  We introduce Generative Infinite-Vocabulary Transformers (GIVT) which
generate vector sequences with real-valued entries, instead of discrete tokens
from a finite vocabulary. To this end, we propose two surprisingly simple
modifications to decoder-only transformers: 1) at the input, we replace the
finite-vocabulary lookup table with a linear projection of the input vectors;
and 2) at the output, we replace the logits prediction (usually mapped to a
categorical distribution) with the parameters of a multivariate Gaussian
mixture model. Inspired by the image-generation paradigm of VQ-GAN and MaskGIT,
where transformers are used to model the discrete latent sequences of a VQ-VAE,
we use GIVT to model the unquantized real-valued latent sequences of a
$\beta$-VAE. In class-conditional image generation GIVT outperforms VQ-GAN (and
improved variants thereof) as well as MaskGIT, and achieves performance
competitive with recent latent diffusion models. Finally, we obtain strong
results outside of image generation when applying GIVT to panoptic segmentation
and depth estimation with a VAE variant of the UViM framework.
