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import torch
from torch import nn
from torch.nn import 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 = F.silu(self.linear_1(x))
return self.linear_2(x)
class SqueezeExcitation(nn.Module):
def __init__(self, channels, reduction=16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channels, channels // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channels // reduction, channels, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
class UNET_ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, n_time=1280, use_se=False):
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)
self.residual_layer = nn.Identity() if in_channels == out_channels else nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
# Add Squeeze-Excitation blocks only if use_se is True
self.use_se = use_se
if use_se:
self.se1 = SqueezeExcitation(out_channels)
self.se2 = SqueezeExcitation(out_channels)
def forward(self, feature, time):
residue = feature
feature = F.silu(self.groupnorm_feature(feature))
feature = self.conv_feature(feature)
if self.use_se:
feature = self.se1(feature) # Apply SE after first conv
time = self.linear_time(F.silu(time))
merged = feature + time.unsqueeze(-1).unsqueeze(-1)
merged = F.silu(self.groupnorm_merged(merged))
merged = self.conv_merged(merged)
if self.use_se:
merged = self.se2(merged) # Apply SE after second conv
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):
residue_long = x
x = self.conv_input(self.groupnorm(x))
n, c, h, w = x.shape
x = x.view((n, c, h * w)).transpose(-1, -2)
residue_short = x
x = self.attention_1(self.layernorm_1(x)) + residue_short
residue_short = x
x = self.attention_2(self.layernorm_2(x), context) + residue_short
residue_short = x
x, gate = self.linear_geglu_1(self.layernorm_3(x)).chunk(2, dim=-1)
x = self.linear_geglu_2(x * F.gelu(gate)) + residue_short
x = x.transpose(-1, -2).view((n, c, h, w))
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):
return self.conv(F.interpolate(x, scale_factor=2, mode='nearest'))
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, use_se=False):
super().__init__()
self.encoders = nn.ModuleList([
SwitchSequential(nn.Conv2d(4, 320, kernel_size=3, padding=1)),
SwitchSequential(UNET_ResidualBlock(320, 320, use_se=use_se), UNET_AttentionBlock(8, 40)),
SwitchSequential(UNET_ResidualBlock(320, 320, use_se=use_se), UNET_AttentionBlock(8, 40)),
SwitchSequential(nn.Conv2d(320, 320, kernel_size=3, stride=2, padding=1)),
SwitchSequential(UNET_ResidualBlock(320, 640, use_se=use_se), UNET_AttentionBlock(8, 80)),
SwitchSequential(UNET_ResidualBlock(640, 640, use_se=use_se), UNET_AttentionBlock(8, 80)),
SwitchSequential(nn.Conv2d(640, 640, kernel_size=3, stride=2, padding=1)),
SwitchSequential(UNET_ResidualBlock(640, 1280, use_se=use_se), UNET_AttentionBlock(8, 160)),
SwitchSequential(UNET_ResidualBlock(1280, 1280, use_se=use_se), UNET_AttentionBlock(8, 160)),
SwitchSequential(nn.Conv2d(1280, 1280, kernel_size=3, stride=2, padding=1)),
SwitchSequential(UNET_ResidualBlock(1280, 1280, use_se=use_se)),
SwitchSequential(UNET_ResidualBlock(1280, 1280, use_se=use_se)),
])
self.bottleneck = SwitchSequential(
UNET_ResidualBlock(1280, 1280, use_se=use_se),
UNET_AttentionBlock(8, 160),
UNET_ResidualBlock(1280, 1280, use_se=use_se),
)
self.decoders = nn.ModuleList([
SwitchSequential(UNET_ResidualBlock(2560, 1280, use_se=use_se)),
SwitchSequential(UNET_ResidualBlock(2560, 1280, use_se=use_se)),
SwitchSequential(UNET_ResidualBlock(2560, 1280, use_se=use_se), Upsample(1280)),
SwitchSequential(UNET_ResidualBlock(2560, 1280, use_se=use_se), UNET_AttentionBlock(8, 160)),
SwitchSequential(UNET_ResidualBlock(2560, 1280, use_se=use_se), UNET_AttentionBlock(8, 160)),
SwitchSequential(UNET_ResidualBlock(1920, 1280, use_se=use_se), UNET_AttentionBlock(8, 160), Upsample(1280)),
SwitchSequential(UNET_ResidualBlock(1920, 640, use_se=use_se), UNET_AttentionBlock(8, 80)),
SwitchSequential(UNET_ResidualBlock(1280, 640, use_se=use_se), UNET_AttentionBlock(8, 80)),
SwitchSequential(UNET_ResidualBlock(960, 640, use_se=use_se), UNET_AttentionBlock(8, 80), Upsample(640)),
SwitchSequential(UNET_ResidualBlock(960, 320, use_se=use_se), UNET_AttentionBlock(8, 40)),
SwitchSequential(UNET_ResidualBlock(640, 320, use_se=use_se), UNET_AttentionBlock(8, 40)),
SwitchSequential(UNET_ResidualBlock(640, 320, use_se=use_se), UNET_AttentionBlock(8, 40)),
])
def forward(self, x, context, time):
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:
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 = F.silu(self.groupnorm(x))
return self.conv(x)
class Diffusion(nn.Module):
def __init__(self, use_se=False):
super().__init__()
self.time_embedding = TimeEmbedding(320)
self.unet = UNET(use_se=use_se)
self.final = UNET_OutputLayer(320, 4)
def forward(self, latent, context, time):
time = self.time_embedding(time)
output = self.unet(latent, context, time)
return self.final(output)
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