239 lines
12 KiB
Python
239 lines
12 KiB
Python
from torch import Tensor
|
|
import torch
|
|
from .utils import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights, linear_conversion
|
|
from .logger import logger
|
|
|
|
|
|
WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
|
|
|
|
|
|
class DefaultWeights:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"optional": {
|
|
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
|
|
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
|
|
|
def load_weights(self, cn_extras: dict[str]={}):
|
|
weights = ControlWeights.default(extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
|
|
|
|
|
class ScaledSoftMaskedUniversalWeights:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"mask": ("MASK", ),
|
|
"min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
|
|
"max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
|
|
#"lock_min": ("BOOLEAN", {"default": False}, ),
|
|
#"lock_max": ("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": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
|
|
|
def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
|
|
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
|
|
# normalize mask
|
|
mask = mask.clone()
|
|
x_min = 0.0 if lock_min else mask.min()
|
|
x_max = 1.0 if lock_max else mask.max()
|
|
if x_min == x_max:
|
|
mask = torch.ones_like(mask) * max_base_multiplier
|
|
else:
|
|
mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
|
|
weights = ControlWeights.universal_mask(weight_mask=mask, uncond_multiplier=uncond_multiplier, extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
|
|
|
|
|
class ScaledSoftUniversalWeightsDeprecated:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "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": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
|
|
|
def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
|
|
weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
|
|
|
|
|
class SoftControlNetWeightsSD15:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"output_0": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_1": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_2": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_3": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_4": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_5": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_6": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_7": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_8": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_9": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
},
|
|
"optional": {
|
|
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
|
|
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
|
|
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
|
|
|
|
def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
|
|
output_7, output_8, output_9, output_10, output_11, middle_0,
|
|
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
|
|
weights_output = [output_0, output_1, output_2, output_3, output_4, output_5, output_6,
|
|
output_7, output_8, output_9, output_10, output_11]
|
|
weights_middle = [middle_0]
|
|
weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
|
|
|
|
|
class CustomControlNetWeightsSD15:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"output_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"output_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
},
|
|
"optional": {
|
|
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
|
|
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
|
|
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
|
|
|
|
def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
|
|
output_7, output_8, output_9, output_10, output_11, middle_0,
|
|
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
|
|
weights_output = [output_0, output_1, output_2, output_3, output_4, output_5, output_6,
|
|
output_7, output_8, output_9, output_10, output_11]
|
|
weights_middle = [middle_0]
|
|
weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
|
|
|
|
|
class SoftT2IAdapterWeights:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"input_0": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"input_1": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"input_2": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
},
|
|
"optional": {
|
|
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
|
|
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
|
|
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
|
|
|
|
def load_weights(self, input_0, input_1, input_2, input_3,
|
|
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
|
|
weights = [input_0, input_1, input_2, input_3]
|
|
weights = get_properly_arranged_t2i_weights(weights)
|
|
weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
|
|
|
|
|
class CustomT2IAdapterWeights:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
"input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
|
},
|
|
"optional": {
|
|
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
|
|
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
|
|
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
|
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
|
FUNCTION = "load_weights"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
|
|
|
|
def load_weights(self, input_0, input_1, input_2, input_3,
|
|
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
|
|
weights = [input_0, input_1, input_2, input_3]
|
|
weights = get_properly_arranged_t2i_weights(weights)
|
|
weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
|
|
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|