The latent channel (4,16,16) in paper is not same with that in the code (64,16,16)

[xiaochengfuhuo]

the latent_channels in scripts/vae/sevirlr/cfg.yaml is 64 but the latent_channels in the paper's Implementation Details is 4.
Will it reduce the training time during the denoise when the latent_channels is set as 64? Because the original data is 128 * 128 = 64 * 16 * 16.

[gaozhihan]

Thank you for your question. We followed the default config in LDM to use latent_channels = 4 and reported the performance in our paper. We found that setting latent_channels = 64 gives a more robust model that is less sensitive to the optimization hyperparameters. In this repo, we set latent_channels = 64 for convenience to reproduce our results. We also release the corresponding pretrained weights for consistency.

[xiaochengfuhuo]

Thank you for your reply. It is helpful for me. How about the training and sampling time? Will latent_channels = 64 be much longer than latent_channels = 4?

[gaozhihan]

Thank you for your follow-up question. The computational costs are not bottlenecked by the hyperparameter latent_channels. In our VAE model, the channel dimensions always increase to 512, regardless of the value of latent_channels. Similarly, in our Earthformer-UNet always increase to 256 and 512. As such, the choices of latent_channels do not significantly impact the training time or inference time. According to our experiments, changing latent_channels to 64 instead of 4 did not even lead to a doubling of the computational costs.

[xiaochengfuhuo]

Thanks a lot. I got it.