T2IAdapter Soft Weights 🛂🅐🅒🅝¶
Documentation¶
- Class name:
SoftT2IAdapterWeights
- Category:
Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter
- Output node:
False
The SoftT2IAdapterWeights node is designed to adjust the influence of control weights within a text-to-image adaptation process, allowing for a more nuanced and customizable image generation based on the specified weights and the option to flip these weights.
Input types¶
Required¶
weight_i
- Specifies a control weight at index 'i', influencing the adaptation process at various stages. The index 'i' represents a sequence of control weights, allowing for detailed customization of the image generation process.
- Comfy dtype:
FLOAT
- Python dtype:
float
flip_weights
- A boolean flag that, when true, reverses the order of control weights, potentially altering the adaptation process.
- Comfy dtype:
BOOLEAN
- Python dtype:
bool
Output types¶
CN_WEIGHTS
- Comfy dtype:
CONTROL_NET_WEIGHTS
- The adjusted control weights after processing through the SoftT2IAdapterWeights node.
- Python dtype:
list
- Comfy dtype:
TK_SHORTCUT
- Comfy dtype:
TIMESTEP_KEYFRAME
- A keyframe group indicating specific timesteps where the control weights have significant influence.
- Python dtype:
TimestepKeyframeGroup
- Comfy dtype:
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}),
},
}
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):
weights = [weight_00, weight_01, weight_02, weight_03]
weights = get_properly_arranged_t2i_weights(weights)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))