Inference Quick Start

Inference Quick Start#

This page contains step-by-step instructions to get you quickly started with video generation using FastVideo.

Requirements#

  • OS: Linux (Tested on Ubuntu 22.04+)

  • Python: 3.10-3.12

  • CUDA: 12.4

  • GPU: At least one NVIDIA GPU

Installation#

We recommend using an environment manager such as Conda to create a clean environment:

# Create and activate a new conda environment
conda create -n fastvideo python=3.12
conda activate fastvideo

# Install FastVideo
pip install fastvideo

For advanced installation options, see the Installation Guide.

Generating Your First Video#

Here’s a minimal example to generate a video using the default settings. Create a file called example.py with the following code:

from fastvideo import VideoGenerator

def main():
    # Create a video generator with a pre-trained model
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
        num_gpus=1,  # Adjust based on your hardware
    )

    # Define a prompt for your video
    prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."

    # Generate the video
    video = generator.generate_video(
        prompt,
        return_frames=True,  # Also return frames from this call (defaults to False)
        output_path="my_videos/",  # Controls where videos are saved
        save_video=True
    )

if __name__ == '__main__':
    main()

Run the script with:

python example.py

The generated video will be saved in the current directory under my_videos/

More inference example scripts can be found in scripts/inference/

Available Models#

Please see the support matrix for the list of supported models and their available optimizations.

Image-to-Video Generation#

You can generate a video starting from an initial image:

from fastvideo import VideoGenerator, SamplingParam

def main():
    # Create the generator
    model_name = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
    generator = VideoGenerator.from_pretrained(model_name, num_gpus=1)

    # Set up parameters with an initial image
    sampling_param = SamplingParam.from_pretrained(model_name)
    sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
    sampling_param.num_frames = 107

    # Generate video based on the image
    prompt = "A photograph coming to life with gentle movement"
    generator.generate_video(prompt, sampling_param=sampling_param,
                             output_path="my_videos/",
                             save_video=True)

if __name__ == '__main__':
    main()

Troubleshooting#

Common issues and their solutions:

Out of Memory Errors#

If you encounter CUDA out of memory errors:

  • Reduce num_frames or video resolution

  • Enable memory optimization with enable_model_cpu_offload

  • Try a smaller model or use distilled versions

  • Use num_gpus > 1 if multiple GPUs are available

Slow Generation#

To speed up generation:

  • Reduce num_inference_steps (20-30 is usually sufficient)

  • Use half precision (fp16) for the VAE

  • Use multiple GPUs if available

Unexpected Results#

If the generated video doesn’t match your prompt:

  • Try increasing guidance_scale (7.0-9.0 works well)

  • Make your prompt more detailed and specific

  • Experiment with different random seeds

  • Try a different model

Next Steps#

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