| | from torch import Tensor |
| | import torch |
| | from .control 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 { |
| | } |
| | |
| | RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
| | RETURN_NAMES = WEIGHTS_RETURN_NAMES |
| | FUNCTION = "load_weights" |
| |
|
| | CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights" |
| |
|
| | def load_weights(self): |
| | weights = ControlWeights.default() |
| | 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}, ), |
| | |
| | |
| | }, |
| | } |
| | |
| | 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): |
| | |
| | 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) |
| | return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
| |
|
| |
|
| | class ScaledSoftUniversalWeights: |
| | @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}), |
| | }, |
| | } |
| | |
| | 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): |
| | weights = ControlWeights.universal(base_multiplier=base_multiplier, flip_weights=flip_weights) |
| | return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
| |
|
| |
|
| | class SoftControlNetWeights: |
| | @classmethod |
| | def INPUT_TYPES(s): |
| | return { |
| | "required": { |
| | "weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_12": ("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/ControlNet" |
| |
|
| | def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
| | weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights): |
| | weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
| | weight_07, weight_08, weight_09, weight_10, weight_11, weight_12] |
| | weights = ControlWeights.controlnet(weights, flip_weights=flip_weights) |
| | return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
| |
|
| |
|
| | class CustomControlNetWeights: |
| | @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}, ), |
| | "weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
| | "weight_12": ("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/ControlNet" |
| |
|
| | def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
| | weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights): |
| | weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
| | weight_07, weight_08, weight_09, weight_10, weight_11, weight_12] |
| | weights = ControlWeights.controlnet(weights, flip_weights=flip_weights) |
| | return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
| |
|
| |
|
| | 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))) |
| |
|
| |
|
| | 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))) |
| |
|
