Instructions to use SceneWorks/wan2.2-t2v-a14b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/wan2.2-t2v-a14b-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download SceneWorks/wan2.2-t2v-a14b-mlx --local-dir wan2.2-t2v-a14b-mlx
- Wan2.2
How to use SceneWorks/wan2.2-t2v-a14b-mlx with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download t5_encoder.safetensors from SceneWorks/wan2.2-t2v-a14b-mlx: direct link, hf CLI and curl.
- Browser
- Download file 11.4 GB
-
https://huggingface.co/SceneWorks/wan2.2-t2v-a14b-mlx/resolve/main/t5_encoder.safetensors
- Command line
-
hf download hf://SceneWorks/wan2.2-t2v-a14b-mlx/t5_encoder.safetensors
-
curl -L -o t5_encoder.safetensors https://huggingface.co/SceneWorks/wan2.2-t2v-a14b-mlx/resolve/main/t5_encoder.safetensors
11.4 GB
- Xet hash:
- 88b740a3917ea9e2af35886683b9b8b1f8016770816d835ae9cefa48e6890395
- Size of remote file:
- 11.4 GB
- SHA256:
- cfffbd1a3d0b0b1508c8edcac4ebd4623e1257102bb07682d333b4bd5de891fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.