mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
5a3195f757
- Replace AnyModelLoader with ModelLoaderRegistry - Fix type check errors in multiple files - Remove apparently unneeded `get_model_config_enum()` method from model manager - Remove last vestiges of old model manager - Updated tests and documentation resolve conflict with seamless.py
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
import contextlib
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from pathlib import Path
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from typing import Optional, Union
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import pytest
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import torch
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from invokeai.app.services.model_manager import ModelManagerServiceBase
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from invokeai.app.services.model_records import UnknownModelException
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from invokeai.backend.model_manager import BaseModelType, LoadedModel, ModelType, SubModelType
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@pytest.fixture(scope="session")
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def torch_device():
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return "cuda" if torch.cuda.is_available() else "cpu"
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def install_and_load_model(
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model_manager: ModelManagerServiceBase,
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model_path_id_or_url: Union[str, Path],
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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submodel_type: Optional[SubModelType] = None,
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) -> LoadedModel:
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"""Install a model if it is not already installed, then get the LoadedModel for that model.
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This is intended as a utility function for tests.
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Args:
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mm2_model_manager (ModelManagerServiceBase): The model manager
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model_path_id_or_url (Union[str, Path]): The path, HF ID, URL, etc. where the model can be installed from if it
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is not already installed.
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model_name (str): The model name, forwarded to ModelManager.get_model(...).
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base_model (BaseModelType): The base model, forwarded to ModelManager.get_model(...).
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model_type (ModelType): The model type, forwarded to ModelManager.get_model(...).
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submodel_type (Optional[SubModelType]): The submodel type, forwarded to ModelManager.get_model(...).
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Returns:
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LoadedModelInfo
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"""
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# If the requested model is already installed, return its LoadedModel
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with contextlib.suppress(UnknownModelException):
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# TODO: Replace with wrapper call
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loaded_model: LoadedModel = model_manager.load_model_by_attr(
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model_name=model_name, base_model=base_model, model_type=model_type
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)
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return loaded_model
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# Install the requested model.
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job = model_manager.install.heuristic_import(model_path_id_or_url)
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model_manager.install.wait_for_job(job, timeout=10)
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assert job.complete
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try:
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loaded_model = model_manager.load_model_by_config(job.config_out)
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return loaded_model
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except UnknownModelException as e:
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raise Exception(
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"Failed to get model info after installing it. There could be a mismatch between the requested model and"
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f" the installation id ('{model_path_id_or_url}'). Error: {e}"
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)
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