What is ACE-Step
ACE-Step is an open-source ACE Studio and StepFun jointly launchedmusic generationfoundation model, based on an innovative architecture design to achieve efficient, coherent, and controllable music creation. ACE-Step combines diffusion models, a deep compressed autoencoder (DCAE), and a lightweight linear transformer to generate high-quality music works in a short time, 15 times faster than traditional LLM models. ACE-Step supports multiple music styles, languages, and controllability features, providing a powerful tool for music creation. ACE-Step is suitable for rapid music generation and, as a foundation model, supports various music creation subtasks, helping musicians, producers, and content creators achieve efficient creation.
ACE-Step's Main Features
- Fast synthesis: Generates high-quality music in a short time, for example, generating 4 minutes of music on an A100 GPU takes only 20 seconds.
- Diverse styles: Supports multiple mainstream music styles (such as pop, rock, electronic, jazz, etc.) and lyrics generation in multiple languages.
- Variation generation: Adjusts the noise ratio to generate different variations, providing diverse music options.
- Repaint function: Regenerates specific parts, modifies style, lyrics, or vocals, while retaining other elements.
- Lyrics editing: Supports local lyrics modification of generated music while keeping the melody and accompaniment unchanged.
- Multilingual support:: Supports 19 languages, among which 10 languages including English, Chinese, Russian, Spanish, and Japanese perform particularly well.
- Lyric2Vocal: Generates vocal audio directly from lyrics based on LoRA fine-tuning.
- Text2Samples: Generates music samples and loops, helping producers quickly create instrument loops, sound effects, etc.
ACE-Step's Technical Principles
- Diffusion Model: Generates data based on gradually removing noise. The model can quickly synthesize music, but traditional diffusion models have deficiencies in long-structure coherence. ACE-Step solves this problem based on an innovative architecture design.
- Deep Compressed Autoencoder: DCAE is used for efficient data compression and decompression, preserving fine-grained audio details of music and reducing computational resource consumption.
- Lightweight Linear Transformer: Used to process sequence information of music, ensuring that the generated music has coherence in melody, harmony, and rhythm.
- Semantic alignment: ACE-Step uses MERT (Music Embedding Representation) and m-hubert technology to align semantic representations (REPA) during training, achieving fast convergence and high-quality generation results.
- Training optimization: Based on semantic alignment and optimization technology during training, ACE-Step can generate high-quality music in a short time, maintaining a balance between generation speed and coherence.
ACE-Step's Project Address
- Project official website：https://ace-step.github.io/
- GitHub repository：https://github.com/ace-step/ACE-Step
- HuggingFace model library：https://huggingface.co/ACE-Step/ACE-Step-v1-3.5B
- Online experience Demo：https://huggingface.co/spaces/ACE-Step/ACE-Step
ACE-Step's Application Scenarios
- Music creation: Quickly generates melodies and lyrics, providing creative inspiration.
- Vocal generation: Directly generates vocal audio from lyrics, suitable for producing vocal demos.
- Music production: Generate instrument loops and sound effects, providing creative materials.
- Multilingual Support: Suitable for cross-language music creation.
- Music Education: As a teaching tool, it helps with learning music creation.