Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining

Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yu Qiao, Hongsheng Li, Peng Gao

Keywords: Autoregressive Image Generation, Multi-modality, LLM

Abstract:

We present Lumina-mGPT, a family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic images from text descriptions. By initializing from multimodal Generative PreTraining (mGPT), Lumina-mGPT demonstrates that decoder-only Autoregressive (AR) model can achieve image generation performance comparable to modern diffusion model with high efficiency through Flexible Progressive Supervised Finetuning (FP-SFT). Equipped with our proposed Unambiguous image Representation} (Uni-Rep), Lumina-mGPT can flexibly generate high-quality images of varying aspect ratios. Building on the strong image generation capabilities, we further explore Ominiponent Supervised Finetuning (Omni-SFT), an initial attempt to elevate Lumina-mGPT into a unified multi-modal generalist. The resulting model demonstrates versatile multimodal capabilities, including visual generation tasks like text-to-image/multiview generation and controllable generation, visual recognition tasks like segmentation and depth estimation, and vision-language tasks like multi-turn visual question answering, casting light on the rosy potential of this direction. We release all code and checkpoints, hoping to facilitate the progress toward building artificial general intelligence.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 2973