silveroxides-ComfyUI-ModelUtils/docs/merger_2_model_modes.md

2-Model Merger Documentation

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Weight-Sum

A * (1 - α) + B * α. A simple linear interpolation between two models.

Models Used: A, B Parameters:

• Alpha: Interpolation weight. 0.0 is 100% Model A, 1.0 is 100% Model B.

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Comparative-Interpolation

Interpolates between A and B based on the relative differences in their tensor values. Creates a unique blend by deciding how much of each model to use for each individual weight.

Models Used: A, B Parameters:

• Alpha: Controls the interpolation curve's strength and shape.
• Beta: Switches the interpolation style. 0.0 focuses on similarity, 1.0 focuses on difference.
• Gamma: Mixes between a randomized (binomial) interpolation at 0.0 and a smooth (linear) one at 1.0.

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Power-Up (DARE)

Adds the unique capabilities of Model B to Model A using the Drop and Rescale (DARE) technique. This implementation handles shape mismatches between models by padding and uses a randomized dropout mask.

Models Used: A, B Parameters:

• Alpha: The dropout rate (p). This is the proportion of delta parameters from Model B that are randomly set to zero.
• Beta: A final multiplier for the rescaled difference before it's added to Model A.
• Rescaling Logic: Remaining weights are automatically rescaled by 1/(1-p) as per the DARE paper to approximate the original embeddings.

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Power-Up (DARE+TIES)

Combines DARE (Drop and Rescale) with TIES (Trim, Elect Sign), the current best-practice algorithm for merging fine-tuned models, particularly LoRA-tuned ones. DARE sparsifies the task vector via random dropout and rescaling; TIES then removes residual low-magnitude noise and enforces sign consistency. The result is a cleaner, more targeted capability transfer from B to A with less interference.

Models Used: A, B Parameters:

• Alpha: DARE drop rate. Fraction of delta parameters randomly zeroed. Higher values produce a sparser, more targeted delta. Typical range: 0.5–0.9.
• Beta: TIES trim quantile. Fraction of the smallest-magnitude delta parameters zeroed after DARE, removing residual noise. 0.0 disables trimming. Typical range: 0.1–0.3.
• Gamma: Lambda scale. Final multiplier applied to the merged delta before adding to Model A. Equivalent to the task arithmetic scaling coefficient. Typical range: 0.5–1.5.
• Seed: Random seed for the DARE dropout mask. Controls reproducibility.

Algorithm:

1. Compute task vector δ = B − A
2. DARE: apply random binary mask with keep probability 1 − alpha; rescale survivors by 1/(1−alpha)
3. TIES trim: zero out parameters below the beta-quantile magnitude threshold
4. TIES elect: determine dominant sign per position; zero out disagreeing parameters
5. Return A + gamma × δ_filtered

Guidance:

• Start with alpha=0.9, beta=0.2, gamma=1.0 for LoRA-derived models
• Lower alpha (e.g. 0.5) for full fine-tunes where more delta mass should be retained
• Increase gamma beyond 1.0 to amplify B's influence; decrease below 1.0 to soften it

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Enhanced Man Interp

Sophisticated interpolation between values from A and B depending on their difference relative to other values, with manual threshold control.

Models Used: A, B Parameters:

• Alpha: Interpolation strength.
• Beta: Lower mean threshold for filtering differences.
• Gamma: Upper mean threshold for filtering differences.
• Delta: Smoothness factor (mix between randomized mask and powered differences).

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Enhanced Auto Interp

Automated version of the enhanced interpolation mode that dynamically calculates thresholds based on mean differences.

Models Used: A, B Parameters:

• Alpha: Interpolation strength.
• Beta: Threshold adjustment factor.
• Gamma: Smoothness factor.

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Weight-Sum Cutoff

A linear interpolation mode that only applies the merge to weights whose differences fall within a specific threshold range.

Models Used: A, B Parameters:

• Alpha: Interpolation weight (multiplier for the difference).
• Beta: Upper threshold for the difference cutoff.
• Gamma: Lower threshold for the difference cutoff.

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SVD LoRA Extraction

Extracts the difference between Model B and Model A and saves it as a new LoRA file. This mode outputs a LoRA, not a full model. The output file will be saved to your loras directory.

Models Used: A (Tuned Model), B (Base Model) Parameters:

• Alpha: The Rank (dimension) for the LoRA's standard layers.
• Beta: The Rank (dimension) specifically for 3x3 convolution layers.
• Gamma: The clamp quantile for weight values. 0.99 is a typical value to prevent outlier weights from dominating.

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Layer Mismatch Handling

When merging models with different layer structures (e.g., fine-tuned models with added/removed layers, or LoRAs with partial layer coverage), the mismatch_mode parameter controls behavior:

Mode	Behavior
skip	Missing layers in B use A's values (default)
zeros	Missing layers in B are treated as zeros
error	Fail if any layer is missing

Common scenarios:

• Merging a fine-tuned model with added/removed layers
• Combining LoRA-extracted differences with base models
• Cross-architecture experiments

Note: Extra layers in Model B that don't exist in Model A are currently ignored. The output will always have the same layer structure as Model A.

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Dtype Preservation & Override

By default, the merger preserves the highest precision dtype for each key found across all active source models (Model A and Model B) and the requested save_dtype.

• Preserve Higher Precision: If a key is float32 in any of the source models, but you requested save_dtype="bf16" or "fp16", that key will be saved as float32 anyway. This prevents degrading critical high-precision layers (such as normalization layers or layers with restored outliers).
• Explicit Override: If you prefer to force the entire model to be saved strictly as the selected save_dtype (e.g. for complete downcasting to bf16), toggle override_dtype to True.

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Layer Filtering (Regex Patterns)

Use regex patterns to control which layers are merged:

exclude_patterns

Layers matching any pattern will keep Model A's values only (no merge).

discard_patterns

Layers matching any pattern will be removed entirely from the output.

Pattern format:

• Whitespace-separated regex patterns
• Patterns use substring matching (not full match)
• Example: text_model lora matches any key containing "text_model" OR "lora"
• Example: layer\.[0-5]\. matches layers 0-5 using regex syntax

Pattern Examples:

Pattern	Matches
text_model	All text encoder layers
\.norm	All normalization layers
attn\.(q|k|v)	Query, key, value attention weights
block\.[0-9]\.	Blocks 0-9