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# CLIPAttentionMultiply - ComfyUI Built-in Node Documentation

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

> 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/CLIPAttentionMultiply/en.md)

The CLIPAttentionMultiply node allows you to adjust the attention mechanism in CLIP models by applying multiplication factors to different components of the self-attention layers. It works by modifying the query, key, value, and output projection weights and biases in the CLIP model's attention mechanism. This experimental node creates a modified copy of the input CLIP model with the specified scaling factors applied.

## Inputs

| Parameter | Data Type | Input Type | Default | Range      | Description                                                    |
| --------- | --------- | ---------- | ------- | ---------- | -------------------------------------------------------------- |
| `clip`    | CLIP      | required   | -       | -          | The CLIP model to modify                                       |
| `q`       | FLOAT     | required   | 1.0     | 0.0 - 10.0 | Multiplication factor for query projection weights and biases  |
| `k`       | FLOAT     | required   | 1.0     | 0.0 - 10.0 | Multiplication factor for key projection weights and biases    |
| `v`       | FLOAT     | required   | 1.0     | 0.0 - 10.0 | Multiplication factor for value projection weights and biases  |
| `out`     | FLOAT     | required   | 1.0     | 0.0 - 10.0 | Multiplication factor for output projection weights and biases |

## Outputs

| Output Name | Data Type | Description                                                                        |
| ----------- | --------- | ---------------------------------------------------------------------------------- |
| `CLIP`      | CLIP      | Returns a modified CLIP model with the specified attention scaling factors applied |