127 lines
6.3 KiB
Markdown
127 lines
6.3 KiB
Markdown
# Documentation
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- Class name: SeargeControlnetAdapterV2
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- Category: UI_PROMPTING
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- Output node: False
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- Repo Ref: https://github.com/jobunk/SeargeSDXL.git
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该节点促进控制网络机制的适配，以增强图像处理任务。它根据输入参数动态选择和应用各种边缘检测和图像增强算法，旨在根据特定要求改善视觉输出。
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# Input types
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## Required
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- controlnet_mode
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- 确定要使用的控制网络类型，这显著影响处理方法和最终图像质量。
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- Comfy dtype: COMBO[str]
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- Python dtype: str
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- controlnet_preprocessor
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- 启用或禁用控制网图像的预处理，可以优化后续的图像处理步骤。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- strength
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- 调整控制网络对图像的影响强度，直接影响最终的视觉结果。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- low_threshold
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- 设置边缘检测灵敏度的下限，影响图像中检测到的边缘的粒度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- high_threshold
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- 定义边缘检测灵敏度的上限，影响最终图像中边缘的显著性。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- start_percent
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- 指定图像处理范围的起始百分比，确定受影响的图像初始段。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- end_percent
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- 指示图像处理范围的结束百分比，建立受影响的图像最终段。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- noise_augmentation
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- 控制应用于图像的噪声增强水平，这可以增强控制网络的鲁棒性。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- revision_enhancer
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- 激活或停用修订增强器，它精炼控制网络的输出以提高准确性。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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## Optional
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- source_image
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- 提供控制网络处理的基础图像，作为所有后续图像操作的基本输入。
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- Comfy dtype: IMAGE
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- Python dtype: PIL.Image
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- data
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- 提供控制网络用于更复杂处理的附加数据流信息。
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- Comfy dtype: SRG_DATA_STREAM
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- Python dtype: List[Dict[str, Any]]
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# Output types
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- data
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- 包含控制网络处理后更新的数据流信息，可以进一步被下游利用。
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- Comfy dtype: SRG_DATA_STREAM
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- Python dtype: Dict[str, Any]
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- preview
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- 展示处理后图像的视觉预览，演示控制网络调整的效果。
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- Comfy dtype: IMAGE
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- Python dtype: PIL.Image
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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 SeargeControlnetAdapterV2:
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def __init__(self):
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self.expected_size = None
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self.hed_annotator = 'ControlNetHED.pth'
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self.leres_annotator = 'res101.pth'
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self.hed_annotator_full_path = get_full_path('annotators', self.hed_annotator)
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self.leres_annotator_full_path = get_full_path('annotators', self.leres_annotator)
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'controlnet_mode': (UI.CONTROLNET_MODES, {'default': UI.NONE}), 'controlnet_preprocessor': ('BOOLEAN', {'default': False}), 'strength': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 10.0, 'step': 0.05}), 'low_threshold': ('FLOAT', {'default': 0.25, 'min': 0.0, 'max': 1.0, 'step': 0.05}), 'high_threshold': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 1.0, 'step': 0.05}), 'start_percent': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.05}), 'end_percent': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.05}), 'noise_augmentation': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.05}), 'revision_enhancer': ('BOOLEAN', {'default': False})}, 'optional': {'data': ('SRG_DATA_STREAM',), 'source_image': ('IMAGE',)}}
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RETURN_TYPES = ('SRG_DATA_STREAM', 'IMAGE')
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RETURN_NAMES = ('data', 'preview')
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FUNCTION = 'get_value'
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CATEGORY = UI.CATEGORY_UI_PROMPTING
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def process_image(self, image, mode, low_threshold, high_threshold):
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if mode == UI.CN_MODE_CANNY:
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image = canny(image, low_threshold, high_threshold)
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elif mode == UI.CN_MODE_DEPTH:
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image = leres(image, low_threshold, high_threshold, self.leres_annotator_full_path)
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elif mode == UI.CN_MODE_SKETCH:
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image = hed(image, self.hed_annotator_full_path)
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else:
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pass
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return image
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def create_dict(self, stack, source_image, controlnet_mode, controlnet_preprocessor, strength, low_threshold, high_threshold, start, end, noise_augmentation, revision_enhancer):
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if controlnet_mode is None or controlnet_mode == UI.NONE:
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cn_image = None
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else:
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cn_image = source_image
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low_threshold = round(low_threshold, 3)
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high_threshold = round(high_threshold, 3)
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if controlnet_mode == UI.CN_MODE_REVISION or controlnet_mode == UI.CUSTOM:
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controlnet_preprocessor = False
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if controlnet_preprocessor and cn_image is not None:
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cn_image = self.process_image(cn_image, controlnet_mode, low_threshold, high_threshold)
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stack += [{UI.F_REV_CN_IMAGE: cn_image, UI.F_REV_CN_IMAGE_CHANGED: True, UI.F_REV_CN_MODE: controlnet_mode, UI.F_CN_PRE_PROCESSOR: controlnet_preprocessor, UI.F_REV_CN_STRENGTH: round(strength, 3), UI.F_CN_LOW_THRESHOLD: low_threshold, UI.F_CN_HIGH_THRESHOLD: high_threshold, UI.F_CN_START: round(start, 3), UI.F_CN_END: round(end, 3), UI.F_REV_NOISE_AUGMENTATION: round(noise_augmentation, 3), UI.F_REV_ENHANCER: revision_enhancer}]
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return ({UI.F_CN_STACK: stack}, cn_image)
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def get_value(self, controlnet_mode, controlnet_preprocessor, strength, low_threshold, high_threshold, start_percent, end_percent, noise_augmentation, revision_enhancer, source_image=None, data=None):
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if data is None:
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data = {}
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stack = retrieve_parameter(UI.F_CN_STACK, retrieve_parameter(UI.S_CONTROLNET_INPUTS, data), [])
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if self.expected_size is None:
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self.expected_size = len(stack)
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elif self.expected_size == 0:
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stack = []
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elif len(stack) > self.expected_size:
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stack = stack[:self.expected_size]
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(stack_entry, image) = self.create_dict(stack, source_image, controlnet_mode, controlnet_preprocessor, strength, low_threshold, high_threshold, start_percent, end_percent, noise_augmentation, revision_enhancer)
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data[UI.S_CONTROLNET_INPUTS] = stack_entry
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return (data, image)
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``` |