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import torch
from torch import nn
from attention import SelfAttention
from torch.nn import functional as F
class VAE_ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.grpnorm_1=nn.GroupNorm(32, in_channels)
self.conv_1=nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.grpnorm_2=nn.GroupNorm(32, out_channels)
self.conv_2=nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
if in_channels == out_channels:
self.residual_layer=nn.Identity()
else:
self.residual_layer=nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
def forward(self, x):
residue=x
x=self.grpnorm_1(x)
x=F.silu(x)
x=self.conv_1(x)
x=self.grpnorm_2(x)
x=F.silu(x)
x=self.conv_2(x)
return x+self.residual_layer(residue)
class VAE_AttentionBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.grpnorm=nn.GroupNorm(32, channels)
self.attention=SelfAttention(1, channels)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: (Batch_Size, Features, Height, Width)
residue=x
# (Batch_Size, Features, Height, Width) -> (Batch_Size, Features, Height, Width)
x=self.grpnorm(x)
n, c, h, w=x.shape
# (Batch_Size, Features, Height, Width) -> (Batch_Size, Features, Height * Width)
x=x.view((n,c,h*w))
# (Batch_Size, Features, Height * Width) -> (Batch_Size, Height * Width, Features)
x=x.transpose(-1, -2)
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x=self.attention(x)
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Features, Height * Width)
x=x.transpose(-1, -2)
# (Batch_Size, Features, Height , Width)
x=x.view((n, c, h, w))
x+=residue
return x
class VAE_Decoder(nn.Sequential):
def __init__(self):
super().__init__(
nn.Conv2d(4, 4, kernel_size=1, padding=0),
nn.Conv2d(4, 512, kernel_size=3, padding=1),
VAE_ResidualBlock(512, 512),
VAE_AttentionBlock(512),
VAE_ResidualBlock(512, 512),
VAE_ResidualBlock(512, 512),
VAE_ResidualBlock(512, 512),
VAE_ResidualBlock(512, 512),
nn.Upsample(scale_factor=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
VAE_ResidualBlock(512, 512),
VAE_ResidualBlock(512, 512),
VAE_ResidualBlock(512, 512),
nn.Upsample(scale_factor=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
VAE_ResidualBlock(512, 256),
VAE_ResidualBlock(256, 256),
VAE_ResidualBlock(256, 256),
nn.Upsample(scale_factor=2),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
VAE_ResidualBlock(256, 128),
VAE_ResidualBlock(128, 128),
VAE_ResidualBlock(128, 128),
nn.GroupNorm(32, 128),
nn.SiLU(),
nn.Conv2d(128, 3, kernel_size=3, padding=1),
)
def forward(self, x):
x/=0.18215
for module in self:
x=module(x)
return x
if __name__ == "__main__":
model = VAE_Decoder()
model.eval()
# Create a dummy input tensor: (batch_size=1, channels=4, height=16, width=16)
x = torch.randn(1, 4, 8, 8)
with torch.no_grad():
output = model(x)
print("Input shape :", x.shape)
print("Output shape:", output.shape)
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