mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import torch
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from typing import Optional
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from .base import (
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ModelBase,
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ModelConfigBase,
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BaseModelType,
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ModelType,
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SubModelType,
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)
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# TODO: naming
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from ..lora import TextualInversionModel as TextualInversionModelRaw
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class TextualInversionModel(ModelBase):
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#model_size: int
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class Config(ModelConfigBase):
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format: None
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def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
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assert model_type == ModelType.TextualInversion
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super().__init__(model_path, base_model, model_type)
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self.model_size = os.path.getsize(self.model_path)
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def get_size(self, child_type: Optional[SubModelType] = None):
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if child_type is not None:
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raise Exception("There is no child models in textual inversion")
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return self.model_size
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def get_model(
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self,
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torch_dtype: Optional[torch.dtype],
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child_type: Optional[SubModelType] = None,
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):
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if child_type is not None:
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raise Exception("There is no child models in textual inversion")
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model = TextualInversionModelRaw.from_checkpoint(
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file_path=self.model_path,
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dtype=torch_dtype,
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)
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self.model_size = model.embedding.nelement() * model.embedding.element_size()
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return model
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@classmethod
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def save_to_config(cls) -> bool:
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return False
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@classmethod
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def detect_format(cls, path: str):
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return None
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@staticmethod
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def convert_if_required(model_path: str, cache_path: str, config: Optional[dict]) -> str:
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return model_path
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