68 lines
3.1 KiB
Markdown
68 lines
3.1 KiB
Markdown
# Documentation
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- Class name: KfKeyframedCondition
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- Category: RootCategory
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- Output node: False
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- Repo Ref: https://github.com/dmarx/ComfyUI-Keyframed
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KfKeyframedCondition节点旨在将条件元素整合到关键帧结构中，允许在不同时间点对数据进行操作和转换。它在序列中条件的动态调整中发挥关键作用，确保状态之间平滑且连贯的过渡。
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# Input types
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## Required
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- conditioning
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- conditioning参数对于定义将在特定关键帧应用的初始状态或条件至关重要。它为后续的转换和插值设定了基础，影响序列的整体行为。
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- Comfy dtype: CONDITIONING
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- Python dtype: Tuple[torch.Tensor, Dict[str, torch.Tensor]]
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## Optional
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- time
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- time参数指定了序列中将附加条件的时间位置。它对于确定条件的顺序及其对系统演变状态的影响至关重要。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- interpolation_method
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- 插值方法决定了条件在关键帧之间如何过渡。它对于创建条件的自然流动非常重要，并且可以极大地影响序列的最终输出。
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- Comfy dtype: COMBO[linear, easeIn, easeOut, easeInOut]
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- Python dtype: str
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# Output types
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- kf_cond_t
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- kf_cond_t输出代表了指定时间的关键帧条件，包含了转换后的条件及其插值方法。它是序列中的关键组成部分，指导系统状态的进展。
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- Comfy dtype: KEYFRAMED_CONDITION
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- Python dtype: kf.Keyframe
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- kf_cond_pooled
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- 当存在kf_cond_pooled输出时，它提供了在指定时间应用的额外条件层。它为系统演变状态的复杂性和细微差别做出了贡献。
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- Comfy dtype: KEYFRAMED_CONDITION
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- Python dtype: Optional[kf.Keyframe]
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- cond_dict
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- cond_dict输出是一个包含条件详细信息的字典，包括任何池化输出。它作为指定时间条件状态的参考，便于进一步的分析和处理。
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- Comfy dtype: Dict[str, torch.Tensor]
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- Python dtype: Dict[str, torch.Tensor]
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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 KfKeyframedCondition:
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"""
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Attaches a condition to a keyframe
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"""
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CATEGORY = CATEGORY
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FUNCTION = 'main'
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RETURN_TYPES = ('KEYFRAMED_CONDITION',)
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'conditioning': ('CONDITIONING', {}), 'time': ('FLOAT', {'default': 0, 'step': 1}), 'interpolation_method': (list(kf.interpolation.EASINGS.keys()), {'default': 'linear'})}}
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def main(self, conditioning, time, interpolation_method):
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(cond_tensor, cond_dict) = conditioning[0]
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cond_tensor = cond_tensor.clone()
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kf_cond_t = kf.Keyframe(t=time, value=cond_tensor, interpolation_method=interpolation_method)
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cond_pooled = cond_dict.get('pooled_output')
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cond_dict = deepcopy(cond_dict)
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kf_cond_pooled = None
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if cond_pooled is not None:
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cond_pooled = cond_pooled.clone()
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kf_cond_pooled = kf.Keyframe(t=time, value=cond_pooled, interpolation_method=interpolation_method)
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cond_dict['pooled_output'] = cond_pooled
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return ({'kf_cond_t': kf_cond_t, 'kf_cond_pooled': kf_cond_pooled, 'cond_dict': cond_dict},)
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``` |