774 lines
36 KiB
Python
774 lines
36 KiB
Python
from typing import Union
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from torch import Tensor
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import torch
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import comfy.utils
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import comfy.controlnet as comfy_cn
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from comfy.controlnet import ControlBase, ControlNet, ControlLora, T2IAdapter, broadcast_image_to
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def get_properly_arranged_t2i_weights(initial_weights: list[float]):
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new_weights = []
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new_weights.extend([initial_weights[0]]*3)
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new_weights.extend([initial_weights[1]]*3)
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new_weights.extend([initial_weights[2]]*3)
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new_weights.extend([initial_weights[3]]*3)
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return new_weights
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class ControlWeightTypeImport:
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DEFAULT = "default"
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UNIVERSAL = "universal"
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T2IADAPTER = "t2iadapter"
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CONTROLNET = "controlnet"
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CONTROLLORA = "controllora"
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CONTROLLLLITE = "controllllite"
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class ControlWeightsImport:
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def __init__(self, weight_type: str, base_multiplier: float=1.0, flip_weights: bool=False, weights: list[float]=None, weight_mask: Tensor=None):
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self.weight_type = weight_type
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self.base_multiplier = base_multiplier
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self.flip_weights = flip_weights
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self.weights = weights
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if self.weights is not None and self.flip_weights:
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self.weights.reverse()
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self.weight_mask = weight_mask
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def get(self, idx: int) -> Union[float, Tensor]:
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# if weights is not none, return index
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if self.weights is not None:
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return self.weights[idx]
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return 1.0
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@classmethod
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def default(cls):
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return cls(ControlWeightTypeImport.DEFAULT)
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@classmethod
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def universal(cls, base_multiplier: float, flip_weights: bool=False):
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return cls(ControlWeightTypeImport.UNIVERSAL, base_multiplier=base_multiplier, flip_weights=flip_weights)
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@classmethod
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def universal_mask(cls, weight_mask: Tensor):
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return cls(ControlWeightTypeImport.UNIVERSAL, weight_mask=weight_mask)
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@classmethod
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def t2iadapter(cls, weights: list[float]=None, flip_weights: bool=False):
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if weights is None:
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weights = [1.0]*12
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return cls(ControlWeightTypeImport.T2IADAPTER, weights=weights,flip_weights=flip_weights)
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@classmethod
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def controlnet(cls, weights: list[float]=None, flip_weights: bool=False):
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if weights is None:
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weights = [1.0]*13
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return cls(ControlWeightTypeImport.CONTROLNET, weights=weights, flip_weights=flip_weights)
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@classmethod
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def controllora(cls, weights: list[float]=None, flip_weights: bool=False):
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if weights is None:
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weights = [1.0]*10
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return cls(ControlWeightTypeImport.CONTROLLORA, weights=weights, flip_weights=flip_weights)
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@classmethod
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def controllllite(cls, weights: list[float]=None, flip_weights: bool=False):
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if weights is None:
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# TODO: make this have a real value
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weights = [1.0]*200
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return cls(ControlWeightTypeImport.CONTROLLLLITE, weights=weights, flip_weights=flip_weights)
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class StrengthInterpolationImport:
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LINEAR = "linear"
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EASE_IN = "ease-in"
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EASE_OUT = "ease-out"
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EASE_IN_OUT = "ease-in-out"
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NONE = "none"
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class LatentKeyframeImport:
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def __init__(self, batch_index: int, strength: float) -> None:
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self.batch_index = batch_index
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self.strength = strength
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# always maintain sorted state (by batch_index of LatentKeyframe)
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class LatentKeyframeGroupImport:
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def __init__(self) -> None:
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self.keyframes: list[LatentKeyframeImport] = []
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def add(self, keyframe: LatentKeyframeImport) -> None:
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added = False
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# replace existing keyframe if same batch_index
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for i in range(len(self.keyframes)):
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if self.keyframes[i].batch_index == keyframe.batch_index:
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self.keyframes[i] = keyframe
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added = True
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break
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if not added:
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self.keyframes.append(keyframe)
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self.keyframes.sort(key=lambda k: k.batch_index)
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def get_index(self, index: int) -> Union[LatentKeyframeImport, None]:
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try:
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return self.keyframes[index]
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except IndexError:
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return None
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def __getitem__(self, index) -> LatentKeyframeImport:
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return self.keyframes[index]
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def is_empty(self) -> bool:
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return len(self.keyframes) == 0
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def clone(self) -> 'LatentKeyframeGroupImport':
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cloned = LatentKeyframeGroupImport()
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for tk in self.keyframes:
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cloned.add(tk)
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return cloned
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class TimestepKeyframeImport:
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def __init__(self,
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start_percent: float = 0.0,
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strength: float = 1.0,
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interpolation: str = StrengthInterpolationImport.NONE,
