ModelScope Text-to-Video Synthesis is a groundbreaking multi-stage text-to-video generation diffusion model that transforms English text input into corresponding video content. Built on three sub-networks, this innovative model boasts a wide range of applications, revolutionizing the way we create and visualize content. In this article, we will delve into the model’s description, applications, usage, limitations, and training data.

Model Description

The text-to-video generation diffusion model comprises three sub-networks:

1. Text feature extraction
2. Text feature-to-video latent space diffusion model
3. Video latent space to video visual space

With approximately 1.7 billion parameters, the model supports English input and utilizes a Unet3D structure. The iterative denoising process transforms pure Gaussian noise video into the final generated video.

Applications and Usage

ModelScope Text-to-Video Synthesis is versatile, capable of reasoning and generating videos from any English text description. To use the model under the ModelScope framework, simply call a Pipeline with a dictionary input containing the legal key value ‘text’ and a short text as the content. Note that the model currently only supports GPU inference.

Hardware requirements for this model include approximately 16GB of RAM and 16GB of GPU memory.

Limitations and Possible Bias

While ModelScope Text-to-Video Synthesis is powerful, it does have some limitations:

  • It is trained on public datasets like Webvid, which may cause deviations in generated results.
  • It cannot achieve perfect film and television quality generation.
  • Clear text generation is not supported.
  • The model is primarily trained with English corpus and does not support other languages.
  • Performance improvement is needed for complex compositional generation tasks.

Misuse, Malicious Use, and Excessive Use

ModelScope Text-to-Video Synthesis is intended for non-commercial and research purposes only. Users should refrain from generating content that:

  1. Realistically represents people or events.
  2. Is demeaning, harmful, or offensive.
  3. Contains pornographic, violent, or bloody material.
  4. Promotes error or false information.

Training Data

The model is trained on public datasets such as LAION5B, ImageNet, and Webvid. To ensure high-quality results, pre-training processes like aesthetic scoring, watermark scoring, and deduplication are employed to filter images and videos.

Conclusion

ModelScope Text-to-Video Synthesis is a game-changing technology that transforms English text into dynamic video content. Though it has some limitations and potential biases, its wide range of applications opens up exciting possibilities for content creation, visualization, and research. By responsibly harnessing this powerful tool, we can unlock new avenues for innovation and exploration.

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