ComfyUI Extension: Bawk Nodes Collection

Authored by juddisjudd

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Updated

5 stars

A complete collection of FLUX-optimized ComfyUI nodes for enhanced AI image generation workflows.

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    README

    <div align="center"> <h1>🐓 ComfyUI Bawk Nodes v2.0.0</h1>

    A collection of FLUX-optimized ComfyUI nodes for efficient AI image generation.

    </div>

    Image Description

    🎯 What's New in v2.0.0

    Major Rewrite: Complete FLUX-first redesign with modular architecture and workflow consolidation.

    • 🎲 Enhanced Wildcard Encoder with 6 LoRA slots
    • 🐓 All-in-One BawkSampler with integrated VAE decoding
    • 📁 Modular Architecture for better maintainability
    • ⚡ Streamlined Workflows - fewer nodes, more power
    • 💾 Enhanced Image Saver with prompt saving

    🚀 Node Collection Overview

    | Node | Description | Category | |------|-------------|----------| | 🚀 Diffusion Model Loader | Advanced FLUX-optimized model loading | Loaders | | 🎲 FLUX Wildcard Encoder | Text encoding + 6 LoRA slots + wildcards | Conditioning | | 🐓 Bawk Sampler | All-in-one latent generation, sampling & VAE decode | Sampling | | 💾 FLUX Image Saver | Organized saving with metadata & prompt files | Image | | 📝 FLUX Prompt Saver | Standalone prompt archiving | Text |

    🔥 Complete FLUX Workflow

    Before BawkNodes (5+ nodes):

    CheckpointLoader → LoraLoader → CLIPTextEncode → EmptyLatent → KSampler → VAEDecode → SaveImage

    After BawkNodes (3 nodes):

    🚀 DiffusionModelLoader → 🎲 FluxWildcardEncode → 🐓 BawkSampler → 💾 FluxImageSaver

    60% fewer nodes, 100% of the power!

    📦 Installation

    Method 1: ComfyUI Manager (Recommended)

    1. Open ComfyUI Manager
    2. Search for "Bawk Nodes"
    3. Click Install
    4. Restart ComfyUI

    Method 2: Manual Installation

    cd ComfyUI/custom_nodes
git clone https://github.com/juddisjudd/ComfyUI-BawkNodes.git
# Restart ComfyUI

    🎲 Node Details

    🚀 Diffusion Model Loader (Advanced)

    FLUX-optimized model loading with advanced features.

    Features:

    • Multiple model formats (FLUX, SDXL, SD1.5)
    • Flexible weight data types (fp8, fp16, bf16, fp32)
    • Separate VAE and CLIP loading
    • Multiple directory support

    Inputs:

    • model_name - Model from diffusion_models folder
    • vae_name - VAE or "baked VAE"
    • clip_name1/2 - CLIP models for FLUX
    • weight_dtype - Precision optimization

    Outputs: MODEL, VAE, CLIP, MODEL_STRING

    🎲 FLUX Wildcard Encoder

    Enhanced text encoder with 6 LoRA slots and wildcard support.

    Features:

    • Wildcard Processing: {option1|option2|option3} syntax
    • 6 LoRA Slots: Individual enable/disable toggles
    • Fuzzy LoRA Matching: Flexible file resolution
    • FLUX Optimization: 16-channel conditioning

    Inputs:

    • model, clip - From model loader
    • prompt - Text with wildcard support
    • wildcard_seed - Seed for consistent wildcard selection
    • lora_X_on - Enable/disable each LoRA (X = 1-6)
    • lora_X_name - LoRA selection dropdown
    • lora_X_strength - Strength adjustment (-10.0 to +10.0)

    Outputs: MODEL, CLIP, CONDITIONING, PROMPT_OUT

    Example Prompt with Wildcards:

    A {beautiful|stunning|gorgeous} {cat|dog|bird} in a {forest|garden|meadow}, 
{photorealistic|artistic|stylized} style

    🐓 Bawk Sampler (All-in-One)

    Complete latent generation, sampling, and VAE decoding in one node.

    Features:

    • Smart Resolution Presets: Pre-configured FLUX-optimized resolutions
    • Custom Resolution Support: Manual width/height with 64px alignment
    • Advanced FLUX Sampling: All FLUX-specific parameters
    • Integrated VAE Decoding: Direct image output
    • Batch Generation: Up to 64 images at once

    Key Inputs:

    • model, conditioning, vae - From previous nodes
    • resolution - Smart presets or custom
    • batch_size - Number of images (default: 4)
    • sampler - Sampling method (default: euler)
    • scheduler - Noise schedule (default: beta)
    • steps - Sampling steps (default: 30)
    • guidance - FLUX guidance scale (default: 3.5)
    • max_shift - FLUX max shift (default: 0.5)
    • base_shift - FLUX base shift (default: 0.3)

    Resolution Presets:

    • FHD 16:9 - 1920x1080 (default)
    • Medium Square - 1024x1024
    • Portrait 9:16 - 1080x1920
    • Ultra-wide - 1792x768
    • And many more...

