76 lines
2.9 KiB
Markdown
76 lines
2.9 KiB
Markdown
# Documentation
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- Class name: KfApplyCurveToCond
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- Category: CONDITIONING
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- Output node: False
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- Repo Ref: https://github.com/dmarx/ComfyUI-Keyframed
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该节点根据指定的曲线动态调整条件数据的强度，允许在生成过程中对条件变量的影响进行细微控制。
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# Input types
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## Required
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- curve
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- 曲线输入是一系列值，将用于调节条件数据，代表随时间或其他变量对条件强度进行的期望调整。
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- Comfy dtype: KEYFRAMED_CURVE
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- Python dtype: List[float]
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- cond
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- 将由曲线输入调整的条件数据，通常包括张量及其关联的元数据，为生成过程提供上下文。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[torch.Tensor, Dict]]
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## Optional
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- latents
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- 可选的潜在数据，可用于进一步细化曲线应用于条件数据，增强节点对不同用例的适应性。
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- Comfy dtype: LATENT
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- Python dtype: Dict
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- start_t
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- 要应用的曲线的起始索引，可用于控制影响条件数据的曲线片段。
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- Comfy dtype: INT
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- Python dtype: int
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- n
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- 要生成的样本数量，如果提供潜在数据，则可以从中推断，它决定了应用于条件数据的曲线片段的长度。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- cond_out
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- 输出是修改后的条件数据，其强度已根据输入曲线进行了调整，从而在生成过程中提供了更定制化的影响。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[torch.Tensor, Dict]]
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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 KfApplyCurveToCond:
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CATEGORY = CATEGORY
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FUNCTION = 'main'
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RETURN_TYPES = ('CONDITIONING',)
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'curve': ('KEYFRAMED_CURVE', {'forceInput': True}), 'cond': ('CONDITIONING', {'forceInput': True})}, 'optional': {'latents': ('LATENT', {}), 'start_t': ('INT', {'default': 0}), 'n': ('INT', {})}}
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def main(self, curve, cond, latents=None, start_t=0, n=0):
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curve = deepcopy(curve)
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cond = deepcopy(cond)
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if isinstance(latents, dict):
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if 'samples' in latents:
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n = latents['samples'].shape[0]
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cond_out = []
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for (c_tensor, c_dict) in cond:
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m = c_tensor.shape[0]
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if c_tensor.shape[0] == 1:
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c_tensor = c_tensor.repeat(n, 1, 1)
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m = n
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weights = [curve[start_t + i] for i in range(m)]
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weights = torch.tensor(weights, device=c_tensor.device)
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c_tensor = c_tensor * weights.view(m, 1, 1)
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if 'pooled_output' in c_dict:
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c_dict = deepcopy(c_dict)
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pooled = c_dict['pooled_output']
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if pooled.shape[0] == 1:
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pooled = pooled.repeat(m, 1)
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c_dict['pooled_output'] = pooled * weights.view(m, 1)
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cond_out.append((c_tensor, c_dict))
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return (cond_out,)
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``` |