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control_weights: ControlWeightsImport = None,
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latent_keyframes: LatentKeyframeGroupImport = None,
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null_latent_kf_strength: float = 0.0,
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inherit_missing: bool = True,
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guarantee_usage: bool = True,
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mask_hint_orig: Tensor = None) -> None:
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self.start_percent = start_percent
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self.start_t = 999999999.9
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self.strength = strength
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self.interpolation = interpolation
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self.control_weights = control_weights
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self.latent_keyframes = latent_keyframes
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self.null_latent_kf_strength = null_latent_kf_strength
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self.inherit_missing = inherit_missing
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self.guarantee_usage = guarantee_usage
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self.mask_hint_orig = mask_hint_orig
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def has_control_weights(self):
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return self.control_weights is not None
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def has_latent_keyframes(self):
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return self.latent_keyframes is not None
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def has_mask_hint(self):
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return self.mask_hint_orig is not None
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@classmethod
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def default(cls) -> 'TimestepKeyframeImport':
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return cls(0.0)
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# always maintain sorted state (by start_percent of TimestepKeyFrame)
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class TimestepKeyframeGroupImport:
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def __init__(self) -> None:
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self.keyframes: list[TimestepKeyframeImport] = []
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self.keyframes.append(TimestepKeyframeImport.default())
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def add(self, keyframe: TimestepKeyframeImport) -> None:
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added = False
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# replace existing keyframe if same start_percent
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for i in range(len(self.keyframes)):
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if self.keyframes[i].start_percent == keyframe.start_percent:
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self.keyframes[i] = keyframe
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added = True
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break
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if not added:
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self.keyframes.append(keyframe)
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self.keyframes.sort(key=lambda k: k.start_percent)
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def get_index(self, index: int) -> Union[TimestepKeyframeImport, None]:
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try:
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return self.keyframes[index]
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except IndexError:
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return None
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def has_index(self, index: int) -> int:
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return index >=0 and index < len(self.keyframes)
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def __getitem__(self, index) -> TimestepKeyframeImport:
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return self.keyframes[index]
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def __len__(self) -> int:
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return len(self.keyframes)
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def is_empty(self) -> bool:
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return len(self.keyframes) == 0
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def clone(self) -> 'TimestepKeyframeGroupImport':
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cloned = TimestepKeyframeGroupImport()
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for tk in self.keyframes:
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cloned.add(tk)
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return cloned
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@classmethod
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def default(cls, keyframe: TimestepKeyframeImport) -> 'TimestepKeyframeGroupImport':
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group = cls()
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group.keyframes[0] = keyframe
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return group
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# used to inject ControlNetAdvancedImport and T2IAdapterAdvancedImport control_merge function
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class AdvancedControlBaseImport:
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def __init__(self, base: ControlBase, timestep_keyframes: TimestepKeyframeGroupImport, weights_default: ControlWeightsImport):
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self.base = base
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self.compatible_weights = [ControlWeightTypeImport.UNIVERSAL]
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self.add_compatible_weight(weights_default.weight_type)
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# mask for which parts of controlnet output to keep
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self.mask_cond_hint_original = None
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self.mask_cond_hint = None
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self.tk_mask_cond_hint_original = None
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self.tk_mask_cond_hint = None
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self.weight_mask_cond_hint = None
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# actual index values
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self.sub_idxs = None
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self.full_latent_length = 0
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self.context_length = 0
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# timesteps
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self.t: Tensor = None
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self.batched_number: int = None
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# weights + override
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self.weights: ControlWeightsImport = None
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self.weights_default: ControlWeightsImport = weights_default
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self.weights_override: ControlWeightsImport = None
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# latent keyframe + override
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self.latent_keyframes: LatentKeyframeGroupImport = None
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self.latent_keyframe_override: LatentKeyframeGroupImport = None
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# initialize timestep_keyframes
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self.set_timestep_keyframes(timestep_keyframes)
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# override some functions
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self.get_control = self.get_control_inject
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self.control_merge = self.control_merge_inject#.__get__(self, type(self))
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self.pre_run = self.pre_run_inject
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self.cleanup = self.cleanup_inject
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def add_compatible_weight(self, control_weight_type: str):
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self.compatible_weights.append(control_weight_type)
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def verify_all_weights(self, throw_error=True):
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# first, check if override exists - if so, only need to check the override
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if self.weights_override is not None:
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if self.weights_override.weight_type not in self.compatible_weights:
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msg = f"Weight override is type {self.weights_override.weight_type}, but loaded {type(self).__name__}" + \
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f"only supports {self.compatible_weights} weights."
