how the model ensure semantic information goes to z rather than x_t? about diffae HOT 2 CLOSED

This is the question we have been pondering for so long as well. I don't pretend to have the full answer. The following is the best intuition I can give you.

DiffAE doesn't learn a "semantic" latent code. It learns a latent code that packs "the most bang for the buck" under the implicit bias of the decoder, in this case, a diffusion model.

There is no reason to believe that a semantic latent code, corresponding to human judgment, is the same as one that packs the most bang for the buck, due to the strong emphasis of the reconstruction loss.
The fact that we observed DiffAE learn a semantically meaningful latent code should be considered accidental, which may be due to the dataset we used, FFHQ256, which is heavily cropped and properly aligned. We should not expect this quality of DiffAE to transfer to other datasets in general.

What to do if I want to control what's kept in the latent code?
There are two ways I could think of: change the loss function to something more semantic, or using a different encoder.

Using a different loss function resulting in a different latent code is easy to understand, but finding a substitute for the reconstruction loss is hard to do.

A more doable way is through the encoder. Let's consider a CLIP encoder. It was trained with correspondence between images and texts. It tends to capture high-level abstract concepts due to the text information given to describe an image. The latent code from this CLIP encoder is expected to capture high-level concepts rather than precise appearances.

One could extend this intuition to another encoder as ArcFace. Since ArcFace is trained to be invariant to most things expect the face identity. The latent code from this encoder should be deprived of most information except identity, i.e., the decoded face may not share the same pose an the original face.

from diffae.

Thx for your reply. It really helps me a lot.

from diffae.