495 lines
19 KiB
Python
495 lines
19 KiB
Python
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple
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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 einops import pack, rearrange, repeat
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from cosyvoice.utils.common import mask_to_bias
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from cosyvoice.utils.mask import add_optional_chunk_mask
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from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
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from matcha.models.components.transformer import BasicTransformerBlock
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class Transpose(torch.nn.Module):
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def __init__(self, dim0: int, dim1: int):
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super().__init__()
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self.dim0 = dim0
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self.dim1 = dim1
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = torch.transpose(x, self.dim0, self.dim1)
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return x
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class CausalConv1d(torch.nn.Conv1d):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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stride: int = 1,
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dilation: int = 1,
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros',
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device=None,
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dtype=None
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) -> None:
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super(CausalConv1d, self).__init__(in_channels, out_channels,
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kernel_size, stride,
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padding=0, dilation=dilation,
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groups=groups, bias=bias,
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padding_mode=padding_mode,
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device=device, dtype=dtype)
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assert stride == 1
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self.causal_padding = kernel_size - 1
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = F.pad(x, (self.causal_padding, 0), value=0.0)
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x = super(CausalConv1d, self).forward(x)
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return x
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class CausalBlock1D(Block1D):
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def __init__(self, dim: int, dim_out: int):
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super(CausalBlock1D, self).__init__(dim, dim_out)
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self.block = torch.nn.Sequential(
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CausalConv1d(dim, dim_out, 3),
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Transpose(1, 2),
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nn.LayerNorm(dim_out),
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Transpose(1, 2),
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nn.Mish(),
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)
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def forward(self, x: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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output = self.block(x * mask)
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return output * mask
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class CausalResnetBlock1D(ResnetBlock1D):
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def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
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super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
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self.block1 = CausalBlock1D(dim, dim_out)
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self.block2 = CausalBlock1D(dim_out, dim_out)
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class ConditionalDecoder(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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channels=(256, 256),
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dropout=0.05,
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attention_head_dim=64,
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n_blocks=1,
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num_mid_blocks=2,
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num_heads=4,
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act_fn="snake",
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):
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"""
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This decoder requires an input with the same shape of the target. So, if your text content
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is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
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"""
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super().__init__()
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channels = tuple(channels)
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.time_embeddings = SinusoidalPosEmb(in_channels)
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time_embed_dim = channels[0] * 4
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self.time_mlp = TimestepEmbedding(
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in_channels=in_channels,
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time_embed_dim=time_embed_dim,
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act_fn="silu",
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)
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self.down_blocks = nn.ModuleList([])
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self.mid_blocks = nn.ModuleList([])
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self.up_blocks = nn.ModuleList([])
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output_channel = in_channels
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for i in range(len(channels)): # pylint: disable=consider-using-enumerate
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input_channel = output_channel
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output_channel = channels[i]
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is_last = i == len(channels) - 1
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resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
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transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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dim=output_channel,
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num_attention_heads=num_heads,
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attention_head_dim=attention_head_dim,
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dropout=dropout,
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activation_fn=act_fn,
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)
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for _ in range(n_blocks)
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]
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)
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downsample = (
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Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
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)
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self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
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for _ in range(num_mid_blocks):
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input_channel = channels[-1]
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out_channels = channels[-1]
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resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
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transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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dim=output_channel,
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num_attention_heads=num_heads,
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attention_head_dim=attention_head_dim,
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dropout=dropout,
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activation_fn=act_fn,
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)
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for _ in range(n_blocks)
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]
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)
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self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
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channels = channels[::-1] + (channels[0],)
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for i in range(len(channels) - 1):
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input_channel = channels[i] * 2
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output_channel = channels[i + 1]
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is_last = i == len(channels) - 2
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resnet = ResnetBlock1D(
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dim=input_channel,
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dim_out=output_channel,
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time_emb_dim=time_embed_dim,
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)
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transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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dim=output_channel,
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num_attention_heads=num_heads,
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attention_head_dim=attention_head_dim,
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dropout=dropout,
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activation_fn=act_fn,
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)
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for _ in range(n_blocks)
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]
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)
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upsample = (
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Upsample1D(output_channel, use_conv_transpose=True)
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if not is_last
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else nn.Conv1d(output_channel, output_channel, 3, padding=1)
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)
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self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
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self.final_block = Block1D(channels[-1], channels[-1])
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self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
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self.initialize_weights()
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def initialize_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d):
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nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.GroupNorm):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
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"""Forward pass of the UNet1DConditional model.
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Args:
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x (torch.Tensor): shape (batch_size, in_channels, time)
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mask (_type_): shape (batch_size, 1, time)
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t (_type_): shape (batch_size)
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spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
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cond (_type_, optional): placeholder for future use. Defaults to None.
