text-to-video-ms-1.7b
Maintainer: ali-vilab
506
⛏️
| 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
The text-to-video-ms-1.7b model is a multi-stage text-to-video generation diffusion model developed by ModelScope. It takes a text description as input and generates a video that matches the text. This model builds on similar efforts in the field of text-to-video synthesis, such as the i2vgen-xl and stable-video-diffusion-img2vid models. However, the text-to-video-ms-1.7b model aims to provide more advanced capabilities in an open-domain setting.
Model inputs and outputs
This model takes an English text description as input and outputs a short video clip that matches the description. The model consists of three sub-networks: a text feature extraction model, a text feature-to-video latent space diffusion model, and a video latent space to video visual space model. The overall model size is around 1.7 billion parameters.
Inputs
- Text description: An English language text description of the desired video content.
Outputs
- Video clip: A short video clip, typically 14 frames at a resolution of 576x1024, that matches the input text description.
Capabilities
The text-to-video-ms-1.7b model can generate a wide variety of video content based on arbitrary English text descriptions. It is capable of reasoning about the content and dynamically creating videos that match the input prompt. This allows for the generation of imaginative and creative video content that goes beyond simple retrieval or editing of existing footage.
What can I use it for?
The text-to-video-ms-1.7b model has potential applications in areas such as creative content generation, educational tools, and research on generative models. Content creators and designers could leverage the model to rapidly produce video assets based on textual ideas. Educators could integrate the model into interactive learning experiences. Researchers could use the model to study the capabilities and limitations of text-to-video synthesis systems.
However, it's important to note that the model's outputs may not always be factual or fully accurate representations of the world. The model should be used responsibly and with an understanding of its potential biases and limitations.
Things to try
One interesting aspect of the text-to-video-ms-1.7b model is its ability to generate videos based on abstract or imaginative prompts. Try providing the model with descriptions of fantastical or surreal scenarios, such as "a robot unicorn dancing in a field of floating islands" or "a flock of colorful origami birds flying through a futuristic cityscape." Observe how the model interprets and visualizes these unique prompts.
Another interesting direction would be to experiment with prompts that require a certain level of reasoning or compositionality, such as "a red cube on top of a blue sphere" or "a person riding a horse on Mars." These types of prompts can help reveal the model's capabilities and limitations in terms of understanding and rendering complex visual scenes.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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