Class GemmaAPITextEncode

gemma_api_conditioning.py:43–147

Encodes text prompts using the LTX Video API, returning CONDITIONING for LTX-2 models. This node replaces the local CLIP encoding step by sending the prompt to an external API for processing. It requires an API key and automatically extracts the model ID from the checkpoint file me

@comfy_node(name="GemmaAPITextEncode")
class GemmaAPITextEncode:
    """
    Encodes text prompts using the LTX Video API, returning CONDITIONING for LTX-2 models.

    This node replaces the local CLIP encoding step by sending the prompt to an external API
    for processing. It requires an API key and automatically extracts the model ID from the
    checkpoint file metadata.

    Inputs:
        - api_key: Authentication key for the LTX Video API
        - prompt: Text prompt to encode
        - ckpt_name: Checkpoint file containing model metadata

    Returns:
        - CONDITIONING: Encoded prompt conditioning ready for LTX-2 video generation
    """

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "api_key": (
                    "STRING",
                    {
                        "default": "",
                        "placeholder": "API_KEY",
                        "multiline": False,
                        "tooltip": "API key for authentication",
                    },
                ),
                "prompt": (
                    "STRING",
                    {
                        "multiline": True,
                        "default": "",
                        "tooltip": "Text prompt to encode",
                    },
                ),
                "enhance_prompt": (
                    "BOOLEAN",
                    {
                        "default": True,
                        "tooltip": "When enabled, the prompt is enhanced using Gemma 3 before encoding",
                    },
                ),
                "ckpt_name": (
                    folder_paths.get_filename_list("checkpoints"),
                    {"tooltip": "The name of the checkpoint (model) to load."},
                ),
            },
        }

    RETURN_TYPES = ("CONDITIONING",)
    RETURN_NAMES = ("conditioning",)
    FUNCTION = "encode"
    CATEGORY = "api node/text/Lightricks"

    def encode(