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import torch
import torch.nn as nn
import torch.nn.functional as F
from attention import SelfAttention, CrossAttention
class TimeEmbedding(nn.Module):
def __init__(self, n_embd):
super().__init__()
self.linear_1 = nn.Linear(n_embd, 4 * n_embd)
self.linear_2 = nn.Linear(4 * n_embd, 4 * n_embd)
def forward(self, x):
# x: (1, 320)
# (1, 320) -> (1, 1280)
x = self.linear_1(x)
# (1, 1280) -> (1, 1280)
x = F.silu(x)
# (1, 1280) -> (1, 1280)
x = self.linear_2(x)
return x
class UNET_ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, n_time=1280):
super().__init__()
self.groupnorm_feature = nn.GroupNorm(32, in_channels)
self.conv_feature = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.linear_time = nn.Linear(n_time, out_channels)
self.groupnorm_merged = nn.GroupNorm(32, out_channels)
self.conv_merged = 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, feature, time):
# feature: (Batch_Size, In_Channels, Height, Width)
# time: (1, 1280)
residue = feature
# (Batch_Size, In_Channels, Height, Width) -> (Batch_Size, In_Channels, Height, Width)
feature = self.groupnorm_feature(feature)
# (Batch_Size, In_Channels, Height, Width) -> (Batch_Size, In_Channels, Height, Width)
feature = F.silu(feature)
# (Batch_Size, In_Channels, Height, Width) -> (Batch_Size, Out_Channels, Height, Width)
feature = self.conv_feature(feature)
# (1, 1280) -> (1, 1280)
time = F.silu(time)
# (1, 1280) -> (1, Out_Channels)
time = self.linear_time(time)
# Add width and height dimension to time.
# (Batch_Size, Out_Channels, Height, Width) + (1, Out_Channels, 1, 1) -> (Batch_Size, Out_Channels, Height, Width)
merged = feature + time.unsqueeze(-1).unsqueeze(-1)
# (Batch_Size, Out_Channels, Height, Width) -> (Batch_Size, Out_Channels, Height, Width)
merged = self.groupnorm_merged(merged)
# (Batch_Size, Out_Channels, Height, Width) -> (Batch_Size, Out_Channels, Height, Width)
merged = F.silu(merged)
# (Batch_Size, Out_Channels, Height, Width) -> (Batch_Size, Out_Channels, Height, Width)
merged = self.conv_merged(merged)
# (Batch_Size, Out_Channels, Height, Width) + (Batch_Size, Out_Channels, Height, Width) -> (Batch_Size, Out_Channels, Height, Width)
return merged + self.residual_layer(residue)
class UNET_AttentionBlock(nn.Module):
def __init__(self, n_head: int, n_embd: int, d_context=768):
super().__init__()
channels = n_head * n_embd
self.groupnorm = nn.GroupNorm(32, channels, eps=1e-6)
self.conv_input = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
self.layernorm_1 = nn.LayerNorm(channels)
self.attention_1 = SelfAttention(n_head, channels, in_proj_bias=False)
self.layernorm_2 = nn.LayerNorm(channels)
self.attention_2 = CrossAttention(n_head, channels, d_context, in_proj_bias=False)
self.layernorm_3 = nn.LayerNorm(channels)
self.linear_geglu_1 = nn.Linear(channels, 4 * channels * 2)
self.linear_geglu_2 = nn.Linear(4 * channels, channels)
self.conv_output = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
def forward(self, x, context):
# x: (Batch_Size, Features, Height, Width)
# context: (Batch_Size, Seq_Len, Dim)
residue_long = x
# (Batch_Size, Features, Height, Width) -> (Batch_Size, Features, Height, Width)
x = self.groupnorm(x)
# (Batch_Size, Features, Height, Width) -> (Batch_Size, Features, Height, Width)
x = self.conv_input(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)
# Normalization + Self-Attention with skip connection
# (Batch_Size, Height * Width, Features)
residue_short = x
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x = self.layernorm_1(x)
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x = self.attention_1(x)
# (Batch_Size, Height * Width, Features) + (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x += residue_short
# (Batch_Size, Height * Width, Features)
residue_short = x
# Normalization + Cross-Attention with skip connection
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x = self.layernorm_2(x)
