# 3-Model Merger Documentation

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## Add-Difference
> `A + (B - C) * α`. This classic technique applies the "difference" between models B and C to model A. For example, it can be used to add a style (B) to a base model (A) while subtracting the style's original base (C).

**Models Used:** A, B, C
**Parameters:**
- **Alpha:** A multiplier for the `(B - C)` difference vector before it's added to A.
- **Beta:** A smoothing toggle. `0` for Off, `1` for On. When On, applies a median and gaussian filter to the difference, which can result in a smoother merge.

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## Train-Difference
> A variation of Add-Difference from the SuperMerger extension that uses a more complex scaling factor based on the relative distances between the tensors of A, B, and C.

**Models Used:** A, B, C
**Parameters:**
- **Alpha:** A multiplier for the final calculated "training" difference.

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## Extract-Features
> A powerful mode that identifies features present in both `(B - A)` and `(C - A)` and adds them to A. It uses per-vector cosine similarity to decide how much of each feature to keep, allowing for fine-grained control over combining aspects.

**Models Used:** A, B, C
**Parameters:**
- **Alpha:** Weights the merge between Model B (`0.0`) and Model C (`1.0`).
- **Beta:** Controls the focus on similarity (`0.0`) versus dissimilarity (`1.0`).
- **Gamma:** A bias exponent for similarity calculation.
- **Delta:** A final multiplier for the extracted features before they are added to Model A.

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## Add-Dissimilarities
> Identifies features that are dissimilar between Model B and Model C (relative to A) and adds them to Model A. Useful for combining unique aspects of two different models.

**Models Used:** A, B, C
**Parameters:**
- **Alpha:** Weights the comparison between Model B (`0.0`) and Model C (`1.0`).
- **Beta:** A multiplier for the final extracted dissimilar features.
- **Gamma:** A bias exponent for the similarity calculation.

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## Layer Mismatch Handling

When merging models with different layer structures, the `mismatch_mode` parameter controls behavior:

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

**Special case for B - C subtraction (Add-Difference, Train-Difference):**
- If **both** B and C have the layer: Normal subtraction is performed
- If **either** B or C is missing the layer: The difference is treated as `None` (no contribution from this layer), and Model A's value is used

**Common scenarios:**
- Merging a fine-tuned model with added/removed layers
- Extracting style differences between models with slightly different architectures
- Combining partial LoRAs or adapters

**Note:** Extra layers in Models B/C 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, B, and C) and the requested `save_dtype`.

- **Preserve Higher Precision**: If a key is `float32` in any of the active 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"