ComfyUI LoRA Block Weight Loader

ComfyUI LoRA Block Weight Loader

A production-ready ComfyUI custom node that provides per-block weight control for LoRA loading. Apply different LoRA strengths to specific transformer blocks for fine-grained control over model behavior. Compatible with Flux, Nunchaku, and standard Stable Diffusion models.

🎯 Key Features

• Per-Block Weight Control: Apply different LoRA strengths to specific transformer blocks
• Universal Compatibility: Works with Flux, Nunchaku, SDXL, and SD 1.5 models
• Multiple Weight Modes: Uniform, linear interpolation, exponential, gaussian, and custom curves
• Block Range Selection: Target specific block ranges (e.g., 0-6, 7-12, 13-18)
• Advanced Presets: Mathematical weight distributions (bell curve, U-shape, emphasis patterns)
• Weight Visualization: Built-in weight editor with ASCII visualization
• Custom Presets: Save and load your own weight configurations
• Production Ready: Fully compatible with ComfyUI Manager

📦 Installation

Via ComfyUI Manager (Recommended)

1. Open ComfyUI Manager
2. Search for "LoRA Block Weight"
3. Click Install

Manual Installation

cd ComfyUI/custom_nodes
git clone https://github.com/bhvbhushan/ComfyUI-LoRABlockWeight.git
# Restart ComfyUI

🚀 Quick Start

Basic Usage

1. Add the "LoRA Block Weight Loader" node to your workflow
2. Connect your model and CLIP
3. Select a LoRA file
4. Choose a weight mode or preset
5. Adjust strengths and connect to sampling nodes

Example Workflows

Uniform Application (Traditional)

• Weight Mode: uniform
• Block Weights: 1.0
• Result: Standard LoRA application across all blocks

Targeted Enhancement

• Weight Mode: block_specific
• Block Range: 0-6
• Block Weights: 1.5, 1.4, 1.3, 1.2, 1.1, 1.0, 0.9
• Result: Stronger influence on early blocks

Smooth Transition

• Weight Mode: linear_interpolation
• Interpolation Start: 1.5
• Interpolation End: 0.5
• Result: Gradual decrease from early to late blocks

🎛️ Node Parameters

LoRA Block Weight Loader

Required Inputs

• model: The base model (Nunchaku/Flux/SD)
• clip: CLIP model
• lora_name: LoRA checkpoint file
• strength_model: Overall LoRA strength for model (−20.0 to 20.0)
• strength_clip: Overall LoRA strength for CLIP (−20.0 to 20.0)

Optional Inputs

• block_weights: Custom weight values (multiple formats supported)
• weight_mode: How weights are applied
  • uniform: Same weight for all blocks
  • block_specific: Custom weights per block
  • linear_interpolation: Linear gradient
  • exponential: Exponential curve
  • gaussian: Bell curve distribution
  • custom_curve: User-defined pattern
• interpolation_start/end: Start and end values for interpolation modes
• interpolation_curve: Curve factor for non-linear interpolations
• block_range: Which blocks to target (e.g., "all", "0-6", "7-12,15-18")
• normalize_weights: Normalize to maintain average strength
• preserve_mean: Keep mean value when normalizing
• verbose: Show detailed weight information
• preset: Use predefined weight patterns

LoRA Block Weight Editor

A companion node for generating and visualizing weight patterns.

Parameters

• total_blocks: Number of blocks in your model
• pattern: Mathematical pattern to generate
• amplitude: Pattern amplitude
• offset: Base offset value
• frequency: Pattern frequency
• phase: Phase shift
• custom_expression: Mathematical expression for custom patterns

📊 Weight Input Formats

The node accepts multiple weight formats for maximum flexibility:

