OmniTokenizer: A Comprehensive Image-Video Tokenizer for Visual Generation
The OmniTokenizer project presents an innovative approach to the encoding and decoding of image and video data through a unified model. Designed by a team of researchers from prominent institutions such as Fudan University and Bytedance Inc., OmniTokenizer is set to redefine the landscape of visual generation.
Key Features
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Single Model and Weight: OmniTokenizer utilizes one model and one set of weights for both image and video tokenization. This approach simplifies the architecture, making it highly efficient for a wide range of tasks.
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State-of-the-Art Performance: The tokenizer achieves remarkable reconstruction accuracy on both image and video datasets, ensuring high-quality outputs.
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Adaptability: It stands out for its ability to process high-resolution images and long video sequences, addressing the needs of applications requiring detailed and extended visual content.
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Integration Potential: OmniTokenizer integrates seamlessly with language models and diffusion models, enhancing their capabilities in visual content creation.
Setting Up OmniTokenizer
To harness the power of OmniTokenizer, users should set up the environment using specific commands to ensure compatibility with the required library versions. Following this, necessary datasets should be downloaded and organized into the appropriate directories.
Model Variants and Performance
The project introduces both VQVAE (Vector Quantized Variational AutoEncoder) and VAE (Variational AutoEncoder) versions of the OmniTokenizer. These have been pretrained on diverse datasets, ranging from standard images to complex video data. Models are evaluated using metrics such as FID (Fréchet Inception Distance) and FVD (Fréchet Video Distance), providing insights into their effectiveness in reconstructing and generating content.
Training and Implementation
The training process for the VQVAE begins with image-only sessions followed by joint image-video training across multiple resolutions. Fine-tuning with KL divergence loss results in the VAE model. Users can customize training parameters like patch size and resolution to optimize performance for specific projects.
Visual Synthesis
OmniTokenizer facilitates two major types of visual synthesis:
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Language Model-Based Synthesis: With provided scripts and checkpoints, users can train and evaluate language models for tasks using datasets like ImageNet, UCF, and Kinetics-600.
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Diffusion Model-Based Synthesis: By leveraging tools like DiT (Diffusion Transformers) and Latte, users can explore diffusion-based visual generation, with detailed guidance available in the project's documents.
Evaluation and Licensing
Comprehensive evaluation procedures are outlined, allowing users to assess the reconstruction or generation capabilities of their implementations fully. The project is made available under the MIT License, promoting open and collaborative development.
Acknowledgments
The development of OmniTokenizer built upon foundational work from projects like VQGAN and TATS. The authors acknowledge the contributions of tools such as pytorch-fid for enhancing video quality evaluations.
In summary, OmniTokenizer offers a robust and versatile solution for visual data processing, serving as a bridge between innovative research and practical applications in image and video tokenization.