(torch.tensor(cd, device=speech_conditioning_input.device))
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max_seq = max(max_seq, codes[-1].shape[-1])
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# Collate
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for i in range(len(codes)):
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if codes[i].shape[-1] < max_seq:
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codes[i] = F.pad(codes[i], (0, max_seq-codes[i].shape[-1]))
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codes = torch.stack(codes, dim=0)
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cond = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1).repeat(1,codes.shape[1],1)
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h = torch.cat([cond, self.initial_embedding(codes)], dim=-1)
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h = self.combiner(h)
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with torch.autocast(codes.device.type):
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logits = self.transformer(h)
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ctc_pred = self.ctc_head(logits)
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generate = torch.argmax(ctc_pred, dim=-1)
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# De-compress the codes from the generated output
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pads = generate % self.max_pad
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repeats = (generate // self.max_pad) + 1
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ctc_batch = []
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max_seq = 0
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for bc, bp, br in zip(codes, pads, repeats):
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ctc = []
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for c, p, r in zip(bc, bp, br):
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for _ in range(p):
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ctc.append(0)
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for _ in range(r):
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ctc.append(c.item())
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ctc_batch.append(torch.tensor(ctc, device=speech_conditioning_input.device))
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max_seq = max(max_seq, ctc_batch[-1].shape[-1])
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# Collate the batch
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for i in range(len(ctc_batch)):
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if ctc_batch[i].shape[-1] < max_seq:
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ctc_batch[i] = F.pad(ctc_batch[i], (0, max_seq-ctc_batch[i].shape[-1]))
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return torch.stack(ctc_batch, dim=0)
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@register_model
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def register_ctc_code_generator(opt_net, opt):
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return CtcCodeGenerator(**opt_get(opt_net, ['kwargs'], {}))
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def inf():
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sd = torch.load('D:\\dlas\\experiments\\train_encoder_build_ctc_alignments_medium\\models\\24000_generator.pth', map_location='cpu')
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model = CtcCodeGenerator(model_dim=1024,layers=32).eval()
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model.load_state_dict(sd)
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with torch.no_grad():
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from data.audio.unsupervised_audio_dataset import load_audio
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from scripts.audio.gen.speech_synthesis_utils import wav_to_mel
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ref_mel = torch.cat([wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\atkins\\1.wav", 22050))[:,:,:450],
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wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\kennard\\1.wav", 22050))[:,:,:450],
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wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\grace\\1.wav", 22050))[:,:,:450],
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wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\atkins\\1.wav", 22050))[:,:,:450]], dim=0)
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ctc = model.generate(ref_mel, (["i suppose though it's too early for them"] * 3) + ["i suppose though it's too early for them, dear"])
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print("Break")
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if __name__ == '__main__':
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#inf()
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mask = clustered_mask(.1, (4,100), 'cpu')
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model = CtcCodeGenerator()
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inps = torch.randint(0,36, (4, 300))
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pads = torch.randint(0,100, (4,300))
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repeats = torch.randint(1,20, (4,300))
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conds = torch.randn(4,80,600)
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loss1, loss2 = model(conds, inps, pads, repeats, torch.tensor([250, 300, 280, 30]))
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print(loss1.shape, loss2.shape)
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@ -1,458 +0,0 @@
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import functools
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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 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.audio.tts.mini_encoder import AudioMiniEncoder
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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
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from x_transformers import Encoder, ContinuousTransformerWrapper
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, **kwargs):
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kw_requires_grad = {}
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kw_no_grad = {}
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for k, v in kwargs.items():
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if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
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kw_requires_grad[k] = v
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else:
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kw_no_grad[k] = v
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partial = functools.partial(self.wrap, **kw_no_grad)
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return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
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class CheckpointedXTransformerEncoder(nn.Module):
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"""
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Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
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to channels-last that XTransformer expects.
