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 …

Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …

Multi-modal large language models (MLLMs) have demonstrated impressive performance in
vision-language tasks across a wide range of domains. However, the large model scale and …

Existing open-source multimodal large language models (MLLMs) generally follow a
training process involving pre-training and supervised fine-tuning. However, these models …

We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-
resolution, photorealistic images following language instruction. Infinity redefines visual …

Recent research arxiv: 2410.15027 has explored the use of diffusion transformers (DiTs) for
task-agnostic image generation by simply concatenating attention tokens across images …

We present TokenFlow, a novel unified image tokenizer that bridges the long-standing gap
between multimodal understanding and generation. Prior research attempt to employ a …

In this work, we propose Visual-Predictive Instruction Tuning (VPiT)-a simple and effective
extension to visual instruction tuning that enables a pretrained LLM to quickly morph into an …

In this work, we introduce Janus-Pro, an advanced version of the previous work Janus.
Specifically, Janus-Pro incorporates (1) an optimized training strategy,(2) expanded training …

We present JanusFlow, a powerful framework that unifies image understanding and
generation in a single model. JanusFlow introduces a minimalist architecture that integrates …