Documentation Index
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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 | Required | Range | Description |
|---|---|---|---|---|
model | MODEL | Yes | - | The diffusion model to apply CFG normalization to |
strength | FLOAT | Yes | 0.0 to 100.0 | Controls the intensity of the normalization effect applied to the CFG scaling (default: 1.0) |
Outputs
| Output Name | Data Type | Description |
|---|---|---|
patched_model | MODEL | Returns the modified model with CFG normalization applied to its sampling process |
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