Instructions to use melmass/liveportrait-decoder-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LivePortrait
How to use melmass/liveportrait-decoder-coreml with LivePortrait:
# 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
Upload README.md with huggingface_hub
Browse files
README.md
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# LivePortrait SPADE decoder, Core ML
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LivePortrait's SPADE decoder (KwaiVGI) as a Core ML ML Program in float16, built with coremltools' MIL builder from [spade_generator.pth](https://huggingface.co/KlingTeam/LivePortrait/blob/main/liveportrait/base_models/spade_generator.pth)
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- Input `feature`: float32 `[1, 256, 64, 64]`, the warping network's output (NCHW).
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- Output `face`: `[1, 3, 512, 512]` in [0, 1].
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- Instance norms read their input 256× smaller and the res-block convs 64× smaller (results scaled back): the activations reach ~10⁴, past float16 otherwise.
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- Run it on the GPU (`.cpuAndGPU`): 32 ms on an M5, 0.15–0.21 levels mean from the float32 decoder. The Neural Engine is slower (41 ms) and 3 levels off.
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