Text-to-Video-MS-1.7B
| Property | Value |
|---|---|
| Model Size | 1.7B parameters |
| License | CC-BY-NC-ND 4.0 |
| Author | ModelScope |
| Architecture | Multi-stage diffusion with UNet3D |
What is text-to-video-ms-1.7b?
Text-to-video-ms-1.7b is an advanced diffusion-based model designed to generate videos from text descriptions. Developed by ModelScope, it employs a sophisticated three-part architecture consisting of text feature extraction, latent space diffusion, and visual space conversion components. The model specifically handles English text inputs and generates corresponding video content through an iterative denoising process.
Implementation Details
The model operates through three distinct sub-networks: a text feature extraction model, a text feature-to-video latent space diffusion model, and a video latent space to visual space converter. It leverages a UNet3D structure for the diffusion process, starting from Gaussian noise and progressively refining it into coherent video content.
- Optimized for memory usage with VAE slicing support
- Capable of generating videos up to 25 seconds on 16GB GPU VRAM
- Supports DPMSolverMultistepScheduler for efficient inference
- Trained on diverse datasets including LAION5B, ImageNet, and Webvid
Core Capabilities
- Generation of videos from English text descriptions
- Support for both short and long video generation
- Memory-efficient processing with model CPU offloading
- Flexible frame count and inference step configuration
- High-quality video synthesis with aesthetic filtering
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its multi-stage architecture that enables high-quality video generation from text, while maintaining memory efficiency through various optimization techniques. It's particularly notable for its ability to handle longer video sequences with reasonable GPU requirements.
Q: What are the recommended use cases?
The model is ideal for research purposes and creative applications requiring text-to-video generation. However, it's important to note that it's not designed for generating realistic representations of people or events, and should not be used for creating misleading or harmful content.