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Raises:
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ValueError: _description_
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ValueError: _description_
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Returns:
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_type_: _description_
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"""
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t = self.time_embeddings(t).to(t.dtype)
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t = self.time_mlp(t)
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x = pack([x, mu], "b * t")[0]
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if spks is not None:
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spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
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x = pack([x, spks], "b * t")[0]
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if cond is not None:
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x = pack([x, cond], "b * t")[0]
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hiddens = []
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masks = [mask]
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for resnet, transformer_blocks, downsample in self.down_blocks:
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mask_down = masks[-1]
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x = resnet(x, mask_down, t)
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x = rearrange(x, "b c t -> b t c").contiguous()
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attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
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attn_mask = mask_to_bias(attn_mask, x.dtype)
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for transformer_block in transformer_blocks:
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x = transformer_block(
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hidden_states=x,
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attention_mask=attn_mask,
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timestep=t,
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)
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x = rearrange(x, "b t c -> b c t").contiguous()
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hiddens.append(x) # Save hidden states for skip connections
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x = downsample(x * mask_down)
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masks.append(mask_down[:, :, ::2])
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masks = masks[:-1]
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mask_mid = masks[-1]
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for resnet, transformer_blocks in self.mid_blocks:
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x = resnet(x, mask_mid, t)
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x = rearrange(x, "b c t -> b t c").contiguous()
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attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
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attn_mask = mask_to_bias(attn_mask, x.dtype)
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for transformer_block in transformer_blocks:
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x = transformer_block(
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hidden_states=x,
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attention_mask=attn_mask,
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timestep=t,
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)
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x = rearrange(x, "b t c -> b c t").contiguous()
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for resnet, transformer_blocks, upsample in self.up_blocks:
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mask_up = masks.pop()
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skip = hiddens.pop()
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x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
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x = resnet(x, mask_up, t)
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x = rearrange(x, "b c t -> b t c").contiguous()
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attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
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attn_mask = mask_to_bias(attn_mask, x.dtype)
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for transformer_block in transformer_blocks:
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x = transformer_block(
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hidden_states=x,
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attention_mask=attn_mask,
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timestep=t,
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)
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x = rearrange(x, "b t c -> b c t").contiguous()
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x = upsample(x * mask_up)
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x = self.final_block(x, mask_up)
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output = self.final_proj(x * mask_up)
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return output * mask
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class CausalConditionalDecoder(ConditionalDecoder):
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def __init__(
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self,
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in_channels,
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out_channels,
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channels=(256, 256),
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dropout=0.05,
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attention_head_dim=64,
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n_blocks=1,
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num_mid_blocks=2,
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num_heads=4,
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act_fn="snake",
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static_chunk_size=50,
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num_decoding_left_chunks=2,
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):
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"""
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This decoder requires an input with the same shape of the target. So, if your text content
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is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
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"""
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torch.nn.Module.__init__(self)
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channels = tuple(channels)
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.time_embeddings = SinusoidalPosEmb(in_channels)
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time_embed_dim = channels[0] * 4
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self.time_mlp = TimestepEmbedding(
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in_channels=in_channels,
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time_embed_dim=time_embed_dim,
