mirror of
https://github.com/invoke-ai/InvokeAI
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77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import os
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import torch
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from enum import Enum
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from typing import Optional, Union, Literal
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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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classproperty,
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)
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# TODO: naming
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from ..lora import LoRAModel as LoRAModelRaw
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class LoRAModelFormat(str, Enum):
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LyCORIS = "lycoris"
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Diffusers = "diffusers"
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class LoRAModel(ModelBase):
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#model_size: int
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class Config(ModelConfigBase):
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model_format: LoRAModelFormat # TODO:
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def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
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assert model_type == ModelType.Lora
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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 lora")
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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 lora")
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model = LoRAModelRaw.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.calc_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 os.path.isdir(path):
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return LoRAModelFormat.Diffusers
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else:
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return LoRAModelFormat.LyCORIS
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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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if cls.detect_format(model_path) == LoRAModelFormat.Diffusers:
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# TODO: add diffusers lora when it stabilizes a bit
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raise NotImplementedError("Diffusers lora not supported")
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else:
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return model_path
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