68 lines
1.9 KiB
Markdown
68 lines
1.9 KiB
Markdown
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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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- [NVIDIA DGX Spark / GB10 (ARM64 + CUDA 13)](installation/spark.md)
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- [Apple silicon](installation/mps.md)
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## Quick Installation
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### Using uv (recommended)
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Use uv as the default environment manager for faster and more stable installs.
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The commands below target NVIDIA CUDA 12; use `UV_TORCH_BACKEND=cu130` on
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CUDA 13. Apple silicon users should follow the [MPS guide](installation/mps.md).
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```bash
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# Create and activate a new uv environment
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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UV_TORCH_BACKEND=cu126 uv pip install fastvideo
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```
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### Using Conda (alternative)
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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 -y
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conda activate fastvideo
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UV_TORCH_BACKEND=cu126 uv pip install fastvideo
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```
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### From source
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FastVideo pins PyTorch 2.12.0. Select its CUDA build explicitly with
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`UV_TORCH_BACKEND=cu126` for CUDA 12 or `UV_TORCH_BACKEND=cu130` for CUDA 13.
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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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UV_TORCH_BACKEND=cu126 uv pip install -e .
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# optional: install flash-attn
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uv pip install flash-attn --no-build-isolation -v
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```
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Alternative with Conda environment (still drives installs through `uv`):
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```bash
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UV_TORCH_BACKEND=cu126 uv pip install -e .
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uv pip install flash-attn --no-build-isolation -v
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```
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## Hardware Requirements
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- **NVIDIA GPUs**: CUDA 12.6+ with compute capability 7.0+
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- **Apple Silicon**: macOS 14.0+ with M1/M2/M3/M4 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/examples_inference_index.md) - Explore example scripts and notebooks
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