PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m
vision encoder and the Gemma-2B language model. It is trained to be a versatile and …

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 …

We present a unified transformer, ie, Show-o, that unifies multimodal understanding and
generation. Unlike fully autoregressive models, Show-o unifies autoregressive and …

Since the emergence of ChatGPT, research on large language models (LLMs) has actively
progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited …

Large language models (LLMs) with vision capabilities (eg, GPT-4o, Gemini 1.5, and Claude
3) are powering countless image-text processing applications, enabling unprecedented …

Long-context capability is critical for multi-modal foundation models, especially for long
video understanding. We introduce LongVILA, a full-stack solution for long-context visual …

We present Lumina-mGPT, a family of multimodal autoregressive models capable of various
vision and language tasks, particularly excelling in generating flexible photorealistic images …

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 …