from .test_nodes import ( TestEventService, ErrorInvocation, TextToImageTestInvocation, PromptTestInvocation, create_edge, wait_until, ) from invokeai.app.services.invocation_queue import MemoryInvocationQueue from invokeai.app.services.processor import DefaultInvocationProcessor from invokeai.app.services.sqlite import SqliteItemStorage, sqlite_memory from invokeai.app.services.invoker import Invoker from invokeai.app.services.invocation_services import InvocationServices from invokeai.app.services.invocation_stats import InvocationStatsService from invokeai.app.services.batch_manager_storage import SqliteBatchProcessStorage from invokeai.app.services.batch_manager import ( Batch, BatchManager, ) from invokeai.app.services.graph import ( Graph, GraphExecutionState, LibraryGraph, ) import pytest @pytest.fixture def simple_graph(): g = Graph() g.add_node(PromptTestInvocation(id="1", prompt="Banana sushi")) g.add_node(TextToImageTestInvocation(id="2")) g.add_edge(create_edge("1", "prompt", "2", "prompt")) return g @pytest.fixture def simple_batches(): batches = [ Batch( node_id=1, data=[ { "prompt": "Tomato sushi", }, { "prompt": "Strawberry sushi", }, { "prompt": "Broccoli sushi", }, { "prompt": "Asparagus sushi", }, { "prompt": "Tea sushi", }, ], ), Batch( node_id="2", data=[ { "prompt2": "Ume sushi", }, { "prompt2": "Ichigo sushi", }, { "prompt2": "Momo sushi", }, { "prompt2": "Mikan sushi", }, { "prompt2": "Cha sushi", }, ], ), ] return batches # This must be defined here to avoid issues with the dynamic creation of the union of all invocation types # Defining it in a separate module will cause the union to be incomplete, and pydantic will not validate # the test invocations. @pytest.fixture def mock_services() -> InvocationServices: # NOTE: none of these are actually called by the test invocations graph_execution_manager = SqliteItemStorage[GraphExecutionState]( filename=sqlite_memory, table_name="graph_executions" ) batch_manager_storage = SqliteBatchProcessStorage(sqlite_memory) return InvocationServices( model_manager=None, # type: ignore events=TestEventService(), logger=None, # type: ignore images=None, # type: ignore latents=None, # type: ignore batch_manager=BatchManager(batch_manager_storage), boards=None, # type: ignore board_images=None, # type: ignore queue=MemoryInvocationQueue(), graph_library=SqliteItemStorage[LibraryGraph](filename=sqlite_memory, table_name="graphs"), graph_execution_manager=graph_execution_manager, processor=DefaultInvocationProcessor(), performance_statistics=InvocationStatsService(graph_execution_manager), configuration=None, # type: ignore ) @pytest.fixture() def mock_invoker(mock_services: InvocationServices) -> Invoker: return Invoker(services=mock_services) def test_can_create_graph_state(mock_invoker: Invoker): g = mock_invoker.create_execution_state() mock_invoker.stop() assert g is not None assert isinstance(g, GraphExecutionState) def test_can_create_graph_state_from_graph(mock_invoker: Invoker, simple_graph): g = mock_invoker.create_execution_state(graph=simple_graph) mock_invoker.stop() assert g is not None assert isinstance(g, GraphExecutionState) assert g.graph == simple_graph # @pytest.mark.xfail(reason = "Requires fixing following the model manager refactor") def test_can_invoke(mock_invoker: Invoker, simple_graph): g = mock_invoker.create_execution_state(graph=simple_graph) invocation_id = mock_invoker.invoke(g) assert invocation_id is not None def has_executed_any(g: GraphExecutionState): g = mock_invoker.services.graph_execution_manager.get(g.id) return len(g.executed) > 0 wait_until(lambda: has_executed_any(g), timeout=5, interval=1) mock_invoker.stop() g = mock_invoker.services.graph_execution_manager.get(g.id) assert len(g.executed) > 0 # @pytest.mark.xfail(reason = "Requires fixing following the model manager refactor") def test_can_invoke_all(mock_invoker: Invoker, simple_graph): g = mock_invoker.create_execution_state(graph=simple_graph) invocation_id = mock_invoker.invoke(g, invoke_all=True) assert invocation_id is not None def has_executed_all(g: GraphExecutionState): g = mock_invoker.services.graph_execution_manager.get(g.id) return g.is_complete() wait_until(lambda: has_executed_all(g), timeout=5, interval=1) mock_invoker.stop() g = mock_invoker.services.graph_execution_manager.get(g.id) assert g.is_complete() # @pytest.mark.xfail(reason = "Requires fixing following the model manager refactor") def test_handles_errors(mock_invoker: Invoker): g = mock_invoker.create_execution_state() g.graph.add_node(ErrorInvocation(id="1")) mock_invoker.invoke(g, invoke_all=True) def has_executed_all(g: GraphExecutionState): g = mock_invoker.services.graph_execution_manager.get(g.id) return g.is_complete() wait_until(lambda: has_executed_all(g), timeout=5, interval=1) mock_invoker.stop() g = mock_invoker.services.graph_execution_manager.get(g.id) assert g.has_error() assert g.is_complete() assert all((i in g.errors for i in g.source_prepared_mapping["1"])) def test_can_create_batch(mock_invoker: Invoker, simple_graph, simple_batches): batch_process_res = mock_invoker.services.batch_manager.create_batch_process( batches=simple_batches, graph=simple_graph, ) assert batch_process_res.batch_id assert len(batch_process_res.session_ids) == 25 for session in batch_process_res.session_ids: assert mock_invoker.services.graph_execution_manager.get(session)