Essential nodes for semantic image editing workflows with Kontext, Qwen Image Edit, and HiDream E1.1 models. Streamlines iterative editing, dataset creation, and batch processing for next-generation semantic editing pipelines. Now with Lora loader for Wan2.2 which automatically selects pairs and any trigger words for you.

Semantic Image Editing Workflow Nodes

Boyo Image Grab

Automatically monitors directories and grabs the most recent image file - perfect for iterative semantic editing workflows where you want to chain multiple edits together without manual file management.

Key Features:

Auto-detects newest image in specified directory
Enables seamless iterative editing chains
Perfect for progressive semantic modifications
Supports all major image formats
Real-time directory monitoring

Inputs:

directory_path: Full path to monitor (e.g., "C:\MyImages")
file_extensions: Comma-separated formats (default: "jpg,jpeg,png,bmp,tiff,webp")
auto_refresh: Enable automatic monitoring (default: True)
refresh_trigger: Manual refresh control (default: 0)

Outputs:

image: Ready-to-use image tensor
filename: Just the filename
full_path: Complete file path
timestamp: Modification timestamp

Usage: Perfect for workflows like: Original → "add a hat" → "make it red" → "change lighting" where each step automatically feeds into the next.

Boyo Paired Image Saver

Saves original and edited image pairs with organised sequential naming - essential for creating datasets from semantic editing workflows.

Boyo Paired Video Saver

Same as above but for video.

Key Features:

Maintains original/edited image relationships
Sequential batch numbering
Organised dataset creation
ControlNet data format compatible
Custom directory placement (inside or outside ComfyUI)

Inputs:

original_image: Source image
controlnet_image: Edited/processed image
folder_name: Target directory
filename_prefix: File naming prefix

Output Format:

generated001_original.png
generated001_controlnet.png
generated002_original.png
generated002_controlnet.png

Boyo Incontext Saver

Specialised saver for semantic editing model outputs with proper dataset formatting.

Key Features:

Handles source and diffusion outputs
Dataset-ready file organisation
Batch processing support
Maintains editing relationships

Inputs:

source_image: Original input
diffusion_output: Semantic edit result
folder_name: Output directory

Boyo Universal Image + Prompt Saver

Saves clean images with accompanying prompt files - perfect for publication and documentation.

Key Features:

Strips metadata for clean publication
Saves actual prompts (crucial for wildcard/enhanced workflows)
Creates organised image/text pairs
Protects workflow secrets whilst maintaining documentation

Perfect for:

Publishing images without exposing ComfyUI metadata
Documenting wildcard-generated prompts you never see
Creating training datasets with prompt annotations
Maintaining reproducible results

LoRA Management System

Revolutionary paired LoRA management with intelligent prompt handling - perfect for complex workflows requiring multiple LoRA types (utility, character, style) with sophisticated prompt strategies.

Boyo LoRA JSON Builder

Create and save LoRA configuration files with flexible prompt and file assignments.

Key Features:

Supports paired LoRAs (high/low noise variants)
Flexible prompt management (multiple prompts per config)
Handles utility LoRAs (no prompts needed)
Graceful handling of single LoRAs or config-only entries
Auto-saves to organised directory structure

Inputs:

name: Configuration name (becomes filename)
prompts: Multi-line text (one prompt per line, optional)
high_noise_lora: LoRA file selector (optional)
low_noise_lora: LoRA file selector (optional)
save_config: Boolean toggle to save JSON

Perfect for:

Lightning/reward LoRAs (utility configs with no prompts)
Character LoRAs with example prompts
Style LoRAs with trigger words
Any LoRA requiring organised management

Boyo LoRA Paired Loader

Load multiple LoRA configurations simultaneously with intelligent prompt handling strategies.

Key Features:

3 simultaneous config slots for layered effects (utility + character + style)
Prompt strategies per config: Mute, Concatenate, Merge
Prompt modes: First Only, Cycle Through, Random (seed-based)
Smart error handling - continues with missing configs
Combined prompt output for easy workflow integration

Inputs:

prompt_mode: Global prompt selection behaviour
lora_config_1/2/3: Configuration file selectors
prompt_strategy_1/2/3: Individual prompt handling per config
seed: For consistent random prompt selection

Outputs:

6 LoRA paths: high_noise_path_1/2/3, low_noise_path_1/2/3 (connect directly to LoRA loaders)
4 prompt strings: individual prompts + combined output

Workflow Integration: Outputs connect directly to any standard LoRA loader. No primitives needed - clean, direct connections.

Boyo LoRA Config Inspector

Preview LoRA configuration contents before loading for informed decision-making.

