  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. Unlike existing
autoregressive image generation approaches, Lumina-mGPT employs a pretrained
decoder-only transformer as a unified framework for modeling multimodal token
sequences. Our key insight is that a simple decoder-only transformer with
multimodal Generative PreTraining (mGPT), utilizing the next-token prediction
objective on massive interleaved text-image sequences, can learn broad and
general multimodal capabilities, thereby illuminating photorealistic
text-to-image generation. Building on these pretrained models, we propose
Flexible Progressive Supervised Finetuning (FP-SFT) on high-quality image-text
pairs to fully unlock their potential for high-aesthetic image synthesis at any
resolution while maintaining their general multimodal capabilities.
Furthermore, we introduce Ominiponent Supervised Finetuning (Omni-SFT),
transforming Lumina-mGPT into a foundation model that seamlessly achieves
omnipotent task unification. The resulting model demonstrates versatile
multimodal capabilities, including visual generation tasks like flexible
text-to-image generation and controllable generation, visual recognition tasks
like segmentation and depth estimation, and vision-language tasks like
multiturn visual question answering. Additionally, we analyze the differences
and similarities between diffusion-based and autoregressive methods in a direct
comparison.
