> ## Documentation Index
> Fetch the complete documentation index at: https://docs.comfy.org/llms.txt
> Use this file to discover all available pages before exploring further.

# UNetCrossAttentionMultiply - ComfyUI Built-in Node Documentation

> Complete documentation for the UNetCrossAttentionMultiply 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/UNetCrossAttentionMultiply/en.md)

The UNetCrossAttentionMultiply node applies multiplication factors to the cross-attention mechanism in a UNet model. It allows you to scale the query, key, value, and output components of the cross-attention layers to experiment with different attention behaviors and effects.

## Inputs

| Parameter | Data Type | Required | Range      | Description                                                            |
| --------- | --------- | -------- | ---------- | ---------------------------------------------------------------------- |
| `model`   | MODEL     | Yes      | -          | The UNet model to modify with attention scaling factors                |
| `q`       | FLOAT     | No       | 0.0 - 10.0 | Scaling factor for query components in cross-attention (default: 1.0)  |
| `k`       | FLOAT     | No       | 0.0 - 10.0 | Scaling factor for key components in cross-attention (default: 1.0)    |
| `v`       | FLOAT     | No       | 0.0 - 10.0 | Scaling factor for value components in cross-attention (default: 1.0)  |
| `out`     | FLOAT     | No       | 0.0 - 10.0 | Scaling factor for output components in cross-attention (default: 1.0) |

## Outputs

| Output Name | Data Type | Description                                                    |
| ----------- | --------- | -------------------------------------------------------------- |
| `model`     | MODEL     | The modified UNet model with scaled cross-attention components |