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## Model Details This is the 1D Variational Autoencoder (VAE) used in [ACE-Step v1.5](https://github.com/ACE-Step/ACE-Step-1.5) for music generation. The weights are provided in **[stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools)** compatible format, making it easy to load, fine-tune, and integrate into your own training pipelines. - **Developed by:** [ACE-STEP](https://github.com/ACE-Step) - **Model type:** Audio VAE (Oobleck Autoencoder) - **License:** [MIT](https://opensource.org/licenses/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 ```bash pip install stable-audio-tools torchaudio ``` ### Load and Use ```python 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 ```bash 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](https://github.com/Stability-AI/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](https://huggingface.co/ACE-Step/acestep-v15-base) | | `acestep-v15-sft` | DiT SFT model (CFG, 50 steps) | [Link](https://huggingface.co/ACE-Step/acestep-v15-sft) | | `acestep-v15-turbo` | DiT turbo model (8 steps) | [Link](https://huggingface.co/ACE-Step/Ace-Step1.5) | | `acestep-v15-xl-base` | XL DiT base (4B, CFG, 50 steps) | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-base) | | `acestep-v15-xl-sft` | XL DiT SFT (4B, CFG, 50 steps) | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-sft) | | `acestep-v15-xl-turbo` | XL DiT turbo (4B, 8 steps) | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-turbo) | ## 🙏 Acknowledgements This project is co-led by ACE Studio and StepFun. ## 📖 Citation If you find this project useful for your research, please consider citing: ```BibTeX @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} } ```