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x = self.attention_2(x, context)
# (Batch_Size, Height * Width, Features) + (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x += residue_short
# (Batch_Size, Height * Width, Features)
residue_short = x
# Normalization + FFN with GeGLU and skip connection
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x = self.layernorm_3(x)
# GeGLU as implemented in the original code: https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/modules/attention.py#L37C10-L37C10
# (Batch_Size, Height * Width, Features) -> two tensors of shape (Batch_Size, Height * Width, Features * 4)
x, gate = self.linear_geglu_1(x).chunk(2, dim=-1)
# Element-wise product: (Batch_Size, Height * Width, Features * 4) * (Batch_Size, Height * Width, Features * 4) -> (Batch_Size, Height * Width, Features * 4)
x = x * F.gelu(gate)
# (Batch_Size, Height * Width, Features * 4) -> (Batch_Size, Height * Width, Features)
x = self.linear_geglu_2(x)
# (Batch_Size, Height * Width, Features) + (Batch_Size, Height * Width, Features) -> (Batch_Size, Height * Width, Features)
x += residue_short
# (Batch_Size, Height * Width, Features) -> (Batch_Size, Features, Height * Width)
x = x.transpose(-1, -2)
# (Batch_Size, Features, Height * Width) -> (Batch_Size, Features, Height, Width)
x = x.view((n, c, h, w))
# Final skip connection between initial input and output of the block
# (Batch_Size, Features, Height, Width) + (Batch_Size, Features, Height, Width) -> (Batch_Size, Features, Height, Width)
return self.conv_output(x) + residue_long
class Upsample(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
def forward(self, x):
# (Batch_Size, Features, Height, Width) -> (Batch_Size, Features, Height * 2, Width * 2)
x = F.interpolate(x, scale_factor=2, mode='nearest')
return self.conv(x)
class SwitchSequential(nn.Sequential):
def forward(self, x, context, time):
for layer in self:
if isinstance(layer, UNET_AttentionBlock):
x = layer(x, context)
elif isinstance(layer, UNET_ResidualBlock):
x = layer(x, time)
else:
x = layer(x)
return x
class UNET(nn.Module):
def __init__(self):
super().__init__()
self.encoders = nn.ModuleList([
# (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
SwitchSequential(nn.Conv2d(4, 320, kernel_size=3, padding=1)),
# (Batch_Size, 320, Height / 8, Width / 8) -> # (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
SwitchSequential(UNET_ResidualBlock(320, 320), UNET_AttentionBlock(8, 40)),
# (Batch_Size, 320, Height / 8, Width / 8) -> # (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
SwitchSequential(UNET_ResidualBlock(320, 320), UNET_AttentionBlock(8, 40)),
# (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 16, Width / 16)
SwitchSequential(nn.Conv2d(320, 320, kernel_size=3, stride=2, padding=1)),
# (Batch_Size, 320, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16)
SwitchSequential(UNET_ResidualBlock(320, 640), UNET_AttentionBlock(8, 80)),
# (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16)
SwitchSequential(UNET_ResidualBlock(640, 640), UNET_AttentionBlock(8, 80)),
# (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 32, Width / 32)
SwitchSequential(nn.Conv2d(640, 640, kernel_size=3, stride=2, padding=1)),
# (Batch_Size, 640, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32)
SwitchSequential(UNET_ResidualBlock(640, 1280), UNET_AttentionBlock(8, 160)),
# (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32)
SwitchSequential(UNET_ResidualBlock(1280, 1280), UNET_AttentionBlock(8, 160)),
# (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 64, Width / 64)
SwitchSequential(nn.Conv2d(1280, 1280, kernel_size=3, stride=2, padding=1)),
# (Batch_Size, 1280, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
SwitchSequential(UNET_ResidualBlock(1280, 1280)),
# (Batch_Size, 1280, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
SwitchSequential(UNET_ResidualBlock(1280, 1280)),
])
self.bottleneck = SwitchSequential(
# (Batch_Size, 1280, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
UNET_ResidualBlock(1280, 1280),
