Multimodal model for vision-language understanding and generation
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Emu3 is a suite of multimodal models that leverage next-token prediction for image and video generation and understanding. It aims to provide a unified, transformer-based approach to multimodal AI, outperforming specialized models without relying on diffusion or separate vision-language models.
How It Works
Emu3 tokenizes images, text, and videos into a discrete space, enabling a single transformer to process and generate multimodal sequences. This approach simplifies the architecture by eliminating the need for separate components like diffusion models or CLIP encoders, allowing for direct next-token prediction for tasks ranging from image generation to video understanding and extension.
Quick Start & Requirements
pip install -r requirements.txt
after cloning the repository.Highlighted Details
Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
4 months ago
1 day