VibeVoice ComfyUI Nodes

A comprehensive ComfyUI integration for Microsoft's VibeVoice text-to-speech model, enabling high-quality single and multi-speaker voice synthesis directly within your ComfyUI workflows.

Features

Core Functionality

• 🎤 Single Speaker TTS: Generate natural speech with optional voice cloning
• 👥 Multi-Speaker Conversations: Support for up to 4 distinct speakers
• 🎯 Voice Cloning: Clone voices from audio samples
• 📝 Text File Loading: Load scripts from text files

Model Options

• 🚀 Two Model Sizes: 1.5B (faster) and 7B (higher quality)
• 🔧 Flexible Configuration: Control temperature, sampling, and guidance scale

Performance & Optimization

• ⚡ Attention Mechanisms: Choose between auto, eager, sdpa, or flash_attention_2
• 🎛️ Diffusion Steps: Adjustable quality vs speed trade-off (default: 20)
• 💾 Memory Management: Toggle automatic VRAM cleanup after generation
• 🧹 Free Memory Node: Manual memory control for complex workflows

Video Demo

Click to watch the demo video

Installation

Automatic Installation (Recommended)

1. Clone this repository into your ComfyUI custom nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/Enemyx-net/VibeVoice-ComfyUI

2. Restart ComfyUI - the nodes will automatically install requirements on first use

Available Nodes

1. VibeVoice Load Text From File

Loads text content from files in ComfyUI's input/output/temp directories.

• Supported formats: .txt
• Output: Text string for TTS nodes

2. VibeVoice Single Speaker

Generates speech from text using a single voice.

• Text Input: Direct text or connection from Load Text node
• Models: VibeVoice-1.5B or VibeVoice-7B-Preview
• Voice Cloning: Optional audio input for voice cloning
• Parameters (in order):
  • text: Input text to convert to speech
  • model: VibeVoice-1.5B, VibeVoice-Large-Preview (deprecated), or VibeVoice-Large
  • attention_type: auto, eager, sdpa, or flash_attention_2 (default: auto)
  • free_memory_after_generate: Free VRAM after generation (default: True)
  • diffusion_steps: Number of denoising steps (5-100, default: 20)
  • seed: Random seed for reproducibility (default: 42)
  • cfg_scale: Classifier-free guidance (1.0-2.0, default: 1.3)
  • use_sampling: Enable/disable deterministic generation (default: False)
• Optional Parameters:
  • voice_to_clone: Audio input for voice cloning
  • temperature: Sampling temperature (0.1-2.0, default: 0.95)
  • top_p: Nucleus sampling parameter (0.1-1.0, default: 0.95)

3. VibeVoice Multiple Speakers

Generates multi-speaker conversations with distinct voices.

• Speaker Format: Use [N]: notation where N is 1-4
• Voice Assignment: Optional voice samples for each speaker
• Recommended Model: VibeVoice-7B-Preview for better multi-speaker quality
• Parameters (in order):
  • text: Input text with speaker labels
  • model: VibeVoice-1.5B, VibeVoice-Large-Preview (deprecated), or VibeVoice-Large
  • attention_type: auto, eager, sdpa, or flash_attention_2 (default: auto)
  • free_memory_after_generate: Free VRAM after generation (default: True)
  • diffusion_steps: Number of denoising steps (5-100, default: 20)
  • seed: Random seed for reproducibility (default: 42)
  • cfg_scale: Classifier-free guidance (1.0-2.0, default: 1.3)
  • use_sampling: Enable/disable deterministic generation (default: False)
• Optional Parameters:
  • speaker1_voice to speaker4_voice: Audio inputs for voice cloning
  • temperature: Sampling temperature (0.1-2.0, default: 0.95)
  • top_p: Nucleus sampling parameter (0.1-1.0, default: 0.95)

4. VibeVoice Free Memory

Manually frees all loaded VibeVoice models from memory.

