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model.txt
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DiffusionUNet(
(encoders): ModuleList(
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1-2): 2 x ResidualBlock(
(groupnorm_1): GroupNorm(32, 32, eps=1e-05, affine=True)
(conv_1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=32, bias=True)
(groupnorm_2): GroupNorm(32, 32, eps=1e-05, affine=True)
(conv_2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Identity()
)
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(4): ResidualBlock(
(groupnorm_1): GroupNorm(32, 32, eps=1e-05, affine=True)
(conv_1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=64, bias=True)
(groupnorm_2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
)
(5): ResidualBlock(
(groupnorm_1): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=64, bias=True)
(groupnorm_2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Identity()
)
(6): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(7): ResidualBlock(
(groupnorm_1): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1))
)
(8): ResidualBlock(
(groupnorm_1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Identity()
)
(9): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(10-11): 2 x ResidualBlock(
(groupnorm_1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Identity()
)
)
(bottleneck): ModuleList(
(0-1): 2 x ResidualBlock(
(groupnorm_1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Identity()
)
)
(decoders): ModuleList(
(0-2): 3 x ResidualBlock(
(groupnorm_1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv_1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
)
(3): UpsampleBlock(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4-5): 2 x ResidualBlock(
(groupnorm_1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv_1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
)
(6): ResidualBlock(
(groupnorm_1): GroupNorm(32, 192, eps=1e-05, affine=True)
(conv_1): Conv2d(192, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=128, bias=True)
(groupnorm_2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1))
)
(7): UpsampleBlock(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(8): ResidualBlock(
(groupnorm_1): GroupNorm(32, 192, eps=1e-05, affine=True)
(conv_1): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=64, bias=True)
(groupnorm_2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))
)
(9): ResidualBlock(
(groupnorm_1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv_1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=64, bias=True)
(groupnorm_2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
)
(10): ResidualBlock(
(groupnorm_1): GroupNorm(32, 96, eps=1e-05, affine=True)
(conv_1): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=64, bias=True)
(groupnorm_2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(96, 64, kernel_size=(1, 1), stride=(1, 1))
)
(11): UpsampleBlock(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(12): ResidualBlock(
(groupnorm_1): GroupNorm(32, 96, eps=1e-05, affine=True)
(conv_1): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=32, bias=True)
(groupnorm_2): GroupNorm(32, 32, eps=1e-05, affine=True)
(conv_2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1))
)
(13-14): 2 x ResidualBlock(
(groupnorm_1): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv_1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(linear_time): Linear(in_features=1024, out_features=32, bias=True)
(groupnorm_2): GroupNorm(32, 32, eps=1e-05, affine=True)
(conv_2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(residual_layer): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
)
)
(time_proj): Sequential(
(0): Linear(in_features=256, out_features=1024, bias=True)
(1): SiLU()
(2): Linear(in_features=1024, out_features=1024, bias=True)
)
(final_layer): Sequential(
(0): GroupNorm(32, 32, eps=1e-05, affine=True)
(1): SiLU()
(2): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)