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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: | |
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 ππ π π /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: | |
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 ππ π π /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 ScaledSoftUniversalWeights: | |
def INPUT_TYPES(s): | |
return { | |
"required": { | |
"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "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 ππ π π /weights" | |
def load_weights(self, base_multiplier, 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: | |
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 ππ π π /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]={}): | |
return CustomControlNetWeightsSD15.load_weights(self, | |
output_0=output_0, output_1=output_1, output_2=output_2, output_3=output_3, | |
output_4=output_4, output_5=output_5, output_6=output_6, output_7=output_7, | |
output_8=output_8, output_9=output_9, output_10=output_10, output_11=output_11, | |
middle_0=middle_0, | |
uncond_multiplier=uncond_multiplier, cn_extras=cn_extras) | |
class CustomControlNetWeightsSD15: | |
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 ππ π π /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 CustomControlNetWeightsFlux: | |
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}, ), | |
"input_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_13": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_14": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_15": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_16": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_17": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), | |
"input_18": ("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 ππ π π /weights/ControlNet" | |
def load_weights(self, input_0, input_1, input_2, input_3, input_4, input_5, input_6, | |
input_7, input_8, input_9, input_10, input_11, input_12, input_13, | |
input_14, input_15, input_16, input_17, input_18, | |
uncond_multiplier: float=1.0, cn_extras: dict[str]={}): | |
weights_input = [input_0, input_1, input_2, input_3, input_4, input_5, | |
input_6, input_7, input_8, input_9, input_10, input_11, | |
input_12, input_13, input_14, input_15, input_16, input_17, input_18] | |
weights = ControlWeights.controlnet(weights_input=weights_input, uncond_multiplier=uncond_multiplier, extras=cn_extras) | |
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) | |
class SoftT2IAdapterWeights: | |
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 ππ π π /weights/T2IAdapter" | |
def load_weights(self, input_0, input_1, input_2, input_3, | |
uncond_multiplier: float=1.0, cn_extras: dict[str]={}): | |
return CustomT2IAdapterWeights.load_weights(self, input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3, | |
uncond_multiplier=uncond_multiplier, cn_extras=cn_extras) | |
class CustomT2IAdapterWeights: | |
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 ππ π π /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))) | |