| --- |
| 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. |
| |
