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A ComfyUI node that automatically analyzes your LoRA stack and selects the best merge strategy — diff-based merging, TIES conflict resolution, and auto-tuned parameters. Two nodes: LoRA Stack (build input) and LoRA Optimizer (analyze + merge).

The Problem

Stacking LoRAs in ComfyUI adds their effects together. On distilled/turbo models (SDXL-Turbo, LCM, Lightning, Flux-schnell), the accumulated effect exceeds what the model can handle, causing overexposure, color blowout, and artifacts.

model += lora1_effect x strength1
model += lora2_effect x strength2
total effect = strength1 + strength2  -->  easily exceeds 1.0

The optimizer solves this by computing full weight diffs, detecting sign conflicts, and merging with the optimal strategy.

Nodes

LoRA Stack

Builds a list of LoRAs for the optimizer. Chain multiple Stack nodes to add any number of LoRAs.

Inputs: LoRA selector, strength, optional previous LORA_STACK

Outputs: LORA_STACK

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LoRA Optimizer

The auto-optimizer. Takes a LORA_STACK, analyzes the LoRAs, and automatically selects the best merge mode and parameters. Outputs the merged result plus a detailed analysis report explaining what it chose and why.

Also accepts standard tuple-format stacks (lora_name, model_strength, clip_strength) from Efficiency Nodes, Comfyroll, and similar packs.

Uses a two-pass streaming architecture for low memory usage:

• Pass 1 (Analysis): Computes weight diffs per prefix, samples conflict and magnitude statistics, then discards the diffs. Only lightweight scalars are kept.
• Pass 2 (Merge): Recomputes diffs per prefix and merges with the auto-selected strategy. Each prefix is freed after merging.

Peak memory is ~one prefix at a time (~260MB) regardless of LoRA count or model size. GPU-accelerated on both passes.

What it analyzes:

• Per-LoRA metrics (rank, key count, effective L2 norms)
• Pairwise sign conflict ratios (sampled for efficiency)
• Magnitude distribution across all weight diffs
• Key overlap between LoRAs

How it decides:

Condition	Decision
Sign conflict > 25%	TIES mode (resolves conflicts)
Sign conflict <= 25%	weighted_average (simple, effective)
Magnitude ratio > 2x between LoRAs	total sign method (stronger LoRA gets more influence)
Magnitude ratio <= 2x	frequency sign method (equal votes)
TIES mode selected	Auto-density estimated from magnitude distribution

Inputs: MODEL, CLIP, LORA_STACK, output strength, clip strength multiplier, auto strength, free VRAM between passes.

Outputs: MODEL, CLIP, STRING (analysis report)

TIES Merging

The optimizer can automatically select TIES-Merging (Trim, Elect Sign, Disjoint Merge — Yadav et al., NeurIPS 2023) when sign conflicts are detected between LoRAs.

Auto-Strength

When auto_strength is set to enabled, the optimizer automatically reduces per-LoRA strengths before merging to prevent overexposure from stacking. This is especially useful on distilled/turbo models where 2+ LoRAs at full strength cause blown-out results even with optimal merge mode selection.

The algorithm uses L2-aware energy normalization: it measures each LoRA's actual weight magnitude (L2 norms collected during analysis) and scales all strengths so the total combined energy matches what the strongest single LoRA would contribute alone.

Scenario	Result
2 equal LoRAs at strength 1.0	Each reduced to ~0.71 (1/sqrt(2))
1 strong + 1 weak LoRA	Proportional reduction, preserving ratio
Single LoRA	No change (scale = 1.0)
auto_strength disabled	No adjustment (default)

Your original strength ratios are always preserved — the algorithm only scales them down uniformly.

Example Report

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LORA OPTIMIZER - ANALYSIS REPORT
==================================================

--- Per-LoRA Analysis ---
  style_lora.safetensors:
    Strength: 1.0
    Keys: 192
    Avg rank: 64
    L2 norm (mean): 0.0847
  detail_lora.safetensors:
    Strength: 0.8
    Keys: 192
    Avg rank: 32
    L2 norm (mean): 0.0423

--- Auto-Strength Adjustment ---
  style_lora.safetensors: 1.0 -> 0.7071
  detail_lora.safetensors: 0.8 -> 0.5657
  Scale factor: 0.7071
  Method: L2-aware energy normalization

--- Pairwise Analysis ---
  style_lora.safetensors vs detail_lora.safetensors:
    Overlapping positions: 89420
    Sign conflicts: 31297 (35.0%)

--- Collection Statistics ---
  Total LoRAs: 2
  Total unique keys: 196
  Avg sign conflict ratio: 35.0%
  Magnitude ratio (max/min L2): 2.00x

--- Auto-Selected Parameters ---
  Merge mode: ties
  Density: 0.42
  Sign method: frequency

--- Reasoning ---
  Sign conflict ratio 35.0% > 25% threshold -> TIES mode selected
    TIES resolves sign conflicts via trim + elect sign + disjoint merge
  Auto-density estimated at 0.42 from magnitude distribution
  Magnitude ratio 2.00x <= 2x -> 'frequency' sign method (equal voting)
    Similar-strength LoRAs get equal votes

--- Merge Summary ---
  Keys processed: 196
  Model patches: 168
  CLIP patches: 28
  Output strength: 1.0
  CLIP strength: 1.0

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Connect the STRING output to a Show Text node to see the report in ComfyUI.

Limitation: The optimizer only analyzes LoRAs in its own stack. It cannot see LoRA patches applied by upstream nodes (Load LoRA, etc.) — those stack additively on top of the optimizer's output. Fully baked merges (safetensors checkpoints) are indistinguishable from base weights and cannot be detected.

Installation

ComfyUI Manager

Search for "LoRA Optimizer" in ComfyUI Manager and install.

Manual

cd ComfyUI/custom_nodes/
git clone https://github.com/ethanfel/ComfyUI-LoRA-Optimizer.git

Restart ComfyUI. Both nodes appear under the loaders/lora category.

Compatibility

• Models: SD 1.5, SDXL, Flux, and other architectures supported by ComfyUI
• LoRA formats: Standard LoRA, LoCon, diffusers formats
• Flux sliced weights: Handled correctly (linear1_qkv offsets)
• Stack formats: Native LoRA Stack dicts, plus standard tuples from Efficiency Nodes / Comfyroll

Credits

• TIES-Merging: Yadav et al., NeurIPS 2023

License

MIT License - see LICENSE.