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59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
from typing import (
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Literal,
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Self,
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)
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from pydantic import Field
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from typing_extensions import Any
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from invokeai.backend.model_manager.configs.base import Config_Base, Diffusers_Config_Base
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from invokeai.backend.model_manager.configs.identification_utils import (
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NotAMatchError,
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get_class_name_from_config_dict_or_raise,
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get_config_dict_or_raise,
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raise_for_override_fields,
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raise_if_not_dir,
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)
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from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
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from invokeai.backend.model_manager.taxonomy import (
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BaseModelType,
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ModelFormat,
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ModelType,
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)
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class CLIPVision_Diffusers_Config(Diffusers_Config_Base, Config_Base):
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"""Model config for CLIPVision."""
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base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
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type: Literal[ModelType.CLIPVision] = Field(default=ModelType.CLIPVision)
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format: Literal[ModelFormat.Diffusers] = Field(default=ModelFormat.Diffusers)
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cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only")
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@classmethod
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def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
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raise_if_not_dir(mod)
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raise_for_override_fields(cls, override_fields)
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cls.raise_if_config_doesnt_look_like_clip_vision(mod)
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return cls(**override_fields)
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@classmethod
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def raise_if_config_doesnt_look_like_clip_vision(cls, mod: ModelOnDisk) -> None:
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config_dict = get_config_dict_or_raise(mod.path / "config.json")
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class_name = get_class_name_from_config_dict_or_raise(config_dict)
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if class_name == "CLIPVisionModelWithProjection":
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looks_like_clip_vision = True
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elif class_name == "CLIPModel" and "vision_config" in config_dict:
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looks_like_clip_vision = True
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else:
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looks_like_clip_vision = False
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if not looks_like_clip_vision:
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raise NotAMatchError(
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f"config class name is {class_name}, not CLIPVisionModelWithProjection or CLIPModel with vision_config"
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)
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