Extensions/Comfyui_CharaConsist
ComfyUI Extension

Comfyui_CharaConsist

Training-free CharaConsist algorithm implementation for ComfyUI - Generate consistent subjects across multiple generations with enhanced mask generation and multi-model support.

By thatname·Created 10 months ago·Updated 9 months ago· 1
thatname/Comfyui_CharaConsist
Nodes9
On cloudLocal install
Categorychara_consist
Stars1
Updated9 months ago

Nodes (9)

Batched Masked Reference Gen

The all-in-one-batch route to consistency

chara_consist
Conditioning Match Mask

Let the pack figure out which prompt words describe which subject

chara_consist
Extract attention data

The reference pass that fills CharaConsist's attention cache

chara_consist
GenConsistent

Where CharaConsist finally pays off

chara_consist
GetCrossSim

The matchmaker between your reference and your new render

chara_consist
Masks to Patches

Pixel masks into the patch-level masks attention actually reads

chara_consist
MaskToPatchMask

The tiny node that decides which patches your subject owns

chara_consist
Preview Subject Mask

See what ExtractAttn actually locked onto

chara_consist
Reference Condition Combine

Stack five references into one batched conditioning

chara_consist
Readme

ComfyUI-CharaConsistent

Training-free CharaConsist algorithm implementation for ComfyUI - Generate consistent subjects across multiple generations with enhanced mask generation and multi-model support.

License

MIT License

Copyright (c) 2025 ZHOU He Email: [email protected]

Repository: https://github.com/thatname/Comfyui_CharaConsist

Credits & References

This is a ComfyUI implementation of the CharaConsist algorithm originally developed by Murray-Wang et al.

This ComfyUI implementation includes significant enhancements and improvements over the original algorithm.

Overview

ComfyUI-CharaConsistent implements the training-free CharaConsist algorithm within ComfyUI, enabling generation of consistent subjects across multiple images. Unlike the original implementation which primarily supported FLUX models, this version extends compatibility to multiple state-of-the-art text-to-image models while introducing improvements to mask generation and workflow integration.

Key Improvements Over Original

Enhanced Mask Generation

  • Original Issue: The original mask generation was not perfect, especially for Chroma models
  • Solution: Support for external GroundingDinoSAM for more accurate mask extraction
  • Flexibility: Users can choose between original or improved mask generation methods

Expanded Model Support

  • Original: Primarily FLUX models only
  • This Implementation:
    • FLUX models
    • Chroma models (optimal performance)
    • Qwen-Image models
    • Chroma1-Radiance T2I models

Multi-Subject Support

  • Supports simultaneous processing of multiple subjects in a single workflow
  • Each subject gets independent attention caching and mask processing

ComfyUI Integration

  • Native ComfyUI node implementation
  • Seamless workflow integration with other ComfyUI custom nodes
  • Visual workflow examples provided

Features

  • ✅ Training-free consistency - No fine-tuning required
  • ✅ Multi-subject support - Generate consistent multiple characters/objects
  • ✅ Enhanced mask generation - GroundingDinoSAM integration for better results
  • ✅ Broad model compatibility - Works with FLUX, Chroma, Qwen-Image, Chroma1-Radiance
  • ✅ Optimal for Chroma - Best performance with Chroma models
  • ✅ Attention caching - Efficient memory management for subject consistency
  • ✅ Cross-similarity matching - Advanced subject matching algorithms
  • ✅ Visual workflow examples - Ready-to-use JSON workflows

Installation

  1. Navigate to your ComfyUI custom_nodes directory
  2. Clone or copy this repository: 
    git clone https://github.com/thatname/Comfyui_CharaConsist.git
  3. Restart ComfyUI
  4. The nodes will appear in the chara_consist category

Supported Models

| Model | Compatibility | Performance Notes | |--------|---------------|------------------| | Chroma Models | ✅ Excellent | Best performance - Recommended | | FLUX Models | ✅ Good | Compatible, original algorithm focus | | Qwen-Image | ✅ Good | Full support with example workflow | | Chroma1-Radiance | ✅ Good | Compatible with T2I workflows |

Model-Specific Notes

  • Chroma Models: Optimal performance, enhanced mask generation recommended
  • FLUX Models: Good compatibility, original mask generation works adequately
  • Qwen-Image: Full support, see example workflow for best practices

Mask Generation Options

Option 1: Enhanced Mask Generation (Recommended)

  • Method: External GroundingDinoSAM
  • Benefits:
    • Superior mask accuracy
    • Better edge detection
    • Improved results for Chroma models
  • Requirements: GroundingDinoSAM custom node
  • Example Workflow: See JSON examples for implementation

Option 2: Original Mask Generation

  • Method: Built-in algorithm
  • Benefits:
    • No additional dependencies
    • Faster processing
    • Works well with FLUX models
  • Limitations:
    • Less accurate for Chroma models
    • May have edge detection issues

Example Workflows

Chroma Workflow

  • File: chroma_chara_consist.json
  • Model: Chroma1-HD
  • Features:
    • Enhanced mask generation with GroundingDinoSAM
    • Multi-step generation pipeline
    • Optimal settings for Chroma models

Qwen-Image Workflow

  • File: qwen_chara_consist.json
  • Model: Qwen-Image
  • Features:
    • LoRA integration
    • CFG normalization
    • Optimized for Qwen architecture

Node Documentation

This implementation includes 9 custom nodes:

Core Nodes

  1. ExtractAttn - Extracts attention data from model layers for subject analysis
  2. GenConsistent - Generates consistent images using cached attention data
  3. GetCrossSim - Computes cross-similarity between subject and target attention
  4. BatchedMaskedReferenceGen - Applies masked attention for consistent generation

Mask Processing Nodes

  1. MasksToPatches - Converts pixel masks to patch-level masks for attention
  2. MaskToPatchMask - Converts masks to patch format with configurable parameters
  3. PreviewSubjectMask - Preview extracted subject masks

Conditioning Nodes

  1. ReferenceConditionCombine - Combines reference and target conditioning
  2. ConditioningMatchMask - Matches reference conditions to target prompts

Memory Requirements

⚠️ Important RAM Considerations

  • Per Subject: 10s of gigabytes of System RAM required
  • Attention Caching: Each subject needs separate attention cache
  • Multi-Subject: Memory usage scales linearly with number of subjects
  • Recommendations:
    • Minimum 32GB RAM for single subject
    • 64GB+ RAM recommended for multiple subjects
    • Monitor RAM usage during generation

Optimization Tips

  • Clear attention cache between sessions
  • Use appropriate batch sizes
  • Consider system RAM when planning multi-subject workflows

Performance Tips

Best Practices

  1. Model Selection: Use Chroma models for best results
  2. Mask Generation: Prefer GroundingDinoSAM for Chroma models
  3. Memory Management: Monitor RAM usage closely
  4. Workflow Optimization: Use provided example workflows as templates

Troubleshooting

  • High RAM Usage: Reduce batch size or number of subjects
  • Poor Mask Quality: Switch to GroundingDinoSAM mask generation
  • Model Compatibility: Ensure you're using supported model versions
  • Performance Issues: Check system RAM availability

Academic Citation

If you use CharaConsist in your research, please cite the original paper:

@inproceedings{CharaConsist,
  title={{CharaConsist}: Fine-Grained Consistent Character Generation},
  author={Wang, Mengyu and Ding, Henghui and Peng, Jianing and Zhao, Yao and Chen, Yunpeng and Wei, Yunchao},
  booktitle={ICCV},
  year={2025}
}

Contributing

Contributions are welcome! Please feel free to submit issues and enhancement requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.