CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/Comfy/docs/CLIPTextEncode.md

tags

tags
CLIP CLIPConditioning CLIPTextEncoding Conditioning

CLIP Text Encode (Prompt)

Documentation

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

The CLIPTextEncode node is designed to encode textual input using a CLIP model, transforming text into a conditioning format suitable for further processing or integration with other models. It focuses on converting text prompts into a structured format that captures the essence of the input for use in conditioned generative tasks.

Input types

Required

• text
  • The 'text' parameter is the primary textual input that the node encodes. It plays a crucial role in determining the output conditioning, as it is directly processed by the CLIP model to generate embeddings.
  • Comfy dtype: STRING
  • Python dtype: str
• clip
  • The 'clip' parameter represents the CLIP model used for text tokenization and encoding. It is essential for transforming the input text into a format that can be utilized for conditioning in generative tasks.
  • Comfy dtype: CLIP
  • Python dtype: object

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • The output is a conditioning format that encapsulates the encoded text information, suitable for guiding generative models in producing content that aligns with the input text.
  • Python dtype: List[Tuple[torch.Tensor, Dict[str, torch.Tensor]]]

Usage tips

• Infra type: CPU
• Common nodes:
  • KSampler
  • ControlNetApplyAdvanced
  • KSampler //Inspire
  • SamplerCustom
  • Reroute
  • KSamplerAdvanced
  • ACN_AdvancedControlNetApply
  • ToBasicPipe
  • FaceDetailer

Source code

class CLIPTextEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "encode"

    CATEGORY = "conditioning"

    def encode(self, clip, text):
        tokens = clip.tokenize(text)
        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
        return ([[cond, {"pooled_output": pooled}]], )