ACE-Step AI
ACE-Step is a state-of-the-art open-source AI music generation model that powers all music creation on AceSteps. This page provides a comprehensive overview of the model's capabilities, architecture, and integration.
Why ACE-Step?
AceSteps chose ACE-Step as its AI backbone for several critical reasons:
Zero Copyright Risk
| Aspect | ACE-Step | Proprietary Models |
|---|---|---|
| License | Apache 2.0 | Restrictive/Proprietary |
| Commercial Use | Full rights | Often limited |
| Training Data | Disclosed | Opaque |
| Copyright Claims | None possible | Risk of infringement |
| NFT Minting | Fully legal | Legal gray area |
:::tip Why This Matters Every song created on AceSteps is 100% copyright-free. Creators can mint, sell, and monetize their AI-generated music without any legal risk. This is foundational to our tokenization model. :::
Open Source Advantages
- Transparency - Model weights publicly available
- Self-Hostable - No vendor lock-in
- Community - Active development and improvements
- Customizable - LoRA fine-tuning support
- Auditable - Training methodology documented
Model Specifications
Core Parameters
| Specification | Value |
|---|---|
| Model Name | ACE-Step-v1-3.5B |
| Parameters | 3.5 billion |
| Architecture | Diffusion + Linear Transformer |
| License | Apache 2.0 |
| Developers | ACE Studio & StepFun |
| Release | 2025 |
Audio Output
| Property | Value |
|---|---|
| Format | MP3 (default), WAV |
| Bitrate | 320 kbps |
| Sample Rate | 44.1 kHz |
| Channels | Stereo |
| Max Duration | 240 seconds (4 minutes) |
| Quality | Professional-grade |
Generation Speed
Real-Time Factor (RTF) measures how fast the model generates audio. Higher RTF = faster generation.
| GPU | RTF (27 steps) | Time for 1 min | RTF (60 steps) | Time for 1 min |
|---|---|---|---|---|
| RTX 4090 | 34.48x | 1.74s | 15.63x | 3.84s |
| A100 | 27.27x | 2.20s | 12.27x | 4.89s |
| RTX 3090 | 12.76x | 4.70s | 6.48x | 9.26s |
| A10G (AceSteps) | ~20x | ~3.0s | ~10x | ~6.0s |
| M2 Max | 2.27x | 26.43s | 1.03x | 58.25s |
:::info AceSteps Performance On our Modal A10G infrastructure, generating 30 seconds of music takes approximately 4-5 seconds. This enables near-instant previews for creators. :::
Language Support
ACE-Step supports 19 languages for lyrics and vocal generation:
Tier 1 - Excellent Performance
| Language | Code | Vocal Quality | Lyric Alignment |
|---|---|---|---|
| English | en | Excellent | Excellent |
| Chinese | zh | Excellent | Excellent |
| Japanese | ja | Excellent | Very Good |
| Korean | ko | Very Good | Very Good |
Tier 2 - Good Performance
| Language | Code | Vocal Quality | Lyric Alignment |
|---|---|---|---|
| Spanish | es | Very Good | Good |
| German | de | Very Good | Good |
| French | fr | Good | Good |
| Portuguese | pt | Good | Good |
| Italian | it | Good | Good |
| Russian | ru | Good | Good |
Tier 3 - Experimental
Other supported languages may have reduced quality due to training data imbalance. Performance varies by genre and complexity.
