MLX port of
AudioSR
—
a
multi-step latent-diffusion audio super-resolution model
— quantized to
INT8
weight-only for on-device inference on Apple Silicon. Upsamples
any-rate input (mono) to
48 kHz
with a 200-step DDIM solver (v-prediction
classifier-free guidance) over an AudioLDM-style VAE latent, then decodes
via a HiFi-GAN vocoder. AudioSR is the
teacher
model that
FlashSR
was distilled
from — FlashSR runs the same architecture in 1 step instead of 200, at the
cost of some quality.
Model
Total parameters
672 M (VAE 223 M + UNet 258 M + Vocoder 190 M)
Diffusion
200-step DDIM, v-prediction, cosine schedule, η=0
Classifier-free guidance
scale 3.5 (cond + uncond passes per step)
Quantization
INT8 weight-only, group size 64, mode
mlx_affine_flat
The three sub-models share one safetensors file with
vae.*
,
ldm.*
,
voc.*
key prefixes.
config.quantized_shapes
records each tensor's pre-flatten
shape so
mx.dequantize
can rebuild conv weight tensors at load time.
Performance (Apple Silicon, M-series, 10.24 s @ 48 kHz)
Metric
Value
Real-time factor (wall / audio)
1.87
Load time
0.06 s
SNR vs FP16 reference
+31.8 dB
Peak amplitude
0.500 (renormalised per upstream)
INT8 is the
transparent-quality
variant — choose it for maximum fidelity
vs the upstream PyTorch reference at twice the on-disk size of INT4. INT4 is
the recommended deployment variant.
Usage
from huggingface_hub import snapshot_download
import mlx.core as mx
import numpy as np
import scipy.io.wavfile as wf
from scipy.signal import resample_poly
bundle = snapshot_download("aufklarer/AudioSR-MLX-8bit")
# See https://github.com/soniqo/speech-swift for production usage.from audiosr import AudioSR
model = AudioSR(bundle)
sr, audio = wf.read("lr.wav")
audio = audio.astype(np.float32) / 32767.0
audio_48 = resample_poly(audio, 48000, sr).astype(np.float32)
hr = model(mx.array(audio_48), steps=200, cfg_scale=3.5, seed=42)
mx.eval(hr)
wf.write("hr.wav", 48000, (np.clip(np.array(hr), -1, 1) * 32767).astype(np.int16))
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