Key Features:

Smart analysis: Detects utility, paired, or single LoRA configs
File status checking: Verifies LoRA files actually exist
Usage recommendations: Suggests optimal prompt strategies
Three output formats: formatted summary, raw JSON, status line
Real-time inspection: See exactly what's in each config

Perfect for:

"What was in that config again?" scenarios
Debugging missing LoRA files
Planning complex multi-LoRA workflows
Understanding prompt availability before loading

Sample Output:

📋 LoRA Configuration: Character_Cyborg
🎯 LoRA FILES:
  📈 High Noise: ✅ cyborg_char_v2.safetensors
  📉 Low Noise: ✅ cyborg_char_v2_low.safetensors
  🎭 Type: PAIRED LoRA (different high/low files)

💬 PROMPTS (3 total):
  1. cyborg woman, metallic skin, glowing eyes
  2. android female, chrome details, futuristic
  3. robotic humanoid, synthetic appearance

💡 USAGE RECOMMENDATIONS:
  • Use 'Cycle Through' for variety
  • Use 'Random' for experimentation

Workflow Enhancement Nodes

Boyo Empty Latent

Smart aspect ratio calculator - input width, select ratio, automatically calculates height.

Features:

Model-aware resolution calculation
Common aspect ratio presets
Prevents invalid dimensions

Load Image List

Batch image processor for mass workflows.

Features:

Mass resize, pad, or crop operations
Perfect for upscaler pipelines
Handles hundreds of images automatically
Set-and-forget batch processing

Utility Nodes

BoyoVision Node

Qwen2.5VL vision node with abliterated version compatibility. The abliterated model is uncensored and therefor does not refuse tasks given to it. Far superior and accurate as a result

Boyo VAE Decode Nodes

Stealth NSFW filtering for controlled environments.

Standard VAE Decode:

Hidden NSFW detection
Disguisable as normal VAE node
Perfect for educational/workplace deployment

Tiled VAE Decode:

Memory-efficient processing for large images
Seamless tile blending
VRAM-friendly for high-resolution workflows
Same stealth NSFW protection

Mandelbrot Video Generator

Fractal art generation for creative projects.

Features:

Random Mandelbrot and Julia sets
Video or frame sequence output
Multiple resolution options
Grayscale mode available

Installation

Quick Install:

git clone https://github.com/DragonDiffusionbyBoyo/Boyonodes.git
cp -r Boyonodes /path/to/ComfyUI/custom_nodes/

Most nodes require only ComfyUI itself.

Mandelbrot Video Node requires additional dependencies:

pip install numpy==1.26 matplotlib pillow tqdm torch

FFmpeg (for video output):

Windows: Download from ffmpeg.org or choco install ffmpeg
macOS: brew install ffmpeg
Linux: sudo apt-get install ffmpeg

Restart ComfyUI after installation.

LoRA Management Workflow Examples

Basic Multi-LoRA Setup:

Create configs for utility (lightning), character, and style LoRAs
Load all three simultaneously in Paired Loader
Set utility to "Mute", character to "Concatenate", style to "Concatenate"
Get combined prompts and all LoRA paths in one node

Progressive LoRA Development:

Build character LoRA config with multiple example prompts
Use "Cycle Through" mode for testing different prompt variations
Inspector node helps verify which prompts are available
Iterate and refine prompt collections

Dataset Creation with LoRAs:

Set up configs for consistent LoRA + prompt combinations
Use "Random" mode with fixed seeds for controlled variation
Paired Image Saver captures results with LoRA-enhanced prompts
Creates organised datasets with known LoRA configurations

Semantic Editing Workflow Examples

Basic Iterative Editing:

Load initial image
Run through Kontext/Qwen/HiDream with prompt
Boyo Paired Image Saver stores original + edit
Boyo Image Grab automatically picks up the edit
Feed back for next iteration
Repeat for progressive modifications

Dataset Creation:

Use semantic editing models on source images
Boyo Paired Image Saver organises results
Creates properly formatted training datasets
Maintains source/target relationships

Publication Workflow:

Create final edited images
Boyo Universal Saver strips metadata
Saves clean images + prompt documentation
Ready for sharing without exposing workflows

Troubleshooting

LoRA Management issues:

Verify LoRA files exist in specified subdirectories
Check JSON syntax in config files
Use Inspector node to debug config problems
Ensure LoRA paths use forward slashes

Boyo Image Grab issues:

Verify directory path exists and is accessible
Check file permissions
Ensure auto_refresh is enabled
Use forward slashes in Windows paths

Paired Saver issues:

Confirm output directory exists
Check file write permissions
Verify input image formats

Performance tips:

Large directories may slow Image Grab scanning
Organise files into smaller subdirectories
Use specific file extensions to reduce scanning
LoRA configs are cached for faster loading

Contributing

Fork, branch (git checkout -b feature-name), commit, push, and open a pull request. Documentation for new features is appreciated.

License

MIT License - see LICENSE file for details.

Built by DragonDiffusionbyBoyo for the semantic editing revolution.