ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance

TL;DR

ILLUME is a multimodal large language model that efficiently integrates understanding and generation, using a novel training scheme and self-assessment to improve performance with less data.

Contribution

The paper introduces ILLUME, a unified multimodal LLM with a new vision tokenizer, progressive training, and self-enhancement, reducing data needs and improving alignment.

Findings

Achieves competitive performance with only 15M pretraining data.

Outperforms existing unified MLLMs like Janus on multiple benchmarks.

Demonstrates effective self-assessment for better image-text alignment.

Abstract

In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model through a unified next-token prediction formulation. To address the large dataset size typically required for image-text alignment, we propose to enhance data efficiency through the design of a vision tokenizer that incorporates semantic information and a progressive multi-stage training procedure. This approach reduces the dataset size to just 15M for pretraining -- over four times fewer than what is typically needed -- while achieving competitive or even superior performance with existing unified MLLMs, such as Janus. Additionally, to promote synergistic enhancement between understanding and generation capabilities, which is under-explored in previous works, we introduce a novel self-enhancing multimodal alignment scheme. This scheme supervises the MLLM to self-assess the consistency between text descriptions and self-generated images, facilitating the model to interpret images more accurately and avoid unrealistic and incorrect predictions caused by misalignment in image generation. Based on extensive experiments, our proposed ILLUME stands out and competes with state-of-the-art unified MLLMs and specialized models across various benchmarks for multimodal understanding, generation, and editing.