unet.txt

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))
)