Is the model only capable of generating songs with random melodies when trying to turn your own musical idea into a complete track?

[bartmobil]

I have tried inference both through the UI and through ComfyUI, but I still cannot figure out what seems like a very basic use case.
I have a melody that I created myself (either as a MIDI file or rendered to WAV), and I would like the model to generate a complete song based on that melody. The exact style is not important — I simply want the generated track to preserve and build upon the melody I provide.
I have read various guides and discussions, but I still cannot get anywhere close to this goal.
The Cover feature does not seem suitable for this purpose. In my experiments it either generates a completely different song, or it reproduces the original melody almost exactly, without turning it into a new composition built around my musical idea.
Am I misunderstanding the intended workflow?
If there is a way to achieve this, I would be very grateful if someone could point me in the right direction.

Thank you.

[FurkanGozukara]

see this tutorial you will understand : https://youtu.be/9C_6qNKjgpA

Video Chapters:
0:00 Intro: ACESTEP XL 1.5 Premium local music, segmentation and processing tutorial
0:52 Fast song generation examples across styles in under one minute
1:55 Output manifest proof, 40-second generation time and supported models
2:29 Turbo/SFT/Base models, LoRA support, GPU presets and Torch Compile boost
3:10 Remix feature preview, same-lyrics requirement and responsible usage note
4:16 Repaint mode: regenerate and merge only a selected song section
5:38 Extract mode: stems, silence trimming, all-stems and batch folders
6:30 Lego mode: add an instrument stem such as guitar into existing audio
7:25 Audio Processing presets and manual enhancement controls for AI songs
8:35 Auto-Editor silent trim for tutorials, videos, audio and workflow export
9:48 DaVinci/Premiere/Final Cut/ShotCut/Kdenlive timeline export demo
11:01 SAM Audio Segment: BF16 models, VRAM presets and advanced segmentation
11:47 SAM outputs demo: vocals, drums, bass, remaining audio and saved files
12:47 Custom SAM prompts, semicolon batch segmenting and speech cleanup example
14:19 Batch processing, load metadata, manifests, saved settings and presets
15:09 Why local open-source models matter and where to run ACESTEP
15:55 Windows install begins: Patreon zip, changelog, attachments and download
16:53 Windows requirements tutorial before Python/CUDA/C++/FFmpeg setup
17:29 Extract zip safely, avoid bad paths and run Windows_Install_or_Update.bat
18:24 Automatic VENV, FFmpeg, UV install, model downloads and hash verification
19:24 Turbo default vs all-model download for SFT/Base and BF16 safetensors
20:32 First Windows launch, default Generate Song test and CMD progress
21:44 Model recommendations, VRAM tiers, languages, vocals and MP4 image output
23:29 Torch Compile setup for faster repeated generations
24:05 Outputs folder, model switching and full remix setup workflow
25:24 Practical remix loop: adapted lyrics, strength, reference audio and seed lock
28:03 Repaint workflow with source range preview, generated result and comparison
29:13 Recap: extraction, Lego, audio processing and SAM text-prompt usage
30:20 Windows wrap-up, LoRA training teaser and move to cloud installs
31:16 RunPod setup: credits, template, CUDA filters, GPU choice and storage
34:53 Upload zip in Jupyter Lab, extract, run instructions and handle installs
35:43 RunPod errors, resume behavior, model downloads and hash verification
38:04 Start ACESTEP on RunPod with Gradio Live, proxy ports and persistence
40:18 Add 7860/7861 ports, verify storage reuse and rerun installer after resume
42:10 RunPod connection troubleshooting and Gradio Live recommendation
44:12 Fix corrupted VENV/stale handle errors, reinstall safely and retest
47:24 Successful RunPod relaunch, default generation, nvitop and loading tips
49:26 RunPod first load vs fast inference, 15-second second generation example
51:02 Download outputs and delete RunPod pods/storage to stop spending
53:30 Massed Compute setup: coupon, Creator image, GPU prices and ThinLinc
57:13 Massed install from extracted folder, Linux notes and ultra-fast downloads
59:18 Start app on Massed Compute via localhost or Gradio Live
1:00:23 Default Massed generation, nvitop, faster loading and speed test
1:02:03 Sync/download outputs and delete Massed Compute instance safely
1:03:25 SimplePod setup: template, persistent volume, pricing and GPU choice
1:06:39 Jupyter upload, direct file browser, install command and model downloads
1:08:21 Start SimplePod, Gradio Live, default generation and one-time load errors
1:09:31 nvitop monitoring, newer driver/CUDA details and generation completion
1:10:42 Direct output/model downloads through SimplePod file browser
1:11:42 Delete instance, keep storage, relaunch GPU and verify install
1:13:15 Discord, subreddit, changelog, update guidance and support links
1:14:30 Final cleanup: terminate servers, delete storage and LoRA training outro

[JohnGWS]

This is exactly my experience with attempting covers. My understanding is that the model is not yet able to interpolate styles into your original demo, thereby creating a proper cover. You either get a virtual 'clone' of your original track, or a very different version using just your lyrics, none of your melody. This is very far from what a commercial system like Suno can produce. My suggestion is to wait for the next version of the model, they may have been working on this aspect since they have made so much progress between Ace Step 1 and 1.5.

[lbryant2004-create]

Probably not it's use-case but creating a LoRA by duplicating a melody created at least 8 times in files then training for about 40 epochs might do the trick, but you would then need to set the weight of the LoRA during inference time to around 0.4 because the LoRA will only remember the instruments and/or the vocals it was trained on. Lessening the strength of the LoRA can then produce vocals / other tracks using something close to the melody you provided, if you strengthen the weights to above 0.6 then all you'll get is the exact data your LoRA was trained on because the LoRA is overfit, it can't create anything new because there wasn't enough data. But it is more deterministic than what you'd otherwise get from the builtin cover / repaint. Another approach that worked reasonably well for me was repaint / cover mode with the XL-Turbo model, however the produced vocals were a bit difficult to understand, things seem to go a bit nicer with a deterministic LoRA, the vocals seem to come through more clearer.

[lbryant2004-create]

Ok so I took my own advice and it's not as easy as I suggested, especially if the song you're training on is not in a style known by ACE-Step-1.5. I am trying it now to see if I can at least get one overfit checkpoint and then reduce the LoRA weights at inference time, down to maybe 0.4 to see what is generated, then maybe generate some stuff with prior checkpoints to find the one that's the most creative. This worked for me very well when I gave it all drums trained at 40 epochs, leaving the weight at 1 when inferencing allowed me to have a very creative drummer without other instruments, but if I lowered the LoRA down to 0.4 during generations, I get my style of drummer with other instruments and even vocals. If interested, I'll keep you all posted. I really do enjoy creating LoRAS.