CLIPEncodeMultiple

Documentation

• Class name: CLIPEncodeMultiple
• Category: Bmad/conditioning
• Output node: False

The CLIPEncodeMultiple node is designed to encode multiple text inputs into a conditioning format using a CLIP model. It iterates over a specified number of inputs, encoding each one separately and aggregating the results into a list of conditionings. This node is useful for scenarios where multiple textual descriptions need to be encoded in parallel and then utilized for further processing or generation tasks.

Input types

Required

• clip
  • The CLIP model used for encoding the text inputs. It is crucial for the text encoding process, affecting the quality and relevance of the generated conditionings.
  • Comfy dtype: CLIP
  • Python dtype: torch.nn.Module
• inputs_len
  • Specifies the number of text inputs to encode. It determines the iteration count for the encoding process, directly influencing the output list's length.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • A list of encoded text inputs, each transformed into a conditioning format suitable for further processing or generation tasks.
  • Python dtype: List[torch.Tensor]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class CLIPEncodeMultiple(nodes.CLIPTextEncode):
    @classmethod
    def INPUT_TYPES(cls):
        return {"required": {
            "clip": ("CLIP",),
            "inputs_len": ("INT", {"default": 9, "min": 0, "max": 32}),
        }}

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "gen2"
    CATEGORY = conditioning_category_path
    OUTPUT_IS_LIST = (True,)

    def gen2(self, clip, 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])[0])
        return (conds,)