Instructions to use SceneWorks/wan2.2-ti2v-5b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/wan2.2-ti2v-5b-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-ti2v-5b-mlx SceneWorks/wan2.2-ti2v-5b-mlx
- Wan2.2
How to use SceneWorks/wan2.2-ti2v-5b-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
Wan2.2-TI2V-5B โ MLX (bf16)
Native MLX (Apple Silicon) conversion of Wan-AI/Wan2.2-TI2V-5B, packaged as a turnkey, self-contained snapshot for the SceneWorks app. Downloading this repo replaces the previous "download the source checkpoint and convert on-device" first-run step.
Contents (self-contained, bf16)
| file | what |
|---|---|
model.safetensors |
Wan2.2 TI2V-5B DiT transformer |
t5_encoder.safetensors |
UMT5-XXL text encoder |
vae.safetensors |
Wan z48 VAE |
tokenizer.json |
UMT5 tokenizer |
config.json |
architecture config |
Quantization (Q4/Q8) is applied at load by the engine โ these weights are full bf16, so one artifact serves every quant tier.
Provenance
- Source:
Wan-AI/Wan2.2-TI2V-5B(Apache-2.0). - Converted with: the SceneWorks native Rust MLX converter (
mlx-gen-wan, converter idwan_ti2v_5b), dtypebfloat16. - No re-quantization; lean snapshot (excludes diffusers-format directories the MLX engine never loads).
License
Apache-2.0, inherited from the upstream model. This repository redistributes a converted copy of the upstream Apache-2.0 weights, with attribution, as permitted by that license. See the source model card.
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Model size
5B params
Tensor type
BF16
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Hardware compatibility
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Quantized
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Base model
Wan-AI/Wan2.2-TI2V-5B