# (Batch_Size, 1280, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
UNET_AttentionBlock(8, 160),
# (Batch_Size, 1280, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
UNET_ResidualBlock(1280, 1280),
)
self.decoders = nn.ModuleList([
# (Batch_Size, 2560, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
SwitchSequential(UNET_ResidualBlock(2560, 1280)),
# (Batch_Size, 2560, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64)
SwitchSequential(UNET_ResidualBlock(2560, 1280)),
# (Batch_Size, 2560, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 64, Width / 64) -> (Batch_Size, 1280, Height / 32, Width / 32)
SwitchSequential(UNET_ResidualBlock(2560, 1280), Upsample(1280)),
# (Batch_Size, 2560, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32)
SwitchSequential(UNET_ResidualBlock(2560, 1280), UNET_AttentionBlock(8, 160)),
# (Batch_Size, 2560, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32)
SwitchSequential(UNET_ResidualBlock(2560, 1280), UNET_AttentionBlock(8, 160)),
# (Batch_Size, 1920, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 32, Width / 32) -> (Batch_Size, 1280, Height / 16, Width / 16)
SwitchSequential(UNET_ResidualBlock(1920, 1280), UNET_AttentionBlock(8, 160), Upsample(1280)),
# (Batch_Size, 1920, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16)
SwitchSequential(UNET_ResidualBlock(1920, 640), UNET_AttentionBlock(8, 80)),
# (Batch_Size, 1280, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16)
SwitchSequential(UNET_ResidualBlock(1280, 640), UNET_AttentionBlock(8, 80)),
# (Batch_Size, 960, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 16, Width / 16) -> (Batch_Size, 640, Height / 8, Width / 8)
SwitchSequential(UNET_ResidualBlock(960, 640), UNET_AttentionBlock(8, 80), Upsample(640)),
# (Batch_Size, 960, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
SwitchSequential(UNET_ResidualBlock(960, 320), UNET_AttentionBlock(8, 40)),
# (Batch_Size, 640, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
SwitchSequential(UNET_ResidualBlock(640, 320), UNET_AttentionBlock(8, 40)),
# (Batch_Size, 640, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
SwitchSequential(UNET_ResidualBlock(640, 320), UNET_AttentionBlock(8, 40)),
])
def forward(self, x, context, time):
# x: (Batch_Size, 4, Height / 8, Width / 8)
# context: (Batch_Size, Seq_Len, Dim)
# time: (1, 1280)
skip_connections = []
for layers in self.encoders:
x = layers(x, context, time)
skip_connections.append(x)
x = self.bottleneck(x, context, time)
for layers in self.decoders:
# Since we always concat with the skip connection of the encoder, the number of features increases before being sent to the decoder's layer
x = torch.cat((x, skip_connections.pop()), dim=1)
x = layers(x, context, time)
return x
class UNET_OutputLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.groupnorm = nn.GroupNorm(32, in_channels)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
def forward(self, x):
# x: (Batch_Size, 320, Height / 8, Width / 8)
# (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
x = self.groupnorm(x)
# (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 320, Height / 8, Width / 8)
x = F.silu(x)
# (Batch_Size, 320, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8)
x = self.conv(x)
# (Batch_Size, 4, Height / 8, Width / 8)
return x
class Diffusion(nn.Module):
def __init__(self):
super().__init__()
self.time_embedding = TimeEmbedding(320)
self.unet = UNET()
self.final = UNET_OutputLayer(320, 4)
def forward(self, latent, context, time):
# latent: (Batch_Size, 4, Height / 8, Width / 8)
# context: (Batch_Size, Seq_Len, Dim)
# time: (1, 320)
# (1, 320) -> (1, 1280)
time = self.time_embedding(time)
# (Batch, 4, Height / 8, Width / 8) -> (Batch, 320, Height / 8, Width / 8)
output = self.unet(latent, context, time)
# (Batch, 320, Height / 8, Width / 8) -> (Batch, 4, Height / 8, Width / 8)
output = self.final(output)
# (Batch, 4, Height / 8, Width / 8)
return output |