SAsanbor·Oct 14, 2026Submission
Ace-Step 1.5 Open-Source Music Generation Model
3 points · Open discussionace-step
ACE-Step-1.5
Python2026–2026
ace-step / ACE-Step-1.5
ACE-Step-1.5
The most powerful local music generation model that outperforms almost all commercial alternatives, supporting Mac, AMD, Intel, and CUDA devices.
5Distinct people
8Mentions
0Explicit recommenders
14.7KGitHub stars
What to know before exploring
What it is
The most powerful local music generation model that outperforms almost all commercial alternatives, supporting Mac, AMD, Intel, and CUDA devices.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month8 dated mentions
Observed mentions: 2026-02-03 17:20:21 UTC to 2026-10-14 07:40:01 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 7 | 3 | 4 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2026 | 2026-08-01 00:00:00 UTC | 2026-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2026 | 2026-09-01 00:00:00 UTC | 2026-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2026 | 2026-10-01 00:00:00 UTC | 2026-11-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
8 sourcesAce step 1.5 is great, only 1.5b params so very easy to run locally.
https://github.com/ace-step/ACE-Step-1.5
In Claude Sonnet 4.6Ace-Step 1.5 prompt tips: how I get more controllable music output
2 points · Open discussionI’ve been experimenting with ACE-Step 1.5 lately and wanted to share a short summary of what actually helped me get more controllable and musical results, based on the official tutorial + hands-on testing.
The biggest realization: ACE-Step works best when you treat prompts as [structured inputs], not a single sentence (same as other LLMs)
1. Separate “Tags” from “Lyrics”
Instead of writing one long prompt, think in two layers:
Tags = global control
Use comma-separated keywords to define:
- genre / vibe (funk, pop, disco)
- tempo (112 bpm, up-tempo)
- instruments (slap bass, drum machine)
- vocal type (male vocals, clean, rhythmic)
- era / production feel (80s style, punchy, dry mix)
Being specific here matters a lot more than being poetic.
2. Use structured lyrics
Lyrics aren’t just text — section labels help a ton:
[intro]
[verse]
[chor
Read the rest
us]
[bridge]
[outro]
Even very simple lines work better when the structure is clear. It pushes the model toward “song form” instead of a continuous loop.
3. Think rhythm, not prose
Short phrases, repetition, and percussive wording generate more stable results than long sentences. Treat vocals like part of the groove.
4. Iterate with small changes
If something feels off:
- tweak tags first (tempo / mood / instruments)
- then adjust one lyric section
No need to rewrite everything each run.
5. LoRA + prompt synergy
LoRAs help with style, but prompts still control:
- structure
- groove
- energy
Over-strong LoRA weights can easily push outputs into parody.
Overall, ACE-Step feels less like “text-to-music” and more like music-conditioned generation. Once you start thinking in tags + structure, results get much more predictable.
Curious how others here are prompting ACE-Step — especially for groove-based music.
resource：https://github.com/ace-step/ACE-Step-1.5
Github https://github.com/ace-step/ACE-Step-1.5
HF https://huggingface.co/collections/ACE-Step/ace-step-15
Paper https://arxiv.org/abs/2602.00744
Demo https://huggingface.co/spaces/ACE-Step/Ace-Step-v1.5
In Released: Ace-Step 1.5: Pushing the Boundaries of Open-Source Music GenerationSHOW HN:AceStep1.5an on-device music model that beats Suno on common eval metric
6 points · Open discussionHi HN! We released ACE-Step 1.5, an open-sourced AI music model.
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.
In SHOW HN:AceStep1.5an on-device music model that beats Suno on common eval metric