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"""
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def __init__(self, **xtransformer_kwargs):
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super().__init__()
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self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x):
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x = x.permute(0,2,1)
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h = self.transformer(x)
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return h.permute(0,2,1)
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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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):
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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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padding = 1 if kernel_size == 3 else 2
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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, 1, padding=0),
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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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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, 1)
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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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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 token prior.
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:param in_channels: channels in the input Tensor.
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:param num_tokens: number of tokens (e.g. characters) which can be provided.
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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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num_tokens=32,
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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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conditioning_inputs_provided=True,
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time_embed_dim_multiplier=4,
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transformer_depths=8,
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nil_guidance_fwd_proportion=.3,
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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.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.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.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
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self.mask_token_id = num_tokens
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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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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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embedding_dim = model_channels * 8
|
||||
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
|
||||
self.conditioning_enabled = conditioning_inputs_provided
|
||||
if conditioning_inputs_provided:
|
||||
self.contextual_embedder = AudioMiniEncoder(in_channels, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
self.conditioning_encoder = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=embedding_dim,
|
||||
depth=transformer_depths,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=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, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(embedding_dim, 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=int(mult * model_channels),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(mult * model_channels)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_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=1, pad=0
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
mid_transformer = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=ch,
|
||||
depth=transformer_depths,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
)
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
mid_transformer,
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
)
|
||||
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,
|
||||
)
|
||||
]
|
||||
ch = int(model_channels * mult)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
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 load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
|
||||
strict: bool = True):
|
||||
# Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
|
||||
lsd = self.state_dict()
|
||||
revised = 0
|
||||
for i, blk in enumerate(self.input_blocks):
|
||||
if isinstance(blk, nn.Embedding):
|
||||
key = f'input_blocks.{i}.weight'
|
||||
if state_dict[key].shape[0] != lsd[key].shape[0]:
|
||||
t = torch.randn_like(lsd[key]) * .02
|
||||
t[:state_dict[key].shape[0]] = state_dict[key]
|
||||
state_dict[key] = t
|
||||
revised += 1
|
||||
print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
|
||||
return super().load_state_dict(state_dict, strict)
|
||||
|
||||
|
||||
|
||||
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], 2048)
|
||||
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])))
|
||||
if self.conditioning_enabled:
|
||||
assert conditioning_input is not None
|
||||
|
||||
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)
|
||||
if self.conditioning_enabled:
|
||||
cond_emb = self.contextual_embedder(conditioning_input)
|
||||
code_emb = cond_emb.unsqueeze(-1) * code_emb
|
||||
code_emb = self.conditioning_encoder(code_emb)
|
||||
|
||||
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_tts5(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
# Test for ~4 second audio clip at 22050Hz
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 1, 32768)
|
||||
tok = torch.randint(0,30, (2,388))
|
||||
cond = torch.randn(2, 1, 44000)
|
||||
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],
|
||||
token_conditioning_resolutions=[1,4,16,64],
|
||||
attention_resolutions=[],
|
||||
num_heads=8,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
conditioning_inputs_provided=True,
|
||||
time_embed_dim_multiplier=4)
|
||||
model(clip, ts, tok, cond)
|
||||
torch.save(model.state_dict(), 'test_out.pth')
|
||||
|
||||
|
|
@ -1,458 +0,0 @@
|
|||
import functools
|
||||
import random
|
||||
|
||||
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.audio.tts.mini_encoder import AudioMiniEncoder
|
||||
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint
|
||||
from x_transformers import Encoder, ContinuousTransformerWrapper
|
||||
|
||||
|
||||
class CheckpointedLayer(nn.Module):
|
||||
"""
|
||||
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
|
||||
checkpoint for all other args.
|
||||
"""
|
||||
def __init__(self, wrap):
|
||||
super().__init__()
|
||||
self.wrap = wrap
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
kw_requires_grad = {}
|
||||
kw_no_grad = {}
|
||||
for k, v in kwargs.items():
|
||||
if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
|
||||
kw_requires_grad[k] = v
|
||||
else:
|
||||
kw_no_grad[k] = v
|
||||
partial = functools.partial(self.wrap, **kw_no_grad)
|
||||
return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
|
||||
|
||||
|
||||
class CheckpointedXTransformerEncoder(nn.Module):
|
||||
"""
|
||||
Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
|
||||
to channels-last that XTransformer expects.
