Spaces:
Runtime error
Runtime error
File size: 7,896 Bytes
f8b823c 64e1ee8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
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 = self.linear_1(x)
x = F.silu(x)
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):
residue = feature
feature = self.groupnorm_feature(feature)
feature = F.silu(feature)
feature = self.conv_feature(feature)
time = F.silu(time)
time = self.linear_time(time)
merged = feature + time.unsqueeze(-1).unsqueeze(-1)
merged = self.groupnorm_merged(merged)
merged = F.silu(merged)
merged = self.conv_merged(merged)
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.groupnorm(x)
x = self.conv_input(x)
n, c, h, w = x.shape
x = x.view((n, c, h * w))
x = x.transpose(-1, -2)
residue_short = x
x = self.layernorm_1(x)
x = self.attention_1(x)
x += residue_short
residue_short = x
x = self.layernorm_2(x)
x = self.attention_2(x, context)
x += residue_short
residue_short = x
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
x, gate = self.linear_geglu_1(x).chunk(2, dim=-1)
x = x * F.gelu(gate)
x = self.linear_geglu_2(x)
x += residue_short
x = x.transpose(-1, -2)
x = x.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):
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([
SwitchSequential(nn.Conv2d(4, 320, kernel_size=3, padding=1)),
SwitchSequential(UNET_ResidualBlock(320, 320), UNET_AttentionBlock(8, 40)),
SwitchSequential(UNET_ResidualBlock(320, 320), UNET_AttentionBlock(8, 40)),
SwitchSequential(nn.Conv2d(320, 320, kernel_size=3, stride=2, padding=1)),
SwitchSequential(UNET_ResidualBlock(320, 640), UNET_AttentionBlock(8, 80)),
SwitchSequential(UNET_ResidualBlock(640, 640), UNET_AttentionBlock(8, 80)),
SwitchSequential(nn.Conv2d(640, 640, kernel_size=3, stride=2, padding=1)),
SwitchSequential(UNET_ResidualBlock(640, 1280), UNET_AttentionBlock(8, 160)),
SwitchSequential(UNET_ResidualBlock(1280, 1280), UNET_AttentionBlock(8, 160)),
SwitchSequential(nn.Conv2d(1280, 1280, kernel_size=3, stride=2, padding=1)),
SwitchSequential(UNET_ResidualBlock(1280, 1280)),
SwitchSequential(UNET_ResidualBlock(1280, 1280)),
])
self.bottleneck = SwitchSequential(
UNET_ResidualBlock(1280, 1280),
UNET_AttentionBlock(8, 160),
UNET_ResidualBlock(1280, 1280),
)
self.decoders = nn.ModuleList([
SwitchSequential(UNET_ResidualBlock(2560, 1280)),
SwitchSequential(UNET_ResidualBlock(2560, 1280)),
SwitchSequential(UNET_ResidualBlock(2560, 1280), Upsample(1280)),
SwitchSequential(UNET_ResidualBlock(2560, 1280), UNET_AttentionBlock(8, 160)),
SwitchSequential(UNET_ResidualBlock(2560, 1280), UNET_AttentionBlock(8, 160)),
SwitchSequential(UNET_ResidualBlock(1920, 1280), UNET_AttentionBlock(8, 160), Upsample(1280)),
SwitchSequential(UNET_ResidualBlock(1920, 640), UNET_AttentionBlock(8, 80)),
SwitchSequential(UNET_ResidualBlock(1280, 640), UNET_AttentionBlock(8, 80)),
SwitchSequential(UNET_ResidualBlock(960, 640), UNET_AttentionBlock(8, 80), Upsample(640)),
SwitchSequential(UNET_ResidualBlock(960, 320), UNET_AttentionBlock(8, 40)),
SwitchSequential(UNET_ResidualBlock(640, 320), UNET_AttentionBlock(8, 40)),
SwitchSequential(UNET_ResidualBlock(640, 320), 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 = self.groupnorm(x)
x = F.silu(x)
x = self.conv(x)
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):
time = self.time_embedding(time)
output = self.unet(latent, context, time)
output = self.final(output)
return output |