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from torch import Tensor |
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import torch |
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from .control import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights, linear_conversion |
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from .logger import logger |
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WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") |
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class DefaultWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights" |
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def load_weights(self): |
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weights = ControlWeights.default() |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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class ScaledSoftMaskedUniversalWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"mask": ("MASK", ), |
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"min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ), |
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"max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ), |
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}, |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights" |
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def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False): |
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mask = mask.clone() |
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x_min = 0.0 if lock_min else mask.min() |
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x_max = 1.0 if lock_max else mask.max() |
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if x_min == x_max: |
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mask = torch.ones_like(mask) * max_base_multiplier |
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else: |
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mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier) |
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weights = ControlWeights.universal_mask(weight_mask=mask) |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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class ScaledSoftUniversalWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ), |
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"flip_weights": ("BOOLEAN", {"default": False}), |
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}, |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights" |
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def load_weights(self, base_multiplier, flip_weights): |
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weights = ControlWeights.universal(base_multiplier=base_multiplier, flip_weights=flip_weights) |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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class SoftControlNetWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"flip_weights": ("BOOLEAN", {"default": False}), |
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}, |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet" |
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights): |
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weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12] |
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weights = ControlWeights.controlnet(weights, flip_weights=flip_weights) |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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class CustomControlNetWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"flip_weights": ("BOOLEAN", {"default": False}), |
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} |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet" |
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights): |
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weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, |
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12] |
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weights = ControlWeights.controlnet(weights, flip_weights=flip_weights) |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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class SoftT2IAdapterWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"flip_weights": ("BOOLEAN", {"default": False}), |
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}, |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter" |
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights): |
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weights = [weight_00, weight_01, weight_02, weight_03] |
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weights = get_properly_arranged_t2i_weights(weights) |
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weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights) |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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class CustomT2IAdapterWeights: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), |
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"flip_weights": ("BOOLEAN", {"default": False}), |
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}, |
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} |
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) |
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RETURN_NAMES = WEIGHTS_RETURN_NAMES |
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FUNCTION = "load_weights" |
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter" |
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights): |
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weights = [weight_00, weight_01, weight_02, weight_03] |
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weights = get_properly_arranged_t2i_weights(weights) |
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weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights) |
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) |
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