Making LLaMA SEE and Draw with SEED Tokenizer
Yuying Ge Sijie Zhao Ziyun Zeng Yixiao Ge Chen Li Xintao Wang Ying Shan
The great success of Large Language Models (LLMs) has expanded the potential
of multimodality, contributing to the gradual evolution of General Artificial
Intelligence (AGI). A true AGI agent should not only possess the capability to
perform predefined multi-tasks but also exhibit emergent abilities in an
open-world context. However, despite the considerable advancements made by
recent multimodal LLMs, they still fall short in effectively unifying
comprehension and generation tasks, let alone open-world emergent abilities. We
contend that the key to overcoming the present impasse lies in enabling text
and images to be represented and processed interchangeably within a unified
autoregressive Transformer. To this end, we introduce SEED, an elaborate image
tokenizer that empowers LLMs with the ability to SEE and Draw at the same time.
We identify two crucial design principles: (1) Image tokens should be
independent of 2D physical patch positions and instead be produced with a 1D
causal dependency, exhibiting intrinsic interdependence that aligns with the
left-to-right autoregressive prediction mechanism in LLMs. (2) Image tokens
should capture high-level semantics consistent with the degree of semantic
abstraction in words, and be optimized for both discriminativeness and
reconstruction during the tokenizer training phase. With SEED tokens, LLM is
able to perform scalable multimodal autoregression under its original training
recipe, i.e., next-word prediction. SEED-LLaMA is therefore produced by
large-scale pretraining and instruction tuning on the interleaved textual and
visual data, demonstrating impressive performance on a broad range of
multimodal comprehension and generation tasks. More importantly, SEED-LLaMA has
exhibited compositional emergent abilities such as multi-turn in-context
multimodal generation, acting like your AI assistant.