Instructions to use mlx-community/ACE-Step1.5-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/ACE-Step1.5-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/ACE-Step1.5-MLX-4bit --local-dir ACE-Step1.5-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
ACE-Step 1.5 MLX (4-bit Quantized)
4-bit quantized MLX weights for ACE-Step/ACE-Step1.5.
- Decoder and encoder quantized to 4-bit (group_size=64)
- VAE, tokenizer, and detokenizer kept in full precision
- 2.2GB main model + 0.7GB VAE + 2.4GB text encoder
Usage
from mlx_audio.tts import load
model = load("mlx-community/ACE-Step1.5-MLX-4bit")
for result in model.generate(
text="upbeat electronic dance music with energetic synthesizers",
duration=30.0,
):
audio = result.audio # [samples, 2] stereo @ 48kHz
sample_rate = result.sample_rate
With Vocals
for result in model.generate(
text="English pop song with clear female vocals, catchy melody",
lyrics="""[verse]
Dance with me tonight
Under the neon lights
[chorus]
We're alive, we're on fire
Dancing higher and higher
""",
duration=60.0,
vocal_language="en",
):
...
The model uses a 5Hz Language Model planner by default (use_lm=True) which generates
a song blueprint before running the diffusion transformer.
- Downloads last month
- 322
Model size
2B params
Tensor type
F32
ยท
U32 ยท
Hardware compatibility
Log In to add your hardware
Quantized
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support