75 lines
3.1 KiB
Markdown
75 lines
3.1 KiB
Markdown
# Documentation
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- Class name: LLLiteLoader
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- Category: EasyUse/Loaders
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- Output node: False
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- Repo Ref: https://github.com/yolain/ComfyUI-Easy-Use.git
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LLLiteLoader节点旨在便于轻量级模型的集成，通过加入额外的模型功能来增强系统的整体处理能力。
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# Input types
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## Required
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- model
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- 模型参数至关重要，它定义了节点将使用和修改的基础架构，显著影响节点的输出和行为。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- model_name
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- 该参数对于从列表中选择适当的模型至关重要，根据所选模型的特性和预期应用引导节点的处理。
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- Comfy dtype: COMBO
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- Python dtype: str
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- cond_image
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- cond_image参数很重要，因为它提供了模型适应其输出到特定条件所需的上下文信息，影响最终结果。
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- Comfy dtype: IMAGE
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- Python dtype: PIL.Image or torch.Tensor
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## Optional
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- strength
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- strength参数调整模型对条件的适应强度，可用于微调节点的输出以满足不同要求。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- steps
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- steps参数定义了模型将经历的迭代次数，这直接影响节点处理的复杂性和准确性。
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- Comfy dtype: INT
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- Python dtype: int
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- start_percent
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- 该参数设置了模型适应过程的起点，影响初始条件和节点执行的进展。
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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参数确定了模型适应的终点，影响节点操作的最终状态和结果。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- model
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- 输出模型是节点处理的结果，包含了适应的特性和条件，准备好在后续任务中使用。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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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 LLLiteLoader:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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def get_file_list(filenames):
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return [file for file in filenames if file != 'put_models_here.txt' and 'lllite' in file]
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return {'required': {'model': ('MODEL',), 'model_name': (get_file_list(folder_paths.get_filename_list('controlnet')),), 'cond_image': ('IMAGE',), 'strength': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.01}), 'steps': ('INT', {'default': 0, 'min': 0, 'max': 200, 'step': 1}), 'start_percent': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 100.0, 'step': 0.1}), 'end_percent': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 100.0, 'step': 0.1})}}
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RETURN_TYPES = ('MODEL',)
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FUNCTION = 'load_lllite'
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CATEGORY = 'EasyUse/Loaders'
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def load_lllite(self, model, model_name, cond_image, strength, steps, start_percent, end_percent):
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model_path = os.path.join(folder_paths.get_full_path('controlnet', model_name))
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model_lllite = model.clone()
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patch = load_control_net_lllite_patch(model_path, cond_image, strength, steps, start_percent, end_percent)
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if patch is not None:
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model_lllite.set_model_attn1_patch(patch)
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model_lllite.set_model_attn2_patch(patch)
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return (model_lllite,)
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``` |