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https://github.com/invoke-ai/InvokeAI.git
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3d6d89feb4
* pass model config to _load_model * make conversion work again * do not write diffusers to disk when convert_cache set to 0 * adding same model to cache twice is a no-op, not an assertion error * fix issues identified by psychedelicious during pr review * following conversion, avoid redundant read of cached submodels * fix error introduced while merging --------- Co-authored-by: Lincoln Stein <lstein@gmail.com>
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
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"""Class for TI model loading in InvokeAI."""
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from pathlib import Path
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from typing import Optional
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from invokeai.backend.model_manager import (
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AnyModel,
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AnyModelConfig,
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BaseModelType,
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ModelFormat,
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ModelType,
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SubModelType,
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)
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from invokeai.backend.textual_inversion import TextualInversionModelRaw
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from .. import ModelLoader, ModelLoaderRegistry
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.TextualInversion, format=ModelFormat.EmbeddingFile)
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@ModelLoaderRegistry.register(
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base=BaseModelType.Any, type=ModelType.TextualInversion, format=ModelFormat.EmbeddingFolder
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)
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class TextualInversionLoader(ModelLoader):
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"""Class to load TI models."""
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def _load_model(
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self,
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config: AnyModelConfig,
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submodel_type: Optional[SubModelType] = None,
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) -> AnyModel:
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if submodel_type is not None:
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raise ValueError("There are no submodels in a TI model.")
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model = TextualInversionModelRaw.from_checkpoint(
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file_path=config.path,
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dtype=self._torch_dtype,
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)
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return model
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# override
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def _get_model_path(self, config: AnyModelConfig) -> Path:
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model_path = self._app_config.models_path / config.path
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if config.format == ModelFormat.EmbeddingFolder:
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path = model_path / "learned_embeds.bin"
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else:
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path = model_path
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if not path.exists():
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raise OSError(f"The embedding file at {path} was not found")
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return path
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