Emu3-Gen
Maintainer: BAAI
99
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Emu3 is a powerful multimodal AI model developed by the Beijing Academy of Artificial Intelligence (BAAI). Unlike traditional models that require separate architectures for different tasks, Emu3 is trained solely on next-token prediction, allowing it to excel at both generation and perception across a wide range of modalities. The model outperforms several well-established task-specific models, including SDXL, LLaVA-1.6, and OpenSora-1.2, while eliminating the need for diffusion or compositional architectures.
Model inputs and outputs
Emu3 is a versatile model that can process and generate a variety of multimodal data, including images, text, and videos. The model takes in sequences of discrete tokens and generates the next token in the sequence, allowing it to perform tasks such as image generation, text-to-image translation, and video prediction.
Inputs
- Sequences of discrete tokens representing images, text, or videos
Outputs
- The next token in the input sequence, which can be used to generate new content or extend existing content
Capabilities
Emu3 demonstrates impressive capabilities in both generation and perception tasks. It can generate high-quality images by simply predicting the next vision token, and it also shows strong vision-language understanding abilities to provide coherent text responses without relying on a CLIP or a pretrained language model. Additionally, Emu3 can generate videos by predicting the next token in a video sequence, and it can also extend existing videos to predict what will happen next.
What can I use it for?
The broad capabilities of Emu3 make it a valuable tool for a wide range of applications, including:
- Content creation: Generating high-quality images, text, and videos to support various creative projects
- Multimodal AI: Developing advanced AI systems that can understand and interact with multimodal data
- Personalization: Tailoring content and experiences to individual users based on their preferences and behavior
- Automation: Streamlining tasks that involve the processing or generation of multimodal data
Things to try
One of the key insights of Emu3 is its ability to learn from a mixture of multimodal sequences, rather than relying on task-specific architectures. This allows the model to develop a more holistic understanding of the relationships between different modalities, which can be leveraged in a variety of ways. For example, you could explore how Emu3 performs on cross-modal tasks, such as generating images from text prompts or translating text into other languages while preserving the original meaning and style.
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