606 lines
25 KiB
Python
606 lines
25 KiB
Python
import sys
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import os
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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import folder_paths
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from .control import ControlNetAdvanced, T2IAdapterAdvanced, load_controlnet, ControlNetWeightsType, T2IAdapterWeightsType,\
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LatentKeyframe, LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup
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from .logger import logger
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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 ScaledSoftControlNetWeights:
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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": 10.0, "step": 0.001}, ),
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"flip_weights": ([False, True], ),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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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 = [(base_multiplier ** float(12 - i)) for i in range(13)]
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if flip_weights:
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weights.reverse()
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_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": ([False, True], ),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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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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if flip_weights:
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weights.reverse()
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_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": ([False, True], ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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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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if flip_weights:
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weights.reverse()
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_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": ([False, True], ),
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},
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}
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RETURN_TYPES = ("T2I_ADAPTER_WEIGHTS", "TIMESTEP_KEYFRAME",)
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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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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if flip_weights:
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weights.reverse()
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weights = get_properly_arranged_t2i_weights(weights)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(t2i_adapter_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": ([False, True], ),
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},
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}
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RETURN_TYPES = ("T2I_ADAPTER_WEIGHTS", "TIMESTEP_KEYFRAME",)
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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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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if flip_weights:
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weights.reverse()
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weights = get_properly_arranged_t2i_weights(weights)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(t2i_adapter_weights=weights)))
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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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},
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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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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, control_net_weights, t2i_adapter_weights, latent_keyframe)
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prev_timestep_keyframe.add(keyframe)
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return (prev_timestep_keyframe,)
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class LatentKeyframeNode:
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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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"batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.00001}, ),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("LATENT_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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batch_index: int,
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strength: float,
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prev_latent_keyframe: LatentKeyframeGroup=None):
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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keyframe = LatentKeyframe(batch_index, strength)
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prev_latent_keyframe.add(keyframe)
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return (prev_latent_keyframe,)
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class LatentKeyframeGroupNode:
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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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"index_strengths": ("STRING", {"multiline": True, "default": ""}),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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"latent_optional": ("LATENT", ),
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}
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}
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RETURN_TYPES = ("LATENT_KEYFRAME", )
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FUNCTION = "load_keyframes"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
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# if part of range, do nothing
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if is_range:
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return index
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# otherwise, validate index
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# validate not out of range - only when latent_count is passed in
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if latent_count > 0 and index > latent_count-1:
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raise IndexError(f"Index '{index}' out of range for the total {latent_count} latents.")
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# if negative, validate not out of range
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if index < 0:
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if not allow_negative:
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raise IndexError(f"Negative indeces not allowed, but was {index}.")
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conv_index = latent_count+index
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if conv_index < 0:
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raise IndexError(f"Index '{index}', converted to '{conv_index}' out of range for the total {latent_count} latents.")
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index = conv_index
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return index
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def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
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try:
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return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
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except ValueError as e:
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raise ValueError(f"index '{raw_index}' must be an integer.", e)
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def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
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if not latent_indeces:
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return set()
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all_indeces = [i for i in range(0, latent_count)]
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allow_negative = latent_count > 0
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chosen_indeces = set()
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# parse string - allow positive ints, negative ints, and ranges separated by ':'
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groups = latent_indeces.split(",")
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groups = [g.strip() for g in groups]
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for g in groups:
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# parse strengths - default to 1.0 if no strength given
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strength = 1.0
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if '=' in g:
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g, strength_str = g.split("=", 1)
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g = g.strip()
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try:
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strength = float(strength_str.strip())
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except ValueError as e:
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raise ValueError(f"strength '{strength_str}' must be a float.", e)
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if strength < 0:
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raise ValueError(f"Strength '{strength}' cannot be negative.")