|
||||
"""
|
||||
def __init__(self, **xtransformer_kwargs):
|
||||
super().__init__()
|
||||
self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
|
||||
|
||||
for i in range(len(self.transformer.attn_layers.layers)):
|
||||
n, b, r = self.transformer.attn_layers.layers[i]
|
||||
self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
|
||||
|
||||
def forward(self, x):
|
||||
x = x.permute(0,2,1)
|
||||
h = self.transformer(x)
|
||||
return h.permute(0,2,1)
|
||||
|
||||
|
||||
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):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
|
||||
Customized to be conditioned on an aligned token prior.
|
||||
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param num_tokens: number of tokens (e.g. characters) which can be provided.
|
||||
: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 attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
: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 dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
: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,
|
||||
num_tokens=32,
|
||||
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
|
||||
token_conditioning_resolutions=(1,16,),
|
||||
attention_resolutions=(512,1024,2048),
|
||||
conv_resample=True,
|
||||
dims=1,
|
||||
use_fp16=False,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
time_embed_dim_multiplier=4,
|
||||
cond_transformer_depth=8,
|
||||
mid_transformer_depth=8,
|
||||
nil_guidance_fwd_proportion=.3,
|
||||
super_sampling=False,
|
||||
super_sampling_max_noising_factor=.1,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
if super_sampling:
|
||||
in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.dtype = torch.float16 if use_fp16 else torch.float32
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
self.dims = dims
|
||||
self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
|
||||
self.mask_token_id = num_tokens
|
||||
self.super_sampling_enabled = super_sampling
|
||||
self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
|
||||
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),
|
||||
)
|
||||
|
||||
embedding_dim = model_channels * 8
|
||||
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
|
||||
self.contextual_embedder = AudioMiniEncoder(1, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
self.conditioning_conv = nn.Conv1d(embedding_dim*2, embedding_dim, 1)
|
||||
self.conditioning_encoder = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=embedding_dim,
|
||||
depth=cond_transformer_depth,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=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, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(embedding_dim, 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=int(mult * model_channels),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(mult * model_channels)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_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=1, pad=0
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
mid_transformer = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=ch,
|
||||
depth=mid_transformer_depth,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
)
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
mid_transformer,
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
)
|
||||
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,
|
||||
)
|
||||
]
|
||||
ch = int(model_channels * mult)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
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=None, conditioning_input=None, lr_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.
|
||||
:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
|
||||
:param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
assert conditioning_input is not None
|
||||
if self.super_sampling_enabled:
|
||||
assert lr_input is not None
|
||||
if self.training and self.super_sampling_max_noising_factor > 0:
|
||||
noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
|
||||
lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
|
||||
lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
|
||||
x = torch.cat([x, lr_input], dim=1)
|
||||
|
||||
with autocast(x.device.type):
|
||||
orig_x_shape = x.shape[-1]
|
||||
cm = ceil_multiple(x.shape[-1], 2048)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
if tokens is not None:
|
||||
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
|
||||
|
||||
hs = []
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
cond_emb = self.contextual_embedder(conditioning_input)
|
||||
if tokens is not None:
|
||||
# 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)
|
||||
code_emb = self.conditioning_conv(torch.cat([cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1]), code_emb], dim=1))
|
||||
else:
|
||||
code_emb = cond_emb.unsqueeze(-1)
|
||||
code_emb = self.conditioning_encoder(code_emb)
|
||||
|
||||
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_tts6(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
# Test for ~4 second audio clip at 22050Hz
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 1, 32768)
|
||||
tok = torch.randint(0,30, (2,388))
|
||||
cond = torch.randn(2, 1, 44000)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
lr = torch.randn(2,1,10000)
|
||||
model = DiffusionTts(128,
|
||||
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=True)
|
||||
model(clip, ts, tok, cond, lr)
|
||||
model(clip, ts, None, cond, lr)
|
||||
torch.save(model.state_dict(), 'test_out.pth')
|
||||
|
||||
|
|
@ -1,312 +0,0 @@
|
|||
import functools
|
||||
import random
|
||||
|
||||
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 TimestepEmbedSequential, \
|
||||
Downsample, Upsample
|
||||
from models.audio.tts.mini_encoder import AudioMiniEncoder
|
||||
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
|
||||
from trainer.networks import register_model
|
||||
from x_transformers import Encoder, ContinuousTransformerWrapper
|
||||
|
||||
|
||||
class CheckpointedLayer(nn.Module):
|
||||
"""
|
||||
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
|
||||
checkpoint for all other args.
