57 lines
2.2 KiB
Markdown
57 lines
2.2 KiB
Markdown
# Sigma Schedule Weighted Mean 🎭🅐🅓
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## Documentation
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- Class name: ADE_SigmaScheduleWeightedAverage
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- Category: Animate Diff 🎭🅐🅓/sample settings/sigma schedule
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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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此节点旨在通过计算两个提供的sigma计划的加权平均值来创建一个新的sigma计划。它允许根据指定的加权因子将两个计划的特性混合成一个新的计划。
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## Input types
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### Required
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- schedule_A
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- 第一个要混合的sigma计划。它是加权平均计算的基础之一。
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- Comfy dtype: SIGMA_SCHEDULE
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- Python dtype: SigmaSchedule
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- schedule_B
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- 第二个与第一个混合的sigma计划。它为加权平均计算做出贡献，补充第一个计划。
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- Comfy dtype: SIGMA_SCHEDULE
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- Python dtype: SigmaSchedule
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- weight_A
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- 第一个sigma计划的加权因子。它决定了第一个计划在最终混合计划中的特性比例。
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- Comfy dtype: FLOAT
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- Python dtype: float
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## Output types
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- sigma_schedule
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- Comfy dtype: SIGMA_SCHEDULE
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- 根据指定的加权因子混合两个输入计划后的结果sigma计划。
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- Python dtype: SigmaSchedule
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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 WeightedAverageSigmaScheduleNode:
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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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"schedule_A": ("SIGMA_SCHEDULE",),
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"schedule_B": ("SIGMA_SCHEDULE",),
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"weight_A": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
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}
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}
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RETURN_TYPES = ("SIGMA_SCHEDULE",)
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CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
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FUNCTION = "get_sigma_schedule"
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def get_sigma_schedule(self, schedule_A: SigmaSchedule, schedule_B: SigmaSchedule, weight_A: float):
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validate_sigma_schedule_compatibility(schedule_A, schedule_B)
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new_sigmas = schedule_A.model_sampling.sigmas * weight_A + schedule_B.model_sampling.sigmas * (1-weight_A)
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combo_schedule = schedule_A.clone()
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combo_schedule.model_sampling.set_sigmas(new_sigmas)
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return (combo_schedule,) |