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raise WeightTypeExceptionImport(msg)
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# otherwise, check all timestep keyframe weights
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else:
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for tk in self.timestep_keyframes.keyframes:
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if tk.has_control_weights() and tk.control_weights.weight_type not in self.compatible_weights:
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msg = f"Weight on Timestep Keyframe with start_percent={tk.start_percent} is type" + \
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f"{tk.control_weights.weight_type}, but loaded {type(self).__name__} only supports {self.compatible_weights} weights."
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raise WeightTypeExceptionImport(msg)
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def set_timestep_keyframes(self, timestep_keyframes: TimestepKeyframeGroupImport):
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self.timestep_keyframes = timestep_keyframes if timestep_keyframes else TimestepKeyframeGroupImport()
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# prepare first timestep_keyframe related stuff
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self.current_timestep_keyframe = None
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self.current_timestep_index = -1
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self.next_timestep_keyframe = None
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self.weights = None
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self.latent_keyframes = None
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def prepare_current_timestep(self, t: Tensor, batched_number: int):
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self.t = t
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self.batched_number = batched_number
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# get current step percent
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curr_t: float = t[0]
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prev_index = self.current_timestep_index
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# if has next index, loop through and see if need to switch
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if self.timestep_keyframes.has_index(self.current_timestep_index+1):
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for i in range(self.current_timestep_index+1, len(self.timestep_keyframes)):
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eval_tk = self.timestep_keyframes[i]
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# check if start percent is less or equal to curr_t
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if eval_tk.start_t >= curr_t:
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self.current_timestep_index = i
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self.current_timestep_keyframe = eval_tk
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# keep track of control weights, latent keyframes, and masks,
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# accounting for inherit_missing
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if self.current_timestep_keyframe.has_control_weights():
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self.weights = self.current_timestep_keyframe.control_weights
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elif not self.current_timestep_keyframe.inherit_missing:
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self.weights = self.weights_default
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if self.current_timestep_keyframe.has_latent_keyframes():
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self.latent_keyframes = self.current_timestep_keyframe.latent_keyframes
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elif not self.current_timestep_keyframe.inherit_missing:
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self.latent_keyframes = None
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if self.current_timestep_keyframe.has_mask_hint():
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self.tk_mask_cond_hint_original = self.current_timestep_keyframe.mask_hint_orig
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elif not self.current_timestep_keyframe.inherit_missing:
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del self.tk_mask_cond_hint_original
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self.tk_mask_cond_hint_original = None
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# if guarantee_usage, stop searching for other TKs
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if self.current_timestep_keyframe.guarantee_usage:
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break
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# if eval_tk is outside of percent range, stop looking further
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else:
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break
