X-Prompt: Towards Universal In-Context Image Generation in
  Auto-Regressive Vision Language Foundation Models
Authors: name: Zeyi Sun, name: Ziyang Chu, name: Pan Zhang, name: Tong Wu, name: Xiaoyi Dong, name: Yuhang Zang, name: Yuanjun Xiong, name: Dahua Lin, name: Jiaqi Wang

In-context generation is a key component of large language models' (LLMs)
open-task generalization capability. By leveraging a few examples as context,
LLMs can perform both in-domain and out-of-domain tasks. Recent advancements in
auto-regressive vision-language models (VLMs) built upon LLMs have showcased
impressive performance in text-to-image generation. However, the potential of
in-context learning for general image generation tasks remains largely
unexplored. To address this, we introduce X-Prompt, a purely auto-regressive
large-vision language model designed to deliver competitive performance across
a wide range of both seen and unseen image generation tasks, all within a
unified in-context learning framework. X-Prompt incorporates a specialized
design that efficiently compresses valuable features from in-context examples,
supporting longer in-context token sequences and improving its ability to
generalize to unseen tasks. A unified training task for both text and image
prediction enables X-Prompt to handle general image generation with enhanced
task awareness from in-context examples. Extensive experiments validate the
model's performance across diverse seen image generation tasks and its capacity
to generalize to previously unseen tasks.