Lumina-mGPT is an impressive set of multimodal autoregressive models with powerful capabilities to perform a wide range of visual and linguistic tasks. Among these tasks, it excels at generating flexible and realistic images based on textual descriptions.
Significantly different from existing autoregressive image generation methods, Lumina-mGPT utilizes a pre-trained decoder-only transformer as a unified framework for modeling multimodal token sequences. In this unique design, a simple decoder-only transformer is ingeniously combined with multimodal generative pre-training (mGPT). By leveraging next Token prediction objectives on a large-scale interlaced text-image sequence, Lumina-mGPT can learn extensive and versatile multimodal capabilities. This capability allows it to achieve highly realistic effects when generating images from text. Whether it's complex scene descriptions or nuanced emotional expressions, it can accurately transform text into vivid images.
Building on these carefully pre-trained models, researchers have proposed a Flexible Progressive Supervised Fine-tuning (FP-SFT) method. This method fully unleashes Lumina-mGPT's potential for high-aesthetic image synthesis at any resolution on high-quality image-text pairs. At the same time, it maintains the model's general multimodal capabilities, enabling Lumina-mGPT to perform excellently in various application scenarios. Whether in artistic creation, advertising design, or other fields that require high-quality image generation, Lumina-mGPT provides users with powerful tools and endless possibilities.