forked from mrq/tortoise-tts
599 lines
26 KiB
Python
599 lines
26 KiB
Python
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"""
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This model is based on OpenAI's UNet from improved diffusion, with modifications to support a MEL conditioning signal
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and an audio conditioning input. It has also been simplified somewhat.
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Credit: https://github.com/openai/improved-diffusion
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"""
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import functools
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import math
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from abc import abstractmethod
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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 torch.nn import Linear
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from torch.utils.checkpoint import checkpoint
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from x_transformers import ContinuousTransformerWrapper, Encoder
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from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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def ceil_multiple(base, multiple):
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res = base % multiple
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if res == 0:
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return base
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return base + (multiple - res)
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def timestep_embedding(timesteps, dim, max_period=10000):
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"""
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Create sinusoidal timestep embeddings.
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:param timesteps: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an [N x dim] Tensor of positional embeddings.
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"""
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
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).to(device=timesteps.device)
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args = timesteps[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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class TimestepBlock(nn.Module):
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"""
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Any module where forward() takes timestep embeddings as a second argument.
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"""
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@abstractmethod
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def forward(self, x, emb):
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"""
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Apply the module to `x` given `emb` timestep embeddings.
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"""
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
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"""
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A sequential module that passes timestep embeddings to the children that
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support it as an extra input.
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"""
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def forward(self, x, emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb)
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else:
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x = layer(x)
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return x
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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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kernel_size=3,
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efficient_config=True,
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use_scale_shift_norm=False,
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_scale_shift_norm = use_scale_shift_norm
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padding = {1: 0, 3: 1, 5: 2}[kernel_size]
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eff_kernel = 1 if efficient_config else 3
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eff_padding = 0 if efficient_config else 1
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding),
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)
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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Linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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nn.Conv1d(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 = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)
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def forward(self, x, emb):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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return checkpoint(
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self._forward, x, emb
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)
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def _forward(self, x, emb):
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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return self.skip_connection(x) + h
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class 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, needs_permute=True, **xtransformer_kwargs):
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super().__init__()
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self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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self.needs_permute = needs_permute
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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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if self.needs_permute:
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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 DiffusionTts(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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Customized to be conditioned on an aligned prior derived from a autoregressive
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GPT-style model.
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:param in_channels: channels in the input Tensor.
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:param in_latent_channels: channels from the input latent.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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def __init__(
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self,
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model_channels,
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in_channels=1,
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in_latent_channels=1024,
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in_tokens=8193,
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conditioning_dim_factor=8,
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conditioning_expansion=4,
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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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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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freeze_main_net=False,
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efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
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use_scale_shift_norm=True,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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# Parameters for super-sampling.
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super_sampling=False,
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super_sampling_max_noising_factor=.1,
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):
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super().__init__()
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if super_sampling:
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in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.super_sampling_enabled = super_sampling
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self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.alignment_size = 2 ** (len(channel_mult)+1)
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self.freeze_main_net = freeze_main_net
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padding = 1 if kernel_size == 3 else 2
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down_kernel = 1 if efficient_convs else 3
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time_embed_dim = model_channels * time_embed_dim_multiplier
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self.time_embed = nn.Sequential(
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Linear(model_channels, time_embed_dim),
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nn.SiLU(),
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Linear(time_embed_dim, time_embed_dim),
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)
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conditioning_dim = model_channels * conditioning_dim_factor
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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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self.code_converter = nn.Sequential(
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nn.Embedding(in_tokens, conditioning_dim),
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CheckpointedXTransformerEncoder(
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needs_permute=False,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=conditioning_dim,
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depth=3,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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))
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self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
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self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
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if in_channels > 60: # It's a spectrogram.
