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docs/CLIPEncodeMultipleAdvanced.md

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+
+# Documentation
+- Class name: CLIPEncodeMultipleAdvanced
+- Category: Bmad/conditioning
+- Output node: False
+- Repo Ref: https://github.com/Stability-AI/StableCascade
+
+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。
+
+# Input types
+## Required
+- clip
+    - 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。
+    - Comfy dtype: CLIP
+    - Python dtype: torch.nn.Module
+- token_normalization
+    - 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。
+    - Comfy dtype: COMBO[STRING]
+    - Python dtype: bool
+- weight_interpretation
+    - 调整编码的权重解释，影响嵌入向量的计算方式。
+    - Comfy dtype: COMBO[STRING]
+    - Python dtype: str
+- inputs_len
+    - 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。
+    - Comfy dtype: INT
+    - Python dtype: int
+
+# Output types
+- conditioning
+    - 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。
+    - Comfy dtype: CONDITIONING
+    - Python dtype: List[torch.Tensor]
+
+
+## Usage tips
+- Infra type: `GPU`
+- Common nodes: unknown
+
+
+## Source code
+```python
+class CLIPEncodeMultipleAdvanced(AdvancedCLIPTextEncode):
+    @classmethod
+    def INPUT_TYPES(s):
+        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
+        types["required"].pop("text")
+        types["required"]["inputs_len"] = ("INT", {"default": 9, "min": 0, "max": 32})
+        return types
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "gen2"
+    CATEGORY = "Bmad/conditioning"
+    OUTPUT_IS_LIST = (True,)
+
+    def gen2(self, clip, token_normalization, weight_interpretation, inputs_len, **kwargs):
+        conds = []
+        for i in range(inputs_len):
+            arg_name = get_arg_name_from_multiple_inputs("string", i)
+            conds.append(super().encode(clip, kwargs[arg_name], token_normalization, weight_interpretation,'disable')[0])
+        return (conds,)
+
+```