🚀 AuK-Flash-MLX-8bit (Apple Silicon 8-Bit Quantized)

🔗 GitHub Source & Web Demo: vanch007/mlx-AuK · Full Precision (FP32/BF16) MLX Weights

This repository provides the 8-Bit affine quantized MLX port of AuK-Flash, Tencent Hunyuan's 1.5B speech foundation model for ultra-fast generation and zero-shot speech/lyric editing on Apple Silicon.

• Main GitHub Repository: https://github.com/vanch007/mlx-AuK
• Original Project: Tencent-Hunyuan/AuK
• Quantization: 8-bit group-wise affine quantization ()
• Weight Size: 0.56 GB (90.1% reduction from 5.70 GB)
• Inference Speed: 1.022s for 10.0s audio on Apple Silicon (RTF 0.1022, ~10x real-time)

Performance Benchmark

Metric	PyTorch MPS	Native MLX FP32	Native MLX 8-Bit (This Model)
4-Step DiT Latency (10s Audio)	3.820s	0.992s	1.022s
Real-Time Factor (RTF)	0.3820	0.0992	0.1022 (9.79x real-time)
Backbone Memory	5.70 GB	5.70 GB	0.56 GB (90.1% saving)

Files Included

• : 8-bit quantized MLX weights for the Flux2Edit diffusion transformer (569MB).
• : MLX weights for the BigVGAN Flow VAE (608MB).
• : Architecture and quantization hyperparameters.

Quickstart

For full inference workflows, CLI tools, and the interactive A/B comparison web demo, visit the project GitHub: 👉 https://github.com/vanch007/mlx-AuK