The CFGNorm node applies a normalization technique to the classifier-free guidance (CFG) process in diffusion models. It adjusts the scale of the denoised prediction by comparing the norms of the conditional and unconditional outputs, then applies a strength multiplier to control the effect. This helps stabilize the generation process by preventing extreme values in the guidance scaling.

## Inputs

| Parameter | Description | Data Type | Required | Range |
| --- | --- | --- | --- | --- |
| `model` | The diffusion model to apply CFG normalization to | MODEL | Yes | - |
| `strength` | Controls the intensity of the normalization effect applied to the CFG scaling (default: 1.0) | FLOAT | Yes | 0.0 to 100.0 |

## Outputs

| Output Name | Description | Data Type |
| --- | --- | --- |
| `patched_model` | Returns the modified model with CFG normalization applied to its sampling process | MODEL |

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