Automatic CFG - Negative¶
Documentation¶
- Class name:
Automatic CFG - Negative
- Category:
model_patches/automatic_cfg/presets
- Output node:
False
This node specializes in applying a dynamic configuration to models for generating content, with a focus on enhancing the generation process by adjusting the model's behavior based on negative prompts. It leverages an advanced configuration technique to fine-tune the model's output, aiming to mitigate the influence of negative aspects specified by the user.
Input types¶
Required¶
model
- The model parameter is the core component that the node modifies, applying a dynamic configuration to adjust its behavior for content generation.
- Comfy dtype:
MODEL
- Python dtype:
MODEL
boost
- The boost parameter determines whether to skip unconditional generation steps, effectively altering the model's generation process to focus more on the specified conditions.
- Comfy dtype:
BOOLEAN
- Python dtype:
BOOLEAN
negative_strength
- This parameter controls the strength of the negative conditioning, allowing for fine-tuning how strongly the model should mitigate or ignore the specified negative aspects during generation.
- Comfy dtype:
FLOAT
- Python dtype:
FLOAT
Output types¶
model
- Comfy dtype:
MODEL
- The modified model with applied dynamic configuration, tailored to enhance content generation by considering negative prompts.
- Python dtype:
MODEL
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes: unknown
Source code¶
class simpleDynamicCFGlerpUncond:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"boost" : ("BOOLEAN", {"default": True}),
"negative_strength": ("FLOAT", {"default": 1, "min": 0.0, "max": 5.0, "step": 0.1, "round": 0.1}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches/automatic_cfg/presets"
def patch(self, model, boost, negative_strength):
advcfg = advancedDynamicCFG()
m = advcfg.patch(model=model,
automatic_cfg="hard", skip_uncond=boost,
uncond_sigma_start = 15, uncond_sigma_end = 1,
lerp_uncond=negative_strength != 1, lerp_uncond_strength=negative_strength,
lerp_uncond_sigma_start = 15, lerp_uncond_sigma_end = 1
)[0]
return (m, )