> ## Documentation Index
> Fetch the complete documentation index at: https://dripart-docs-recommend-assets-api.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# CLIPMergeSimple - ComfyUI Built-in Node Documentation

> Complete documentation for the CLIPMergeSimple node in ComfyUI. Learn its inputs, outputs, parameters and usage.

`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 | Data Type | Description                                                                                                                                                                                                                       |
| --------- | --------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `clip1`   | CLIP      | The first CLIP model to be merged. It serves as the base model for the merging process.                                                                                                                                           |
| `clip2`   | CLIP      | 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.                                                                        |
| `ratio`   | FLOAT     | Range `0.0 - 1.0`, 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. |

## Outputs

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

## 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