76 lines
3.8 KiB
Markdown
76 lines
3.8 KiB
Markdown
# Documentation
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- Class name: BasicPipeToDetailerPipe
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- Category: ImpactPack/Pipe
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- Output node: False
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- Repo Ref: https://github.com/ltdrdata/ComfyUI-Impact-Pack.git
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BasicPipeToDetailerPipe 节点旨在将基本管道结构转换为更详细的管道，增强后续任务的处理能力。它通过整合各种组件，如bbox_detector、通配符和可选模型，共同完善输入数据以进行更复杂的分析。
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# Input types
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## Required
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- basic_pipe
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- basic_pipe 参数至关重要，因为它提供了节点操作所需的基础数据结构。它是节点促进的转换过程的先决条件。
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- Comfy dtype: BASIC_PIPE
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- Python dtype: Tuple[Any, ...]
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- bbox_detector
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- bbox_detector 是识别和定位数据中兴趣区域的关键组件。其整合允许在节点的工作流程中进行更精确的操控和分析。
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- Comfy dtype: BBOX_DETECTOR
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- Python dtype: Any
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- wildcard
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- wildcard 参数通过允许在执行期间动态替换占位符，为节点的处理引入了灵活性。这一特性增强了节点对各种数据场景的适应性。
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- Comfy dtype: STRING
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- Python dtype: str
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## Optional
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- Select to add LoRA
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- LoRA 选择参数提供了对节点功能的可选增强，允许将额外的抽象层或复杂性纳入处理管道中。
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- Comfy dtype: COMBO[Select the LoRA to add to the text]
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- Python dtype: Union[str, None]
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- Select to add Wildcard
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- 此可选参数允许用户在处理流程中引入额外的通配符，可用于更动态和多样化的数据操作。
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- Comfy dtype: COMBO[Select the Wildcard to add to the text]
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- Python dtype: Union[str, None]
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- sam_model_opt
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- 可选的 sam_model_opt 参数使节点能够使用高级模型进行更复杂的分析，以防默认处理不够充分。
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- Comfy dtype: SAM_MODEL
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- Python dtype: Union[Any, None]
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- segm_detector_opt
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- 当提供 segm_detector_opt 参数时，允许在节点操作中进行更细粒度的分割检测，可能提高结果的准确性。
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- Comfy dtype: SEGM_DETECTOR
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- Python dtype: Union[Any, None]
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- detailer_hook
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- detailer_hook 是一个可选参数，可用于自定义或扩展节点的功能，允许针对特定用例进行定制处理。
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- Comfy dtype: DETAILER_HOOK
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- Python dtype: Union[Any, None]
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# Output types
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- detailer_pipe
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- detailer_pipe 输出代表了节点处理后得到的增强型管道。它是一个准备用于进一步分析或下游任务的详细结构。
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- Comfy dtype: DETAILER_PIPE
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- Python dtype: Tuple[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 BasicPipeToDetailerPipe:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'basic_pipe': ('BASIC_PIPE',), 'bbox_detector': ('BBOX_DETECTOR',), 'wildcard': ('STRING', {'multiline': True, 'dynamicPrompts': False}), 'Select to add LoRA': (['Select the LoRA to add to the text'] + folder_paths.get_filename_list('loras'),), 'Select to add Wildcard': (['Select the Wildcard to add to the text'],)}, 'optional': {'sam_model_opt': ('SAM_MODEL',), 'segm_detector_opt': ('SEGM_DETECTOR',), 'detailer_hook': ('DETAILER_HOOK',)}}
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RETURN_TYPES = ('DETAILER_PIPE',)
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RETURN_NAMES = ('detailer_pipe',)
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FUNCTION = 'doit'
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CATEGORY = 'ImpactPack/Pipe'
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def doit(self, *args, **kwargs):
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basic_pipe = kwargs['basic_pipe']
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bbox_detector = kwargs['bbox_detector']
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wildcard = kwargs['wildcard']
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sam_model_opt = kwargs.get('sam_model_opt', None)
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segm_detector_opt = kwargs.get('segm_detector_opt', None)
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detailer_hook = kwargs.get('detailer_hook', None)
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(model, clip, vae, positive, negative) = basic_pipe
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pipe = (model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None)
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return (pipe,)
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``` |