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aufklarer
/
FlashSR-MLX-4bit

Audio-to-Audio
MLX
Safetensors
English
audio
music
audio-super-resolution
flashsr
latent-diffusion
quantized
int4
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  1. README.md +101 -0
  2. config.json +2951 -0
  3. model.safetensors +3 -0
README.md ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-4.0
3
+ language:
4
+ - en
5
+ tags:
6
+ - mlx
7
+ - audio
8
+ - music
9
+ - audio-super-resolution
10
+ - flashsr
11
+ - latent-diffusion
12
+ - quantized
13
+ - int4
14
+ base_model: jakeoneijk/FlashSR_Inference
15
+ library_name: mlx
16
+ pipeline_tag: audio-to-audio
17
+ ---
18
+
19
+ # FlashSR-MLX-4bit
20
+
21
+ - [speech-swift](https://github.com/soniqo/speech-swift) — Apple SDK
22
+ - [soniqo.audio](https://soniqo.audio) — website
23
+ - [blog](https://soniqo.audio/blog) — blog
24
+
25
+ MLX port of [FlashSR](https://github.com/jakeoneijk/FlashSR_Inference) — a
26
+ **single-step** distilled latent-diffusion audio super-resolution model —
27
+ quantized to **INT4** weight-only for on-device inference on Apple Silicon.
28
+ Upsamples any-rate input (mono) to **48 kHz** in one diffusion pass with a
29
+ specialised BigVGAN-flavour SR vocoder. FlashSR is the distilled student of
30
+ [AudioSR](https://github.com/haoheliu/versatile_audio_super_resolution).
31
+
32
+ ## Model
33
+
34
+ | | |
35
+ |---|---|
36
+ | Total parameters | 638 M (VAE 223 M + UNet 258 M + Vocoder 157 M) |
37
+ | Diffusion steps | **1** (distilled v-prediction student, cosine schedule, t=999) |
38
+ | Quantization | INT4 weight-only, group size 64, mode `mlx_affine_flat` |
39
+ | Format | MLX safetensors (single combined bundle) |
40
+ | Sample rate | 48 kHz mono out (any-rate mono in) |
41
+ | Frame length | 5.12 s (245 760 samples) per forward |
42
+ | Bundle size | 346 MB on disk |
43
+ | Source | [jakeoneijk/FlashSR_Inference](https://github.com/jakeoneijk/FlashSR_Inference) |
44
+
45
+ ## Files
46
+
47
+ | File | Size | Description |
48
+ |---|---|---|
49
+ | `model.safetensors` | 346 MB | INT4-quantized VAE + UNet + SR Vocoder weights |
50
+ | `config.json` | ~70 KB | Sub-model configs + quantization metadata + original shape table for dequant-on-load |
51
+
52
+ The three sub-models share one safetensors file with `vae.*`, `ldm.*`, `voc.*`
53
+ key prefixes. `config.quantized_shapes` records each tensor's pre-flatten shape
54
+ so `mx.dequantize` can rebuild conv weight tensors at load time.
55
+
56
+ ## Performance (Apple Silicon, M-series, 5.12 s @ 48 kHz)
57
+
58
+ | Metric | Value |
59
+ |---|---|
60
+ | Real-time factor (wall / audio) | **1.10** |
61
+ | Load time | 0.17 s (dequant materialises bf16 weights once) |
62
+ | SNR vs FP16 reference | **+29.4 dB** |
63
+ | Cosine similarity vs FP16 | **0.9994** |
64
+ | Peak amplitude preservation | 1.000 |
65
+
66
+ INT4 is the recommended deployment variant — well above music-perceptual
67
+ threshold and the smallest viable on-device bundle.
68
+
69
+ ## Usage
70
+
71
+ ```python
72
+ from huggingface_hub import snapshot_download
73
+ import mlx.core as mx
74
+ import numpy as np
75
+ import scipy.io.wavfile as wf
76
+ from scipy.signal import resample_poly
77
+
78
+ bundle = snapshot_download("aufklarer/FlashSR-MLX-4bit")
79
+ # See https://github.com/soniqo/speech-swift for production usage.
80
+
81
+ # Toy Python demo (requires the matching MLX FlashSR runtime):
82
+ sr, audio = wf.read("lr.wav")
83
+ audio = audio.astype(np.float32) / 32767.0
84
+ audio_48 = resample_poly(audio, 48000, sr).astype(np.float32)
85
+
86
+ from flashsr import FlashSR # from this repo's export/
87
+ model = FlashSR(bundle)
88
+ hr = model(mx.array(audio_48), seed=42)
89
+ mx.eval(hr)
90
+ wf.write("hr.wav", 48000, (np.clip(np.array(hr), -1, 1) * 32767).astype(np.int16))
91
+ ```
92
+
93
+ ## Source
94
+
95
+ - Upstream: [jakeoneijk/FlashSR_Inference](https://github.com/jakeoneijk/FlashSR_Inference)
96
+ - Paper: [FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation](https://arxiv.org/abs/2501.10295)
97
+ - Teacher model: [AudioSR](https://github.com/haoheliu/versatile_audio_super_resolution)
98
+
99
+ ## License
100
+
101
+ **CC-BY-NC 4.0** — inherited from upstream FlashSR weights. Non-commercial use only.
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