🛠️ Easy LoRA Merger Suite

Complexity Made Simple — Merge Anything

The Easy LoRA Merger Suite gives you a complete toolkit to merge, convert, extract, bake, and audit LoRAs and checkpoints — without worrying about tensor dimensions, sparsity mismatches, or arcane scaling math.

Every node is built with the same philosophy: powerful defaults when you need simplicity, full control when you need precision.

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🔍 All Nodes at a Glance

The complete node suite — drag, connect, and merge.

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📦 Node Catalog

Node	Display Name	Purpose
🎨	Easy LoRA Merger	Merge 2–3 LoRAs with 15+ methods, auto‑format detection, smart defaults, and forensic reports. The core workhorse.
🛡️	Easy LoRA Studio	Universal LoRA analyzer and converter. Transforms between Standard WebUI, Comfy Native, and Forge‑Optimized formats. SVD compression, TE scaling, weight adjustment.
🛡️	Easy Checkpoint Studio	Precision surgery for full checkpoints — precision casting FP8/BF16/FP32, component stripping VAE/TE/CLIP, SVD compression, and structural remapping.
🎨	Easy Checkpoint Merger	Merge 2–3 full model checkpoints with Weight Block Map component‑wise scaling. Streaming engine for low RAM usage.
🔬	Easy LoRA Extractor	Extract a LoRA from the delta between a base and fine‑tuned checkpoint. SVD decomposition with auto‑energy rank selection.
🔧	Easy Component Extractor	Split a checkpoint into its building blocks — extract UNet, CLIP, or VAE as separate files with precision control.
🧩	Easy Component Combiner	Reassemble extracted UNet + CLIP + VAE components back into a single full checkpoint.
🧩	Easy Component Merger	Merge 2–3 component state dicts (e.g., two different CLIP sets or UNets) using the full merge method suite.
🔥	Easy LoRA Baker	Bake a LoRA (or merged LoRA) directly into a full checkpoint at the tensor level. Produces MODEL+CLIP+VAE with RAM Guard fallback.

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🎯 Merge Methods

Method	Best for	Works on Checkpoints?
linear	Simple weighted average — the safe starting point for any merge	✅ Yes
cross	Blending with a cross‑magnitude interaction term for richer mixes	✅ Yes
ties_strict	Conflicting styles — only keeps weights where both sources agree	⚠️ Limited (all-positive weights reduce effect)
ties_gentle	Softer version of TIES — applies only to strong disagreements	⚠️ Limited (all-positive weights reduce effect)
ties_contrast	Amplify differences between two sources, mute agreements	⚠️ Limited (best on LoRA deltas)
dare_lite	Random dropout — creates sparse, stochastic blends	❌ LoRA only (risky on checkpoint weights)
dare_rescale	Random dropout with rescaling — preserves magnitude distribution	❌ LoRA only (risky on checkpoint weights)
magnitude	Keep the stronger signal per-element from either source	✅ Yes
subtract	Remove unwanted features by subtracting one source from another	✅ Yes
feature_mix	Preserve unique features from each source — great for style + character	✅ Yes
slerp	Smooth spherical interpolation between two vectors	✅ Yes (2‑way only)
svd_preserve	SVD‑based rank reduction — keeps core structure while reducing noise	✅ Yes
block_swap	Swap blocks between sources using a seeded random pattern	✅ Yes
noise_aware	Suppress small noise values before merging for cleaner results	✅ Yes
gradient_alignment	Weight contributions by directional similarity between sources	✅ Yes

💡 Tip: When in doubt, start with linear or magnitude — they're the most versatile and work well across LoRAs and checkpoints alike.

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🧠 Smart Diagnostics

Every merge node outputs a detailed forensic report with:

• ✅ Alignment verification — cross‑architecture key matching stats
• 📊 Layer‑by‑layer statistics — energy distribution, sparsity, component breakdown
• ⚠️ Clear warnings — mismatched trainers, conflicting trigger words, density risks
• 🔍 Sparsity + scaling reports — magnitude distribution per layer

Console output keeps you informed at every step without overwhelming.

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🏗️ Supported Architectures (aspirational — not all fully tested)

Architecture	Status
Flux.1‑Dev / Flux.1‑S	Tested
Flux Klein 4B / 9B	Tested
Z‑Image Turbo / Base	Tested
SDXL	Tested
SD1.5	Tested
Anima	Early support
Lumina 2	Early support
SD3	Experimental

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🚀 Get Started

• Download: github.com/Terpentinas/EasyLoRAMerger
• Install: Drop into ComfyUI/custom_nodes/ and restart ComfyUI.
• Explore: Drag assets/nodes.png into the workflow area to see the suite in action.
• Experiment: Connect a Easy LoRA Merger → Easy LoRA Baker pipeline, or use Easy Checkpoint Studio to shrink a 12GB checkpoint to FP8.
• Feedback: Open an issue on GitHub — contributions and ideas welcome.

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⚙️ Precision Options

Different nodes expose different precision tiers depending on their capabilities:

Standard Tier — all float formats

Used by: Easy LoRA Merger, Easy LoRA Studio

Option	Behavior
auto	Auto-select — defaults to BF16 on supported GPUs
float32	Full precision, 4 bytes/param — maximum quality
bfloat16	Half precision, 2 bytes/param — good balance
float16	Half precision, 2 bytes/param — good balance

Extended Tier — adds FP8 support

Used by: Easy Checkpoint Merger, Easy Component Extractor, Easy Component Combiner, Easy Component Merger, Easy LoRA Baker

Option	Behavior
(all Standard options above)
fp8_e4m3fn	FP8 quantization, 1 byte/param — memory efficient
fp8_e5m2	FP8 quantization (wider range) — memory efficient

Studio Tier — full conversion toolkit

Used by: Easy Checkpoint Studio

Option	Behavior
(all Extended options above)
int8	Explicit INT8 quantization, 1 byte/param
int8_convrot	INT8 with convolution rotation handling
svd_only	SVD compression only, no dtype conversion

💡 Tip: For everyday use, just leave it on auto. The nodes will pick the best format for your hardware automatically.

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📐 Design Philosophy

The suite is engineered for stability and SSD-friendly operation:

• No unnecessary disk writes — intermediate data stays in RAM. Temp files are created only when RAM Guard activates or save_trigger=True.
• Clean resource management — file handles from checkpoint loading are closed immediately after model objects are constructed, preventing stale handles from causing I/O during garbage collection.
• Predictable performance — no background cleanup threads. All file I/O is explicit and happens at known points in the pipeline.
• Memory-efficient streaming — large checkpoints are processed in configurable batch sizes, keeping peak RAM usage under control.

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Built because merging different model formats shouldn't require a PhD in tensor math. 💪