CLIP Text Encode Sequence (Advanced)

Documentation

• Class name: CLIPTextEncodeList
• Category: conditioning
• Output node: False

This node is designed for encoding textual inputs using a CLIP model, transforming them into a structured format that can be utilized for further computational tasks or analyses. It focuses on processing multiple text inputs, applying tokenization and encoding to extract relevant features for downstream applications.

Input types

Required

• clip
  • The 'clip' parameter represents the CLIP model used for text encoding. It is crucial for the node's operation as it determines how text inputs are processed and encoded.
  • Comfy dtype: CLIP
  • Python dtype: torch.nn.Module
• token_normalization
  • Specifies the method for normalizing tokens, affecting the encoding process's outcome. It plays a significant role in adjusting the encoding based on the chosen normalization strategy.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• weight_interpretation
  • Defines how weights are interpreted during the encoding process, influencing the final encoded output. It's important for tailoring the encoding to specific weight interpretation preferences.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• text
  • The textual input to be encoded. This parameter is essential as it provides the raw data for the encoding process.
  • Comfy dtype: STRING
  • Python dtype: str

Output types

• conditioning_sequence
  • Comfy dtype: CONDITIONING_SEQ
  • The output is a sequence of conditionings derived from the encoded text inputs. It's significant for subsequent computational tasks that rely on these encoded representations.
  • Python dtype: List[torch.Tensor]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class CLIPTextEncodeSequence:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "clip": ("CLIP", ),
                "token_normalization": (["none", "mean", "length", "length+mean"],),
                "weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
                "text": ("STRING", {"multiline": True, "default": '''0:A portrait of a rosebud
5:A portrait of a blooming rosebud
10:A portrait of a blooming rose
15:A portrait of a rose'''}),
                }
            }

    RETURN_TYPES = ("CONDITIONING_SEQ",)
    RETURN_NAMES = ("conditioning_sequence",)
    IS_LIST_OUTPUT = (True,)

    FUNCTION = "encode"
    CATEGORY = "conditioning"

    def encode(self, clip, text, token_normalization, weight_interpretation):
        text = text.strip()
        conditionings = []
        for l in text.splitlines():
            match = re.match(r'(\d+):', l)

            if match:
                idx = int(match.group(1))
                _, line = l.split(":", 1)
                line = line.strip()

                if USE_BLK:
                    encoded = blk_adv.encode(clip=clip, text=line, token_normalization=token_normalization, weight_interpretation=weight_interpretation)
                else:
                    encoded = CLIPTextEncode.encode(clip=clip, text=line)

                conditioning = (idx, [encoded[0][0][0], encoded[0][0][1]])
                conditionings.append(conditioning)

        return (conditionings, )