Key traits of ACE-Step 1.5: Quality: beats Suno on common eval scores Speed: full song under 2s on A100 Local: ~4GB VRAM, under 10s on RTX 3090 LoRA: train your own style with a few songs License: MIT, free for commercial use Data: fully authorized plus synthetic
GitHub: https://github.com/ace-step/ACE-Step-1.5
Weights/Training code/LoRA code/Paper are all open.
Closed-source commercial models dominate AI music today, tying creators to a single app and model. If access disappears, or the model changes, your creative power can vanish overnight.
ACE-Step 1.5 breaks that lock-in with a competitive open-source alternative: run locally, own it, fine-tune with your songs, and reduce privacy/data-leak risk.