Files
hao-ai-lab-FastVideo/examples/inference/basic
SolitaryThinker 85642a44f7 [misc]: DreamX — point registry/examples at published Diffusers weights
Verified on GB200: both FastVideo/DreamX-World-5B-Cam-Diffusers and
FastVideo/DreamX-World-5B-Diffusers strict-load from the Hub with no
missing/unexpected keys, and the Cam example generates a video end-to-end
from the Hub repo id.

- registry: hf_model_paths now reference the published FastVideo Diffusers
  repos instead of the official GD-ML checkpoints (which are raw-format and
  not directly loadable).
- example + support matrix: default/documented model ids updated likewise.
- SSIM test: model paths default to the Hub repos like sibling SSIM tests;
  the local-converted-path skip machinery is gone (env overrides remain).
2026-07-06 22:24:14 -07:00
..
2026-02-21 16:06:55 -08:00

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

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()