import torch import torch.nn as nn import torch.nn.functional as F from decoder import VAE_AttentionBlock, VAE_ResidualBlock class VAE_Encoder(nn.Sequential): def __init__(self): super().__init__( # (Batch_Size, Channel, Height, Width) -> (Batch_Size, 128, Height, Width) nn.Conv2d(3, 128, kernel_size=3, padding=1), # (Batch_Size, 128, Height, Width) -> (Batch_Size, 128, Height, Width) VAE_ResidualBlock(128, 128), # (Batch_Size, 128, Height, Width) -> (Batch_Size, 128, Height, Width) VAE_ResidualBlock(128, 128), # (Batch_Size, 128, Height, Width) -> (Batch_Size, 128, Height / 2, Width / 2) nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=0), # (Batch_Size, 128, Height / 2, Width / 2) -> (Batch_Size, 256, Height / 2, Width / 2) VAE_ResidualBlock(128, 256), # (Batch_Size, 256, Height / 2, Width / 2) -> (Batch_Size, 256, Height / 2, Width / 2) VAE_ResidualBlock(256, 256), # (Batch_Size, 256, Height / 2, Width / 2) -> (Batch_Size, 256, Height / 4, Width / 4) nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=0), # (Batch_Size, 256, Height / 4, Width / 4) -> (Batch_Size, 512, Height / 4, Width / 4) VAE_ResidualBlock(256, 512), # (Batch_Size, 512, Height / 4, Width / 4) -> (Batch_Size, 512, Height / 4, Width / 4) VAE_ResidualBlock(512, 512), # (Batch_Size, 512, Height / 4, Width / 4) -> (Batch_Size, 512, Height / 8, Width / 8) nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=0), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) VAE_ResidualBlock(512, 512), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) VAE_ResidualBlock(512, 512), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) VAE_ResidualBlock(512, 512), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) VAE_AttentionBlock(512), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) VAE_ResidualBlock(512, 512), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) nn.GroupNorm(32, 512), # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) nn.SiLU(), # Because the padding=1, it means the width and height will increase by 2 # Out_Height = In_Height + Padding_Top + Padding_Bottom # Out_Width = In_Width + Padding_Left + Padding_Right # Since padding = 1 means Padding_Top = Padding_Bottom = Padding_Left = Padding_Right = 1, # Since the Out_Width = In_Width + 2 (same for Out_Height), it will compensate for the Kernel size of 3 # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 8, Height / 8, Width / 8). nn.Conv2d(512, 8, kernel_size=3, padding=1), # (Batch_Size, 8, Height / 8, Width / 8) -> (Batch_Size, 8, Height / 8, Width / 8) nn.Conv2d(8, 8, kernel_size=1, padding=0), ) def forward(self, x, noise): # x: (Batch_Size, Channel, Height, Width) # noise: (Batch_Size, 4, Height / 8, Width / 8) for module in self: if getattr(module, 'stride', None) == (2, 2): # Padding at downsampling should be asymmetric (see #8) # Pad: (Padding_Left, Padding_Right, Padding_Top, Padding_Bottom). # Pad with zeros on the right and bottom. # (Batch_Size, Channel, Height, Width) -> (Batch_Size, Channel, Height + Padding_Top + Padding_Bottom, Width + Padding_Left + Padding_Right) = (Batch_Size, Channel, Height + 1, Width + 1) x = F.pad(x, (0, 1, 0, 1)) x = module(x) # (Batch_Size, 8, Height / 8, Width / 8) -> two tensors of shape (Batch_Size, 4, Height / 8, Width / 8) mean, log_variance = torch.chunk(x, 2, dim=1) # Clamp the log variance between -30 and 20, so that the variance is between (circa) 1e-14 and 1e8. # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) log_variance = torch.clamp(log_variance, -30, 20) # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) variance = log_variance.exp() # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) stdev = variance.sqrt() # Transform N(0, 1) -> N(mean, stdev) # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) x = mean + stdev * noise # Scale by a constant # Constant taken from: https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/configs/stable-diffusion/v1-inference.yaml#L17C1-L17C1 x *= 0.18215 return x