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ACE-Step/ace-step-v1.5-1d-vae-stable-audio-format

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ACE-Step v1.5 1D VAE

Stable Audio Tools Format

GitHub |

Project |

Hugging Face |

Space Demo |

Discord |

Tech Report

Model Details

This is the 1D Variational Autoencoder (VAE) used in ACE-Step v1.5 for music generation. The weights are provided in stable-audio-tools compatible format, making it easy to load, fine-tune, and integrate into your own training pipelines.

  • Developed by: ACE-STEP
  • Model type: Audio VAE (Oobleck Autoencoder)
  • License: MIT

| Parameter | Value |

|-----------|-------|

| Architecture | Oobleck Autoencoder (VAE) |

| Audio Channels | 2 (Stereo) |

| Sampling Rate | 48,000 Hz |

| Latent Dim | 64 |

| Encoder Latent Dim | 128 |

| Downsampling Ratio | 1,920 |

| Encoder/Decoder Channels | 128 |

| Channel Multipliers | [1, 2, 4, 8, 16] |

| Strides | [2, 4, 4, 6, 10] |

| Activation | Snake |

🏗️ Architecture

The VAE is a core component of the ACE-Step v1.5 pipeline, responsible for compressing raw stereo audio (48kHz) into a compact latent representation with a 1920x downsampling ratio and 64-dimensional latent space. The DiT operates in this latent space to generate music.

Quick Start

Installation

pip install stable-audio-tools torchaudio

Load and Use

from stable_audio_vae import StableAudioVAE

# Load model
vae = StableAudioVAE(
    config_path="config.json",
    checkpoint_path="checkpoint.ckpt",
)
vae = vae.cuda().eval()

# Encode audio
wav = vae.load_wav("input.wav")
wav = wav.cuda()
latent = vae.encode(wav)
print(f"Latent shape: {latent.shape}")  # [batch, 64, time/1920]

# Decode back to audio
output = vae.decode(latent)

Command Line

python stable_audio_vae.py -i input.wav -o output.wav

# For long audio, use chunked processing
python stable_audio_vae.py -i input.wav -o output.wav --chunked

Fine-Tuning

This checkpoint is compatible with stable-audio-tools training pipelines. The config.json includes full training configuration (optimizer, loss, discriminator settings) that you can use as a starting point for fine-tuning.

File Structure

.
├── config.json            # Model architecture and training config
├── checkpoint.ckpt        # Model weights (PyTorch checkpoint)
├── stable_audio_vae.py    # Inference script with StableAudioVAE wrapper
└── README.md

🦁 Related Models

| Model | Description | Hugging Face |

|-------|-------------|--------------|

| acestep-v15-base | DiT base model (CFG, 50 steps) | Link |

| acestep-v15-sft | DiT SFT model (CFG, 50 steps) | Link |

| acestep-v15-turbo | DiT turbo model (8 steps) | Link |

| acestep-v15-xl-base | XL DiT base (4B, CFG, 50 steps) | Link |

| acestep-v15-xl-sft | XL DiT SFT (4B, CFG, 50 steps) | Link |

| acestep-v15-xl-turbo | XL DiT turbo (4B, 8 steps) | Link |

🙏 Acknowledgements

This project is co-led by ACE Studio and StepFun.

📖 Citation

If you find this project useful for your research, please consider citing:

@misc{gong2026acestep,
	title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
	author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
	howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
	year={2026},
	note={GitHub repository}
}