hao-ai-lab-FastVideo/examples/inference/basic

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 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.

# 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

For an example on Apple silicon:

python examples/inference/basic/basic_mps.py

For an example running DMD+VSA inference:

python examples/inference/basic/basic_dmd.py

For the typed config/request path added during the inference API refactor:

python examples/inference/basic/basic_dmd_new_api.py

Basic Walkthrough

All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!

from fastvideo import VideoGenerator
from fastvideo.api import EngineConfig, GenerationRequest, GeneratorConfig

def main():
    generator = VideoGenerator.from_config(
        GeneratorConfig(
            model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
            engine=EngineConfig(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.")
    result = generator.generate(GenerationRequest(prompt=prompt))

if __name__ == "__main__":
    main()