206 lines
8.4 KiB
Python
206 lines
8.4 KiB
Python
import numpy as np
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import folder_paths
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from .control import load_controlnet, ControlNetWeightsType, T2IAdapterWeightsType,\
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LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup, is_advanced_controlnet
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from .control import StrengthInterpolation as SI
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from .weight_nodes import ScaledSoftControlNetWeights, SoftControlNetWeights, CustomControlNetWeights, \
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SoftT2IAdapterWeights, CustomT2IAdapterWeights
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from .latent_keyframe_nodes import LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode
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from .deprecated_nodes import LoadImagesFromDirectory
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from .logger import logger
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class TimestepKeyframeNode:
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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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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT, SI.NONE], ),
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"default_latent_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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},
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"optional": {
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"control_net_weights": ("CONTROL_NET_WEIGHTS", ),
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"t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
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"latent_keyframe": ("LATENT_KEYFRAME", ),
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"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
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FUNCTION = "load_keyframe"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def load_keyframe(self,
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start_percent: float,
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strength: float,
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interpolation: str,
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default_latent_strength: float,
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control_net_weights: ControlNetWeightsType=None,
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t2i_adapter_weights: T2IAdapterWeightsType=None,
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latent_keyframe: LatentKeyframeGroup=None,
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prev_timestep_keyframe: TimestepKeyframeGroup=None):
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if not prev_timestep_keyframe:
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prev_timestep_keyframe = TimestepKeyframeGroup()
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keyframe = TimestepKeyframe(start_percent=start_percent, strength=strength, interpolation=interpolation, default_latent_strength=default_latent_strength,
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control_net_weights=control_net_weights, t2i_adapter_weights=t2i_adapter_weights, latent_keyframes=latent_keyframe)
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prev_timestep_keyframe.add(keyframe)
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return (prev_timestep_keyframe,)
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class ControlNetLoaderAdvanced:
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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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"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
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},
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"optional": {
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"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
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def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup=None):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, timestep_keyframe)
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return (controlnet,)
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class DiffControlNetLoaderAdvanced:
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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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"model": ("MODEL",),
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"control_net_name": (folder_paths.get_filename_list("controlnet"), )
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},
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"optional": {
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"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
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def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup, model):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
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return (controlnet,)
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class AdvancedControlNetApply:
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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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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"control_net": ("CONTROL_NET", ),
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"image": ("IMAGE", ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
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},
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"optional": {
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"mask_optional": ("MASK", ),
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}
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}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING")
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RETURN_NAMES = ("positive", "negative")
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FUNCTION = "apply_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/conditioning"
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def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, mask_optional=None):
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if strength == 0:
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return (positive, negative)
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control_hint = image.movedim(-1,1)
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cnets = {}
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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prev_cnet = d.get('control', None)
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if prev_cnet in cnets:
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c_net = cnets[prev_cnet]
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else:
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c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
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# set cond hint mask
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if mask_optional is not None:
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if is_advanced_controlnet(c_net):
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# if not in the form of a batch, make it so
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if len(mask_optional.shape) < 3:
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mask_optional = mask_optional.unsqueeze(0)
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c_net.set_cond_hint_mask(mask_optional)
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c_net.set_previous_controlnet(prev_cnet)
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cnets[prev_cnet] = c_net
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d['control'] = c_net
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d['control_apply_to_uncond'] = False
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return (out[0], out[1])
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# NODE MAPPING
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NODE_CLASS_MAPPINGS = {
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# Keyframes
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"TimestepKeyframe": TimestepKeyframeNode,
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"LatentKeyframe": LatentKeyframeNode,
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"LatentKeyframeGroup": LatentKeyframeGroupNode,
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"LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
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"LatentKeyframeTiming": LatentKeyframeInterpolationNode,
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# Loaders
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"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
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"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
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# Conditioning
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"ACN_AdvancedControlNetApply": AdvancedControlNetApply,
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# Weights
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"ScaledSoftControlNetWeights": ScaledSoftControlNetWeights,
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"SoftControlNetWeights": SoftControlNetWeights,
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"CustomControlNetWeights": CustomControlNetWeights,
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"SoftT2IAdapterWeights": SoftT2IAdapterWeights,
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"CustomT2IAdapterWeights": CustomT2IAdapterWeights,
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# Image
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"LoadImagesFromDirectory": LoadImagesFromDirectory
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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# Keyframes
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"TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
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"LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
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"LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
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"LatentKeyframeBatchedGroup": "Latent Keyframe Batched Group 🛂🅐🅒🅝",
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"LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
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# Loaders
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"ControlNetLoaderAdvanced": "Load ControlNet Model (Advanced) 🛂🅐🅒🅝",
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"DiffControlNetLoaderAdvanced": "Load ControlNet Model (diff Advanced) 🛂🅐🅒🅝",
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# Conditioning
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"ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
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# Weights
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"ScaledSoftControlNetWeights": "Scaled Soft ControlNet Weights 🛂🅐🅒🅝",
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"SoftControlNetWeights": "Soft ControlNet Weights 🛂🅐🅒🅝",
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"CustomControlNetWeights": "Custom ControlNet Weights 🛂🅐🅒🅝",
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"SoftT2IAdapterWeights": "Soft T2IAdapter Weights 🛂🅐🅒🅝",
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"CustomT2IAdapterWeights": "Custom T2IAdapter Weights 🛂🅐🅒🅝",
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# Image
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"LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
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}
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