Design axis
Representation
What space does the model actually generate in?
Continuous tokens
Per-position continuous vectors generated autoregressively, each sampled by a small diffusion loss instead of a softmax over a discrete codebook.
Deep-compression latent
A latent pushed to much higher spatial compression ratios (up to 128x) than standard VAEs, via residual autoencoding and staged high-resolution adaptation.
Frozen encoder + trained decoder
Keep a pretrained vision encoder entirely frozen as the latent space; train only a lightweight decoder to map that space back to pixels.
Hybrid semantic + detail codebook
Two coupled codebooks/branches: one capturing semantic content, one capturing pixel-level detail, kept aligned so both understanding and generation/reconstruction stay strong.
Multi-scale (next-scale) tokens
A coarse-to-fine pyramid of discrete tokens generated scale by scale, rather than a single flat token grid.
Pixels
Generate directly on raw image pixels; no learned tokenizer or latent space at all.
Semantic / foundation latent
A latent taken from (or aligned to) a pretrained vision foundation encoder's feature space, so it is high-dimensional and semantically structured rather than reconstruction-only.
VAE latent
A compact continuous latent from a KL-regularized autoencoder (e.g. SD-VAE, 4-16 channels), trained purely for pixel reconstruction.