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# parse range of indeces (e.g. 2:16)
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if ':' in g:
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index_range = g.split(":", 1)
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index_range = [r.strip() for r in index_range]
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start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
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end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
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for i in all_indeces[start_index:end_index]:
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chosen_indeces.add(LatentKeyframe(i, strength))
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# parse individual indeces
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else:
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chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
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return chosen_indeces
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def load_keyframes(self,
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index_strengths: str,
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prev_latent_keyframe: LatentKeyframeGroup=None,
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latent_image_opt=None):
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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curr_latent_keyframe = LatentKeyframeGroup()
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latent_count = -1
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if latent_image_opt:
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latent_count = latent_image_opt['samples'].size()[0]
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latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
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for latent_keyframe in latent_keyframes:
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logger.info(f"keyframe {latent_keyframe.batch_index}:{latent_keyframe.strength}")
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curr_latent_keyframe.add(latent_keyframe)
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for latent_keyframe in prev_latent_keyframe.keyframes:
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curr_latent_keyframe.add(latent_keyframe)
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return (curr_latent_keyframe,)
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class LatentKeyframeInterpolationNode:
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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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"batch_index_from": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
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"batch_index_to_excl": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
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"strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
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"strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
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"interpolation": (["linear", "ease-in", "ease-out", "ease-in-out"], ),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("LATENT_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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batch_index_from: int,
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strength_from: float,
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batch_index_to_excl: int,
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strength_to: float,
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interpolation: str,
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prev_latent_keyframe: LatentKeyframeGroup=None):
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if (batch_index_from > batch_index_to_excl):
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raise ValueError("batch_index_from must be less than or equal to batch_index_to.")
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if (batch_index_from < 0 and batch_index_to_excl >= 0):
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raise ValueError("batch_index_from and batch_index_to must be either both positive or both negative.")
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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curr_latent_keyframe = LatentKeyframeGroup()
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steps = batch_index_to_excl - batch_index_from
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diff = strength_to - strength_from
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if interpolation == "linear":
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weights = np.linspace(strength_from, strength_to, steps)
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elif interpolation == "ease-in":
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index = np.linspace(0, 1, steps)
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weights = diff * np.power(index, 2) + strength_from
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elif interpolation == "ease-out":
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index = np.linspace(0, 1, steps)
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weights = diff * (1 - np.power(1 - index, 2)) + strength_from
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elif interpolation == "ease-in-out":
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index = np.linspace(0, 1, steps)
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weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
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for i in range(steps):
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keyframe = LatentKeyframe(batch_index_from + i, float(weights[i]))
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logger.info(f"keyframe {batch_index_from + i}:{weights[i]}")
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curr_latent_keyframe.add(keyframe)
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# replace values with prev_latent_keyframes
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for latent_keyframe in prev_latent_keyframe.keyframes:
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curr_latent_keyframe.add(latent_keyframe)
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return (curr_latent_keyframe,)
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class ControlNetLoaderAdvanced:
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@classmethod
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def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
|
|
},
|
|
"optional": {
|
|
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET", )
|
|
FUNCTION = "load_controlnet"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
|
|
|
|
def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup=None):
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
controlnet = load_controlnet(controlnet_path, timestep_keyframe)
|
|
return (controlnet,)
|
|
|
|
|
|
class DiffControlNetLoaderAdvanced:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
"control_net_name": (folder_paths.get_filename_list("controlnet"), )
|
|
},
|
|
"optional": {
|
|
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET", )
|
|
FUNCTION = "load_controlnet"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
|
|
|
|
def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup, model):
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
|
|
return (controlnet,)
|
|
|
|
|
|
class ControlNetApplyAdvanced_AdvControlNet:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"control_net": ("CONTROL_NET", ),
|
|
"image": ("IMAGE", ),
|
|
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
|
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
|
|
},
|
|
"optional": {
|
|
"mask_opt": ("MASK", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONDITIONING","CONDITIONING")
|
|
RETURN_NAMES = ("positive", "negative")
|
|
FUNCTION = "apply_controlnet"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders/conditioning"
|
|
|
|
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, mask_opt=None):
|
|
if strength == 0:
|
|
return (positive, negative)
|
|
|
|
if mask_opt is not None:
|
|
mask_hint = mask_opt.movedim(-1,1)
|
|
control_hint = image.movedim(-1,1)
|
|
cnets = {}
|
|
|
|
out = []
|
|
for conditioning in [positive, negative]:
|
|
c = []
|
|
for t in conditioning:
|
|
d = t[1].copy()
|
|
|
|
prev_cnet = d.get('control', None)
|
|
if prev_cnet in cnets:
|
|
c_net = cnets[prev_cnet]
|
|
else:
|
|
c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
|
|
# TODO: finish mask implemention, does nothing right now
|
|
if mask_opt is not None:
|
|
if isinstance(c_net, ControlNetAdvanced) or isinstance(c_net, T2IAdapterAdvanced):
|
|
c_net.set_cond_hint_mask(mask_hint)
|
|
else:
|
|
logger
|
|
c_net.set_previous_controlnet(prev_cnet)
|
|
cnets[prev_cnet] = c_net
|
|
|
|
d['control'] = c_net
|
|
d['control_apply_to_uncond'] = False
|
|
n = [t[0], d]
|
|
c.append(n)
|
|
out.append(c)
|
|
return (out[0], out[1])
|
|
|
|
|
|
class LoadImagesFromDirectory:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"directory": ("STRING", {"default": ""}),
|
|
},
|
|
"optional": {
|
|
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
|
|
"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK", "INT")
|
|
FUNCTION = "load_images"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated"
|
|
|
|
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
|
|
if not os.path.isdir(directory):
|
|
raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
|
|
dir_files = os.listdir(directory)
|
|
if len(dir_files) == 0:
|
|
raise FileNotFoundError(f"No files in directory '{directory}'.")
