The UNetTemporalAttentionMultiply node applies multiplication factors to different types of attention mechanisms in a temporal UNet model. It modifies the model by adjusting the weights of self-attention and cross-attention layers, distinguishing between structural and temporal components. This allows fine-tuning of how much influence each attention type has on the model's output.

## Inputs

| Parameter | Description | Data Type | Required | Range |
| --- | --- | --- | --- | --- |
| `model` | The input model to modify with attention multipliers | MODEL | Yes | - |
| `self_structural` | Multiplier for self-attention structural components (default: 1.0) | FLOAT | No | 0.0 - 10.0 |
| `self_temporal` | Multiplier for self-attention temporal components (default: 1.0) | FLOAT | No | 0.0 - 10.0 |
| `cross_structural` | Multiplier for cross-attention structural components (default: 1.0) | FLOAT | No | 0.0 - 10.0 |
| `cross_temporal` | Multiplier for cross-attention temporal components (default: 1.0) | FLOAT | No | 0.0 - 10.0 |

## Outputs

| Output Name | Description | Data Type |
| --- | --- | --- |
| `model` | The modified model with adjusted attention weights | MODEL |

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