Emu3: Next-Token Prediction is All You Need
📌 核心贡献
Emu3引入了一套最先进的多模态模型，仅通过“下一令牌预测”进行训练，旨在解决该方法在多模态任务中表现不如扩散模型和组合方法的困境。通过将图像、文本和视频离散化为令牌，并在多模态序列混合数据上从头开始训练单一Transformer，Emu3在多个既定任务专用模型上取得了更优异的性能。
📖 摘要
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e.g., Stable Diffusion) and compositional approaches (e.g., CLIP combined with LLMs). In this paper, we introduce Emu3, a new suite of state-of-the-art multimodal models trained solely with next-token prediction. By tokenizing images, text, and videos into a discrete space, we train a single transformer from scratch on a mixture of multimodal sequences. Emu3 outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship models such as SDXL and LLaVA-1.6, while eliminating the need for diffusion or compositional architectures. Emu3 is also capable of generating high-fidelity video via predicting the next token in a video sequence. We simplify complex multimodal model designs by converging on a singular focus: tokens, unlocking great potential for scaling both during training and inference. Our results demonstrate that next-token prediction is a promising path towards building general multimodal intelligence beyond language. We open-source key techniques and models to support further research in this direction.
🔍 关键信息
| 字段 | 内容 |
|---|---|
| 发表 | 2024-09-27 |
| 分类 | cs.CV |
| 链接 | arXiv |