|
||||
"""
|
||||
def __init__(self, wrap):
|
||||
super().__init__()
|
||||
self.wrap = wrap
|
||||
|
||||
def forward(self, x, *args, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
|
||||
partial = functools.partial(self.wrap, **kwargs)
|
||||
return torch.utils.checkpoint.checkpoint(partial, x, *args)
|
||||
|
||||
|
||||
class CheckpointedXTransformerEncoder(nn.Module):
|
||||
"""
|
||||
Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
|
||||
to channels-last that XTransformer expects.
|
||||
"""
|
||||
def __init__(self, **xtransformer_kwargs):
|
||||
super().__init__()
|
||||
self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
|
||||
|
||||
for i in range(len(self.transformer.attn_layers.layers)):
|
||||
n, b, r = self.transformer.attn_layers.layers[i]
|
||||
self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
x = x.permute(0,2,1)
|
||||
h = self.transformer(x, **kwargs)
|
||||
return h.permute(0,2,1)
|
||||
|
||||
|
||||
class DiffusionTts(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
model_channels,
|
||||
in_channels=1,
|
||||
num_tokens=32,
|
||||
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),
|
||||
# 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
|
||||
token_conditioning_resolutions=(1,16,),
|
||||
dims=1,
|
||||
use_fp16=False,
|
||||
time_embed_dim_multiplier=4,
|
||||
cond_transformer_depth=8,
|
||||
mid_transformer_depth=8,
|
||||
nil_guidance_fwd_proportion=.3,
|
||||
# Parameters for super-sampling.
|
||||
super_sampling=False,
|
||||
super_sampling_max_noising_factor=.1,
|
||||
# Parameters for unaligned inputs.
|
||||
enabled_unaligned_inputs=False,
|
||||
num_unaligned_tokens=164,
|
||||
unaligned_encoder_depth=8,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if super_sampling:
|
||||
in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
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
|
||||
self.super_sampling_enabled = super_sampling
|
||||
self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
|
||||
|
||||
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),
|
||||
)
|
||||
|
||||
embedding_dim = model_channels * 8
|
||||
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
|
||||
self.contextual_embedder = AudioMiniEncoder(1, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
self.conditioning_conv = nn.Conv1d(embedding_dim*3, embedding_dim, 1)
|
||||
|
||||
self.enable_unaligned_inputs = enabled_unaligned_inputs
|
||||
if enabled_unaligned_inputs:
|
||||
self.unaligned_embedder = nn.Embedding(num_unaligned_tokens, embedding_dim)
|
||||
self.unaligned_encoder = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1,
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=embedding_dim,
|
||||
depth=unaligned_encoder_depth,
|
||||
heads=embedding_dim//128,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_emb_dim=True,
|
||||
)
|
||||
)
|
||||
|
||||
self.conditioning_encoder = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=embedding_dim,
|
||||
depth=cond_transformer_depth,
|
||||
heads=embedding_dim//128,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
cross_attend=self.enable_unaligned_inputs,
|
||||
)
|
||||
)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, 3, padding=1)
|
||||
)
|
||||
]
|
||||
)
|
||||
token_conditioning_blocks = []
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
|
||||
for level, mult in enumerate(channel_mult):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
|
||||
token_conditioning_block.weight.data *= .02
|
||||
self.input_blocks.append(token_conditioning_block)
|
||||
token_conditioning_blocks.append(token_conditioning_block)
|
||||
|
||||
out_ch = int(mult * model_channels)
|
||||
if level != len(channel_mult) - 1:
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
Downsample(
|
||||
ch, use_conv=True, dims=dims, out_channels=out_ch, factor=2, ksize=3, pad=1
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
|
||||
