64 lines
2.3 KiB
Markdown
64 lines
2.3 KiB
Markdown
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# Documentation
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- Class name: CLIPEncodeMultipleAdvanced
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- Category: Bmad/conditioning
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- Output node: False
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- Repo Ref: https://github.com/Stability-AI/StableCascade
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CLIPEncodeMultipleAdvanced节点是为使用CLIP模型进行高级文本编码而设计的，允许自定义标记归一化和权重解释。它支持同时编码多个输入，根据提供的参数调整编码过程，以生成一系列条件约束。
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# Input types
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## Required
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- clip
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- 用于编码的CLIP模型。它对于将输入文本解释为嵌入向量至关重要。
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- Comfy dtype: CLIP
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- Python dtype: torch.nn.Module
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- token_normalization
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- 决定在编码过程中是否对标记进行归一化，从而影响最终的嵌入向量。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: bool
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- weight_interpretation
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- 调整编码的权重解释，影响嵌入向量的计算方式。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- inputs_len
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- 指定要编码的输入数量。这允许节点同时处理多个输入，生成一系列条件约束。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- conditioning
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- 基于编码输入生成的条件约束列表。这对于依赖这些条件约束的后续处理步骤至关重要。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[torch.Tensor]
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class CLIPEncodeMultipleAdvanced(AdvancedCLIPTextEncode):
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@classmethod
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def INPUT_TYPES(s):
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types = super().INPUT_TYPES() # TODO should refactor Grid class above to this too, so if original is changed, all the new options are added there too
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types["required"].pop("text")
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types["required"]["inputs_len"] = ("INT", {"default": 9, "min": 0, "max": 32})
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return types
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "gen2"
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CATEGORY = "Bmad/conditioning"
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OUTPUT_IS_LIST = (True,)
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def gen2(self, clip, token_normalization, weight_interpretation, inputs_len, **kwargs):
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conds = []
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for i in range(inputs_len):
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arg_name = get_arg_name_from_multiple_inputs("string", i)
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conds.append(super().encode(clip, kwargs[arg_name], token_normalization, weight_interpretation,'disable')[0])
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return (conds,)
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```
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