T2IAdapter Soft Weights 🛂🅐🅒🅝

Documentation

• Class name: SoftT2IAdapterWeights
• Category: Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter
• Output node: False

This node is designed to adapt text-to-image (T2I) control weights for a softer blending or transition effect in generated images. It manipulates a set of weights to achieve a desired balance between different control aspects, enhancing the flexibility and precision of image generation processes.

Input types

Required

• weight_i
  • Specifies the weight for a control aspect, influencing the blending or transition effect in the generated images. The index 'i' ranges from 0 to 3, each representing a different aspect of control.
  • Comfy dtype: FLOAT
  • Python dtype: float
• flip_weights
  • When enabled, reverses the order of weights, offering an alternative approach to control the blending or transition effects.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Optional

• uncond_multiplier
  • Adjusts the unconditional aspect of the weights, providing an additional layer of control over the image generation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• cn_extras
  • Includes extra control net weights for extended customization and fine-tuning capabilities in the image generation process.
  • Comfy dtype: CN_WEIGHTS_EXTRAS
  • Python dtype: dict[str]
• autosize
  • Configures automatic sizing parameters, affecting how control net weights are applied and adjusted.
  • Comfy dtype: ACNAUTOSIZE
  • Python dtype: dict

Output types

• CN_WEIGHTS
  • Comfy dtype: CONTROL_NET_WEIGHTS
  • The computed control net weights after processing input parameters.
  • Python dtype: Tensor
• TK_SHORTCUT
  • Comfy dtype: TIMESTEP_KEYFRAME
  • A shortcut representation for timestep keyframes, facilitating easier manipulation and application in the control net process.
  • Python dtype: Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class SoftT2IAdapterWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "flip_weights": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 55}),
            }
        }

    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"

    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        weights = [weight_00, weight_01, weight_02, weight_03]
        weights = get_properly_arranged_t2i_weights(weights)
        weights.reverse()  # to account for recent ComfyUI changes
        weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))