back to top
HomeTechAI ModelsHeartMuLa: An Open-Source Suno-Style AI Music Generator You Can Run Locally with...

HeartMuLa: An Open-Source Suno-Style AI Music Generator You Can Run Locally with ComfyUI

- Advertisement -

If you’ve been playing with AI music tools lately, here’s some genuinely good news.

Heartmula has released an open-source AI music foundation model that’s surprisingly close to what tools like Suno AI can do but with a very different philosophy. It gives you something many creators actually want: full control.

With this model, you can generate music directly on your own PC, offline, with no usage limits. What you run, you own & once it’s set up, you can generate as much music as your hardware allows.

In this guide, I’ll show you exactly how to run Heartmula on your PC, step by step, without skipping the confusing parts.

Before we get into the setup, let’s quickly look at what this model can do and why it’s worth trying in the first place.

Demo of HeartMula

Below is the demo video of HeartMula music model showcasing some of its music generations in different styles & languages.

Features of HeartMula

FeatureWhat It DoesWhy It Matters
Open-Source (Apache 2.0)Fully open code and model weightsOpen-Source Freedom: No subscriptions
Suno-Style Music ScriptingSupports [Verse], [Chorus], [Bridge], etc.Structure Control: Custom songs generation
12.5 Hz HeartCodecUltra-efficient audio encoding & decodingHigh Fidelity: Pro-level sound on consumer GPUs
ComfyUI IntegrationVisual node-based workflowCreator Friendly: No scripts, easy experimentation
Full-Length Music OutputGenerates tracks up to ~6 minutesLong-Form Ready: Songs, not just short clips
Multilingual EngineSupports EN, ZH, JP, KR, ESGlobal Reach: Localized music & ads
Expressive Vocal ControlLyrics formatting affects vocal styleMore Emotion: Singing, spoken, and hybrid vocals
HeartTranscriptorWhisper-tuned audio-to-text modelSync-Ready: Lyrics, subtitles, karaoke
Local & Offline ExecutionRuns 100% on your PCData Sovereignty: Prompts never leave your system
VRAM-Optimized LoadingLazy loading + BF16 pipelineAccessible Power: Works on 12–16 GB GPUs

Before You Start

To keep things simple, this guide assumes you’re using ComfyUI’s portable Windows build. If you’re new to ComfyUI, this is the easiest and safest way to get started.

Recommended ComfyUI Version (Windows)

Why CU126?
It’s more widely compatible and tends to be more stable with custom nodes and AI audio models right now.

Minimum System Requirements

GPU: NVIDIA GPU

  • VRAM:
    • 12 GB minimum
    • 16 GB recommended (best audio quality)
  • Enough disk space for model downloads

If ComfyUI runs on your system, you’re good to continue.

Check if Hugging Face CLI Is Installed

HeartMuLa uses Hugging Face to download model files.

  1. Open Command Prompt or Terminal
  2. Navigate to your ComfyUI folder
  3. Run one of the following commands:
hf --help

or

huggingface-cli --help

What to expect:

  • If you see a list of commands → you’re ready
  • If you see command not found → install it

Install Hugging Face CLI (If Needed)

Run this inside the same Python environment ComfyUI uses:

pip install huggingface-hub

This ComfyUI workflow and custom node integration was created by Benji, and it’s an excellent contribution to the open-source community. His work makes it possible to run HeartMuLa directly inside ComfyUI with a clean, minimal workflow. We’ll use Benji’s HeartMuLa ComfyUI workflow to install and run HeartMuLa locally.

Step 1: Install HeartMuLa ComfyUI Custom Nodes

HeartMuLa uses custom nodes in ComfyUI for music generation and lyrics/audio transcription. Follow these steps:

  1. Open Command Prompt and navigate to your ComfyUI folder and in address bar, type cmd and hit enter then in command prompt type:
cd custom_nodes
  1. Download the custom nodes from GitHub:
git clone https://github.com/benjiyaya/HeartMuLa_ComfyUI
HeartMula Comfyui installation process
  1. Install the required Python dependencies:
  2. Stay in custom_nodes folder and run:
..\..\python_embeded\python.exe -m pip install -r .\HeartMuLa_ComfyUI\requirements.txt

This ensures all the libraries needed for HeartMuLa nodes are installed in your ComfyUI environment.

  1. Check that everything is ready
  • Start ComfyUI by simply double-clicking the file named: run_nvidia_gpu.bat
  • Look for messages confirming the custom nodes loaded successfully

File Structure

ComfyUI/custom_nodes/HeartMuLa_ComfyUI/
├── init.py <– The code provided below
├── util/ <– Create this folder
│ └── heartlib/ <– Paste the heartlib SOURCE CODE here
│ ├── init.py
│ ├── pipelines.py
│ ├── models.py
│ └── … (other python files)
└── requirements.txt (Optional: torch, transformers, torchaudio, etc.)

You’re now ready for Step 2

Step 2: Download the HeartMuLa Model Files.

HeartMuLa has multiple model components: the music generator, 3B model, codec, and transcriptor. We’ll use the Hugging Face CLI to download them directly into the correct folder.

1. Go to your ComfyUI models folder

HeartMula Comfyui installation

ComfyUI\models

2. Look for HeartMuLa folder, if it doesn’t exist, you can create it:

Create a folder namedHeartMuLa & don’t open it yet.

