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hao-ai-lab-FastVideo/docs/source/inference/inference_quick_start.md
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2025-06-03 02:54:12 -04:00

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# 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:
```bash
# 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](installation.md).
## 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:
```python
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:
```bash
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](#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:
```python
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
- Learn about [Advanced Inference Configurations](#inference-configuration)
- Learn about using [Optimizations](#inference-optimizations)
- See [Examples](../examples/examples_inference_index.md) for more usage scenarios
- Join our [Community Discord](https://discord.gg/JA7cksDz86).
- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg).