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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>
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
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"""Class for Onnx model loading in InvokeAI."""
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# This should work the same as Stable Diffusion pipelines
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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 .. import ModelLoaderRegistry
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from .generic_diffusers import GenericDiffusersLoader
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.ONNX)
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.Olive)
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class OnnyxDiffusersModel(GenericDiffusersLoader):
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"""Class to load onnx 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 not submodel_type is not None:
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raise Exception("A submodel type must be provided when loading onnx pipelines.")
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model_path = Path(config.path)
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load_class = self.get_hf_load_class(model_path, submodel_type)
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repo_variant = getattr(config, "repo_variant", None)
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variant = repo_variant.value if repo_variant else None
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model_path = model_path / submodel_type.value
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result: AnyModel = load_class.from_pretrained(
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model_path,
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torch_dtype=self._torch_dtype,
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variant=variant,
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)
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return result
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