50 lines
3.3 KiB
Markdown
50 lines
3.3 KiB
Markdown
# Documentation
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- Class name: CustomControlNetWeights
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- Category: Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet
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- Output node: False
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- Repo Ref: https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet.git
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CustomControlNetWeights 类用于管理和应用自定义权重到控制网络中，允许微调网络的行为。它封装了加载和处理权重的逻辑，确保权重正确地集成到网络的运行中。
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# Input types
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## Required
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- weight_00
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- 权重参数对于调整网络内特定控制机制的影响至关重要。它允许对网络如何响应输入数据的不同方面进行精确控制。
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- Comfy dtype: FLOAT
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- Python dtype: float
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## Optional
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- flip_weights
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- flip_weights 参数决定在处理过程中是否应翻转权重，这对于网络内的某些类型的转换或调整可能很重要。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- weights
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- 权重输出代表处理过的和自定义的控制网络权重，准备应用于网络以执行特定任务或转换。
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- Comfy dtype: CONTROL_NET_WEIGHTS
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- Python dtype: ControlWeights
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- timestep_keyframe
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- timestep_keyframe 输出提供了控制网络在特定时间点的状态的结构化表示，可用于网络运行中精确的时间控制。
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- Comfy dtype: TIMESTEP_KEYFRAME
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- Python dtype: TimestepKeyframe
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class CustomControlNetWeights:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'weight_00': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_01': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_02': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_03': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_04': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_05': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_06': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_07': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_08': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_09': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_10': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_11': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'weight_12': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.001}), 'flip_weights': ('BOOLEAN', {'default': False})}}
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RETURN_TYPES = ('CONTROL_NET_WEIGHTS', 'TIMESTEP_KEYFRAME')
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RETURN_NAMES = WEIGHTS_RETURN_NAMES
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FUNCTION = 'load_weights'
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CATEGORY = 'Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet'
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 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, weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
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weights = ControlWeights.controlnet(weights, flip_weights=flip_weights)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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``` |