self.middle_block = CheckpointedXTransformerEncoder(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=ch,
|
||||
depth=mid_transformer_depth,
|
||||
heads=ch//128,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
)
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, mult in list(enumerate(channel_mult))[::-1]:
|
||||
ich = ch + input_block_chans.pop()
|
||||
out_ch = int(model_channels * mult)
|
||||
if level != 0:
|
||||
self.output_blocks.append(
|
||||
TimestepEmbedSequential(Upsample(ich, use_conv=True, dims=dims, out_channels=out_ch, factor=2))
|
||||
)
|
||||
else:
|
||||
self.output_blocks.append(
|
||||
TimestepEmbedSequential(conv_nd(dims, ich, out_ch, 3, padding=1))
|
||||
)
|
||||
ch = out_ch
|
||||
ds //= 2
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
|
||||
)
|
||||
|
||||
|
||||
def forward(self, x, timesteps, tokens=None, conditioning_input=None, lr_input=None, unaligned_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.
|
||||
:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
|
||||
:param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
|
||||
:param unaligned_input: A structural input that is not properly aligned with the output of the diffusion model.
|
||||
Can be combined with a conditioning input to produce more robust conditioning.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
assert conditioning_input is not None
|
||||
if self.super_sampling_enabled:
|
||||
assert lr_input is not None
|
||||
if self.training and self.super_sampling_max_noising_factor > 0:
|
||||
noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
|
||||
lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
|
||||
lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
|
||||
x = torch.cat([x, lr_input], dim=1)
|
||||
|
||||
if self.enable_unaligned_inputs:
|
||||
assert unaligned_input is not None
|
||||
unaligned_h = self.unaligned_embedder(unaligned_input).permute(0,2,1)
|
||||
unaligned_h = self.unaligned_encoder(unaligned_h).permute(0,2,1)
|
||||
|
||||
with autocast(x.device.type):
|
||||
orig_x_shape = x.shape[-1]
|
||||
cm = ceil_multiple(x.shape[-1], 2048)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
if tokens is not None:
|
||||
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
|
||||
|
||||
hs = []
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
cond_emb = self.contextual_embedder(conditioning_input)
|
||||
if tokens is not None:
|
||||
# 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 = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
|
||||
cond_time_emb = timestep_embedding(torch.zeros_like(timesteps), code_emb.shape[1]) # This was something I was doing (adding timesteps into this computation), but removed on second thought. TODO: completely remove.
|
||||
cond_time_emb = cond_time_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
|
||||
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb, cond_time_emb], dim=1))
|
||||
else:
|
||||
code_emb = cond_emb.unsqueeze(-1)
|
||||
if self.enable_unaligned_inputs:
|
||||
code_emb = self.conditioning_encoder(code_emb, context=unaligned_h)
|
||||
else:
|
||||
code_emb = self.conditioning_encoder(code_emb)
|
||||
|
||||
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)
|
||||
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_tts8(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
# Test for ~4 second audio clip at 22050Hz
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 1, 32768)
|
||||
tok = torch.randint(0,30, (2,388))
|
||||
cond = torch.randn(2, 1, 44000)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
lr = torch.randn(2,1,10000)
|
||||
un = torch.randint(0,120, (2,100))
|
||||
model = DiffusionTts(128,
|
||||
channel_mult=[1,1.5,2, 3, 4, 6, 8],
|
||||
token_conditioning_resolutions=[1,4,16,64],
|
||||
time_embed_dim_multiplier=4, super_sampling=False,
|
||||
enabled_unaligned_inputs=True)
|
||||
model(clip, ts, tok, cond, lr, un)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user