• Input: audio - Connect audio output to trigger memory cleanup
• Output: audio - Passes through the input audio unchanged
• Use Case: Insert between nodes to free VRAM/RAM at specific workflow points
• Example: [VibeVoice Node] → [Free Memory] → [Save Audio]

Multi-Speaker Text Format

For multi-speaker generation, format your text using the [N]: notation:

[1]: Hello, how are you today?
[2]: I'm doing great, thanks for asking!
[1]: That's wonderful to hear.
[3]: Hey everyone, mind if I join the conversation?
[2]: Not at all, welcome!

Important Notes:

• Use [1]:, [2]:, [3]:, [4]: for speaker labels
• Maximum 4 speakers supported
• The system automatically detects the number of speakers from your text
• Each speaker can have an optional voice sample for cloning

Model Information

VibeVoice-1.5B

• Size: ~5GB download
• Speed: Faster inference
• Quality: Good for single speaker
• Use Case: Quick prototyping, single voices

VibeVoice-Large-Preview (Deprecated)

• Size: ~17GB download
• Speed: Slower inference
• Quality: Superior, especially for multi-speaker
• Use Case: Legacy support only - use VibeVoice-Large instead
• Note: This is the preview version, now superseded by VibeVoice-Large

VibeVoice-Large

• Size: ~17GB download
• Speed: Slower inference but optimized
• Quality: Best available quality
• Use Case: Highest quality production, multi-speaker conversations
• Note: Latest official release from Microsoft

Models are automatically downloaded on first use and cached in ComfyUI/models/vibevoice/.

Generation Modes

Deterministic Mode (Default)

• use_sampling = False
• Produces consistent, stable output
• Recommended for production use

Sampling Mode

• use_sampling = True
• More variation in output
• Uses temperature and top_p parameters
• Good for creative exploration

Voice Cloning

To clone a voice:

1. Connect an audio node to the voice_to_clone input (single speaker)
2. Or connect to speaker1_voice, speaker2_voice, etc. (multi-speaker)
3. The model will attempt to match the voice characteristics

Requirements for voice samples:

• Clear audio with minimal background noise
• Minimum 3–10 seconds. Recommended at least 30 seconds for better quality
• Automatically resampled to 24kHz

Tips for Best Results

1. Text Preparation:

   • Use proper punctuation for natural pauses
   • Break long texts into paragraphs
   • For multi-speaker, ensure clear speaker transitions

2. Model Selection:

   • Use 1.5B for quick single-speaker tasks (fastest, ~8GB VRAM)
   • Use Large-Preview (deprecated) for legacy workflows (~16GB VRAM)
   • Use Large for best quality and multi-speaker (~16GB VRAM)

3. Seed Management:

   • Default seed (42) works well for most cases
   • Save good seeds for consistent character voices
   • Try random seeds if default doesn't work well

4. Performance:

   • First run downloads models (5-17GB)
   • Subsequent runs use cached models
   • GPU recommended for faster inference

System Requirements

Hardware

• Minimum: 8GB VRAM for VibeVoice-1.5B
• Recommended: 16GB+ VRAM for VibeVoice-7B
• RAM: 16GB+ system memory

Software

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.8+ (for GPU acceleration)
• ComfyUI (latest version)

Troubleshooting

Installation Issues

• Ensure you're using ComfyUI's Python environment
• Try manual installation if automatic fails
• Restart ComfyUI after installation

Generation Issues

• If voices sound unstable, try deterministic mode
• For multi-speaker, ensure text has proper [N]: format
• Check that speaker numbers are sequential (1,2,3 not 1,3,5)

Memory Issues

• 7B model requires ~16GB VRAM
• Use 1.5B model for lower VRAM systems
• Models use bfloat16 precision for efficiency

Examples

Single Speaker

Text: "Welcome to our presentation. Today we'll explore the fascinating world of artificial intelligence."
Model: VibeVoice-1.5B
cfg_scale: 1.3
use_sampling: False

Two Speakers

[1]: Have you seen the new AI developments?
[2]: Yes, they're quite impressive!
[1]: I think voice synthesis has come a long way.
[2]: Absolutely, it sounds so natural now.

