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.

The paper presents BLIP3-o, a family of fully open unified multimodal models that enhance both image understanding and generation. It introduces a diffusion transformer for generating CLIP image features, advocates for a sequential pretraining strategy, and proposes a high-quality dataset, BLIP3o-60k, to improve performance across various benchmarks. The models, along with code and datasets, are open-sourced to foster further research.

HunyuanImage-3.0 has been released as an open-source image generation model, featuring a unified multimodal architecture that integrates text and image understanding. It boasts the largest Mixture of Experts model with 80 billion parameters, enabling superior image generation capabilities while supporting extensive customization through various checkpoints and performance optimizations.