Music Flamingo Analysis

Stop describing tracks by hand

This is the closest ComfyUI has to "JoyCaption, but for music." You drop in a song, and out comes a paragraph - genre, tempo, key, instrumentation, song structure. Behind it is NVIDIA's Music Flamingo, a real audio-captioning model (Audio Flamingo 3) that got wrapped up as a single, dead-simple ComfyUI node. One audio input, one text output, nothing else in the way. If you've ever wanted to generate a Suno prompt from a reference track, caption a pile of music for a training set, or just get a second opinion on what a song actually is, this is the node you reach for.

The author, C0untFloyd (CountFloyd_ on Reddit), built it after asking r/StableDiffusion for a local "joycaption for audio" and then announcing it as vibe-coded. Take that framing seriously: it's a thin but genuinely working wrapper around a strong model, not a polished ecosystem. It does one thing, and it does it fine.

How it works

Mechanically it's a chat model wearing an audio hat. The node takes the standard Comfy AUDIO dict from a plain Load Audio node (a waveform tensor plus a sample rate), writes it to a temporary WAV file with torchaudio, then builds a chat-template conversation containing your prompt text and that audio file. AutoProcessor tokenizes it, and AudioFlamingo3ForConditionalGeneration generates a response token by token - which is why a full song takes minutes, not milliseconds. Only the generated continuation is decoded and returned.

Two nice touches buried in the source: it loads in bfloat16 when your GPU supports it (falling back to fp16, then fp32), and it wires a real ComfyUI progress bar plus the Stop button into generation via a custom stopping criterion. So at least it's honest about how long it's taking.

The inputs and output that matter

Only four inputs, and you'll mostly touch two:

• audio - the AUDIO output of a Load Audio node. Single clip only; the code rejects anything that isn't shape [1, C, T], so don't try to batch.
• prompt - the question you're asking the model. The default asks for genre, tempo, key, instruments, and structure, and it's a sensible starting point. This is where you tune the output: ask for "a Suno-style prompt" or "production notes" and you'll get very different answers.
• max_new_tokens - 512 by default, up to 1024. Raise it if the description cuts off mid-thought.
• device - gpu or cpu. Default gpu; CPU loads fp32 and crawls. Only use it to prove the model works on a cardless box.

The single output, description, is a plain STRING - wire it into a Show Text node to read it, or feed it onward if you're building prompts or logging captions.

Installing it

ComfyUI Manager (search "comfyui-musicflamingo"), or the manual way:

cd ComfyUI/custom_nodes
git clone https://github.com/C0untFloyd/comfyui-musicflamingo

Then restart ComfyUI. The catch is the first run: it downloads the NVIDIA weights, roughly 16 GB, and stashes them in ComfyUI/models/checkpoints/musicflamingo - not the standard Hugging Face cache, so it'll show up in your checkpoints folder. Go make a coffee.

Where people get burned

The real trap is the dependency: the pack requires transformers>=5.0.0, a very new release, because it imports AudioFlamingo3 straight out of transformers.models.audioflamingo3. If any of your other custom nodes pin an older transformers, that upgrade can silently break them - the classic Comfy dependency hell where fixing one node breaks two others. Worth knowing before you click install in Manager.

After that, set expectations: ~16 GB of disk, minutes per full-length track (the author quotes about five minutes on a 5060 Ti), and no batching. It's a captioning LLM for audio, not a fingerprinting tool. For anything real, use a GPU and give the prompt a moment to do its job.