Automatic CFG

Documentation

• Class name: Automatic CFG
• Category: model_patches/automatic_cfg/presets
• Output node: False

The Automatic CFG node applies a dynamic configuration to models, enhancing their performance by adjusting parameters based on the 'boost' flag. This adjustment aims to optimize the model's operation, potentially improving efficiency and output quality.

Input types

Required

• model
  • The 'model' parameter represents the model to which the dynamic configuration will be applied. It is crucial for defining the base upon which the adjustments and optimizations are performed.
  • Comfy dtype: MODEL
  • Python dtype: torch.nn.Module
• boost
  • The 'boost' flag determines the intensity of the configuration adjustments. Enabling 'boost' applies a more aggressive optimization strategy, potentially leading to significant enhancements in model performance.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• model
  • Comfy dtype: MODEL
  • The output is a modified version of the input model, optimized through dynamic configuration adjustments for improved performance.
  • Python dtype: torch.nn.Module

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class simpleDynamicCFG:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                                "model": ("MODEL",),
                                "boost" : ("BOOLEAN", {"default": True}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "model_patches/automatic_cfg/presets"

    def patch(self, model, boost):
        advcfg = advancedDynamicCFG()
        m = advcfg.patch(model,
                         skip_uncond = boost,
                         uncond_sigma_start = 15,  uncond_sigma_end = 1,
                         automatic_cfg = "hard" if boost else "soft"
                         )[0]
        return (m, )