[docs] port to mkdocs (#855)
This commit is contained in:
Mihir Jagtap
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# 🔧 Installation
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FastVideo supports the following hardware platforms:
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- [NVIDIA CUDA](installation/gpu.md)
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- [Apple silicon](installation/mps.md)
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## Quick Installation
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### Using pip
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```bash
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pip install fastvideo
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```
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### Using conda
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```bash
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conda install -c conda-forge fastvideo
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```
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### From source
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```bash
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git clone https://github.com/hao-ai-lab/FastVideo.git
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cd FastVideo
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pip install -e .
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```
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## Hardware Requirements
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- **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+
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- **Apple Silicon**: macOS 12.0+ with M1/M2/M3 chips
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- **CPU**: x86_64 architecture (for CPU-only inference)
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## Next Steps
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- [Quick Start Guide](quick_start.md) - Get started with your first video generation
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- [Configuration](../inference/configuration.md) - Learn about configuration options
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- [Examples](../inference/examples/) - Explore example scripts and notebooks
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# NVIDIA GPU
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Instructions to install FastVideo for NVIDIA CUDA GPUs.
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## Requirements
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- **OS: Linux or Windows WSL**
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- **Python: 3.10-3.12**
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- **CUDA 12.8**
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- **At least 1 NVIDIA GPU**
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## Set up using Python
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### Create a new Python environment
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#### Conda
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You can create a new python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html)
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##### 1. Install Miniconda (if not already installed)
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```bash
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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```
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##### 2. Create and activate a Conda environment for FastVideo
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```bash
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# (Recommended) Create a new conda environment.
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conda create -n fastvideo python=3.12 -y
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conda activate fastvideo
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```
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#### uv
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Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
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```console
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# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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```
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### Installation
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```bash
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pip install fastvideo
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# or if you are using uv
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uv pip install fastvideo
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```
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Also optionally install flash-attn:
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```bash
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pip install flash-attn --no-build-isolation
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```
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### Installation from Source
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#### 1. Clone the FastVideo repository
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```bash
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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```
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#### 2. Install FastVideo
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Basic installation:
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```bash
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pip install -e .
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# or if you are using uv
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uv pip install -e .
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```
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### Optional Dependencies
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#### Flash Attention
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```bash
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pip install flash-attn --no-build-isolation
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```
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## Set up using Docker
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We also have prebuilt docker images with FastVideo dependencies pre-installed:
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[Docker Images](#docker)
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## Development Environment Setup
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If you're planning to contribute to FastVideo please see the following page:
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[Contributor Guide](#developer-overview)
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## Hardware Requirements
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### For Basic Inference
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- NVIDIA GPU with CUDA 12.8 support
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### For Lora Finetuning
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- 40GB GPU memory each for 2 GPUs with lora
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- 30GB GPU memory each for 2 GPUs with CPU offload and lora
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### For Full Finetuning/Distillation
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- Multiple high-memory GPUs recommended (e.g., H100)
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## Troubleshooting
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If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).
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You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
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# MPS (Apple Silicon)
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Instructions to install FastVideo for Apple Silicon.
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## Requirements
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- **OS: MacOS**
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- **Python: 3.12.4**
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## Set up using Python
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### Create a new Python environment
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#### Conda
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You can create a new python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html)
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##### 1. Install Miniconda (if not already installed)
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```bash
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh
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bash Miniconda3-latest-MacOSX-arm64.sh
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source ~/.zshrc
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```
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##### 2. Create and activate a Conda environment for FastVideo
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```bash
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# (Recommended) Create a new conda environment.
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conda create -n fastvideo python=3.12.4 -y
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conda activate fastvideo
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```
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#### uv
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Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
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```console
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# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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```
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### Dependencies
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```
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brew install ffmpeg
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```
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### Installation
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```bash
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pip install fastvideo
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# or if you are using uv
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uv pip install fastvideo
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```
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### Installation from Source
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#### 1. Clone the FastVideo repository
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```bash
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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```
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#### 2. Install FastVideo
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Basic installation:
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```bash
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pip install -e .
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# or if you are using uv
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uv pip install -e .
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```
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## Development Environment Setup
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If you're planning to contribute to FastVideo please see the following page:
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[Contributor Guide](#developer-overview)
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## Hardware Requirements
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### For Basic Inference
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- Mac M1, M2, M3, or M4 (at least 32 GB RAM is preferable for high quality video generation)
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## Troubleshooting
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If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).
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You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
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@@ -0,0 +1,55 @@
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# 🚀 Quick Start
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Get up and running with FastVideo in minutes!
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## Installation
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First, install FastVideo:
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```bash
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pip install fastvideo
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```
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## Basic Usage
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### Text-to-Video Generation
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```python
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from fastvideo import FastVideoPipeline
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# Initialize the pipeline
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pipe = FastVideoPipeline.from_pretrained("wan2.1-t2v-1.3B")
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# Generate a video
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prompt = "A cat playing with a ball of yarn"
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video = pipe(prompt, num_frames=16, height=512, width=512)
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# Save the video
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video.save("output.mp4")
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```
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### Image-to-Video Generation
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```python
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from fastvideo import FastVideoPipeline
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from PIL import Image
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# Load an image
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image = Image.open("input.jpg")
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# Initialize the pipeline
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pipe = FastVideoPipeline.from_pretrained("wan2.1-i2v-14B-480p")
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# Generate a video from the image
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video = pipe(image, num_frames=16, height=480, width=480)
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# Save the video
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video.save("output.mp4")
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```
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## Next Steps
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- [Installation Guide](installation.md) - Detailed installation instructions
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- [Configuration](../inference/configuration.md) - Learn about configuration options
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- [Examples](../inference/examples/) - Explore more examples
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- [Optimizations](../inference/optimizations.md) - Performance optimization tips
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@@ -0,0 +1,42 @@
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# V1 API
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FastVideo's V1 API provides a streamlined interface for video generation tasks with powerful customization options. This page documents the primary components of the API.
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## Video Generator
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This class will be the primary Python API for generating videos and images.
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::: fastvideo.entrypoints.video_generator.VideoGenerator
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options:
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show_root_heading: true
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show_source: false
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members:
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- from_pretrained
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heading_level: 3
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`VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
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## Configuring FastVideo
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The following two classes `PipelineConfig` and `SamplingParam` are used to configure initialization and sampling parameters, respectively.
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### PipelineConfig
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::: fastvideo.configs.pipelines.base.PipelineConfig
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options:
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show_root_heading: true
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show_source: false
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members:
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- from_pretrained
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- dump_to_json
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heading_level: 4
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### SamplingParam
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::: fastvideo.configs.sample.base.SamplingParam
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options:
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show_root_heading: true
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show_source: false
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members:
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- from_pretrained
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heading_level: 4
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