Instructions to use neithsan/wan21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neithsan/wan21 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("neithsan/wan21", 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:
- c3fc0d485be3656a65439fed87c39df2cfb9c81a202b431b1816516bdccc41f3
- Size of remote file:
- 6.26 GB
- SHA256:
- e5c9b431cbd9d2bb5ebb0ffe845ec352ae414f85814a822179cac53423ccdf5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.