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self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,conditioning_dim,3,padding=1,stride=2),
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CheckpointedXTransformerEncoder(
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needs_permute=True,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=conditioning_dim,
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depth=4,
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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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else:
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self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
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self.conditioning_timestep_integrator = TimestepEmbedSequential(
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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)
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self.conditioning_expansion = conditioning_expansion
|
||
|
|
|
||
|
|
self.input_blocks = nn.ModuleList(
|
||
|
|
[
|
||
|
|
TimestepEmbedSequential(
|
||
|
|
nn.Conv1d(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(conditioning_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),
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
efficient_config=efficient_convs,
|
||
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
||
|
|
)
|
||
|
|
]
|
||
|
|
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, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
ch = out_ch
|
||
|
|
input_block_chans.append(ch)
|
||
|
|
ds *= 2
|
||
|
|
self._feature_size += ch
|
||
|
|
|
||
|
|
self.middle_block = TimestepEmbedSequential(
|
||
|
|
ResBlock(
|
||
|
|
ch,
|
||
|
|
time_embed_dim,
|
||
|
|
dropout,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
efficient_config=efficient_convs,
|
||
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
||
|
|
),
|
||
|
|
AttentionBlock(
|
||
|
|
ch,
|
||
|
|
num_heads=num_heads,
|
||
|
|
num_head_channels=num_head_channels,
|
||
|
|
),
|
||
|
|
ResBlock(
|
||
|
|
ch,
|
||
|
|
time_embed_dim,
|
||
|
|
dropout,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
efficient_config=efficient_convs,
|
||
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
||
|
|
),
|
||
|
|
)
|
||
|
|
self._feature_size += ch
|
||
|
|
|
||
|
|
self.output_blocks = nn.ModuleList([])
|
||
|
|
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
|
||
|
|
for i in range(num_blocks + 1):
|
||
|
|
ich = input_block_chans.pop()
|
||
|
|
layers = [
|
||
|
|
ResBlock(
|
||
|
|
ch + ich,
|
||
|
|
time_embed_dim,
|
||
|
|
dropout,
|
||
|
|
out_channels=int(model_channels * mult),
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
efficient_config=efficient_convs,
|
||
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
||
|
|
)
|
||
|
|
]
|
||
|
|
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, 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(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)),
|
||
|
|
)
|
||
|
|
|
||
|
|
def fix_alignment(self, x, aligned_conditioning):
|
||
|
|
"""
|
||
|
|
The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
|
||
|
|
padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
|
||
|
|
"""
|
||
|
|
cm = ceil_multiple(x.shape[-1], self.alignment_size)
|
||
|
|
if cm != 0:
|
||
|
|
pc = (cm-x.shape[-1])/x.shape[-1]
|
||
|
|
x = F.pad(x, (0,cm-x.shape[-1]))
|
||
|
|
# Also fix aligned_latent, which is aligned to x.
|
||
|
|
if is_latent(aligned_conditioning):
|
||
|
|
aligned_conditioning = torch.cat([aligned_conditioning,
|
||
|
|
self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1)
|
||
|
|
else:
|
||
|
|
aligned_conditioning = F.pad(aligned_conditioning, (0, int(pc*aligned_conditioning.shape[-1])))
|
||
|
|
return x, aligned_conditioning
|
||
|
|
|
||
|
|
def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_input=None, conditioning_free=False):
|
||
|
|
"""
|
||
|
|
Apply the model to an input batch.
|
||
|
|
|
||
|
|
:param x: an [N x C x ...] Tensor of inputs.
|
||
|
|
:param timesteps: a 1-D batch of timesteps.
|
||
|
|
:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
|
||
|
|
: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 conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
|
||
|
|
:return: an [N x C x ...] Tensor of outputs.
|
||
|
|
"""
|
||
|
|
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)
|
||
|
|
|
||
|
|
# Shuffle aligned_latent to BxCxS format
|
||
|
|
if is_latent(aligned_conditioning):
|
||
|
|
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
|
||
|
|
|
||
|
|
# Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
|
||
|
|
orig_x_shape = x.shape[-1]
|
||
|
|
x, aligned_conditioning = self.fix_alignment(x, aligned_conditioning)
|
||
|
|
|
||
|
|
with autocast(x.device.type, enabled=self.enable_fp16):
|
||
|
|
hs = []
|
||
|
|
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||
|
|
|
||
|
|
# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
|
||
|
|
if conditioning_free:
|
||
|
|
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
|
||
|
|
else:
|
||
|
|
cond_emb = self.contextual_embedder(conditioning_input)
|
||
|
|
if len(cond_emb.shape) == 3: # Just take the first element.
|
||
|
|
cond_emb = cond_emb[:, :, 0]
|
||
|
|
if is_latent(aligned_conditioning):
|
||
|
|
code_emb = self.latent_converter(aligned_conditioning)
|
||
|
|
else:
|
||
|
|
code_emb = self.code_converter(aligned_conditioning)
|
||
|
|
cond_emb = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
|
||
|
|
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
# Everything after this comment is timestep dependent.
|
||
|
|
code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1)
|
||
|
|
code_emb = self.conditioning_timestep_integrator(code_emb, time_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=self.enable_fp16 and not first):
|
||
|
|
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
|
||
|
|
h = module(h, time_emb)
|
||
|
|
hs.append(h)
|
||
|
|
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)
|
||
|
|
|
||
|
|
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
|
||
|
|
extraneous_addition = 0
|
||
|
|
params = [self.aligned_latent_padding_embedding, self.unconditioned_embedding] + list(self.latent_converter.parameters())
|
||
|
|
for p in params:
|
||
|
|
extraneous_addition = extraneous_addition + p.mean()
|
||
|
|
out = out + extraneous_addition * 0
|
||
|
|
|
||
|
|
return out[:, :, :orig_x_shape]
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
clip = torch.randn(2, 1, 32868)
|
||
|
|
aligned_latent = torch.randn(2,388,1024)
|
||
|
|
aligned_sequence = torch.randint(0,8192,(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,
|
||
|
|
time_embed_dim_multiplier=4,
|
||
|
|
super_sampling=False,
|
||
|
|
efficient_convs=False)
|
||
|
|
# Test with latent aligned conditioning
|
||
|
|
o = model(clip, ts, aligned_latent, cond)
|
||
|
|
# Test with sequence aligned conditioning
|
||
|
|
o = model(clip, ts, aligned_sequence, cond)
|