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# if index changed, apply overrides
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if prev_index != self.current_timestep_index:
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if self.weights_override is not None:
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self.weights = self.weights_override
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if self.latent_keyframe_override is not None:
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self.latent_keyframes = self.latent_keyframe_override
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# make sure weights and latent_keyframes are in a workable state
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# Note: each AdvancedControlBaseImport should create their own get_universal_weights class
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self.prepare_weights()
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def prepare_weights(self):
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if self.weights is None or self.weights.weight_type == ControlWeightTypeImport.DEFAULT:
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self.weights = self.weights_default
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elif self.weights.weight_type == ControlWeightTypeImport.UNIVERSAL:
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# if universal and weight_mask present, no need to convert
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if self.weights.weight_mask is not None:
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return
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self.weights = self.get_universal_weights()
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def get_universal_weights(self) -> ControlWeightsImport:
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return self.weights
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def set_cond_hint_mask(self, mask_hint):
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self.mask_cond_hint_original = mask_hint
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return self
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def pre_run_inject(self, model, percent_to_timestep_function):
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self.base.pre_run(model, percent_to_timestep_function)
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self.pre_run_advanced(model, percent_to_timestep_function)
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def pre_run_advanced(self, model, percent_to_timestep_function):
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# for each timestep keyframe, calculate the start_t
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for tk in self.timestep_keyframes.keyframes:
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tk.start_t = percent_to_timestep_function(tk.start_percent)
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# clear variables
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self.cleanup_advanced()
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def get_control_inject(self, x_noisy, t, cond, batched_number):
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# prepare timestep and everything related
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self.prepare_current_timestep(t=t, batched_number=batched_number)
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# if should not perform any actions for the controlnet, exit without doing any work
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if self.strength == 0.0 or self.current_timestep_keyframe.strength == 0.0:
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control_prev = None
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if self.previous_controlnet is not None:
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control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
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if control_prev is not None:
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return control_prev
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else:
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return None
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# otherwise, perform normal function
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return self.get_control_advanced(x_noisy, t, cond, batched_number)
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def get_control_advanced(self, x_noisy, t, cond, batched_number):
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pass
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def calc_weight(self, idx: int, x: Tensor, layers: int) -> Union[float, Tensor]:
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if self.weights.weight_mask is not None:
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# prepare weight mask
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self.prepare_weight_mask_cond_hint(x, self.batched_number)
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# adjust mask for current layer and return
