forked from mrq/DL-Art-School
528 lines
22 KiB
Python
528 lines
22 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.x_transformers import AbsolutePositionalEmbedding, AttentionLayers, CrossAttender
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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 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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def clustered_mask(probability, shape, dev, lateral_expansion_radius_max=3, inverted=False):
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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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"""
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# Each masked token spreads out to 1+lateral_expansion_radius_max on average, therefore reduce the probability in
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# kind
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probability = probability / (1+lateral_expansion_radius_max)
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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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if inverted:
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return torch.bernoulli(torch.clamp(mask, 0, 1)) != 0
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else:
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return torch.bernoulli(torch.clamp(mask, 0, 1)) == 0
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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, *args, **kwargs):
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for k, v in kwargs.items():
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assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
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partial = functools.partial(self.wrap, **kwargs)
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return torch.utils.checkpoint.checkpoint(partial, x, *args)
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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, **kwargs):
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x = x.permute(0,2,1)
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h = self.transformer(x, **kwargs)
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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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time_embed_dim_multiplier=4,
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cond_transformer_depth=8,
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mid_transformer_depth=8,
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# Parameters for regularization.
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nil_guidance_fwd_proportion=.3,
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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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# Parameters for unaligned inputs.
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enabled_unaligned_inputs=False,
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num_unaligned_tokens=164,
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unaligned_encoder_depth=8,
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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.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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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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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
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self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
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self.contextual_embedder = AudioMiniEncoder(1, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(embedding_dim*3, embedding_dim, 1)
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self.enable_unaligned_inputs = enabled_unaligned_inputs
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if enabled_unaligned_inputs:
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self.unaligned_embedder = nn.Embedding(num_unaligned_tokens, embedding_dim)
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self.unaligned_encoder = CheckpointedXTransformerEncoder(
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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=embedding_dim,
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depth=unaligned_encoder_depth,
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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.conditioning_encoder = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=embedding_dim,
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depth=cond_transformer_depth,
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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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cross_attend=self.enable_unaligned_inputs,
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)
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,embedding_dim,1))
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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(embedding_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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)
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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=1, pad=0
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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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mid_transformer = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=ch,
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depth=mid_transformer_depth,
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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.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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),
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mid_transformer,
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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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),
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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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)
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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 forward(self, x, timesteps, tokens=None, conditioning_input=None, lr_input=None, unaligned_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 tokens: an aligned text input.
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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 unaligned_input: A structural input that is not properly aligned with the output of the diffusion model.
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Can be combined with a conditioning input to produce more robust conditioning.
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:param conditioning_free: When set, all conditioning inputs (including tokens, conditioning_input and unaligned_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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with autocast(x.device.type):
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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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if tokens is not None:
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tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
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hs = []
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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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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if self.enable_unaligned_inputs:
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assert unaligned_input is not None
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unaligned_h = self.unaligned_embedder(unaligned_input).permute(0,2,1)
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unaligned_h = self.unaligned_encoder(unaligned_h).permute(0,2,1)
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cond_emb = self.contextual_embedder(conditioning_input)
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if tokens is not None:
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# Mask out guidance tokens for un-guided diffusion.
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if self.training and self.nil_guidance_fwd_proportion > 0:
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token_mask = clustered_mask(self.nil_guidance_fwd_proportion, tokens.shape, tokens.device, inverted=True)
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tokens = torch.where(token_mask, self.mask_token_id, tokens)
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code_emb = self.code_embedding(tokens).permute(0,2,1)
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cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
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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.
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cond_time_emb = cond_time_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, 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)
|
|
|
|
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
|
|
if self.training and self.unconditioned_percentage > 0:
|
|
unconditioned_batches = torch.rand((code_emb.shape[0],1,1), device=code_emb.device) < self.unconditioned_percentage
|
|
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1), 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_tts7(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],
|
|
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,
|
|
enabled_unaligned_inputs=True)
|
|
model(clip, ts, tok, cond, lr, un)
|
|
model(clip, ts, None, cond, lr)
|
|
torch.save(model.state_dict(), 'test_out.pth')
|
|
|