84 lines
3.3 KiB
Markdown
84 lines
3.3 KiB
Markdown
# 3-Model Merger Documentation
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---
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## Add-Difference
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> `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).
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**Models Used:** A, B, C
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**Parameters:**
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- **Alpha:** A multiplier for the `(B - C)` difference vector before it's added to A.
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- **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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---
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## Train-Difference
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> 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.
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**Models Used:** A, B, C
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**Parameters:**
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- **Alpha:** A multiplier for the final calculated "training" difference.
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---
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## Extract-Features
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> 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.
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**Models Used:** A, B, C
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**Parameters:**
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- **Alpha:** Weights the merge between Model B (`0.0`) and Model C (`1.0`).
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- **Beta:** Controls the focus on similarity (`0.0`) versus dissimilarity (`1.0`).
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- **Gamma:** A bias exponent for similarity calculation.
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- **Delta:** A final multiplier for the extracted features before they are added to Model A.
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---
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## Add-Dissimilarities
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> 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.
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**Models Used:** A, B, C
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**Parameters:**
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- **Alpha:** Weights the comparison between Model B (`0.0`) and Model C (`1.0`).
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- **Beta:** A multiplier for the final extracted dissimilar features.
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- **Gamma:** A bias exponent for the similarity calculation.
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---
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## Layer Mismatch Handling
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When merging models with different layer structures, the `mismatch_mode` parameter controls behavior:
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| Mode | Behavior |
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|------|----------|
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| `skip` | Missing layers in B/C use A's values **(default)** |
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| `zeros` | Missing layers in B/C are treated as zeros |
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| `error` | Fail if any layer is missing |
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**Special case for B - C subtraction (Add-Difference, Train-Difference):**
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- If **both** B and C have the layer: Normal subtraction is performed
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- 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
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**Common scenarios:**
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- Merging a fine-tuned model with added/removed layers
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- Extracting style differences between models with slightly different architectures
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- Combining partial LoRAs or adapters
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**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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---
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## Layer Filtering (Regex Patterns)
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Use regex patterns to control which layers are merged:
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### `exclude_patterns`
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Layers matching any pattern will **keep Model A's values only** (no merge).
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### `discard_patterns`
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Layers matching any pattern will be **removed entirely** from the output.
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**Pattern format:**
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- Whitespace-separated regex patterns
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- Patterns use **substring matching** (not full match)
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- Example: `text_model lora` matches any key containing "text_model" OR "lora"
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