97 lines
3.8 KiB
Markdown
97 lines
3.8 KiB
Markdown
# Set Props and Combine Conds 🎭🅐🅓
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## Documentation
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- Class name: ADE_PairedConditioningSetMaskAndCombine
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- Category: Animate Diff 🎭🅐🅓/conditioning
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- Output node: False
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- Repo Ref: https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved.git
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此节点专门用于生成模型的成对条件数据的高级操作，特别关注掩码和附加条件层的应用和组合。它通过应用掩码、强度调整和整合新的条件元素来增强或修改现有条件，从而更精确地控制生成过程。
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## Input types
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### Required
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- positive
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- 要增强或修改的原始正向条件数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: torch.Tensor
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- negative
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- 要增强或修改的原始负向条件数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: torch.Tensor
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- positive_ADD
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- 要与原始条件结合的附加正向条件数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: torch.Tensor
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- negative_ADD
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- 要与原始条件结合的附加负向条件数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: torch.Tensor
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- strength
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- 定义掩码或条件修改强度的参数。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- set_cond_area
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- 指定掩码或修改应用于条件的区域。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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### Optional
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- opt_mask
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- 一个可选掩码，可以应用于条件数据以进行选择性修改。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- opt_lora_hook
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- 一个可选参数，允许将Lora挂钩应用于条件数据，提供额外的转换或控制层。
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- Comfy dtype: LORA_HOOK
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- Python dtype: LoraHookGroup
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- opt_timesteps
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- 可选的时间步条件，允许进行时间调整。
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- Comfy dtype: TIMESTEPS_COND
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- Python dtype: TimestepsCond
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## Output types
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- positive
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- Comfy dtype: CONDITIONING
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- 应用掩码、强度调整并与附加条件结合后的增强或修改后的正向条件数据。
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- Python dtype: torch.Tensor
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- negative
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- Comfy dtype: CONDITIONING
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- 应用掩码、强度调整并与附加条件结合后的增强或修改后的负向条件数据。
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: CPU
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- Common nodes: unknown
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## Source code
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```python
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class PairedConditioningSetMaskAndCombineHooked:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"positive_ADD": ("CONDITIONING",),
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"negative_ADD": ("CONDITIONING",),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"set_cond_area": (COND_CONST._LIST_COND_AREA,),
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},
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"optional": {
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"opt_mask": ("MASK", ),
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"opt_lora_hook": ("LORA_HOOK",),
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"opt_timesteps": ("TIMESTEPS_COND",)
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}
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}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
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RETURN_NAMES = ("positive", "negative")
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CATEGORY = "Animate Diff 🎭🅐🅓/conditioning"
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FUNCTION = "append_and_combine"
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def append_and_combine(self, positive, negative, positive_ADD, negative_ADD,
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strength: float, set_cond_area: str,
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opt_mask: Tensor=None, opt_lora_hook: LoraHookGroup=None, opt_timesteps: TimestepsCond=None):
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final_positive, final_negative = set_mask_and_combine_conds(conds=[positive, negative], new_conds=[positive_ADD, negative_ADD],
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strength=strength, set_cond_area=set_cond_area,
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opt_mask=opt_mask, opt_lora_hook=opt_lora_hook, opt_timesteps=opt_timesteps)
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return (final_positive, final_negative,) |