Instructions to use jander87/Wan2.2-Animate-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jander87/Wan2.2-Animate-14B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jander87/Wan2.2-Animate-14B", 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:
- 9c2722554ebf225d807218ab8355b357b5f2c80833edeb00ca148fbd2c346310
- Size of remote file:
- 9.95 GB
- SHA256:
- e2aa343b0ba04f563566e9959a439611a18189cd9accdc04d59681be9ce5be50
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.