CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/ComfyUI_IPAdapter_plus/docs/IPAdapterWeightsFromStrategy.md

tags

tags
ControlNet Weight

IPAdapter Weights From Strategy

Documentation

• Class name: IPAdapterWeightsFromStrategy
• Category: ipadapter/weights
• Output node: False

The IPAdapterWeightsFromStrategy node is designed to generate weights for image processing based on a specified strategy. It allows for the dynamic adjustment of image processing parameters, facilitating tailored image manipulation according to the strategy chosen.

Input types

Required

• weights_strategy
  • Specifies the strategy to be used for generating weights, influencing how images are processed and manipulated.
  • Comfy dtype: WEIGHTS_STRATEGY
  • Python dtype: str

Optional

• image
  • An optional image input that can be used in conjunction with the weights strategy to further customize the image processing.
  • Comfy dtype: IMAGE
  • Python dtype: str

Output types

• weights
  • Comfy dtype: FLOAT
  • The generated weights based on the specified strategy.
  • Python dtype: List[float]
• weights_invert
  • Comfy dtype: FLOAT
  • The inverted weights derived from the original weights, used for alternative processing effects.
  • Python dtype: List[float]
• total_frames
  • Comfy dtype: INT
  • The total number of frames calculated based on the weights strategy, affecting the duration of the image processing.
  • Python dtype: int
• image_1
  • Comfy dtype: IMAGE
  • The first image output, modified according to the weights and strategy applied.
  • Python dtype: torch.Tensor
• image_2
  • Comfy dtype: IMAGE
  • The second image output, modified in a different manner based on the weights and strategy for varied effects.
  • Python dtype: torch.Tensor
• weights_strategy
  • Comfy dtype: WEIGHTS_STRATEGY
  • The weights strategy used for processing, including all parameters and settings involved.
  • Python dtype: Dict

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class IPAdapterWeightsFromStrategy(IPAdapterWeights):
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "weights_strategy": ("WEIGHTS_STRATEGY",),
            }, "optional": {
                "image": ("IMAGE",),
            }
        }