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https://github.com/invoke-ai/InvokeAI.git
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a16d0b8301
Overhaul of model identification (probing) tests. Previously we didn't test the correctness of probing except in a few narrow cases - now we do. See tests/model_identification/README.md for a detailed overview of the new test setup. It includes instructions for adding a new test case. In brief: - Download the model you want to add as a test case - Run a script against it to generate the test model files - Fill in the expected model type/format/base/etc in the generated test metadata JSON file Included test cases: - All starter models - A handful of other models that I had installed - Models present in the previous test cases as smoke tests, now also tested for correctness
79 lines
3.6 KiB
Python
79 lines
3.6 KiB
Python
# conftest.py is a special pytest file. Fixtures defined in this file will be accessible to all tests in this directory
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# without needing to explicitly import them. (https://docs.pytest.org/en/6.2.x/fixture.html)
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# We import the model_installer and torch_device fixtures here so that they can be used by all tests. Flake8 does not
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# play well with fixtures (F401 and F811), so this is cleaner than importing in all files that use these fixtures.
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import logging
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import shutil
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from pathlib import Path
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import pytest
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from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
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from invokeai.app.services.board_records.board_records_sqlite import SqliteBoardRecordStorage
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from invokeai.app.services.bulk_download.bulk_download_default import BulkDownloadService
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from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.app.services.images.images_default import ImageService
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from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
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from invokeai.app.services.invocation_services import InvocationServices
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from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
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from invokeai.app.services.invoker import Invoker
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from invokeai.backend.util.logging import InvokeAILogger
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from tests.backend.model_manager.model_manager_fixtures import * # noqa: F403
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from tests.fixtures.sqlite_database import create_mock_sqlite_database # noqa: F401
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from tests.test_nodes import TestEventService
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@pytest.fixture
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def mock_services() -> InvocationServices:
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configuration = InvokeAIAppConfig(use_memory_db=True, node_cache_size=0)
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logger = InvokeAILogger.get_logger()
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db = create_mock_sqlite_database(configuration, logger)
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# NOTE: none of these are actually called by the test invocations
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return InvocationServices(
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board_image_records=SqliteBoardImageRecordStorage(db=db),
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board_images=None, # type: ignore
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board_records=SqliteBoardRecordStorage(db=db),
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boards=None, # type: ignore
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bulk_download=BulkDownloadService(),
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configuration=configuration,
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events=TestEventService(),
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image_files=None, # type: ignore
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image_records=None, # type: ignore
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images=ImageService(),
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invocation_cache=MemoryInvocationCache(max_cache_size=0),
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logger=logging, # type: ignore
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model_images=None, # type: ignore
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model_manager=None, # type: ignore
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download_queue=None, # type: ignore
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names=None, # type: ignore
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performance_statistics=InvocationStatsService(),
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session_processor=None, # type: ignore
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session_queue=None, # type: ignore
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urls=None, # type: ignore
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workflow_records=None, # type: ignore
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tensors=None, # type: ignore
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conditioning=None, # type: ignore
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style_preset_records=None, # type: ignore
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style_preset_image_files=None, # type: ignore
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workflow_thumbnails=None, # type: ignore
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model_relationship_records=None, # type: ignore
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model_relationships=None, # type: ignore
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client_state_persistence=None, # type: ignore
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)
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@pytest.fixture()
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def mock_invoker(mock_services: InvocationServices) -> Invoker:
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return Invoker(services=mock_services)
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@pytest.fixture(scope="module")
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def invokeai_root_dir(tmp_path_factory) -> Path:
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root_template = Path(__file__).parent.resolve() / "backend/model_manager/data/invokeai_root"
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temp_dir: Path = tmp_path_factory.mktemp("data") / "invokeai_root"
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shutil.copytree(root_template, temp_dir)
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return temp_dir
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