|
|
|
|
dir_files = sorted(dir_files)
|
|
dir_files = [os.path.join(directory, x) for x in dir_files]
|
|
# start at start_index
|
|
dir_files = dir_files[start_index:]
|
|
|
|
images = []
|
|
masks = []
|
|
|
|
limit_images = False
|
|
if image_load_cap > 0:
|
|
limit_images = True
|
|
image_count = 0
|
|
|
|
for image_path in dir_files:
|
|
if os.path.isdir(image_path):
|
|
continue
|
|
if limit_images and image_count >= image_load_cap:
|
|
break
|
|
i = Image.open(image_path)
|
|
i = ImageOps.exif_transpose(i)
|
|
image = i.convert("RGB")
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
if 'A' in i.getbands():
|
|
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
else:
|
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
|
images.append(image)
|
|
masks.append(mask)
|
|
image_count += 1
|
|
|
|
if len(images) == 0:
|
|
raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
|
|
|
|
return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
|
|
|
|
|
|
|
|
|
|
# NODE MAPPING
|
|
NODE_CLASS_MAPPINGS = {
|
|
# Keyframes
|
|
"TimestepKeyframe": TimestepKeyframeNode,
|
|
"LatentKeyframe": LatentKeyframeNode,
|
|
"LatentKeyframeGroup": LatentKeyframeGroupNode,
|
|
"LatentKeyframeTiming": LatentKeyframeInterpolationNode,
|
|
# Loaders
|
|
"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
|
|
"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
|
|
# Weights
|
|
"ScaledSoftControlNetWeights": ScaledSoftControlNetWeights,
|
|
"SoftControlNetWeights": SoftControlNetWeights,
|
|
"CustomControlNetWeights": CustomControlNetWeights,
|
|
"SoftT2IAdapterWeights": SoftT2IAdapterWeights,
|
|
"CustomT2IAdapterWeights": CustomT2IAdapterWeights,
|
|
# Image
|
|
"LoadImagesFromDirectory": LoadImagesFromDirectory
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
# Keyframes
|
|
"TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
|
|
"LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
|
|
"LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
|
|
"LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
|
|
# Loaders
|
|
"ControlNetLoaderAdvanced": "Load ControlNet Model (Advanced) 🛂🅐🅒🅝",
|
|
"DiffControlNetLoaderAdvanced": "Load ControlNet Model (diff Advanced) 🛂🅐🅒🅝",
|
|
# Weights
|
|
"ScaledSoftControlNetWeights": "Scaled Soft ControlNet Weights 🛂🅐🅒🅝",
|
|
"SoftControlNetWeights": "Soft ControlNet Weights 🛂🅐🅒🅝",
|
|
"CustomControlNetWeights": "Custom ControlNet Weights 🛂🅐🅒🅝",
|
|
"SoftT2IAdapterWeights": "Soft T2IAdapter Weights 🛂🅐🅒🅝",
|
|
"CustomT2IAdapterWeights": "Custom T2IAdapter Weights 🛂🅐🅒🅝",
|
|
# Image
|
|
"LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
|
|
}
|