42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
# Basic Video Generation Tutorial
|
|
The `VideoGenerator` class provides the primary Python interface for doing offline video generation, which is interacting with a diffusion pipeline without using a separate inference api server.
|
|
|
|
## Requirements
|
|
- At least a single NVIDIA GPU with CUDA 12.4.
|
|
- Python 3.10-3.12
|
|
|
|
## Installation
|
|
If you have not installed FastVideo, please following these [instructions](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html) first.
|
|
|
|
## Usage
|
|
The first script in this example shows the most basic usage of FastVideo. If you are new to Python and FastVideo, you should start here.
|
|
|
|
```bash
|
|
# if you have not cloned the directory:
|
|
git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
|
|
|
|
python examples/inference/basic/basic.py
|
|
```
|
|
|
|
## Basic Walkthrough
|
|
|
|
All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!
|
|
|
|
```python
|
|
from fastvideo import VideoGenerator
|
|
|
|
def main():
|
|
generator = VideoGenerator.from_pretrained(
|
|
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
|
|
num_gpus=1,
|
|
)
|
|
|
|
prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
|
|
"wide with interest. The playful yet serene atmosphere is complemented by soft "
|
|
"natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
|
|
video = generator.generate_video(prompt)
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
```
|