| | import torch
|
| | import torch.nn as nn
|
| | import torch.nn.functional as F
|
| | from einops import rearrange
|
| |
|
| | from ...ext.autoencoder.edm2_utils import (MPConv1D, mp_silu, mp_sum, normalize)
|
| |
|
| |
|
| | def nonlinearity(x):
|
| |
|
| | return mp_silu(x)
|
| |
|
| |
|
| | class ResnetBlock1D(nn.Module):
|
| |
|
| | def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):
|
| | super().__init__()
|
| | self.in_dim = in_dim
|
| | out_dim = in_dim if out_dim is None else out_dim
|
| | self.out_dim = out_dim
|
| | self.use_conv_shortcut = conv_shortcut
|
| | self.use_norm = use_norm
|
| |
|
| | self.conv1 = MPConv1D(in_dim, out_dim, kernel_size=kernel_size)
|
| | self.conv2 = MPConv1D(out_dim, out_dim, kernel_size=kernel_size)
|
| | if self.in_dim != self.out_dim:
|
| | if self.use_conv_shortcut:
|
| | self.conv_shortcut = MPConv1D(in_dim, out_dim, kernel_size=kernel_size)
|
| | else:
|
| | self.nin_shortcut = MPConv1D(in_dim, out_dim, kernel_size=1)
|
| |
|
| | def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| |
|
| |
|
| | if self.use_norm:
|
| | x = normalize(x, dim=1)
|
| |
|
| | h = x
|
| | h = nonlinearity(h)
|
| | h = self.conv1(h)
|
| |
|
| | h = nonlinearity(h)
|
| | h = self.conv2(h)
|
| |
|
| | if self.in_dim != self.out_dim:
|
| | if self.use_conv_shortcut:
|
| | x = self.conv_shortcut(x)
|
| | else:
|
| | x = self.nin_shortcut(x)
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| |
|
| | return mp_sum(x, h, t=0.3)
|
| |
|
| |
|
| | class AttnBlock1D(nn.Module):
|
| |
|
| | def __init__(self, in_channels, num_heads=1):
|
| | super().__init__()
|
| | self.in_channels = in_channels
|
| |
|
| | self.num_heads = num_heads
|
| | self.qkv = MPConv1D(in_channels, in_channels * 3, kernel_size=1)
|
| | self.proj_out = MPConv1D(in_channels, in_channels, kernel_size=1)
|
| |
|
| | def forward(self, x):
|
| | h = x
|
| | y = self.qkv(h)
|
| | y = y.reshape(y.shape[0], self.num_heads, -1, 3, y.shape[-1])
|
| | q, k, v = normalize(y, dim=2).unbind(3)
|
| |
|
| | q = rearrange(q, 'b h c l -> b h l c')
|
| | k = rearrange(k, 'b h c l -> b h l c')
|
| | v = rearrange(v, 'b h c l -> b h l c')
|
| |
|
| | h = F.scaled_dot_product_attention(q, k, v)
|
| | h = rearrange(h, 'b h l c -> b (h c) l')
|
| |
|
| | h = self.proj_out(h)
|
| |
|
| | return mp_sum(x, h, t=0.3)
|
| |
|
| |
|
| | class Upsample1D(nn.Module):
|
| |
|
| | def __init__(self, in_channels, with_conv):
|
| | super().__init__()
|
| | self.with_conv = with_conv
|
| | if self.with_conv:
|
| | self.conv = MPConv1D(in_channels, in_channels, kernel_size=3)
|
| |
|
| | def forward(self, x):
|
| | x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact')
|
| | if self.with_conv:
|
| | x = self.conv(x)
|
| | return x
|
| |
|
| |
|
| | class Downsample1D(nn.Module):
|
| |
|
| | def __init__(self, in_channels, with_conv):
|
| | super().__init__()
|
| | self.with_conv = with_conv
|
| | if self.with_conv:
|
| |
|
| | self.conv1 = MPConv1D(in_channels, in_channels, kernel_size=1)
|
| | self.conv2 = MPConv1D(in_channels, in_channels, kernel_size=1)
|
| |
|
| | def forward(self, x):
|
| |
|
| | if self.with_conv:
|
| | x = self.conv1(x)
|
| |
|
| | x = F.avg_pool1d(x, kernel_size=2, stride=2)
|
| |
|
| | if self.with_conv:
|
| | x = self.conv2(x)
|
| |
|
| | return x
|
| |
|