# Single value (applies to all blocks)
"1.5"

# Space-separated
"1.0 1.2 1.4 1.2 1.0 0.8"

# Comma-separated
"1.0, 1.2, 1.4, 1.2, 1.0, 0.8"

# JSON array
"[1.0, 1.2, 1.4, 1.2, 1.0, 0.8]"

# JSON object with indices
'{"0": 1.5, "5": 1.2, "10": 0.8}'

# Multi-line block-specific (for Flux models)
"""
double_blocks: 1.0, 1.2, 1.4
single_blocks: 0.8, 0.9, 1.0
"""

🎨 Built-in Presets

• linear_decay: Gradual decrease from 1.5 to 0.5
• linear_growth: Gradual increase from 0.5 to 1.5
• bell_curve: Peak in middle blocks
• u_shape: Peak at extremes, trough in middle
• emphasis_early: Strong early blocks (first third)
• emphasis_middle: Strong middle blocks
• emphasis_late: Strong late blocks (last third)

🔧 Advanced Features

Custom Presets

Create your own presets by adding JSON files to the presets/ directory:

{
  "name": "my_custom_preset",
  "description": "My custom weight distribution",
  "weights": [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, ...]
}

For Flux-specific presets:

{
  "name": "flux_custom",
  "description": "Custom Flux weights",
  "weights": {
    "double_blocks": [1.0, 1.1, 1.2, ...],
    "single_blocks": [0.9, 1.0, 1.1, ...]
  }
}

Weight Visualization

The LoRA Block Weight Editor provides ASCII visualization:

Weight Distribution (blocks: 57)
Range: [0.500, 1.500]
Mean: 1.000, Std: 0.289

          ████████
       ███        ███
     ██              ██
   ██                  ██
 ██                      ██
─────────────────────────────

Mathematical Expressions

Use custom expressions with the Weight Editor:

# Sine wave
"1.0 + 0.5 * sin(2 * pi * i / n)"

# Exponential decay
"2.0 * exp(-i / (n / 3))"

# Step function
"1.5 if i < n/2 else 0.5"

🏗️ Architecture Support

Flux Models

• Automatically detects double_blocks (19) and single_blocks (38)
• Proper weight mapping for Flux transformer architecture
• Native support for Nunchaku quantized models

Stable Diffusion Models

• Compatible with SD 1.5, SDXL, and variants
• Detects input/output/middle blocks
• Falls back gracefully for unsupported architectures

🎯 Use Cases

Creative Control

• Composition Control: Strengthen early blocks for layout/pose
• Style Transfer: Adjust middle blocks for artistic style
• Detail Enhancement: Boost late blocks for fine details
• Character Consistency: Target specific blocks for facial features

Technical Applications

• LoRA Merging: Different strengths for different LoRA aspects
• Fine-tuning: Selective layer updates
• A/B Testing: Compare different weight distributions
• Research: Analyze block contributions to generation

🐛 Troubleshooting

LoRA Not Loading

• Ensure LoRA file is in ComfyUI/models/loras/
• Check console for specific error messages
• Verify model compatibility

Unexpected Results

• Enable verbose mode to see actual weights applied
• Start with uniform weights as baseline
• Check if normalization is affecting results

Performance Issues

• Hierarchical weighting adds minimal overhead
• Weight calculations are cached
• Consider reducing block range for testing

📈 Performance

• Memory: Minimal additional memory usage
• Speed: < 1% overhead vs standard LoRA loading
• Compatibility: Works with all ComfyUI samplers

🤝 Contributing

Contributions welcome! Areas of interest:

• Additional mathematical presets
• Model architecture detection improvements
• Weight optimization algorithms
• Integration with other ComfyUI nodes

📄 License

MIT License - See LICENSE file for details

🙏 Acknowledgments

• ComfyUI community for the framework
• Nunchaku and Flux model developers
• Contributors and testers

📚 References

• ComfyUI Documentation
• Flux Architecture
• LoRA Paper

💬 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Discord: ComfyUI Discord #custom-nodes channel

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Note: This node provides a general-purpose solution for per-block LoRA weight control. It works with any ComfyUI-compatible model that benefits from block-level weight control, including Flux, Nunchaku, SDXL, and SD 1.5 models.