Liquid: Language Models are Scalable and Unified Multi-modal Generators
This work provides a scalable solution for enhancing vision-language understanding and generation, demonstrating that existing LLMs can serve as powerful multimodal generators, which is significant for researchers and developers in AI and computer vision.
The paper tackles the problem of integrating visual comprehension and generation in multimodal AI by proposing Liquid, an auto-regressive paradigm that tokenizes images into discrete codes and trains them alongside text in a shared feature space using a single large language model (LLM). The result shows that Liquid outperforms models like Chameleon in multimodal capabilities, achieves an FID of 5.47 on MJHQ-30K, and saves 100x in training costs while maintaining language performance comparable to LLAMA2.
We present Liquid, an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation by tokenizing images into discrete codes and learning these code embeddings alongside text tokens within a shared feature space for both vision and language. Unlike previous multimodal large language model (MLLM), Liquid achieves this integration using a single large language model (LLM), eliminating the need for external pretrained visual embeddings such as CLIP. For the first time, Liquid uncovers a scaling law that performance drop unavoidably brought by the unified training of visual and language tasks diminishes as the model size increases. Furthermore, the unified token space enables visual generation and comprehension tasks to mutually enhance each other, effectively removing the typical interference seen in earlier models. We show that existing LLMs can serve as strong foundations for Liquid, saving 100x in training costs while outperforming Chameleon in multimodal capabilities and maintaining language performance comparable to mainstream LLMs like LLAMA2. Liquid also outperforms models like SD v2.1 and SD-XL (FID of 5.47 on MJHQ-30K), excelling in both vision-language and text-only tasks. This work demonstrates that LLMs such as Qwen2.5 and GEMMA2 are powerful multimodal generators, offering a scalable solution for enhancing both vision-language understanding and generation. The code and models will be released at https://github.com/FoundationVision/Liquid.