While next-token prediction is considered a promising path towards artificial general
intelligence, it has struggled to excel in multimodal tasks, which are still dominated by …

VILA-U is a Unified foundation model that integrates Video, Image, Language understanding
and generation. Traditional visual language models (VLMs) use separate modules for …

In this work, we introduce OmniGen, a new diffusion model for unified image generation.
Unlike popular diffusion models (eg, Stable Diffusion), OmniGen no longer requires …

In this paper, we introduce Janus, an autoregressive framework that unifies multimodal
understanding and generation. Prior research often relies on a single visual encoder for …

We present Open-MAGVIT2, a family of auto-regressive image generation models ranging
from 300M to 1.5 B. The Open-MAGVIT2 project produces an open-source replication of …

We introduce Hybrid Autoregressive Transformer (HART), an autoregressive (AR) visual
generation model capable of directly generating 1024x1024 images, rivaling diffusion …

Building on the foundations of language modeling in natural language processing, Next
Token Prediction (NTP) has evolved into a versatile training objective for machine learning …

Diffusion models have become the dominant approach for visual generation. They are
trained by denoising a Markovian process which gradually adds noise to the input. We …

Image tokenizers are crucial for visual generative models, eg, diffusion models (DMs) and
autoregressive (AR) models, as they construct the latent representation for modeling …

Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o,
have captured significant attention in the speech domain. Compared to traditional three-tier …