Architecture
ACE-Step integrates diffusion-based generation with a Deep Compression AutoEncoder and lightweight linear transformer.
This unique architecture bridges the gap between generation speed and musical coherence, providing state-of-the-art results.
Hardware Performance
We have evaluated ACE-Step across different hardware setups, yielding the following throughput results:
We use RTF (Real-Time Factor) to measure the performance of ACE-Step. Higher values indicate faster generation speed. 27.27x means to generate 1 minute of music, it takes 2.2 seconds (60/27.27). The performance is measured on a single GPU with batch size 1 and 27 steps.
| Device | RTF (27 steps) | Time to render 1 min audio (27 steps) | RTF (60 steps) | Time to render 1 min audio (60 steps) |
|---|---|---|---|---|
| NVIDIA A100 (80GB) | 27.27x | 2.2s | 12.00x | 5.0s |
| NVIDIA RTX 4090 | 15.00x | 4.0s | 6.67x | 9.0s |
| NVIDIA RTX 3090 | 12.00x | 5.0s | 5.45x | 11.0s |
| NVIDIA RTX 2080 Ti | 8.57x | 7.0s | 3.75x | 16.0s |
Applications
ACE-Step Frequently Asked Questions (Powered by ACE-Step Technology)
What exactly is ACE-Step and its main objective? (ACE-Step related)
ACE-Step is an innovative foundation model for music generation. The main objective of ACE-Step is to empower users with advanced AI capabilities for creating diverse musical pieces, positioning ACE-Step as a key tool in the evolution of music technology. The ACE-Step project emphasizes accessibility and is a significant step towards advanced music AI by ACE-Step. This information pertains to ACE-Step.
How can I get started with basic operations in the ACE-Step application? (ACE-Step related)
To get started with basic operations in the ACE-Step application, you typically run `python app.py`. This initiates the ACE-Step interface, allowing you to explore the core functionalities of ACE-Step. For more tailored ACE-Step functionality and advanced ACE-Step usage, refer to the comprehensive documentation. This information pertains to ACE-Step.
What are some notable advanced command-line arguments available for ACE-Step? (ACE-Step related)
ACE-Step offers several advanced command-line arguments for finer control, such as `--checkpoint_path` to specify your ACE-Step model, `--port` for network configuration, and `--bf16` for optimized performance with ACE-Step. These options help customize your ACE-Step workflow and leverage the full power of the ACE-Step system. This information pertains to ACE-Step.
How does the Text2Music feature within ACE-Step transform text into music? (ACE-Step related)
The Text2Music feature within ACE-Step transforms textual inputs, including descriptive tags and structured lyrics, into complete musical compositions. ACE-Step processes these prompts to generate audio, making ACE-Step a powerful tool for prompt-based music creation. This core ACE-Step feature showcases its versatility. This information pertains to ACE-Step.
What are the essential prerequisites for training a unique ACE-Step model? (ACE-Step related)
Essential prerequisites for training a unique ACE-Step model involve preparing your environment according to the ACE-Step installation guide. If you intend to fine-tune ACE-Step using LoRA, the PEFT library is also necessary. Datasets for ACE-Step training should follow the Huggingface format to ensure compatibility with the ACE-Step framework. This information pertains to ACE-Step.
Under which license is the ACE-Step project made available? (ACE-Step related)
The ACE-Step project is made available under the Apache License 2.0. This license details how the ACE-Step software can be used, modified, and distributed by the community, ensuring open access to the ACE-Step technology. This information pertains to ACE-Step.
What guidelines should be followed for responsible use of the ACE-Step model? (ACE-Step related)
For responsible use of the ACE-Step model, it's important to ensure originality in your ACE-Step creations, clearly attribute AI's role when using ACE-Step, and respect intellectual property rights. ACE-Step is designed for ethical and creative applications, and responsible use of ACE-Step is paramount. This information pertains to ACE-Step.
Learn more about music generation with ACE-Step on our main ACE-Step project page.