125 lines
3.8 KiB
Markdown
125 lines
3.8 KiB
Markdown
# Inference Quick Start
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This page contains step-by-step instructions to get you quickly started with video generation using FastVideo.
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## Requirements
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- **OS**: Linux (Tested on Ubuntu 22.04+)
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- **Python**: 3.10-3.12
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- **CUDA**: 12.4
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- **GPU**: At least one NVIDIA GPU
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## Installation
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We recommend using an environment manager such as `Conda` to create a clean environment:
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```bash
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# Create and activate a new conda environment
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conda create -n fastvideo python=3.12
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conda activate fastvideo
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# Install FastVideo
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pip install fastvideo
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```
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For advanced installation options, see the [Installation Guide](installation.md).
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## Generating Your First Video
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Here's a minimal example to generate a video using the default settings. Create a file called `example.py` with the following code:
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```python
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from fastvideo import VideoGenerator
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def main():
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# Create a video generator with a pre-trained model
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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num_gpus=1, # Adjust based on your hardware
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)
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# Define a prompt for your video
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prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."
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# Generate the video
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video = generator.generate_video(
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prompt,
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return_frames=True, # Also return frames from this call (defaults to False)
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output_path="my_videos/", # Controls where videos are saved
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save_video=True
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)
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if __name__ == '__main__':
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main()
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```
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Run the script with:
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```bash
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python example.py
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```
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The generated video will be saved in the current directory under `my_videos/`
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More inference example scripts can be found in `scripts/inference/`
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## Available Models
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Please see the [support matrix](#support-matrix) for the list of supported models and their available optimizations.
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## Image-to-Video Generation
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You can generate a video starting from an initial image:
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```python
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from fastvideo import VideoGenerator, SamplingParam
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def main():
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# Create the generator
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model_name = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
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generator = VideoGenerator.from_pretrained(model_name, num_gpus=1)
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# Set up parameters with an initial image
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sampling_param = SamplingParam.from_pretrained(model_name)
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sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
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sampling_param.num_frames = 107
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# Generate video based on the image
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prompt = "A photograph coming to life with gentle movement"
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generator.generate_video(prompt, sampling_param=sampling_param,
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output_path="my_videos/",
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save_video=True)
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if __name__ == '__main__':
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main()
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```
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## Troubleshooting
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Common issues and their solutions:
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### Out of Memory Errors
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If you encounter CUDA out of memory errors:
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- Reduce `num_frames` or video resolution
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- Enable memory optimization with `enable_model_cpu_offload`
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- Try a smaller model or use distilled versions
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- Use `num_gpus` > 1 if multiple GPUs are available
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### Slow Generation
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To speed up generation:
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- Reduce `num_inference_steps` (20-30 is usually sufficient)
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- Use half precision (`fp16`) for the VAE
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- Use multiple GPUs if available
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### Unexpected Results
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If the generated video doesn't match your prompt:
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- Try increasing `guidance_scale` (7.0-9.0 works well)
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- Make your prompt more detailed and specific
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- Experiment with different random seeds
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- Try a different model
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## Next Steps
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- Learn about [Advanced Inference Configurations](#inference-configuration)
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- Learn about using [Optimizations](#inference-optimizations)
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- See [Examples](../examples/examples_inference_index.md) for more usage scenarios
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- Join our [Community Discord](https://discord.gg/JA7cksDz86).
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- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg).
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