FastVideo Tutorial: Generate Videos & Fine-Tune Diffusion Models
Step-by-step tutorial: deploy FastVideo with Docker, run text-to-video inference, and fine-tune video diffusion models with LoRA.
In this tutorial, you'll take FastVideo from zero to generating videos — and then fine-tune a diffusion model with LoRA. FastVideo by Hao AI Lab combines high-performance inference with a complete post-training stack, so one framework covers both sides of your video-generation workflow.
🚀 Want to deploy FastVideo yourself?
Docker configs, system requirements, and installation guides — all on one page.
View FastVideo Tool Page →Prerequisites
FastVideo is GPU-native. The community Docker image (junwha/fastvideo) bundles a CUDA development environment, so you mainly need the right hardware:
| Component | Minimum | Recommended |
|---|---|---|
| GPU | NVIDIA GPU, 24 GB VRAM | A100 / H100 or multi-GPU |
| CPU | 2 cores | 4+ cores |
| RAM | 4 GB | 8 GB+ |
Step 1 — Pull the Docker image
The fastest path is the community CUDA image:
docker run --gpus all -it junwha/fastvideo bash
Step 2 — Run text-to-video inference
Inside the container, use the FastVideo inference entrypoint with a model like CogVideoX or HunyuanVideo:
python -m fastvideo.sample \
--model CogVideoX-5B \
--prompt "a red panda surfing on a wave"
FlashAttention and optimized sampling kick in automatically, which keeps generation far faster than naive pipelines.
Step 3 — Fine-tune with LoRA
To adapt the model to your own footage, FastVideo ships ready-to-run post-training recipes:
python -m fastvideo.train.lora \
--config configs/lora.yaml \
--data /path/to/dataset
💡 Pro tip: Start with LoRA before full SFT — you get most of the quality gain at a fraction of the VRAM and training time. Then, if you need smaller or faster models, explore FastVideo's distillation recipes to compress your fine-tuned checkpoint.
Wrap-up
FastVideo gives you an end-to-end video-generation platform: fast inference, modern post-training, and reproducible Docker deployment. Whether you're prototyping text-to-video or building a production fine-tuning pipeline, it's one of the strongest open-source choices available today.
🚀 Want to deploy FastVideo yourself?
Docker configs, system requirements, and installation guides — all on one page.
View FastVideo Tool Page →