(I think you might need a flatten in 1, too.)
We have a short discussion of the more general number of channels and width in our book (Stevens/Antiga/Viehmann: Deep Learning with PyTorch, Section “8.3.1 Our network as an nn.Module”).
So the idea behind “channels should increase while width should decrease” is mainly “total size should decrease but not too much”. That the channels increase is more a way to achieve “not too much” in general (so halving the spatial resolution + doubling channels halves the number of elements).
Aside from the size (196 vs 128), the key difference between 1 and 2 is that the first evicts all explicitly spatial information while the second keeps it but reduces the information “per pixel”. Which is more appropriate is likely task dependent. (And you could mix, too, if you feel like it.)
For the VAE, it might make sense to to keep some spatial information as might want to be able reconstruct flipped/rotated images (as a vague intuition), but the typical thing might be to keep more than one channel. But I would recommend to check out the common VAE architectures. It is still good to ask “why” they did it this way, but it’d give you a starting point.
Best regards
Thomas