HeartMula Comfyui

3. Download the model files using Hugging Face CLI

Open Cmd & Run these commands one by one:

hf download HeartMuLa/HeartMuLaGen --local-dir ./HeartMuLa
hf download HeartMuLa/HeartMuLa-oss-3B --local-dir ./HeartMuLa/HeartMuLa-oss-3B
hf download HeartMuLa/HeartCodec-oss --local-dir ./HeartMuLa/HeartCodec-oss
hf download HeartMuLa/HeartTranscriptor-oss --local-dir ./HeartMuLa/HeartTranscriptor-oss

These commands will automatically place the files into the correct subfolders inside ComfyUI\models\HeartMuLa. Below is how the HeartMula folder should look like:

HeartMula Comfyui installation scr

Step 3: Verify the folder structure

ComfyUI
└── models
    └── HeartMuLa
        ├── HeartMuLa-oss-3B
        ├── HeartCodec-oss
        ├── HeartTranscriptor-oss
        └── gen_config.json
        └── tokenizer.json

This structure is required for the custom nodes to find the models correctly.


Tip

  • If your GPU has 12 GB VRAM, lazy loading will help manage memory.
  • The 7B model isn’t released yet — stick with 3B for now.

Also Read: Forget AI Videos Yume 1.5 Creates Interactive AI Worlds on Your PC

Step 4: Run Your First Music Generation in ComfyUI

music generation ai
  1. Run ComfyUI
  2. In the HeartMuLa custom nodes folder, you’ll find example workflows:
    • Generate Music.json → Music generation
    • Lyrics Transcriber.json → Audio-to-text transcription
  3. Drag & drop the workflow into ComfyUI.
  4. For music generation:
    • In the lyrics node, type your lyrics
    • Below it, type music styles as keywords/tags (piano,happy,wedding)
    • Adjust any settings you want and run → enjoy your generated song
  5. For lyrics transcription:
    • Import Lyrics Transcriber.json
    • Load any audio into the input node
    • Run → get a transcribed text output

That’s it! play around with the nodes, tweak lyrics or styles, and see what your AI can create!

Also Read: Run TRELLIS 2 Locally: Generate High-Quality 3D Models from Images

Need Help or Have Questions?

If you run into any issues, get stuck, or just want tips on better results, drop a comment below
I’ll do my best to help you out.

Wrapping Up

HeartMuLa brings Suno-style AI music generation fully offline, open-source, and ComfyUI-friendly. With portable ComfyUI, drag-and-drop workflows, and simple lyric + style inputs, you can go from idea to full track in minutes.

Install it once, experiment freely, tweak the settings, and let the model do the heavy lifting
If this guide helped you, try pushing the limits, different genres, structures, and languages.

Happy creating!!

Don’t miss any Tech Story

Subscribe To Firethering NewsLetter

You Can Unsubscribe Anytime! Read more in our privacy policy

LEAVE A REPLY

Please enter your comment!
Please enter your name here

YOU MAY ALSO LIKE
glm 5.2 ai open weights

GLM-5.2 Is the Closest an Open Model Has Come to Claude

0
What does it take for an open-weight model to stop chasing Claude and actually beat it? Every open-weight release for two years has told some version of the same story: closer, but not quite. The chart shrinks, the wording softens to "competitive with," and the conversation moves on until the next model repeats the cycle. GLM-5.2 breaks that pattern. The model is built to survive long, messy coding work, the kind that runs for hours without losing the thread. That's the pitch its maker is leading with. But scroll down their own benchmark table and something else is sitting there quietly: on a couple of standard math evals, this open model isn't approaching Claude Opus 4.8, GPT-5.5, or Gemini 3.1 Pro. It's beating all three, on the same table. It loses plenty of ground elsewhere, and that part matters just as much as the wins. But a model anyone can download under an MIT license, with no usage restrictions attached, coming out ahead of the lab everyone else measures themselves against, is worth pausing on before getting to what the rest of the numbers actually say.
Open-Source AI Tools Worth Trying Right Now

5 Open-Source AI Tools You Probably Haven’t Tried Yet

0
Every week brings another open source AI release, and most of them require setting up a Python environment. Find out the model card lied about VRAM requirements. By the time something actually runs, the appeal has mostly worn off. The five tools below skip most of that. One turns image and video generation into something closer to a desktop app. One gives DeepSeek an actual workspace instead of a browser tab. One builds UI prototypes using coding agents you probably already have installed. One quietly builds a memory system out of your own apps. And one is, literally, a desktop pet.
Claude Mythos 5 and Claude Fable 5

Claude Mythos 5 Was Too Powerful to Ship. Anthropic Released Fable 5 Instead.

0
Anthropic gave stripe early access to Fable 5 and set it loose on a 50 million line Ruby codebase. The migration that would have taken a full engineering team over two months got done in a day. That's a real company's real codebase and a task with real consequences if it goes wrong. Anthropic leads with it because it's the kind of result that's hard to argue with & because it sets up everything else they need to tell you about why this launch looks the way it does. Because here's the thing. The model Anthropic actually built Claude Mythos 5, isn't what most people are getting today. What's going live for general use is Claude Fable 5. Same underlying model. Different version. The parts Anthropic decided were too dangerous for public release got a separate wrapper, a separate name, and a separate approval process controlled in part by the US government.