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act_fn="silu",
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)
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self.static_chunk_size = static_chunk_size
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self.num_decoding_left_chunks = num_decoding_left_chunks
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self.down_blocks = nn.ModuleList([])
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self.mid_blocks = nn.ModuleList([])
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self.up_blocks = nn.ModuleList([])
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output_channel = in_channels
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for i in range(len(channels)): # pylint: disable=consider-using-enumerate
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input_channel = output_channel
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output_channel = channels[i]
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is_last = i == len(channels) - 1
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resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
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transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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dim=output_channel,
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num_attention_heads=num_heads,
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attention_head_dim=attention_head_dim,
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dropout=dropout,
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activation_fn=act_fn,
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)
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for _ in range(n_blocks)
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]
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)
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downsample = (
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Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3)
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)
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self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
|
||
|
|
|
||
|
|
for _ in range(num_mid_blocks):
|
||
|
|
input_channel = channels[-1]
|
||
|
|
out_channels = channels[-1]
|
||
|
|
resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
|
||
|
|
|
||
|
|
transformer_blocks = nn.ModuleList(
|
||
|
|
[
|
||
|
|
BasicTransformerBlock(
|
||
|
|
dim=output_channel,
|
||
|
|
num_attention_heads=num_heads,
|
||
|
|
attention_head_dim=attention_head_dim,
|
||
|
|
dropout=dropout,
|
||
|
|
activation_fn=act_fn,
|
||
|
|
)
|
||
|
|
for _ in range(n_blocks)
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
|
||
|
|
|
||
|
|
channels = channels[::-1] + (channels[0],)
|
||
|
|
for i in range(len(channels) - 1):
|
||
|
|
input_channel = channels[i] * 2
|
||
|
|
output_channel = channels[i + 1]
|
||
|
|
is_last = i == len(channels) - 2
|
||
|
|
resnet = CausalResnetBlock1D(
|
||
|
|
dim=input_channel,
|
||
|
|
dim_out=output_channel,
|
||
|
|
time_emb_dim=time_embed_dim,
|
||
|
|
)
|
||
|
|
transformer_blocks = nn.ModuleList(
|
||
|
|
[
|
||
|
|
BasicTransformerBlock(
|
||
|
|
dim=output_channel,
|
||
|
|
num_attention_heads=num_heads,
|
||
|
|
attention_head_dim=attention_head_dim,
|
||
|
|
dropout=dropout,
|
||
|
|
activation_fn=act_fn,
|
||
|
|
)
|
||
|
|
for _ in range(n_blocks)
|
||
|
|
]
|
||
|
|
)
|
||
|
|
upsample = (
|
||
|
|
Upsample1D(output_channel, use_conv_transpose=True)
|
||
|
|
if not is_last
|
||
|
|
else CausalConv1d(output_channel, output_channel, 3)
|
||
|
|
)
|
||
|
|
self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
|
||
|
|
self.final_block = CausalBlock1D(channels[-1], channels[-1])
|
||
|
|
self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
|
||
|
|
self.initialize_weights()
|
||
|
|
|
||
|
|
def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
|
||
|
|
"""Forward pass of the UNet1DConditional model.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x (torch.Tensor): shape (batch_size, in_channels, time)
|
||
|
|
mask (_type_): shape (batch_size, 1, time)
|
||
|
|
t (_type_): shape (batch_size)
|
||
|
|
spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
|
||
|
|
cond (_type_, optional): placeholder for future use. Defaults to None.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: _description_
|
||
|
|
ValueError: _description_
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
_type_: _description_
|
||
|
|
"""
|
||
|
|
t = self.time_embeddings(t).to(t.dtype)
|
||
|
|
t = self.time_mlp(t)
|
||
|
|
|
||
|
|
x = pack([x, mu], "b * t")[0]
|
||
|
|
|
||
|
|
if spks is not None:
|
||
|
|
spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
|
||
|
|
x = pack([x, spks], "b * t")[0]
|
||
|
|
if cond is not None:
|
||
|
|
x = pack([x, cond], "b * t")[0]
|
||
|
|
|
||
|
|
hiddens = []
|
||
|
|
masks = [mask]
|
||
|
|
for resnet, transformer_blocks, downsample in self.down_blocks:
|
||
|
|
mask_down = masks[-1]
|
||
|
|
x = resnet(x, mask_down, t)
|
||
|
|
x = rearrange(x, "b c t -> b t c").contiguous()
|
||
|
|
if streaming is True:
|
||
|
|
attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1)
|
||
|
|
else:
|
||
|
|
attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
|
||
|
|
attn_mask = mask_to_bias(attn_mask, x.dtype)
|
||
|
|
for transformer_block in transformer_blocks:
|
||
|
|
x = transformer_block(
|
||
|
|
hidden_states=x,
|
||
|
|
attention_mask=attn_mask,
|
||
|
|
timestep=t,
|
||
|
|
)
|
||
|
|
x = rearrange(x, "b t c -> b c t").contiguous()
|
||
|
|
hiddens.append(x) # Save hidden states for skip connections
|
||
|
|
x = downsample(x * mask_down)
|
||
|
|
masks.append(mask_down[:, :, ::2])
|
||
|
|
masks = masks[:-1]
|
||
|
|
mask_mid = masks[-1]
|
||
|
|
|
||
|
|
for resnet, transformer_blocks in self.mid_blocks:
|
||
|
|
x = resnet(x, mask_mid, t)
|
||
|
|
x = rearrange(x, "b c t -> b t c").contiguous()
|
||
|
|
if streaming is True:
|
||
|
|
attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1)
|
||
|
|
else:
|
||
|
|
attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
|
||
|
|
attn_mask = mask_to_bias(attn_mask, x.dtype)
|
||
|
|
for transformer_block in transformer_blocks:
|
||
|
|
x = transformer_block(
|
||
|
|
hidden_states=x,
|
||
|
|
attention_mask=attn_mask,
|
||
|
|
timestep=t,
|
||
|
|
)
|
||
|
|
x = rearrange(x, "b t c -> b c t").contiguous()
|
||
|
|
|
||
|
|
for resnet, transformer_blocks, upsample in self.up_blocks:
|
||
|
|
mask_up = masks.pop()
|
||
|
|
skip = hiddens.pop()
|
||
|
|
x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
|
||
|
|
x = resnet(x, mask_up, t)
|
||
|
|
x = rearrange(x, "b c t -> b t c").contiguous()
|
||
|
|
if streaming is True:
|
||
|
|
attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1)
|
||
|
|
else:
|
||
|
|
attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
|
||
|
|
attn_mask = mask_to_bias(attn_mask, x.dtype)
|
||
|
|
for transformer_block in transformer_blocks:
|
||
|
|
x = transformer_block(
|
||
|
|
hidden_states=x,
|
||
|
|
attention_mask=attn_mask,
|
||
|
|
timestep=t,
|
||
|
|
)
|
||
|
|
x = rearrange(x, "b t c -> b c t").contiguous()
|
||
|
|
x = upsample(x * mask_up)
|
||
|
|
x = self.final_block(x, mask_up)
|
||
|
|
output = self.final_proj(x * mask_up)
|
||
|
|
return output * mask
|