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Unified Latents (UL): How to train your latents

Intermediate

Jonathan Heek, Emiel Hoogeboom et al.Feb 19arXiv

Unified Latents (UL) is a way to learn the hidden code (latents) for images and videos by training three parts together: an encoder, a diffusion prior, and a diffusion decoder.

#Unified Latents#diffusion prior#diffusion decoder

Balancing Understanding and Generation in Discrete Diffusion Models

Intermediate

Yue Liu, Yuzhong Zhao et al.Feb 1arXiv

This paper introduces XDLM, a single model that blends two popular diffusion styles (masked and uniform) so it both understands and generates text and images well.

#XDLM#discrete diffusion#stationary noise kernel

VA-$π$: Variational Policy Alignment for Pixel-Aware Autoregressive Generation

Intermediate

Xinyao Liao, Qiyuan He et al.Dec 22arXiv

Autoregressive (AR) image models make pictures by choosing tokens one-by-one, but they were judged only on picking likely tokens, not on how good the final picture looks in pixels.

#autoregressive image generation#tokenizer–generator alignment#pixel-space reconstruction

Scaling Behavior of Discrete Diffusion Language Models

Intermediate

Dimitri von Rütte, Janis Fluri et al.Dec 11arXiv

This paper studies how a newer kind of language model, called a discrete diffusion language model (DLM), gets better as we give it more data, bigger models, and more compute.

#discrete diffusion#language models#scaling laws