ACE-Step Text to Music

This is the flagship node of the Kaola ACE-Step pack and the one you'll reach for first. You type a description, it generates a full song - vocals, instruments, structure - as an audio file on your disk. No API, no key, no cloud round-trip: the whole thing runs on your GPU. People are already posting 10-song test runs of ACE-Step 1.5 on r/StableDiffusion and it holds up for a local generator. If you've been waiting for a serious local alternative to the hosted music services, this is it.

ACE-Step 1.5 is a diffusion-transformer music model with an attached language model. The DiT renders the audio; the language model (lm_model_path) writes the lyrics and metadata. Keep thinking on and the LLM invents the lyrics for you; turn it off when you're supplying your own and want a faster, more deterministic pass. On the default acestep-v15-turbo config it runs in about 8 diffusion steps - that's the distilled/few-step variant, and it's the same "turbo = fast but slightly less refined than the 50-step base" tradeoff you already know from image models.

The inputs that matter

The only thing you actually must type is caption - and specificity is the whole game. "Upbeat electronic dance music with heavy bass and synthesizer leads" gets you somewhere; "good music" gets you a generic blob. Then the useful knobs:

• duration - seconds, 10 to 600. Start at 30.
• inference_steps - 8 for turbo. That's the sweet spot, not a suggestion.
• lyrics - leave empty for auto-generation, or paste your own (use [Verse 1]/[Chorus] style section markers).
• bpm, keyscale, timesignature - 0/empty means auto-detect.
• vocal_language - auto/unknown for the model to guess, or force en, zh, ja, ko, etc.
• instrumental - true for no vocals.
• batch_size - generate 2-8 variations in one go; each comes back with its real seed in metadata.
• guidance_scale and shift - prompt adherence and sequence-length scaling. Defaults are fine until you're tuning.

Outputs: audio (into ComfyUI's Save Audio), audio_path, and metadata - a JSON string showing what was actually used (caption, bpm, duration, seed). Read it once; it teaches you how the model interpreted your prompt.

Install

The pack wraps ACE-Step 1.5, so there are two installs plus a model download:

git clone https://github.com/ACE-Step/ACE-Step-1.5.git
cd ACE-Step-1.5 && pip install -e .
cd ComfyUI/custom_nodes
git clone https://github.com/kana112233/ComfyUI-kaola-ace-step.git
cd ComfyUI-kaola-ace-step && pip install -r requirements.txt
huggingface-cli download ACE-Step/Ace-Step1.5 --local-dir ComfyUI/models/Ace-Step1.5

ComfyUI Manager installs just the node pack ("ComfyUI-kaola-ace-step"); the ACE-Step package and the ~8GB of models are manual. The checkpoint_dir dropdown lists folders under ComfyUI/models/, so keep the acestep-v15-turbo/ and acestep-5Hz-lm-1.7B/ subfolders inside Ace-Step1.5/. Then restart ComfyUI.

Where people get burned

• "Model path not found" - the models aren't in ComfyUI/models/Ace-Step1.5/ with the right subfolder names. That error is almost always a download/layout problem, not a code problem.
• OOM - drop batch_size to 1 and use the smaller acestep-5Hz-lm-0.6B language model. README's own advice for <8GB VRAM: batch 1, small LM, thinking off.
• Doesn't match your prompt - get more specific, and remember the turbo model is a fast impression, not a faithful rendering. Switch to acestep-v15-base with 32-64 steps when a track matters.

A last note on quantization/compile_model: int8_weight_only cuts VRAM but needs torchao installed, compile_model on, and it won't work with LoRA. Only reach for it when you're actually bumping the VRAM ceiling.