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UNet2DModel(
(conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_proj): Timesteps()
(time_embedding): TimestepEmbedding(
(linear_1): LoRACompatibleLinear(in_features=128, out_features=512, bias=True)
(act): SiLU()
(linear_2): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
)
(down_blocks): ModuleList(
(0-1): 2 x DownBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=128, bias=True)
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(2): DownBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(128, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(3): DownBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(4): AttnDownBlock2D(
(attentions): ModuleList(
(0-1): 2 x Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_k): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_v): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(256, 512, kernel_size=(1, 1), stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(5): DownBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
)
(up_blocks): ModuleList(
(0): UpBlock2D(
(resnets): ModuleList(
(0-2): 3 x ResnetBlock2D(
(norm1): GroupNorm(32, 1024, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(1024, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(1): AttnUpBlock2D(
(attentions): ModuleList(
(0-2): 3 x Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_k): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_v): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 1024, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(1024, 512, kernel_size=(1, 1), stride=(1, 1))
)
(2): ResnetBlock2D(
(norm1): GroupNorm(32, 768, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(768, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(768, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(2): UpBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 768, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(768, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(3): UpBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): ResnetBlock2D(
(norm1): GroupNorm(32, 384, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=256, bias=True)
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(384, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(4): UpBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 384, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=128, bias=True)
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(384, 128, kernel_size=(1, 1), stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=128, bias=True)
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(256, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(5): UpBlock2D(
(resnets): ModuleList(
(0-2): 3 x ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=128, bias=True)
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(256, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
)
)
(mid_block): UNetMidBlock2D(
(attentions): ModuleList(
(0): Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_k): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_v): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
(conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv_act): SiLU()
(conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
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