ComfyUI-VoxCPM

ComfyUI-VoxCPM is a full-featured ComfyUI custom node for VoxCPM, with TTS, voice cloning, and in-node LoRA training support.

It wraps VoxCPM into ComfyUI’s visual node-based workflow, allowing users to integrate speech synthesis into their generation pipelines.

Note

For a lighter alternative with auto-transcription, see ComfyUI-VoxCPMTTS.

Supported VoxCPM Versions

VoxCPM 1.0 (0.5B)

✅ Supported (openbmb/VoxCPM-0.5B, 16 kHz)

VoxCPM 1.5

✅ Supported (openbmb/VoxCPM1.5, 44.1 kHz)

VoxCPM 2

❌ Not supported

Features

  • Supports both VoxCPM 1.5 (44.1 kHz, ~800M params) and VoxCPM-0.5B (16 kHz, ~640M params)

  • Auto-download models to ComfyUI/models/tts/VoxCPM/

  • LoRA inference and training directly in ComfyUI:

    • VoxCPM Train Config — configure training hyperparameters

    • VoxCPM Dataset Maker — prepare fine-tuning data

    • VoxCPM LoRA Trainer — run LoRA fine-tuning within ComfyUI

  • All generation parameters exposed as node inputs

  • Built-in denoiser (ZipEnhancer) disabled by default to keep dependencies light

  • Multi-device support: CUDA, CPU, MPS (Apple Silicon), DirectML, HIP (AMD ROCm, if available)

Prerequisites

Installation

  • Via ComfyUI Manager: search for ComfyUI-VoxCPM and install.

Manual installation:

cd ComfyUI/custom_nodes/
git clone https://github.com/wildminder/ComfyUI-VoxCPM.git
pip install -r ComfyUI-VoxCPM/requirements.txt
# Restart ComfyUI

Models are auto-downloaded on first use.

Basic usage

Text-to-Speech

  1. Add a VoxCPM TTS node from the audio/tts category

  2. Connect text input with the target text to synthesize

  3. Adjust cfg_value, inference_timesteps, and device as needed

  4. Connect output to a Save Audio or Preview Audio node

Voice cloning

  1. Add a Load Audio node and load your reference audio

  2. Connect it to the prompt_audio input of the VoxCPM TTS node

  3. Provide the verbatim transcript in prompt_text (not a description — must match the reference audio exactly)

  4. Enter the target text to synthesize in text

Tip

For high-quality voice clones, use clear reference audio (5–15 seconds) and ensure prompt_text is an accurate transcript.

LoRA fine-tuning

  1. Add VoxCPM Train Config → VoxCPM Dataset Maker → VoxCPM LoRA Trainer nodes

  2. Configure your training data and hyperparameters

  3. Run the workflow to train a LoRA adapter

  4. Load the trained LoRA from models/loras (use the refresh button and lora_name dropdown)

See readme-lora-training.md in the repository for detailed training instructions.

Parameters

Parameter

Description

model_name

Select VoxCPM 1.5 or 0.5B

cfg_value

Classifier-free guidance scale (default: 2.0; higher = closer to prompt, may reduce quality)

inference_timesteps

LocDiT diffusion steps (default: 10; higher = better quality, slower)

device

cuda / cpu / mps / directml / hip (varies by hardware)

normalize_text

Enable external text normalization (disable for phoneme input)

seed

Random seed for reproducibility (default: -1 for random)

lora_name

Select a LoRA adapter from models/loras

min_tokens / max_tokens

Token length range for generation

force_offload

Offload model from GPU after inference to save VRAM

retry_max_attempts

Maximum retries for bad generation outputs

retry_threshold

Threshold for bad-case detection

Troubleshooting

Out of memory

  • Set force_offload to True to release GPU memory after each generation

  • Switch to cpu device (slower but uses system RAM)

  • Use VoxCPM-0.5B instead of 1.5 for lower memory usage