This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHubThe 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 | Data Type | Input Type | Default | Range | Description |
|---|---|---|---|---|---|
model | MODEL | required | - | - | The diffusion model to apply CFG normalization to |
strength | FLOAT | required | 1.0 | 0.0 - 100.0 | Controls the intensity of the normalization effect applied to the CFG scaling |
Outputs
| Output Name | Data Type | Description |
|---|---|---|
patched_model | MODEL | Returns the modified model with CFG normalization applied to its sampling process |