mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
88 lines
2.4 KiB
Python
88 lines
2.4 KiB
Python
import os
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from typing import Optional
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import torch
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# TODO: naming
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from ..lora import TextualInversionModel as TextualInversionModelRaw
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from .base import (
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BaseModelType,
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InvalidModelException,
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ModelBase,
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ModelConfigBase,
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ModelNotFoundException,
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ModelType,
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SubModelType,
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classproperty,
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)
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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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model_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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checkpoint_path = self.model_path
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if os.path.isdir(checkpoint_path):
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checkpoint_path = os.path.join(checkpoint_path, "learned_embeds.bin")
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if not os.path.exists(checkpoint_path):
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raise ModelNotFoundException()
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model = TextualInversionModelRaw.from_checkpoint(
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file_path=checkpoint_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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@classproperty
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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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if not os.path.exists(path):
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raise ModelNotFoundException()
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if os.path.isdir(path):
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if os.path.exists(os.path.join(path, "learned_embeds.bin")):
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return None # diffusers-ti
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if os.path.isfile(path):
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if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]]):
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return None
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raise InvalidModelException(f"Not a valid model: {path}")
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@classmethod
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def convert_if_required(
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cls,
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model_path: str,
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output_path: str,
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config: ModelConfigBase,
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base_model: BaseModelType,
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) -> str:
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return model_path
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