The number of channels is weird

I run the unet.py on cifar-10 dataset, and I print the structure of UNet as follows, It seems not right ?

Input shape: torch.Size([128, 3, 32, 32]) Time embedding shape: torch.Size([128, 256]) After image_proj shape: torch.Size([128, 64, 32, 32])

Downsampling process: Down block 1 output shape: torch.Size([128, 64, 32, 32]) Down block 2 output shape: torch.Size([128, 64, 32, 32]) Down block 3 output shape: torch.Size([128, 64, 16, 16]) Down block 4 output shape: torch.Size([128, 128, 16, 16]) Down block 5 output shape: torch.Size([128, 128, 16, 16]) Down block 6 output shape: torch.Size([128, 128, 8, 8]) Down block 7 output shape: torch.Size([128, 256, 8, 8]) Down block 8 output shape: torch.Size([128, 256, 8, 8]) Down block 9 output shape: torch.Size([128, 256, 4, 4]) Down block 10 output shape: torch.Size([128, 1024, 4, 4]) Down block 11 output shape: torch.Size([128, 1024, 4, 4])

Middle block: Middle block output shape: torch.Size([128, 1024, 4, 4])

Upsampling process: Concatenated input shape before up block 1: torch.Size([128, 2048, 4, 4]) Up block 1 output shape: torch.Size([128, 1024, 4, 4]) Concatenated input shape before up block 2: torch.Size([128, 2048, 4, 4]) Up block 2 output shape: torch.Size([128, 1024, 4, 4]) Concatenated input shape before up block 3: torch.Size([128, 1280, 4, 4]) Up block 3 output shape: torch.Size([128, 256, 4, 4]) Upsample 4 output shape: torch.Size([128, 256, 8, 8]) Concatenated input shape before up block 5: torch.Size([128, 512, 8, 8]) Up block 5 output shape: torch.Size([128, 256, 8, 8]) Concatenated input shape before up block 6: torch.Size([128, 512, 8, 8]) Up block 6 output shape: torch.Size([128, 256, 8, 8]) Concatenated input shape before up block 7: torch.Size([128, 384, 8, 8]) Up block 7 output shape: torch.Size([128, 128, 8, 8]) Upsample 8 output shape: torch.Size([128, 128, 16, 16]) Concatenated input shape before up block 9: torch.Size([128, 256, 16, 16]) Up block 9 output shape: torch.Size([128, 128, 16, 16]) Concatenated input shape before up block 10: torch.Size([128, 256, 16, 16]) Up block 10 output shape: torch.Size([128, 128, 16, 16]) Concatenated input shape before up block 11: torch.Size([128, 192, 16, 16]) Up block 11 output shape: torch.Size([128, 64, 16, 16]) Upsample 12 output shape: torch.Size([128, 64, 32, 32]) Concatenated input shape before up block 13: torch.Size([128, 128, 32, 32]) Up block 13 output shape: torch.Size([128, 64, 32, 32]) Concatenated input shape before up block 14: torch.Size([128, 128, 32, 32]) Up block 14 output shape: torch.Size([128, 64, 32, 32])

Final output shape: torch.Size([128, 3, 32, 32])