Generation Capabilities
Text-to-Music
Generate complete songs from natural language descriptions:
Input: "upbeat electronic dance music with energetic synth leads
and a driving four-on-the-floor beat, festival anthem style"
Output: 30-second EDM track with synthesizers, bass drops, and builds
Style Control
Genres Supported
| Category | Genres |
|---|---|
| Electronic | EDM, House, Techno, Trance, Dubstep, Ambient |
| Hip-Hop | Trap, Boom Bap, Lo-fi Hip-hop, Drill |
| Rock | Alternative, Indie, Metal, Punk, Classic Rock |
| Pop | Synth-pop, K-pop, J-pop, Dance Pop |
| Classical | Orchestral, Piano, Chamber, Cinematic |
| World | Latin, Afrobeat, Reggae, Folk |
| Other | Jazz, R&B, Soul, Country, Blues |
Mood Control
Energetic / Calm / Happy / Sad / Dark / Uplifting / Mysterious / Aggressive
Romantic / Nostalgic / Triumphant / Melancholic / Peaceful / Intense
Instrument Specification
Piano, Guitar (acoustic/electric), Drums, Bass, Synthesizer, Strings,
Brass, Woodwinds, Percussion, Violin, Cello, Saxophone, Trumpet, Flute
Lyrics Generation with Llama-Song-Stream-3B
AceSteps uses two open-source AI models working together:
┌─────────────────────────────────────────────────────────────────────────────┐
│ DUAL-MODEL LYRICS PIPELINE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ User Prompt: "love song about summer nights by the ocean" │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ LLAMA-SONG-STREAM-3B (Lyrics Generation) │ │
│ │ │ │
│ │ • Fine-tuned Llama 3.2 3B model │ │
│ │ • 57.7k lyrical training examples │ │
│ │ • Maintains rhyme, meter, thematic consistency │ │
│ │ • Apache 2.0 license │ │
│ │ │ │
│ │ Output: "Verse 1: Walking down the sandy shore..." │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ │ Generated lyrics + original prompt │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ ACE-STEP 3.5B (Music + Vocal Synthesis) │ │
│ │ │ │
│ │ • Combines lyrics with musical generation │ │
│ │ • Synthesizes vocals matching melody │ │
│ │ • Aligns lyrics to beat and rhythm │ │
│ │ │ │
│ │ Output: Complete song with vocals │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Final Audio: Love song with AI vocals and lyrics │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Llama-Song-Stream-3B Specifications
| Specification | Value |
|---|---|
| Model Name | Llama-Song-Stream-3B-Instruct |
| Base Model | Meta Llama 3.2 3B |
| Parameters | 3 billion |
| Training Data | 57.7k lyrical examples |
| License | Apache 2.0 |
| Developer | prithivMLmods |
Lyrics Generation Capabilities
| Feature | Description |
|---|---|
| Genre Awareness | Pop, rock, rap, R&B, country, classical, etc. |
| Rhyme Schemes | ABAB, AABB, free verse, and more |
| Song Structure | Verse, chorus, bridge, pre-chorus, outro |
| Thematic Control | Love, heartbreak, party, motivation, storytelling |
| Multilingual | Best in English, supports other languages |
Example Usage
Input Theme: "motivational workout anthem"
Genre: "EDM / electronic"
Generated Lyrics:
[Verse 1]
Push through the fire, feel the burn ignite
Every rep is power, every step is right
No more excuses, leave them all behind
Champions are built one rep at a time
[Chorus]
Rise up, rise up, we're unstoppable tonight
Rise up, rise up, reaching for the light...
:::tip Best Results For optimal lyrics generation:
- Specify genre and mood clearly
- Mention desired song structure (verse/chorus)
- Include thematic keywords
- Use Tier 1 languages (English, Chinese, Japanese, Korean) :::
Resources
Advanced Generation Modes
| Mode | Description | Use Case |
|---|---|---|
| Variations | Generate alternatives from same prompt | Explore different interpretations |
| Repainting | Regenerate specific sections | Fix parts you don't like |
| Lyric Editing | Modify lyrics while keeping melody | Adjust words post-generation |
| Extend | Continue an existing generation | Create longer compositions |
Architecture Overview
ACE-Step uses a novel architecture that combines the best of diffusion models and transformers:
┌────────────────────────────────────────────────────────────────────────┐
│ ACE-STEP GENERATION PIPELINE │
├────────────────────────────────────────────────────────────────────────┤
│ │
│ USER PROMPT │
│ "chill lofi beat with rain sounds and soft piano" │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ TEXT ENCODER (T5) │ │
│ │ Converts natural language to semantic embeddings │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │ │
│ │ Text Embeddings │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ LINEAR TRANSFORMER + DiT BLOCKS │ │
│ │ │ │
│ │ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ │
│ │ │ DiT │───▶│ DiT │───▶│ DiT │──▶ ... │ │
│ │ │ Block 1 │ │ Block 2 │ │ Block 3 │ │ │
│ │ └───────────┘ └───────────┘ └───────────┘ │ │
│ │ ▲ ▲ ▲ │ │
│ │ │ │ │ │ │
│ │ ┌─────┴────────────────┴────────────────┴─────┐ │ │
│ │ │ TIMESTEP EMBEDDINGS │ │ │
│ │ │ (Diffusion step conditioning) │ │ │
│ │ └─────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │ │
│ │ Latent Representations │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ DCAE DECODER │ │
│ │ Deep Compression AutoEncoder (from Sana) │ │
│ │ Converts latent space → high-fidelity audio waveform │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ AUDIO OUTPUT │ │
│ │ 44.1kHz • Stereo • 320kbps MP3 │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────────────┘
Key Components
| Component | Purpose | Innovation |
|---|---|---|
| DCAE | Audio encoding/decoding | Deep compression preserves acoustic details |
| Linear Transformer | Sequence modeling | Lightweight, efficient attention |
| REPA | Semantic alignment | MERT + m-hubert for faster convergence |
| Flow-Matching | Generation process | Faster than pure diffusion |
For detailed architecture information, see AI Architecture Deep-Dive.
