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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 RenormCFG node modifies the classifier-free guidance (CFG) process in diffusion models by applying conditional scaling and normalization. It adjusts the denoising process based on specified timestep thresholds and renormalization factors to control the influence of conditional versus unconditional predictions during image generation.
Inputs
| Parameter | Data Type | Required | Range | Description |
|---|---|---|---|---|
model | MODEL | Yes | - | The diffusion model to apply renormalized CFG to |
cfg_trunc | FLOAT | No | 0.0 - 100.0 | Timestep threshold for applying CFG scaling. When the current timestep is below this value, CFG scaling is applied; otherwise, only the conditional prediction is used (default: 100.0) |
renorm_cfg | FLOAT | No | 0.0 - 100.0 | Renormalization factor that limits the maximum norm of the CFG-scaled prediction relative to the original conditional prediction. A value of 0.0 disables renormalization (default: 1.0) |
Outputs
| Output Name | Data Type | Description |
|---|---|---|
model | MODEL | The modified model with renormalized CFG function applied |
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