    Outputs: IMAGE, LATENT

    💾 FLUX Image Saver

    Organized image saving with metadata and prompt archiving.

    Features:

    • Smart Folder Organization: [MODEL]-DD-MM-YYYY structure
    • Multiple Formats: PNG, JPG, WebP support
    • Metadata Embedding: PNG metadata support
    • Prompt File Saving: Separate .txt files with processed prompts
    • JSON Metadata: Complete generation parameters

    Inputs:

    • images - From BawkSampler
    • model_string - From model loader
    • processed_prompt - From wildcard encoder
    • save_prompt - Enable prompt file saving (default: True)
    • format - Image format (PNG/JPG/WebP)
    • quality - Compression quality (1-100)

    File Output Example:

    ComfyUI/output/[FLUX_Model]-01-08-2025/
├── flux_image_01-08-2025_14-30-15_001.png
├── flux_image_01-08-2025_14-30-15_002.png
├── flux_image_01-08-2025_14-30-15_prompt.txt
├── flux_image_01-08-2025_14-30-15_001_metadata.json
└── flux_image_01-08-2025_14-30-15_002_metadata.json

    📝 FLUX Prompt Saver

    Standalone prompt and parameter archiving.

    Features:

    • JSON Format: Structured data storage
    • Complete Parameters: All generation settings
    • Organized Storage: Matches image saver folder structure
    • Workflow Integration: Links with other BawkNodes

    🛠️ Advanced Usage

    Wildcard Examples

    Basic Wildcards:

    A {red|blue|green} car in the {city|countryside}

    Nested Concepts:

    {A majestic|An elegant|A powerful} {dragon|phoenix|griffin} 
{soaring through|perched upon|emerging from} {clouds|mountains|flames}

    Style Variations:

    Portrait of a woman, {photorealistic|oil painting|digital art|watercolor} style,
{studio lighting|natural lighting|dramatic lighting}

    LoRA Management

    Best Practices:

    1. Enable LoRAs individually for precise control
    2. Use strength between 0.5-1.5 for most LoRAs
    3. Combine complementary LoRAs (style + subject)
    4. Test different combinations for unique results

    Example LoRA Setup:

    • LoRA 1: realistic_skin_v2.safetensors (0.8)
    • LoRA 2: dramatic_lighting.safetensors (0.6)
    • LoRA 3: detail_enhancer.safetensors (0.4)

    Resolution Guidelines

    Recommended Presets:

    • Square: Medium Square - 1024x1024
    • Landscape: FHD 16:9 - 1920x1080
    • Portrait: Portrait 9:16 - 1080x1920
    • Widescreen: Ultra-wide - 1792x768

    Custom Resolution Rules:

    • Must be multiples of 64 pixels
    • Keep total pixel count reasonable (<4MP for speed)
    • Consider VRAM limitations for large batches

    🔧 Configuration

    Model Setup

    1. FLUX Models: Place in models/diffusion_models/
    2. VAE Files: Place in models/vae/
    3. CLIP Models: Place in models/text_encoders/
    4. LoRA Files: Place in models/loras/

    Recommended Settings

    For Speed:

    • Resolution: Medium Square - 1024x1024
    • Batch Size: 4
    • Steps: 20-25
    • Sampler: euler

    For Quality:

    • Resolution: FHD 16:9 - 1920x1080
    • Batch Size: 1-2
    • Steps: 30-40
    • Sampler: dpmpp_2m

    For Experimentation:

    • Use wildcards with high variation
    • Enable multiple LoRAs
    • Try different guidance scales (2.0-5.0)

    Troubleshooting

    Common Issues

    Node Not Appearing:

    # Check ComfyUI console for errors
# Ensure all files are in correct directories
# Restart ComfyUI completely

    LoRA Not Loading:

    • Check file is in models/loras/
    • Verify file isn't corrupted
    • Check console for specific error messages

    Memory Issues:

    • Reduce batch size
    • Use lower resolution
    • Enable fp8 weight dtype in loader

    Generation Errors:

    • Verify all connections are correct
    • Check that VAE is connected to BawkSampler
    • Ensure CLIP and MODEL are from same loader

    Performance Optimization

    VRAM Usage:

    • Use fp8_e4m3fn_fast for weight dtype
    • Reduce batch size for large images
    • Close other GPU applications

    Speed Improvements:

    • Use euler sampler with beta scheduler
    • Reduce step count (20-30 is often sufficient)
    • Use medium resolution presets

    📄 License

    GPL-3.0 license - see LICENSE file for details.

    🙏 Acknowledgments

    • rgthree - Inspiration for dynamic UI patterns

    🔗 Links

    ko-fi