In this work, we introduce Vision-Language Generative Pre-trained Transformer
(VL-GPT), a transformer model proficient at concurrently perceiving and
generating visual and linguistic data. VL-GPT achieves a unified pre-training
approach for both image and text modalities by employing a straightforward
auto-regressive objective, thereby enabling the model to process image and text
as seamlessly as a language model processes text. To accomplish this, we
initially propose a novel image tokenizer-detokenizer framework for visual
data, specifically designed to transform raw images into a sequence of
continuous embeddings and reconstruct them accordingly. In combination with the
existing text tokenizer and detokenizer, this framework allows for the encoding
of interleaved image-text data into a multimodal sequence, which can
subsequently be fed into the transformer model. Consequently, VL-GPT can
perform large-scale pre-training on multimodal corpora utilizing a unified
auto-regressive objective (i.e., next-token prediction). Upon completion of
pre-training, VL-GPT exhibits remarkable zero-shot and few-shot performance
across a diverse range of vision and language understanding and generation
tasks, including image captioning, visual question answering, text-to-image
generation, and more. Additionally, the pre-trained model retrains in-context
learning capabilities when provided with multimodal prompts. We further conduct
instruction tuning on our VL-GPT, highlighting its exceptional potential for
multimodal assistance. The source code and model weights shall be released.

VL-GPT 是一种同时感知和生成视觉和语言数据的变压器模型，通过采用直观的自回归目标，实现了图像和文本两种模态的统一预训练方法，从而使模型可以像处理文本一样无缝地处理图像和文本。预训练完成后，VL-GPT 在包括图像字幕生成、视觉问答、文本到图像生成等多样的视觉和语言理解以及生成任务中表现出了令人瞩目的零样本和少样本性能。