Instructions to use FastVideo/FastWan2.1-T2V-1.3B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastWan2.1-T2V-1.3B-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/FastWan2.1-T2V-1.3B-Diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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- It supports 3-step inference and achieves up to **20 FPS** on a single **H100** GPU.
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- Both [finetuning](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/distill/v1_distill_dmd_wan_VSA.sh) and [inference](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/inference/v1_inference_wan_dmd.sh) scripts are available in the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository.
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- Try it out on **FastVideo** — we support a wide range of GPUs from **H100** to **4090**, and even support **Mac** users!
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- It supports 3-step inference and achieves up to **20 FPS** on a single **H100** GPU.
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- Both [finetuning](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/distill/v1_distill_dmd_wan_VSA.sh) and [inference](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/inference/v1_inference_wan_dmd.sh) scripts are available in the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository.
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- Try it out on **FastVideo** — we support a wide range of GPUs from **H100** to **4090**, and even support **Mac** users!
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- We use [FastVideo 480P Synthetic Wan dataset](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) for training.
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