Wan2.1-T2V-14B
| Property | Value |
|---|---|
| Model Size | 14B parameters |
| Architecture | Diffusion Transformer with T5 Encoder |
| License | Apache 2.0 |
| Supported Resolutions | 480P and 720P |
| Framework | Flow Matching with Diffusion Transformers |
What is Wan2.1-T2V-14B?
Wan2.1-T2V-14B is a state-of-the-art text-to-video generation model that represents a significant advancement in video synthesis technology. It's built on a 14B parameter architecture that combines a novel spatio-temporal VAE with advanced diffusion transformer techniques. The model stands out for its ability to generate both Chinese and English text in videos and support multiple resolutions up to 720P.
Implementation Details
The model utilizes a sophisticated architecture with 5120 dimensions, 40 transformer layers, and 40 attention heads. It implements a unique 3D causal VAE design that enables efficient video processing while maintaining temporal consistency. The architecture includes specialized MLP components for time embedding processing and cross-attention mechanisms for text integration.
- Advanced Flow Matching framework with Diffusion Transformers
- T5 Encoder for multilingual text processing
- Shared MLP across transformer blocks with individual bias learning
- Novel spatio-temporal VAE for efficient video compression
Core Capabilities
- High-quality video generation at both 480P and 720P resolutions
- Bilingual text generation support (Chinese and English)
- Superior motion dynamics and temporal consistency
- Efficient processing with various GPU configurations
- Extensive prompt extension capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to generate both Chinese and English text, support for multiple resolutions, and state-of-the-art performance in video generation make it stand out. It's the first of its kind to offer such comprehensive capabilities while maintaining high quality across various use cases.
Q: What are the recommended use cases?
The model excels in text-to-video generation, particularly for creating high-quality videos with text elements. It's suitable for content creation, educational materials, and creative projects requiring sophisticated video synthesis with text integration.