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return torch.pow(self.weight_mask_cond_hint, self.get_calc_pow(idx=idx, layers=layers))
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return self.weights.get(idx=idx)
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def get_calc_pow(self, idx: int, layers: int) -> int:
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return (layers-1)-idx
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def apply_advanced_strengths_and_masks(self, x: Tensor, batched_number: int):
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# apply strengths, and get batch indeces to null out
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# AKA latents that should not be influenced by ControlNet
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if self.latent_keyframes is not None:
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latent_count = x.size(0)//batched_number
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indeces_to_null = set(range(latent_count))
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mapped_indeces = None
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# if expecting subdivision, will need to translate between subset and actual idx values
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if self.sub_idxs:
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mapped_indeces = {}
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for i, actual in enumerate(self.sub_idxs):
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mapped_indeces[actual] = i
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for keyframe in self.latent_keyframes:
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real_index = keyframe.batch_index
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# if negative, count from end
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if real_index < 0:
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real_index += latent_count if self.sub_idxs is None else self.full_latent_length
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# if not mapping indeces, what you see is what you get
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if mapped_indeces is None:
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if real_index in indeces_to_null:
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indeces_to_null.remove(real_index)
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# otherwise, see if batch_index is even included in this set of latents
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else:
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real_index = mapped_indeces.get(real_index, None)
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if real_index is None:
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continue
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indeces_to_null.remove(real_index)
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# if real_index is outside the bounds of latents, don't apply
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if real_index >= latent_count or real_index < 0:
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continue
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# apply strength for each batched cond/uncond
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for b in range(batched_number):
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x[(latent_count*b)+real_index] = x[(latent_count*b)+real_index] * keyframe.strength
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# null them out by multiplying by null_latent_kf_strength
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for batch_index in indeces_to_null:
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# apply null for each batched cond/uncond
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for b in range(batched_number):
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x[(latent_count*b)+batch_index] = x[(latent_count*b)+batch_index] * self.current_timestep_keyframe.null_latent_kf_strength
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# apply masks, resizing mask to required dims
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if self.mask_cond_hint is not None:
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masks = prepare_mask_batch(self.mask_cond_hint, x.shape)
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x[:] = x[:] * masks
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if self.tk_mask_cond_hint is not None:
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masks = prepare_mask_batch(self.tk_mask_cond_hint, x.shape)
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x[:] = x[:] * masks
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# apply timestep keyframe strengths
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if self.current_timestep_keyframe.strength != 1.0:
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x[:] *= self.current_timestep_keyframe.strength
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def control_merge_inject(self: 'AdvancedControlBaseImport', control_input, control_output, control_prev, output_dtype):
