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SEED Tokenizer v1

[arXiv]

image

Abstract

We present SEED, an elaborate image tokenizer that empowers Large Language Models (LLMs) with the emergent ability to SEE and Draw at the same time. Research on image tokenizers has previously reached an impasse, as frameworks employing quantized visual tokens have lost prominence due to subpar performance and convergence in multimodal comprehension (compared to BLIP-2, etc.) or generation (compared to Stable Diffusion, etc.). Despite the limitations, we remain confident in its natural capacity to unify visual and textual representations, facilitating scalable multimodal training with LLM’s original recipe. In this study, we identify two crucial principles for the architecture and training of SEED that effectively ease subsequent alignment with LLMs. (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a 1D causal dependency, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs. (2) Image tokens should capture high-level semantics consistent with the degree of semantic abstraction in words, and be optimized for both discriminativeness and reconstruction during the tokenizer training phase. As a result, the off-the-shelf LLM is able to perform both image-to-text and text-to-image generation by incorporating our SEED through efficient LoRA tuning. Comprehensive multimodal pretraining and instruction tuning, which may yield improved results, are reserved for future investigation. This version of SEED was trained in 5.7 days using only 64 V100 GPUs and 5M publicly available image-text pairs. Our preliminary study emphasizes the great potential of discrete visual tokens in versatile multimodal LLMs and the importance of proper image tokenizers in broader research.

SEED Tokenizer for Image Reconstruction

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SEED-OPT2.7B for Multimodal Comprehension

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SEED-OPT2.7B for Multimodal Generation

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Dependencies and Installation

Installation

  1. Clone repo

    git clone https://github.com/AILab-CVC/SEED.git
cd SEED

  2. Install dependent packages

    sh install.sh

Model Weights

We release the pre-trained SEED Visual Tokenizer in google drive.

Inference

To discretize an image to 1D vision codes with causal dependency, and reconstruct the image from the vision codes using stable diffusion UNet,

  1. Download the pre-trained SEED Visual Tokenizer and stable diffusion model in google drive and put them under the folder "pretrained".
  2. run the inference code.
    python demo_recon.py

To Do

  • Release SEED Tokenizer

License

SEED is released under Apache License Version 2.0.

Acknowledgement

We utilize Stable Diffusion to decode images from our visual codes, and use its implementation and pre-trained model in https://github.com/CompVis/stable-diffusion.git.

Our code is based on the implementation of BLIP-2 in https://github.com/salesforce/LAVIS.git.

Citation

If you find the work helpful, please consider citing:

@misc{ge2023planting,
      title={Planting a SEED of Vision in Large Language Model},
      author={Yuying Ge and Yixiao Ge and Ziyun Zeng and Xintao Wang and Ying Shan},
      year={2023},
      eprint={2307.08041},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

The project is still in progress. Stay tuned for more updates!