Stable-Diffusion-Model/decoder.py at main · karan-nanda/Stable-Diffusion-Model · GitHub

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import torch
from torch import nn
from torch import functional as F
from attention import SelfAttention

class VAE_AttentionBlock(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.groupnorm = nn.GroupNorm(32, channels)
        self.attention = SelfAttention(1,channels)

    def forward(self,x):

        residue = x

        x = self.groupnorm(x)

        n, c, h, w = x.shape

        x = x.view(n ,c,h *w)

        x = x.transpose(-1,-2)

        #Performing SelfAttention without MASK

        x = self.attention(x)
        x = x.transpose(-1,-2)

        x = x.view(n,c,h,w)

        x += residue
        return x

class VAE_ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.groupnorm1 = nn.GroupNorm(32, in_channels)
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)

        self.groupnorm2 = nn.GroupNorm(32, out_channels)
        self.conv2 = 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 forwarf(self,x):

        residue = x

        x = self.groupnorm1(x)
        x = F.silu(x)
        x = self.conv1(x)

        x = self.groupnorm2(x)
        x = F.silu(x)
        x = self.conv2(x)

        return x + self.residual_layer(residue)

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