---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0/blob/main/README.md
pipeline_tag: text-to-audio
base_model: OpenMOSS-Team/MOSS-SoundEffect-v2.0
tags:
  - mlx
---

# mlx-community/MOSS-SoundEffect-v2.0-4bit

This model [mlx-community/MOSS-SoundEffect-v2.0-4bit](https://huggingface.co/mlx-community/MOSS-SoundEffect-v2.0-4bit)
was converted to MLX format from
[OpenMOSS-Team/MOSS-SoundEffect-v2.0](https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0)
— a text-to-sound-effect diffusion pipeline (foley / ambience / creature /
action audio, 48 kHz, up to 30 s) with a 1.3B Wan-style flow-matching DiT, a
continuous 128-d DAC VAE (50 Hz latents), and a frozen Qwen3-1.7B text encoder.

Precision: DiT int4 (group_size 64, transformer-block Linears only — embeddings, time/text projections, head, and norms stay bf16), DAC-VAE fp32, Qwen3 text encoder bf16.

## Use with mlx

```bash
pip install moss-sfx-mlx  # https://github.com/xocialize/moss-soundeffect-mlx
```

```python
from moss_sfx_mlx.pipeline_mlx import MossSoundEffectPipeline

pipe = MossSoundEffectPipeline.from_pretrained("mlx-community/MOSS-SoundEffect-v2.0-4bit")
audio = pipe(prompt="a heavy wooden door creaks open slowly",
             seconds=5, num_inference_steps=100, cfg_scale=4.0, seed=0)
# audio: (1, 1, samples) mx.array at 48 kHz
```

## Parity

Validated against the upstream PyTorch reference (fp32, CPU stream, per-module
and end-to-end golden tensors; full suite in the GitHub repo):

- End-to-end waveform vs PyTorch golden (10-step CFG denoise): max_abs < 1e-2 fp32
- Full-DiT velocity field at production scale (T=1500): max_abs < 1e-2 fp32
- DAC-VAE decode vs reference: max_abs < 1e-2 fp32 (no scale constant — the
  learned post_quant_conv is faithful)
- Qwen3 hidden states: cosine 1.0, max_abs 4.4e-4 (fp32 accumulation floor)
- int4 DiT per-pass cosine vs bf16 on identical injected inputs: 0.999425 (gate 0.99)

- 10-prompt perceptual A/B at 100 steps: passed human review (correct content,
  duration, no tonal artifacts)

## Performance (Apple M5 Max)

100 steps, cfg 4.0, full 30 s latent: 45 s wall clock, 12.2 GB peak memory; DiT shrinks 2.83 GB -> 0.83 GB.

## License

Apache-2.0, matching the upstream model, code, and all components.