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55 lines
2.2 KiB
Python
55 lines
2.2 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
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from invokeai.backend.model_manager.configs.identification_utils import (
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NotAMatchError,
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raise_for_override_fields,
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raise_if_not_file,
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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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from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
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class Spandrel_Checkpoint_Config(Config_Base):
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"""Model config for Spandrel Image to Image models."""
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base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
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type: Literal[ModelType.SpandrelImageToImage] = Field(default=ModelType.SpandrelImageToImage)
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format: Literal[ModelFormat.Checkpoint] = Field(default=ModelFormat.Checkpoint)
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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_file(mod)
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raise_for_override_fields(cls, override_fields)
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cls._validate_spandrel_loads_model(mod)
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return cls(**override_fields)
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@classmethod
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def _validate_spandrel_loads_model(cls, mod: ModelOnDisk) -> None:
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try:
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# It would be nice to avoid having to load the Spandrel model from disk here. A couple of options were
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# explored to avoid this:
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# 1. Call `SpandrelImageToImageModel.load_from_state_dict(ckpt)`, where `ckpt` is a state_dict on the meta
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# device. Unfortunately, some Spandrel models perform operations during initialization that are not
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# supported on meta tensors.
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# 2. Spandrel has internal logic to determine a model's type from its state_dict before loading the model.
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# This logic is not exposed in spandrel's public API. We could copy the logic here, but then we have to
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# maintain it, and the risk of false positive detections is higher.
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SpandrelImageToImageModel.load_from_file(mod.path)
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except Exception as e:
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raise NotAMatchError("model does not match SpandrelImageToImage heuristics") from e
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