Four Speaker Conversation

[1]: Welcome everyone to our meeting.
[2]: Thanks for having us!
[3]: Glad to be here.
[4]: Looking forward to the discussion.
[1]: Let's begin with the agenda.

Performance Benchmarks

Model	VRAM Usage	Context Length	Max Audio Duration
VibeVoice-1.5B	~8GB	64K tokens	~90 minutes
VibeVoice-Large-Preview	~16GB	32K tokens	~45 minutes
VibeVoice-Large	~16GB	32K tokens	~45 minutes

Known Limitations

• Maximum 4 speakers in multi-speaker mode
• Works best with English and Chinese text
• Some seeds may produce unstable output
• Background music generation cannot be directly controlled

License

This ComfyUI wrapper is released under the MIT License. See LICENSE file for details.

Note: The VibeVoice model itself is subject to Microsoft's licensing terms:

• VibeVoice is for research purposes only
• Check Microsoft's VibeVoice repository for full model license details

Links

• Original VibeVoice Repository - Official Microsoft VibeVoice repository (currently unavailable)

Credits

• VibeVoice Model: Microsoft Research
• ComfyUI Integration: Fabio Sarracino
• Base Model: Built on Qwen2.5 architecture

Support

For issues or questions:

1. Check the troubleshooting section
2. Review ComfyUI logs for error messages
3. Ensure VibeVoice is properly installed
4. Open an issue with detailed error information

Contributing

Contributions welcome! Please:

1. Test changes thoroughly
2. Follow existing code style
3. Update documentation as needed
4. Submit pull requests with clear descriptions

Changelog

Version 1.0.9

• Embedded VibeVoice code directly into the wrapper
  • Added vvembed folder containing the complete VibeVoice code (MIT licensed)
  • No longer requires external VibeVoice installation
  • Ensures continued functionality for all users

Version 1.0.8

• BFloat16 Compatibility Fix
  • Fixed tensor type compatibility issues with audio processing nodes
  • Input audio tensors are now converted from BFloat16 to Float32 for numpy compatibility
  • Output audio tensors are explicitly converted to Float32 to ensure compatibility with downstream nodes
  • Resolves "Got unsupported ScalarType BFloat16" errors when using voice cloning or saving audio

Version 1.0.7

• Added interruption handler to detect user's cancel request
• Bug fixing

Version 1.0.6

• Fixed a bug that prevented VibeVoice nodes from receiving audio directly from another VibeVoice node

Version 1.0.5

• Added support for Microsoft's official VibeVoice-Large model (stable release)

Version 1.0.4

• Improved tokenizer dependency handling

Version 1.0.3

• Added attention_type parameter to both Single Speaker and Multi Speaker nodes for performance optimization
  • auto (default): Automatic selection of best implementation
  • eager: Standard implementation without optimizations
  • sdpa: PyTorch's optimized Scaled Dot Product Attention
  • flash_attention_2: Flash Attention 2 for maximum performance (requires compatible GPU)
• Added diffusion_steps parameter to control generation quality vs speed trade-off
  • Default: 20 (VibeVoice default)
  • Higher values: Better quality, longer generation time
  • Lower values: Faster generation, potentially lower quality

Version 1.0.2

• Added free_memory_after_generate toggle to both Single Speaker and Multi Speaker nodes
• New dedicated "Free Memory Node" for manual memory management in workflows
• Improved VRAM/RAM usage optimization
• Enhanced stability for long generation sessions
• Users can now choose between automatic or manual memory management

Version 1.0.1

• Fixed issue with line breaks in speaker text (both single and multi-speaker nodes)
• Line breaks within individual speaker text are now automatically removed before generation
• Improved text formatting handling for all generation modes

Version 1.0.0

• Initial release
• Single speaker node with voice cloning
• Multi-speaker node with automatic speaker detection
• Text file loading from ComfyUI directories
• Deterministic and sampling generation modes
• Support for VibeVoice 1.5B and 7B models