Liquid is an innovative auto-regressive model that integrates visual comprehension and generation by tokenizing images into discrete codes and learning them alongside text tokens. This multimodal large language model operates within a shared feature space, allowing for seamless understanding and generation without relying on external visual embeddings. Liquid is available in multiple sizes and explores the scaling laws of multimodal models, revealing mutual benefits between understanding and generation tasks.

GigaTok is a novel method designed for scaling visual tokenizers to 3 billion parameters, addressing the reconstruction vs. generation dilemma through semantic regularization. It offers a comprehensive framework for training and evaluating tokenizers, alongside various model configurations and instructions for setup and usage. The project is a collaboration involving extensive research and experimentation, with resources available for further exploration.