ACE-Step

ACE-Step: A Step Towards Music Generation Foundation Model ACE-Step is a novel open-source foundation model for music generation that overcomes key limitations of existing approaches through a holistic architectural design. It integrates diffusion-based generation with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer, achieving state-of-the-art performance in generation speed, musical coherence, and controllability. Key Features: - 15× faster than LLM-based baselines (20s for 4-minute music on A100) - Superior musical coherence across melody, harmony, and rhythm - full-song generation, duration control and accepts natural language descriptions Direct Use ACE-Step can be used for: - Generating original music from text descriptions - Music remixing and style transfer - edit song lyrics Downstream Use The model serves as a foundation for: - Voice cloning applications - Specialized music generation (rap, jazz, etc.) - Music production tools - Creative AI assistants Out-of-Scope Use The model should not be used for: - Generating copyrighted content without permission - Creating harmful or offensive content - Misrepresenting AI-generated music as human-created | Device | 27 Steps | 60 Steps | |---------------|----------|----------| | NVIDIA A100 | 27.27x | 12.27x | | RTX 4090 | 34.48x | 15.63x | | RTX 3090 | 12.76x | 6.48x | | M2 Max | 2.27x | 1.03x | RTF (Real-Time Factor) shown - higher values indicate faster generation - Performance varies by language (top 10 languages perform best) - Longer generations (>5 minutes) may lose structural coherence - Rare instruments may not render perfectly - Output Inconsistency: Highly sensitive to random seeds and input duration, leading to varied "gacha-style" results. - Style-specific Weaknesses: Underperforms on certain genres (e.g. Chinese rap/zhrap) Limited style adherence and musicality ceiling - Continuity Artifacts: Unnatural transitions in repainting/extend operations - Vocal Quality: Coarse vocal synthesis lacking nuance - Control Granularity: Needs finer-grained musical parameter control Users should: - Verify originality of generated works - Disclose AI involvement - Respect cultural elements and copyrights - Avoid harmful content generation Developed by: ACE Studio and StepFun Model type: Diffusion-based music generation with transformer conditioning License: Apache 2.0 Resources: - Project Page - Demo Space - GitHub Repository Acknowledgements This project is co-led by ACE Studio and StepFun.