Instructions to use Avdpro/FlashHead-Pro-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Avdpro/FlashHead-Pro-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Avdpro/FlashHead-Pro-MLX --local-dir FlashHead-Pro-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 986 Bytes
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license: apache-2.0
library_name: mlx
tags:
- image-to-video
- audio-driven-video
- ai2apps
---
# FlashHead Pro MLX
Self-contained checkpoint for AI2Apps FlashHead native MLX Model Worker. Runtime and model Python code are distributed separately.
Includes Pro DiT, its VAE and Wav2Vec2 base. Original tensor layouts are converted to MLX on load; Pro Wan VAE was converted offline from a pure tensor state dictionary to safetensors. No Torch is required for inference.
Fixed upstream revisions, exact runtime file list, sizes and SHA-256 values are in `ai2apps-checkpoint.json`.
Sources: [SoulX-FlashHead](https://github.com/Soul-AILab/SoulX-FlashHead), [official weights](https://huggingface.co/Soul-AILab/SoulX-FlashHead-1_3B), [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h). Upstream model cards declare Apache-2.0. Preserve LICENSE and NOTICE.md.
512x512, 25 FPS, four denoising steps. This is a local offline generation path; no real-time streaming claim.
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