mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
118 lines
3.3 KiB
Python
118 lines
3.3 KiB
Python
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 rearrange
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from ...ext.autoencoder.edm2_utils import (MPConv1D, mp_silu, mp_sum, normalize)
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def nonlinearity(x):
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# swish
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return mp_silu(x)
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class ResnetBlock1D(nn.Module):
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def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):
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super().__init__()
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self.in_dim = in_dim
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out_dim = in_dim if out_dim is None else out_dim
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self.out_dim = out_dim
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self.use_conv_shortcut = conv_shortcut
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self.use_norm = use_norm
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self.conv1 = MPConv1D(in_dim, out_dim, kernel_size=kernel_size)
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self.conv2 = MPConv1D(out_dim, out_dim, kernel_size=kernel_size)
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if self.in_dim != self.out_dim:
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if self.use_conv_shortcut:
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self.conv_shortcut = MPConv1D(in_dim, out_dim, kernel_size=kernel_size)
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else:
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self.nin_shortcut = MPConv1D(in_dim, out_dim, kernel_size=1)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# pixel norm
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if self.use_norm:
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x = normalize(x, dim=1)
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h = x
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h = nonlinearity(h)
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h = self.conv1(h)
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h = nonlinearity(h)
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h = self.conv2(h)
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if self.in_dim != self.out_dim:
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if self.use_conv_shortcut:
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x = self.conv_shortcut(x)
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else:
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x = self.nin_shortcut(x)
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return mp_sum(x, h, t=0.3)
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class AttnBlock1D(nn.Module):
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def __init__(self, in_channels, num_heads=1):
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super().__init__()
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self.in_channels = in_channels
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self.num_heads = num_heads
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self.qkv = MPConv1D(in_channels, in_channels * 3, kernel_size=1)
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self.proj_out = MPConv1D(in_channels, in_channels, kernel_size=1)
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def forward(self, x):
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h = x
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y = self.qkv(h)
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y = y.reshape(y.shape[0], self.num_heads, -1, 3, y.shape[-1])
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q, k, v = normalize(y, dim=2).unbind(3)
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q = rearrange(q, 'b h c l -> b h l c')
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k = rearrange(k, 'b h c l -> b h l c')
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v = rearrange(v, 'b h c l -> b h l c')
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h = F.scaled_dot_product_attention(q, k, v)
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h = rearrange(h, 'b h l c -> b (h c) l')
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h = self.proj_out(h)
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return mp_sum(x, h, t=0.3)
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class Upsample1D(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = MPConv1D(in_channels, in_channels, kernel_size=3)
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def forward(self, x):
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x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T)
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if self.with_conv:
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x = self.conv(x)
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return x
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class Downsample1D(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv1 = MPConv1D(in_channels, in_channels, kernel_size=1)
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self.conv2 = MPConv1D(in_channels, in_channels, kernel_size=1)
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def forward(self, x):
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if self.with_conv:
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x = self.conv1(x)
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x = F.avg_pool1d(x, kernel_size=2, stride=2)
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if self.with_conv:
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x = self.conv2(x)
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return x
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