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out = {'input':[], 'middle':[], 'output': []}
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if control_input is not None:
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for i in range(len(control_input)):
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key = 'input'
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x = control_input[i]
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if x is not None:
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self.apply_advanced_strengths_and_masks(x, self.batched_number)
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x *= self.strength * self.calc_weight(i, x, len(control_input))
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if x.dtype != output_dtype:
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x = x.to(output_dtype)
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out[key].insert(0, x)
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if control_output is not None:
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for i in range(len(control_output)):
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if i == (len(control_output) - 1):
|
|
key = 'middle'
|
|
index = 0
|
|
else:
|
|
key = 'output'
|
|
index = i
|
|
x = control_output[i]
|
|
if x is not None:
|
|
self.apply_advanced_strengths_and_masks(x, self.batched_number)
|
|
|
|
if self.global_average_pooling:
|
|
x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
|
|
|
|
x *= self.strength * self.calc_weight(i, x, len(control_output))
|
|
if x.dtype != output_dtype:
|
|
x = x.to(output_dtype)
|
|
|
|
out[key].append(x)
|
|
if control_prev is not None:
|
|
for x in ['input', 'middle', 'output']:
|
|
o = out[x]
|
|
for i in range(len(control_prev[x])):
|
|
prev_val = control_prev[x][i]
|
|
if i >= len(o):
|
|
o.append(prev_val)
|
|
elif prev_val is not None:
|
|
if o[i] is None:
|
|
o[i] = prev_val
|
|
else:
|
|
o[i] += prev_val
|
|
return out
|
|
|
|
def prepare_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
|
|
self._prepare_mask("mask_cond_hint", self.mask_cond_hint_original, x_noisy, t, cond, batched_number, dtype)
|
|
self.prepare_tk_mask_cond_hint(x_noisy, t, cond, batched_number, dtype)
|
|
|
|
def prepare_tk_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
|
|
return self._prepare_mask("tk_mask_cond_hint", self.current_timestep_keyframe.mask_hint_orig, x_noisy, t, cond, batched_number, dtype)
|
|
|
|
def prepare_weight_mask_cond_hint(self, x_noisy: Tensor, batched_number, dtype=None):
|
|
return self._prepare_mask("weight_mask_cond_hint", self.weights.weight_mask, x_noisy, t=None, cond=None, batched_number=batched_number, dtype=dtype, direct_attn=True)
|
|
|
|
def _prepare_mask(self, attr_name, orig_mask: Tensor, x_noisy: Tensor, t, cond, batched_number, dtype=None, direct_attn=False):
|
|
# make mask appropriate dimensions, if present
|
|
if orig_mask is not None:
|
|
out_mask = getattr(self, attr_name)
|
|
if self.sub_idxs is not None or out_mask is None or x_noisy.shape[2] * 8 != out_mask.shape[1] or x_noisy.shape[3] * 8 != out_mask.shape[2]:
|
|
self._reset_attr(attr_name)
|
|
del out_mask
|
|
# TODO: perform upscale on only the sub_idxs masks at a time instead of all to conserve RAM
|
|
# resize mask and match batch count
|
|
multiplier = 1 if direct_attn else 8
|
|
out_mask = prepare_mask_batch(orig_mask, x_noisy.shape, multiplier=multiplier)
|
|
actual_latent_length = x_noisy.shape[0] // batched_number
|
|
out_mask = comfy.utils.repeat_to_batch_size(out_mask, actual_latent_length if self.sub_idxs is None else self.full_latent_length)
|
|
if self.sub_idxs is not None:
|
|
out_mask = out_mask[self.sub_idxs]
|
|
# make cond_hint_mask length match x_noise
|
|
if x_noisy.shape[0] != out_mask.shape[0]:
|
|
out_mask = broadcast_image_to(out_mask, x_noisy.shape[0], batched_number)
|
|
# default dtype to be same as x_noisy
|
|
if dtype is None:
|
|
dtype = x_noisy.dtype
|
|
setattr(self, attr_name, out_mask.to(dtype=dtype).to(self.device))
|
|
del out_mask
|
|
|
|
def _reset_attr(self, attr_name, new_value=None):
|
|
if hasattr(self, attr_name):
|
|
delattr(self, attr_name)
|
|
setattr(self, attr_name, new_value)
|
|
|
|
def cleanup_inject(self):
|
|
self.base.cleanup()
|
|
self.cleanup_advanced()
|
|
|
|
def cleanup_advanced(self):
|
|
self.sub_idxs = None
|
|
self.full_latent_length = 0
|
|
self.context_length = 0
|
|
self.t = None
|
|
self.batched_number = None
|
|
self.weights = None
|
|
self.latent_keyframes = None
|
|
# timestep stuff
|
|
self.current_timestep_keyframe = None
|
|
self.next_timestep_keyframe = None
|
|
self.current_timestep_index = -1
|
|
# clear mask hints
|
|
if self.mask_cond_hint is not None:
|
|
del self.mask_cond_hint
|
|
self.mask_cond_hint = None
|
|
if self.tk_mask_cond_hint_original is not None:
|
|
del self.tk_mask_cond_hint_original
|
|
self.tk_mask_cond_hint_original = None
|
|
if self.tk_mask_cond_hint is not None:
|
|
del self.tk_mask_cond_hint
|
|
self.tk_mask_cond_hint = None
|
|
if self.weight_mask_cond_hint is not None:
|
|
