CLIPEncodeMultiple

Documentation

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

The CLIPEncodeMultiple node is designed to encode multiple inputs using a CLIP model, producing a list of conditionings based on the inputs. It abstracts the complexity of handling multiple inputs and leverages the CLIP model's capabilities to generate relevant conditionings for each input.

Input types

Required

• clip
  • The CLIP model used for encoding the inputs. It is crucial for determining how the inputs are processed and encoded into conditionings.
  • Comfy dtype: CLIP
  • Python dtype: torch.nn.Module
• inputs_len
  • Specifies the number of inputs to be encoded. It affects the number of iterations and consequently the size of the output list of conditionings.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • A list of conditionings generated from the inputs. Each element in the list corresponds to the conditioning of an individual input.
  • Python dtype: List[torch.Tensor]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

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

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "gen2"
    CATEGORY = "Bmad/conditioning"
    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,)