| 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)) | |
| ) | |