del self.weight_mask_cond_hint
|
|
self.weight_mask_cond_hint = None
|
|
|
|
def copy_to_advanced(self, copied: 'AdvancedControlBaseImport'):
|
|
copied.mask_cond_hint_original = self.mask_cond_hint_original
|
|
copied.weights_override = self.weights_override
|
|
copied.latent_keyframe_override = self.latent_keyframe_override
|
|
|
|
|
|
class ControlNetAdvancedImport(ControlNet, AdvancedControlBaseImport):
|
|
def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroupImport, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
|
|
super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
|
|
AdvancedControlBaseImport.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeightsImport.controlnet())
|
|
|
|
def get_universal_weights(self) -> ControlWeightsImport:
|
|
raw_weights = [(self.weights.base_multiplier ** float(12 - i)) for i in range(13)]
|
|
return ControlWeightsImport.controlnet(raw_weights, self.weights.flip_weights)
|
|
|
|
def get_control_advanced(self, x_noisy, t, cond, batched_number):
|
|
# perform special version of get_control that supports sliding context and masks
|
|
return self.sliding_get_control(x_noisy, t, cond, batched_number)
|
|
|
|
def sliding_get_control(self, x_noisy: Tensor, t, cond, batched_number):
|
|
control_prev = None
|
|
if self.previous_controlnet is not None:
|
|
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
|
|
|
|
if self.timestep_range is not None:
|
|
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
|
|
if control_prev is not None:
|
|
return control_prev
|
|
else:
|
|
return None
|
|
|
|
dtype = self.control_model.dtype
|
|
if self.manual_cast_dtype is not None:
|
|
dtype = self.manual_cast_dtype
|
|
|
|
output_dtype = x_noisy.dtype
|
|
# make cond_hint appropriate dimensions
|
|
# TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
|
|
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
# if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
|
|
if self.sub_idxs is not None and self.cond_hint_original.size(0) >= self.full_latent_length:
|
|
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
|
|
else:
|
|
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
|
|
if x_noisy.shape[0] != self.cond_hint.shape[0]:
|
|
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
|
|
|
|
# prepare mask_cond_hint
|
|
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=dtype)
|
|
|
|
context = cond['c_crossattn']
|
|
# uses 'y' in new ComfyUI update
|
|
y = cond.get('y', None)
|
|
if y is None: # TODO: remove this in the future since no longer used by newest ComfyUI
|
|
y = cond.get('c_adm', None)
|
|
if y is not None:
|
|
y = y.to(dtype)
|
|
timestep = self.model_sampling_current.timestep(t)
|
|
x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
|
|
|
|
control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
|
|
return self.control_merge(None, control, control_prev, output_dtype)
|
|
|
|
def copy(self):
|
|
c = ControlNetAdvancedImport(self.control_model, self.timestep_keyframes, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
|
|
self.copy_to(c)
|
|
self.copy_to_advanced(c)
|
|
return c
|
|
|
|
@staticmethod
|
|
def from_vanilla(v: ControlNet, timestep_keyframe: TimestepKeyframeGroupImport=None) -> 'ControlNetAdvancedImport':
|
|
return ControlNetAdvancedImport(control_model=v.control_model, timestep_keyframes=timestep_keyframe,
|
|
global_average_pooling=v.global_average_pooling, device=v.device, load_device=v.load_device, manual_cast_dtype=v.manual_cast_dtype)
|
|
|
|
|
|
class T2IAdapterAdvancedImport(T2IAdapter, AdvancedControlBaseImport):
|
|
def __init__(self, t2i_model, timestep_keyframes: TimestepKeyframeGroupImport, channels_in, device=None):
|
|
super().__init__(t2i_model=t2i_model, channels_in=channels_in, device=device)
|
|
AdvancedControlBaseImport.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeightsImport.t2iadapter())
|
|
|
|
def get_universal_weights(self) -> ControlWeightsImport:
|
|
raw_weights = [(self.weights.base_multiplier ** float(7 - i)) for i in range(8)]
|
|
raw_weights = [raw_weights[-8], raw_weights[-3], raw_weights[-2], raw_weights[-1]]
|
|
raw_weights = get_properly_arranged_t2i_weights(raw_weights)
|
|
return ControlWeightsImport.t2iadapter(raw_weights, self.weights.flip_weights)
|
|
|
|
def get_calc_pow(self, idx: int, layers: int) -> int:
|
|
# match how T2IAdapterAdvancedImport deals with universal weights
|
|
indeces = [7 - i for i in range(8)]
|
|
indeces = [indeces[-8], indeces[-3], indeces[-2], indeces[-1]]
|
|
indeces = get_properly_arranged_t2i_weights(indeces)
|
|
return indeces[idx]
|
|
|
|
def get_control_advanced(self, x_noisy, t, cond, batched_number):
|
|
# prepare timestep and everything related
|
|
self.prepare_current_timestep(t=t, batched_number=batched_number)
|
|
try:
|
|
# if sub indexes present, replace original hint with subsection
|
|
if self.sub_idxs is not None:
|
|
# cond hints
|
|
full_cond_hint_original = self.cond_hint_original
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
self.cond_hint_original = full_cond_hint_original[self.sub_idxs]
|
|
# mask hints
|
|
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number)
