Step-Video-T2V-Turbo
| Property | Value |
|---|---|
| Parameters | 30 billion |
| Max Resolution | 544px x 992px |
| Max Frames | 204 |
| Paper | arXiv:2502.10248 |
| Model Type | Text-to-Video Generation |
What is stepvideo-t2v-turbo?
Step-Video-T2V-Turbo is a cutting-edge text-to-video generation model that represents a significant advancement in AI-powered video creation. Built on a sophisticated architecture combining Video-VAE compression and DiT with 3D Full Attention, this model can generate high-quality videos from text descriptions in both English and Chinese.
Implementation Details
The model employs a deep compression Video-VAE achieving 16x16 spatial and 8x temporal compression ratios. Its architecture includes a DiT with 48 layers and 48 attention heads, each with 128 dimensions. The model incorporates AdaLN-Single for timestep conditioning and QK-Norm in self-attention for training stability.
- Utilizes dual bilingual text encoders for English and Chinese support
- Implements 3D RoPE for handling variable video lengths and resolutions
- Features Direct Preference Optimization (DPO) for enhanced visual quality
- Supports fast inference with specialized turbo mode
Core Capabilities
- Generate videos up to 204 frames in length
- Support for high-resolution output (544px x 992px)
- Bilingual prompt understanding
- Optimized inference with configurable parameters for quality/speed trade-offs
- Enhanced visual quality through DPO fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
The model's combination of high compression ratios, extensive parameter count (30B), and DPO optimization sets it apart. Its ability to handle long video sequences and bilingual input makes it particularly versatile.
Q: What are the recommended use cases?
The model excels in creating high-quality videos across various categories including sports, food, scenery, animals, festivals, and more. It's particularly suitable for applications requiring detailed video generation from text descriptions.
Q: What are the hardware requirements?
The model requires NVIDIA GPUs with CUDA support, with recommended 80GB of GPU memory for optimal generation quality. It's tested on Linux systems and requires multiple GPUs for efficient operation.
