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hao-ai-lab /FastVideo

Trains and speeds up open video models on your own GPU
Model toolingApache-2.0English README
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About FastVideo

FastVideo lets you run and retrain open video models yourself instead of paying a hosting bill. You get finetuning, LoRA training, and tricks that cut the number of denoising passes by more than half. Setup takes some work, since it assumes you have a decent GPU.

Best for: Hands-on makers with a solid GPU who would rather tune a model than rent someone else's.

What it does

  • Finetunes open video models fully, or trains a smaller LoRA version
  • Skips ahead during generation with DMD2 and sparse distillation
  • Uses Video Sparse Attention and Sliding Tile Attention kernels
  • Shares the load across GPUs with sequence parallelism
  • Generates and edits live video in the browser via the Dreamverse app

Quickstart

Rewritten from the README. Check the repo for the latest steps.

  1. 1
    Start a fresh environment: uv venv --python 3.12 --seed, then source .venv/bin/activate
  2. 2
    If you have an NVIDIA card, install with the command below (swap in UV_TORCH_BACKEND=cu130 for newer drivers)
    UV_TORCH_BACKEND=cu126 uv pip install fastvideo
  3. 3
    Clone the repo and run uv pip install -e '.[mlx]' on an Apple Silicon Mac
  4. 4
    Save a script named example.py that loads a generator and writes a clip
  5. 5
    Generate your first video with python example.py

Models and languages

Models it works with

  • FastWan2.1-T2V-1.3B
  • FastWan2.2-TI2V-5B
  • MiniMax H3
  • CausalWan2.2 I2V A14B
  • FastWan-QAD
  • FastMetal-QAD

Interface and docs

  • English

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Reviewed Oct 1, 2026. We write these descriptions ourselves. The repo's own docs are the final word.