|
|
return super().get_control(x_noisy, t, cond, batched_number)
|
|
finally:
|
|
if self.sub_idxs is not None:
|
|
# replace original cond hint
|
|
self.cond_hint_original = full_cond_hint_original
|
|
del full_cond_hint_original
|
|
|
|
def copy(self):
|
|
c = T2IAdapterAdvancedImport(self.t2i_model, self.timestep_keyframes, self.channels_in)
|
|
self.copy_to(c)
|
|
self.copy_to_advanced(c)
|
|
return c
|
|
|
|
def cleanup(self):
|
|
super().cleanup()
|
|
self.cleanup_advanced()
|
|
|
|
@staticmethod
|
|
def from_vanilla(v: T2IAdapter, timestep_keyframe: TimestepKeyframeGroupImport=None) -> 'T2IAdapterAdvancedImport':
|
|
return T2IAdapterAdvancedImport(t2i_model=v.t2i_model, timestep_keyframes=timestep_keyframe, channels_in=v.channels_in, device=v.device)
|
|
|
|
|
|
class ControlLoraAdvancedImport(ControlLora, AdvancedControlBaseImport):
|
|
def __init__(self, control_weights, timestep_keyframes: TimestepKeyframeGroupImport, global_average_pooling=False, device=None):
|
|
super().__init__(control_weights=control_weights, global_average_pooling=global_average_pooling, device=device)
|
|
AdvancedControlBaseImport.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeightsImport.controllora())
|
|
# use some functions from ControlNetAdvancedImport
|
|
self.get_control_advanced = ControlNetAdvancedImport.get_control_advanced.__get__(self, type(self))
|
|
self.sliding_get_control = ControlNetAdvancedImport.sliding_get_control.__get__(self, type(self))
|
|
|
|
def get_universal_weights(self) -> ControlWeightsImport:
|
|
raw_weights = [(self.weights.base_multiplier ** float(9 - i)) for i in range(10)]
|
|
return ControlWeightsImport.controllora(raw_weights, self.weights.flip_weights)
|
|
|
|
def copy(self):
|
|
c = ControlLoraAdvancedImport(self.control_weights, self.timestep_keyframes, global_average_pooling=self.global_average_pooling)
|
|
self.copy_to(c)
|
|
self.copy_to_advanced(c)
|
|
return c
|
|
|
|
def cleanup(self):
|
|
super().cleanup()
|
|
self.cleanup_advanced()
|
|
|
|
@staticmethod
|
|
def from_vanilla(v: ControlLora, timestep_keyframe: TimestepKeyframeGroupImport=None) -> 'ControlLoraAdvancedImport':
|
|
return ControlLoraAdvancedImport(control_weights=v.control_weights, timestep_keyframes=timestep_keyframe,
|
|
global_average_pooling=v.global_average_pooling, device=v.device)
|
|
|
|
|
|
class ControlLLLiteAdvancedImport(ControlNet, AdvancedControlBaseImport):
|
|
def __init__(self, control_weights, timestep_keyframes: TimestepKeyframeGroupImport, device=None):
|
|
AdvancedControlBaseImport.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeightsImport.controllllite())
|
|
|
|
|
|
def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroupImport=None, model=None):
|
|
control = comfy_cn.load_controlnet(ckpt_path, model=model)
|
|
# TODO: support controlnet-lllite
|
|
# if is None, see if is a non-vanilla ControlNet
|
|
# if control is None:
|
|
# controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
|
|
# # check if lllite
|
|
# if "lllite_unet" in controlnet_data:
|
|
# pass
|
|
return convert_to_advanced(control, timestep_keyframe=timestep_keyframe)
|
|
|
|
|
|
def convert_to_advanced(control, timestep_keyframe: TimestepKeyframeGroupImport=None):
|
|
# if already advanced, leave it be
|
|
if is_advanced_controlnet(control):
|
|
return control
|
|
# if exactly ControlNet returned, transform it into ControlNetAdvancedImport
|
|
if type(control) == ControlNet:
|
|
return ControlNetAdvancedImport.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
|
|
# if exactly ControlLora returned, transform it into ControlLoraAdvancedImport
|
|
elif type(control) == ControlLora:
|
|
return ControlLoraAdvancedImport.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
|
|
# if T2IAdapter returned, transform it into T2IAdapterAdvancedImport
|
|
elif isinstance(control, T2IAdapter):
|
|
return T2IAdapterAdvancedImport.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
|
|
# otherwise, leave it be - might be something I am not supporting yet
|
|
return control
|
|
|
|
|
|
def is_advanced_controlnet(input_object):
|
|
return hasattr(input_object, "sub_idxs")
|
|
|
|
|
|
# adapted from comfy/sample.py
|
|
def prepare_mask_batch(mask: Tensor, shape: Tensor, multiplier: int=1, match_dim1=False):
|
|
mask = mask.clone()
|
|
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2]*multiplier, shape[3]*multiplier), mode="bilinear")
|
|
if match_dim1:
|
|
mask = torch.cat([mask] * shape[1], dim=1)
|
|
return mask
|
|
|
|
|
|
# applies min-max normalization, from:
|
|
# https://stackoverflow.com/questions/68791508/min-max-normalization-of-a-tensor-in-pytorch
|
|
def normalize_min_max(x: Tensor, new_min = 0.0, new_max = 1.0):
|
|
x_min, x_max = x.min(), x.max()
|
|
return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
|
|
|
|
def linear_conversion(x, x_min=0.0, x_max=1.0, new_min=0.0, new_max=1.0):
|
|
return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
|
|
|
|
|
|
class WeightTypeExceptionImport(TypeError):
|
|
"Raised when weight not compatible with AdvancedControlBaseImport object"
|
|
pass
|