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
File size: 914 Bytes
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license: mit
library_name: coreml
base_model: KlingTeam/LivePortrait
pipeline_tag: image-to-video
tags:
- liveportrait
- coreml
- mlpackage
- float16
- apple-silicon
---
# LivePortrait SPADE decoder, Core ML
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)
- Input `feature`: float32 `[1, 256, 64, 64]`, the warping network's output (NCHW).
- Output `face`: `[1, 3, 512, 512]` in [0, 1].
- Instance norms read their input 256× smaller and the res-block convs 64× smaller (results scaled back): the activations reach ~10⁴, past float16 otherwise.
- 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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