`CLIPMergeSimple` is an advanced model merging node used to combine two CLIP text encoder models based on a specified ratio.

This node specializes in merging two CLIP models based on a specified ratio, effectively blending their characteristics. It selectively applies patches from one model to another, excluding specific components like position IDs and logit scale, to create a hybrid model that combines features from both source models.

## Inputs

| Parameter | Description | Data Type | Input Type | Default | Range |
| --- | --- | --- | --- | --- | --- |
| `clip1` | The first CLIP model to be merged. It serves as the base model for the merging process. | CLIP | REQUIRED | - | - |
| `clip2` | The second CLIP model to be merged. Its key patches, except for position IDs and logit scale, are applied to the first model based on the specified ratio. | CLIP | REQUIRED | - | - |
| `ratio` | Determines the proportion of features from the second model to blend into the first model. A ratio of 1.0 means fully adopting the second model's features, while 0.0 retains only the first model's features. | FLOAT | REQUIRED | 1.0 | 0.0 - 1.0 (step: 0.01) |

## Outputs

| Output Name | Description | Data Type |
| --- | --- | --- |
| `clip` | The resulting merged CLIP model, incorporating features from both input models according to the specified ratio. | CLIP |

## Merging Mechanism Explained

### Merging Algorithm

The node uses weighted averaging to merge the two models:

1. **Clone Base Model**: First clones `clip1` as the base model
2. **Get Patches**: Obtains all key patches from `clip2`
3. **Filter Special Keys**: Skips keys ending with `.position_ids` and `.logit_scale`
4. **Apply Weighted Merge**: Uses the formula `(1.0 - ratio) * clip1 + ratio * clip2`

### Ratio Parameter Explained

- **ratio = 0.0**: Fully uses clip1, ignores clip2
- **ratio = 0.5**: 50% contribution from each model
- **ratio = 1.0**: Fully uses clip2, ignores clip1

## Use Cases

1. **Model Style Fusion**: Combine characteristics of CLIP models trained on different data
2. **Performance Optimization**: Balance strengths and weaknesses of different models
3. **Experimental Research**: Explore combinations of different CLIP encoders

> This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! [Edit on GitHub](https://github.com/Comfy-Org/embedded-docs/blob/main/comfyui_embedded_docs/docs/CLIPMergeSimple/en.md)

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