AceSteps Integration
Generation Flow
┌──────────────────────────────────────────────────────────────────────┐
│ ACESTEPS GENERATION FLOW │
├──────────────────────────────────────────────────────────────────────┤
│ │
│ CREATOR ACESTEPS BLOCKCHAIN │
│ │ │ │ │
│ │ 1. Enter prompt │ │ │
│ │─────────────────────────▶│ │ │
│ │ │ │ │
│ │ 2. Validate & queue │ │
│ │ │ │ │
│ │ 3. GPU inference │ │
│ │ (Modal A10G) │ │
│ │ │ │ │
│ │ 4. Stream preview │ │ │
│ │◀─────────────────────────│ │ │
│ │ │ │ │
│ │ 5. Click "Save" │ │ │
│ │─────────────────────────▶│ │ │
│ │ │ │ │
│ │ 6. Generate signature │ │
│ │ (ECDSA sign) │ │
│ │ │ │ │
│ │ │ 7. mint(signature) │ │
│ │ │───────────────────────────▶│ │
│ │ │ │ │
│ │ │ 8. Verify & mint NFT │ │
│ │ │◀───────────────────────────│ │
│ │ │ │ │
│ │ 9. NFT in wallet! │ │ │
│ │◀─────────────────────────│ │ │
│ │
└──────────────────────────────────────────────────────────────────────┘
Signature Verification
Only AI-generated music can be minted on AceSteps. This is enforced through cryptographic signatures:
- Generation - Backend runs ACE-Step, stores
audioHash - Signing - Backend signs
hash(userAddress + metadataURI + audioHash) - Minting - Smart contract verifies signature via ECDSA
- Security - Prevents uploading copyrighted or non-AI content
For more details, see Backend Integration.
Prompt Engineering Tips
Effective Prompts
| Element | Good Example | Poor Example |
|---|---|---|
| Genre | "lo-fi hip-hop beat" | "good music" |
| Mood | "melancholic, nostalgic" | "sad" |
| Instruments | "soft piano, vinyl crackle, muted drums" | "instruments" |
| Tempo | "slow, 70 BPM" | (not specified) |
| Style Reference | "Nujabes-inspired jazz hop" | "like that one song" |
Prompt Structure
[genre] + [mood/atmosphere] + [instruments] + [additional details]
Example:
"ambient electronic music with ethereal pads, gentle arpeggios,
and atmospheric textures, peaceful and meditative, space-themed"
For comprehensive prompt guidance, see Prompts Guide.
Limitations
Current Constraints
| Limitation | Details | Workaround |
|---|---|---|
| Duration | Max 240 seconds | Use "extend" for longer pieces |
| Seed Sensitivity | Same prompt can yield different results | Save seeds you like |
| Niche Genres | Some genres underperform | Combine with well-supported genres |
| Vocal Nuance | Vocals can sound artificial | Focus on instrumental or simple vocals |
| Long Coherence | Structure may drift >3 min | Keep generations under 2 minutes |
Not Supported
:::warning Intentionally Excluded These features are by design not supported to ensure copyright compliance: :::
| Feature | Reason |
|---|---|
| Artist Imitation | Copyright and likeness rights |
| Song Recreation | Direct copyright infringement |
| Voice Cloning | Privacy and consent concerns |
| Cover Songs | Requires licensing |
Comparison with Other Models
| Feature | ACE-Step | Suno | Udio | MusicGen |
|---|---|---|---|---|
| Open Source | Yes | No | No | Yes |
| Commercial License | Apache 2.0 | Proprietary | Proprietary | CC-BY-NC |
| NFT Minting Legal | Yes | Unclear | Unclear | No (NC) |
| Max Duration | 4 min | 4 min | 2 min | 30s |
| Speed (1 min) | ~3s | ~30s | ~60s | ~10s |
| Self-Hostable | Yes | No | No | Yes |
| Fine-tuning | LoRA | No | No | Limited |
Resources
Official Links
- ACE-Step GitHub - Source code and documentation
- Hugging Face Model - Model weights
- Project Page - Demos and examples
- Research Paper - Technical details
Citation
@misc{gong2025acestep,
title={ACE-Step: A Step Towards Music Generation Foundation Model},
author={Junmin Gong and Wenxiao Zhao and Sen Wang and Shengyuan Xu and Jing Guo},
howpublished={\url{https://github.com/ace-step/ACE-Step}},
year={2025}
}
Related
- AI Architecture Deep-Dive - Technical architecture details
- Backend Integration - Infrastructure and API
- Custom Models & LoRA - Fine-tuning guide
- Prompts Guide - Master prompt engineering
- API Integration - Developer API reference