ComfyUI-ZImage-LoRA-Merger

Z-Image LoRA Merger for ComfyUI

Custom nodes for combining multiple LoRAs without the "burned/overexposed" look on distilled models like Z-Image Turbo.

🔥 The Problem

When you chain multiple LoRAs in ComfyUI, their effects are added together:

model += lora1_effect × strength1
model += lora2_effect × strength2
Total effect = strength1 + strength2  ← Can exceed 1.0!

On distilled/turbo models this causes overexposure and artifacts because these models are already optimized for fewer inference steps and can't handle the accumulated LoRA effects.

✨ The Solution

This pack provides 5 nodes with different strategies:

1. Z-Image LoRA Merger

Applies multiple LoRAs with automatic strength normalization.

| Mode | What it does | |------|-------------| | normalize | Keeps total "energy" (sum of squares) at target level — recommended | | average | Divides each strength by number of LoRAs | | sqrt_scale | Scales by 1/√n — good for independent effects | | linear_decay | First LoRA strongest, others progressively weaker | | geometric_decay | Aggressive decay: 1, 0.5, 0.25, 0.125... | | additive | Standard behavior (for comparison) |

Example:

Input:  LoRA1=0.6, LoRA2=1.0
Mode:   normalize, target=1.0

Output: LoRA1=0.51, LoRA2=0.86  (total "energy" normalized)

2. Z-Image LoRA True Merge ⭐

Properly merges LoRAs of ANY rank!

Standard merging can't combine LoRAs with different ranks (e.g., rank-32 + rank-256). This node computes the full weight diff for each LoRA first, then averages them:

Standard: A₁[×32] + A₂[×256] = ❌ ERROR (different shapes)

True Merge:
  diff1 = A₁ @ B₁ × alpha → [4096×4096] ✓
  diff2 = A₂ @ B₂ × alpha → [4096×4096] ✓  
  merged = average(diff1, diff2) → Works! ✓

⚠️ Uses more memory and is slower, but gives true averaging for any rank combination.

3-4. Z-Image LoRA Stack + Stack Apply

Flexible node-based LoRA chaining with blend modes.

5. Z-Image LoRA Merge to Single

Merges LoRA weights before applying (works best with same-rank LoRAs).

📊 Comparison

| Method | Different Ranks | Memory | Speed | Best For | |--------|----------------|--------|-------|----------| | Standard chaining | ✅ | Low | Fast | Can overexpose | | LoRA Merger (normalize) | ✅ | Low | Fast | Most cases | | LoRA True Merge | ✅ | High | Slow | Mixed ranks | | Merge to Single | ❌ Same rank | Medium | Medium | Same rank LoRAs |

🎯 Recommended Settings

For Z-Image Turbo / Distilled Models:

1. Use Z-Image LoRA Merger with normalize mode
2. Set target_strength to 0.7-0.9
3. If still overexposed, try sqrt_scale

For LoRAs with Different Ranks:

1. Use Z-Image LoRA True Merge
2. Mode: weighted_average
3. Adjust output_strength as needed

📦 Installation

Option 1: ComfyUI Manager (Recommended)

Search for "Z-Image LoRA Merger" in ComfyUI Manager and install.

Option 2: Manual Installation

1. Navigate to your ComfyUI/custom_nodes/ folder
2. Clone this repository:

git clone https://github.com/DanrisiUA/ComfyUI-ZImage-LoRA-Merger.git

3. Restart ComfyUI

Option 3: Download ZIP

1. Download this repository as ZIP
2. Extract to ComfyUI/custom_nodes/ComfyUI-ZImage-LoRA-Merger
3. Restart ComfyUI

🖼️ Nodes

After installation, you'll find these nodes in the loaders/lora category:

• Z-Image LoRA Merger — Main node with blend modes
• Z-Image LoRA True Merge — For different rank LoRAs
• Z-Image LoRA Stack — Build LoRA chains
• Z-Image LoRA Stack Apply — Apply LoRA chains
• Z-Image LoRA Merge to Single — Pre-merge LoRA weights

📝 Blend Modes Explained

normalize (recommended)

Normalizes so that sum of squared strengths equals target_strength². Preserves relative proportions while controlling total effect.

scale = target_strength / √(Σstrength²)
new_strength[i] = strength[i] × scale

average

Simply divides each strength by number of LoRAs.

new_strength[i] = strength[i] × (target_strength / n)

sqrt_scale

Scales by 1/√n — mathematically sound for independent effects.

new_strength[i] = strength[i] / √n

linear_decay

First LoRA gets full weight, others progressively less: 1, 1/2, 1/3, 1/4...

geometric_decay

Aggressive decay: 1, 0.5, 0.25, 0.125... Good when you want one dominant LoRA.

🤝 Contributing

Contributions are welcome! Feel free to:

• Report bugs
• Suggest features
• Submit pull requests

📄 License

MIT License - see LICENSE file.

🙏 Credits

• Developed by DanrisiUA
• For the ComfyUI community

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If this helped you, please ⭐ star the repo!