Wan2.1-T2V-14B-Diffusers
| Property | Value |
|---|---|
| Model Size | 14B parameters |
| License | Apache 2.0 |
| Architecture | Diffusion Transformer with T5 Encoder |
| Supported Resolutions | 480P and 720P |
| Release Date | February 2025 |
What is Wan2.1-T2V-14B-Diffusers?
Wan2.1-T2V-14B-Diffusers is a state-of-the-art text-to-video generation model that establishes new benchmarks in video generation capabilities. Built on a diffusion transformer architecture, it features 14 billion parameters and can generate high-quality videos at both 480P and 720P resolutions. The model uniquely supports both Chinese and English text generation, making it versatile for global applications.
Implementation Details
The model utilizes a sophisticated architecture with 5120 dimensions, 40 attention heads, and 40 layers. It incorporates a novel 3D causal VAE (Wan-VAE) specifically designed for video generation, enabling efficient spatio-temporal compression while maintaining temporal causality. The model processes inputs through a T5 Encoder with cross-attention mechanisms in each transformer block, complemented by an MLP for time embedding processing.
- Advanced prompt extension capabilities using either Dashscope API or local Qwen models
- Supports multi-GPU inference using FSDP + xDiT USP
- Efficient processing with options for reduced VRAM usage
- Implements Flow Matching framework within the Diffusion Transformer paradigm
Core Capabilities
- High-quality video generation at 480P and 720P resolutions
- Bilingual text generation support (Chinese and English)
- Exceptional motion dynamics and temporal consistency
- Compatible with consumer-grade GPUs through various optimization options
- Supports both text-to-video and image-to-video generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of high-performance capabilities, bilingual text support, and ability to generate videos at multiple resolutions while maintaining quality. It's the first video model capable of producing both Chinese and English text in generated content.
Q: What are the recommended use cases?
The model excels in creating high-quality videos from text descriptions, making it suitable for content creation, educational material generation, and creative applications. It's particularly valuable when multilingual text generation is needed or when high-resolution output is required.
