Liquid: Language Models are Scalable and Unified Multi-modal Generators

• URL: http://arxiv.org/abs/2412.04332v4
• Date: Thu, 10 Apr 2025 18:28:11 GMT
• Title: Liquid: Language Models are Scalable and Unified Multi-modal Generators
• Authors: Junfeng Wu, Yi Jiang, Chuofan Ma, Yuliang Liu, Hengshuang Zhao, Zehuan Yuan, Song Bai, Xiang Bai,
• Abstract summary: Liquid is an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation.<n>Unlike previous multimodal large language model (MLLM), Liquid achieves this integration using a single large language model.<n>For the first time, Liquid uncovers a scaling law that performance drop unavoidably brought by the unified training of visual and language tasks.
• Score: 112.71734051183726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding [91.0552157725366]
  This paper presents a novel high-performance monolithic VLM named HoVLE. It converts visual and textual inputs into a shared space, allowing LLMs to process images in the same way as texts. Our experiments show that HoVLE achieves performance close to leading compositional models on various benchmarks.
  arXiv  Detail & Related papers  (2024-12-20T18:59:59Z)
• LLaVA-KD: A Framework of Distilling Multimodal Large Language Models [70.19607283302712]
  We propose a novel framework to transfer knowledge from l-MLLM to s-MLLM. Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM. We also propose a three-stage training scheme to fully exploit the potential of s-MLLM.
  arXiv  Detail & Related papers  (2024-10-21T17:41:28Z)
• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings. EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• NVLM: Open Frontier-Class Multimodal LLMs [64.00053046838225]
  We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks. We propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. We develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks.
  arXiv  Detail & Related papers  (2024-09-17T17:59:06Z)
• SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs [40.74693126923826]
  Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities. Training adapters with image-level supervision often results in significant misalignment. We introduce Supervised Embedding Alignment (SEA), a token-level alignment method that leverages vision-language pre-trained models.
  arXiv  Detail & Related papers  (2024-08-21T17:58:02Z)
• Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models [42.891427362223176]
  Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities. We propose a novel framework to fully harness the capabilities of LLMs. We further design an LLM-Infused Diffusion Transformer (LI-DiT) based on the framework.
  arXiv  Detail & Related papers  (2024-06-17T17:59:43Z)
• InfMLLM: A Unified Framework for Visual-Language Tasks [44.29407348046122]
  multimodal large language models (MLLMs) have attracted growing interest. This work delves into enabling LLMs to tackle more vision-language-related tasks. InfMLLM achieves either state-of-the-art (SOTA) performance or performance comparable to recent MLLMs.
  arXiv  Detail & Related papers  (2023-11-12T09:58:16Z)
• mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs [50.17767479660832]
  Vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to understand' the image input. We present mBLIP, the first Vision-LLM leveraging multilingual LLMs, which we obtain in a computationally efficient manner on consumer-level hardware.
  arXiv  Detail & Related papers  (2023-07-13T17:51:58Z)
• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality [95.76661165594884]
  mPLUG-Owl is a training paradigm that equips large language models (LLMs) with multi-modal abilities. The training paradigm involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM. Experimental results show that our model outperforms existing multi-modal models.
  arXiv  Detail & Related papers  (2023-04-27T13:27:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.