T2IAdapter Custom Weights 🛂🅐🅒🅝¶
Documentation¶
- Class name:
CustomT2IAdapterWeights
- Category:
Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter
- Output node:
False
This node is designed to load and configure weights for a custom Text-to-Image (T2I) Adapter within the Advanced ControlNet framework. It allows for the dynamic adjustment of weight parameters to fine-tune the control over the image generation process, incorporating options to flip weights for varied effects.
Input types¶
Required¶
weight_i
- unknown
- Comfy dtype:
FLOAT
- Python dtype:
unknown
flip_weights
- A boolean parameter that, when enabled, reverses the order of weights, potentially altering the image generation outcome for creative variations.
- Comfy dtype:
BOOLEAN
- Python dtype:
bool
Output types¶
CN_WEIGHTS
- Comfy dtype:
CONTROL_NET_WEIGHTS
- Outputs the configured weights as a set, ready for application within the T2I Adapter for controlling image generation.
- Python dtype:
list[float]
- Comfy dtype:
TK_SHORTCUT
- Comfy dtype:
TIMESTEP_KEYFRAME
- Generates a keyframe group based on the configured weights, facilitating precise control over the image generation timeline.
- Python dtype:
TimestepKeyframeGroup
- Comfy dtype:
Usage tips¶
- Infra type:
CPU
- Common nodes: unknown
Source code¶
class CustomT2IAdapterWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_02": ("FLOAT", {"default": 1.0, "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)))