88 lines
4.0 KiB
Markdown
88 lines
4.0 KiB
Markdown
# Documentation
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- Class name: ControlNetApplyAdvanced
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- Category: conditioning
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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ControlNetApplyAdvanced节点旨在将控制网络应用于一组条件输入，提高生成过程的精度和控制。它通过调整积极和消极条件因素的影响来操作，允许根据特定的图像特征和所需的强度对输出进行微调。
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# Input types
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## Required
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- positive
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- “positive”输入代表应在输出中强调的所需特征。它对于引导生成过程朝向期望的结果至关重要。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[Any, Dict[str, Any]]]
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- negative
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- “negative”输入包含应在输出中避免的特征。它在提炼生成过程中以排除不需要的元素方面发挥重要作用。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[Any, Dict[str, Any]]]
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- control_net
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- “control_net”参数是用于管理控制提示应用的预配置网络。它对节点的功能至关重要，决定了条件应用的方式。
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- Comfy dtype: CONTROL_NET
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- Python dtype: ControlNet
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- image
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- “image”输入是将应用控制网络的视觉数据。它是基本组成部分，因为它定义了条件过程的上下文。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- strength
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- “strength”参数决定了控制网络对输出影响的强度。它是控制条件引起的变化程度的关键因素。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- start_percent
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- “start_percent”参数指定控制网络效果相对于图像尺寸的起始点。它在定义控制应用的初始范围方面很重要。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- end_percent
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- “end_percent”参数标记了控制网络影响的终点。它在定义控制网络影响输出的范围方面至关重要。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- positive
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- “positive”输出是经过控制网络调整的精炼条件数据，确保所需特征在最终输出中更加突出。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[Any, Dict[str, Any]]]
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- negative
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- “negative”输出反映了通过控制网络应用最小化不需要的特征的条件数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[Any, Dict[str, Any]]]
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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 ControlNetApplyAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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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})}}
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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 = 'conditioning'
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def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
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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, (start_percent, end_percent))
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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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``` |