InvokeAI/invokeai/backend/model_management/models/textual_inversion.py

import torch
from typing import Optional
from .base import (
    ModelBase,
    ModelConfigBase,
    BaseModelType,
    ModelType,
    SubModelType,
)
# TODO: naming
from ..lora import TextualInversionModel as TextualInversionModelRaw

class TextualInversionModel(ModelBase):
    #model_size: int

    class Config(ModelConfigBase):
        format: None

    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
        assert model_type == ModelType.TextualInversion
        super().__init__(model_path, base_model, model_type)

        self.model_size = os.path.getsize(self.model_path)

    def get_size(self, child_type: Optional[SubModelType] = None):
        if child_type is not None:
            raise Exception("There is no child models in textual inversion")
        return self.model_size

    def get_model(
        self,
        torch_dtype: Optional[torch.dtype],
        child_type: Optional[SubModelType] = None,
    ):
        if child_type is not None:
            raise Exception("There is no child models in textual inversion")

        model = TextualInversionModelRaw.from_checkpoint(
            file_path=self.model_path,
            dtype=torch_dtype,
        )

        self.model_size = model.embedding.nelement() * model.embedding.element_size()
        return model

    @classmethod
    def save_to_config(cls) -> bool:
        return False

    @classmethod
    def detect_format(cls, path: str):
        return None

    @staticmethod
    def convert_if_required(model_path: str, cache_path: str, config: Optional[dict]) -> str:
        return model_path