size mismatch: Model built with autoencoder_kl_16x16x16.yaml not compatible with cin-ldm-vq-f8.yaml

[humanely]

I trained an autoencoder but when I use the generated last.ckpt, it gives the following error. Has anyone encountered this?
How to match the dimensions in both encoders and LDM?

raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for VQModelInterface:
	size mismatch for encoder.down.2.block.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for encoder.down.2.block.0.conv1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.0.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.0.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.0.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for encoder.down.2.block.0.conv2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.1.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.1.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for encoder.down.2.block.1.conv1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.1.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.1.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.block.1.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for encoder.down.2.block.1.conv2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.2.downsample.conv.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for encoder.down.2.downsample.conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.3.block.0.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.3.block.0.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for encoder.down.3.block.0.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 256, 3, 3]).
	size mismatch for encoder.conv_out.weight: copying a param with shape torch.Size([8, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([4, 512, 3, 3]).
	size mismatch for encoder.conv_out.bias: copying a param with shape torch.Size([8]) from checkpoint, the shape in current model is torch.Size([4]).
	size mismatch for decoder.up.1.block.0.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.1.block.0.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.1.block.0.conv1.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.block.0.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 3, 3]).
	size mismatch for decoder.up.2.block.0.conv1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.0.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.0.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.0.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.block.0.conv2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.1.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.1.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.block.1.conv1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.1.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.1.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.1.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.block.1.conv2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.2.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.2.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.block.2.conv1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.2.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.2.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.block.2.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.block.2.conv2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for decoder.up.2.upsample.conv.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for decoder.up.2.upsample.conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for quant_conv.weight: copying a param with shape torch.Size([8, 8, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 4, 1, 1]).
	size mismatch for quant_conv.bias: copying a param with shape torch.Size([8]) from checkpoint, the shape in current model is torch.Size([16]).
	size mismatch for post_quant_conv.weight: copying a param with shape torch.Size([4, 4, 1, 1]) from checkpoint, the shape in current model is torch.Size([4, 16, 1, 1]).

During handling of the above exception, another exception occurred:

[zzj311]

Do you defeat this problem?