Instructions to use wangbei1/wan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wangbei1/wan with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wangbei1/wan", 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
- Xet hash:
- cbb3dd4e4854e3175d36e972e5b4a27de8eef41e86782ca810875ded7ac72fbe
- Size of remote file:
- 3.79 GB
- SHA256:
- 4663f373fffca661dce51c2808e31c2b0e2b7776926b4ca4fd24eb61a359f4a6
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