mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
c42d692ea6
* chore: bump pydantic to 2.5.2 This release fixes pydantic/pydantic#8175 and allows us to use `JsonValue` * fix(ui): exclude public/en.json from prettier config * fix(workflow_records): fix SQLite workflow insertion to ignore duplicates * feat(backend): update workflows handling Update workflows handling for Workflow Library. **Updated Workflow Storage** "Embedded Workflows" are workflows associated with images, and are now only stored in the image files. "Library Workflows" are not associated with images, and are stored only in DB. This works out nicely. We have always saved workflows to files, but recently began saving them to the DB in addition to in image files. When that happened, we stopped reading workflows from files, so all the workflows that only existed in images were inaccessible. With this change, access to those workflows is restored, and no workflows are lost. **Updated Workflow Handling in Nodes** Prior to this change, workflows were embedded in images by passing the whole workflow JSON to a special workflow field on a node. In the node's `invoke()` function, the node was able to access this workflow and save it with the image. This (inaccurately) models workflows as a property of an image and is rather awkward technically. A workflow is now a property of a batch/session queue item. It is available in the InvocationContext and therefore available to all nodes during `invoke()`. **Database Migrations** Added a `SQLiteMigrator` class to handle database migrations. Migrations were needed to accomodate the DB-related changes in this PR. See the code for details. The `images`, `workflows` and `session_queue` tables required migrations for this PR, and are using the new migrator. Other tables/services are still creating tables themselves. A followup PR will adapt them to use the migrator. **Other/Support Changes** - Add a `has_workflow` column to `images` table to indicate that the image has an embedded workflow. - Add handling for retrieving the workflow from an image in python. The image file must be fetched, the workflow extracted, and then sent to client, avoiding needing the browser to parse the image file. With the `has_workflow` column, the UI knows if there is a workflow to be fetched, and only fetches when the user requests to load the workflow. - Add route to get the workflow from an image - Add CRUD service/routes for the library workflows - `workflow_images` table and services removed (no longer needed now that embedded workflows are not in the DB) * feat(ui): updated workflow handling (WIP) Clientside updates for the backend workflow changes. Includes roughed-out workflow library UI. * feat: revert SQLiteMigrator class Will pursue this in a separate PR. * feat(nodes): do not overwrite custom node module names Use a different, simpler method to detect if a node is custom. * feat(nodes): restore WithWorkflow as no-op class This class is deprecated and no longer needed. Set its workflow attr value to None (meaning it is now a no-op), and issue a warning when an invocation subclasses it. * fix(nodes): fix get_workflow from queue item dict func * feat(backend): add WorkflowRecordListItemDTO This is the id, name, description, created at and updated at workflow columns/attrs. Used to display lists of workflowsl * chore(ui): typegen * feat(ui): add workflow loading, deleting to workflow library UI * feat(ui): workflow library pagination button styles * wip * feat: workflow library WIP - Save to library - Duplicate - Filter/sort - UI/queries * feat: workflow library - system graphs - wip * feat(backend): sync system workflows to db * fix: merge conflicts * feat: simplify default workflows - Rename "system" -> "default" - Simplify syncing logic - Update UI to match * feat(workflows): update default workflows - Update TextToImage_SD15 - Add TextToImage_SDXL - Add README * feat(ui): refine workflow list UI * fix(workflow_records): typo * fix(tests): fix tests * feat(ui): clean up workflow library hooks * fix(db): fix mis-ordered db cleanup step It was happening before pruning queue items - should happen afterwards, else you have to restart the app again to free disk space made available by the pruning. * feat(ui): tweak reset workflow editor translations * feat(ui): split out workflow redux state The `nodes` slice is a rather complicated slice. Removing `workflow` makes it a bit more reasonable. Also helps to flatten state out a bit. * docs: update default workflows README * fix: tidy up unused files, unrelated changes * fix(backend): revert unrelated service organisational changes * feat(backend): workflow_records.get_many arg "filter_text" -> "query" * feat(ui): use custom hook in current image buttons Already in use elsewhere, forgot to use it here. * fix(ui): remove commented out property * fix(ui): fix workflow loading - Different handling for loading from library vs external - Fix bug where only nodes and edges loaded * fix(ui): fix save/save-as workflow naming * fix(ui): fix circular dependency * fix(db): fix bug with releasing without lock in db.clean() * fix(db): remove extraneous lock * chore: bump ruff * fix(workflow_records): default `category` to `WorkflowCategory.User` This allows old workflows to validate when reading them from the db or image files. * hide workflow library buttons if feature is disabled --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
180 lines
6.6 KiB
Python
180 lines
6.6 KiB
Python
import logging
|
|
|
|
import pytest
|
|
|
|
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
|
from invokeai.backend.util.logging import InvokeAILogger
|
|
|
|
# This import must happen before other invoke imports or test in other files(!!) break
|
|
from .test_nodes import ( # isort: split
|
|
ErrorInvocation,
|
|
PromptTestInvocation,
|
|
TestEventService,
|
|
TextToImageTestInvocation,
|
|
create_edge,
|
|
wait_until,
|
|
)
|
|
|
|
from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
|
|
from invokeai.app.services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
|
|
from invokeai.app.services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
|
|
from invokeai.app.services.invocation_services import InvocationServices
|
|
from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
|
|
from invokeai.app.services.invoker import Invoker
|
|
from invokeai.app.services.item_storage.item_storage_sqlite import SqliteItemStorage
|
|
from invokeai.app.services.session_queue.session_queue_common import DEFAULT_QUEUE_ID
|
|
from invokeai.app.services.shared.graph import Graph, GraphExecutionState, GraphInvocation, LibraryGraph
|
|
from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
|
|
|
|
|
|
@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 graph_with_subgraph():
|
|
sub_g = Graph()
|
|
sub_g.add_node(PromptTestInvocation(id="1", prompt="Banana sushi"))
|
|
sub_g.add_node(TextToImageTestInvocation(id="2"))
|
|
sub_g.add_edge(create_edge("1", "prompt", "2", "prompt"))
|
|
g = Graph()
|
|
g.add_node(GraphInvocation(id="1", graph=sub_g))
|
|
return g
|
|
|
|
|
|
# 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:
|
|
db = SqliteDatabase(InvokeAIAppConfig(use_memory_db=True), InvokeAILogger.get_logger())
|
|
configuration = InvokeAIAppConfig(use_memory_db=True, node_cache_size=0)
|
|
|
|
# NOTE: none of these are actually called by the test invocations
|
|
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
|
|
return InvocationServices(
|
|
board_image_records=None, # type: ignore
|
|
board_images=None, # type: ignore
|
|
board_records=None, # type: ignore
|
|
boards=None, # type: ignore
|
|
configuration=configuration,
|
|
events=TestEventService(),
|
|
graph_execution_manager=graph_execution_manager,
|
|
graph_library=SqliteItemStorage[LibraryGraph](db=db, table_name="graphs"),
|
|
image_files=None, # type: ignore
|
|
image_records=None, # type: ignore
|
|
images=None, # type: ignore
|
|
invocation_cache=MemoryInvocationCache(max_cache_size=0),
|
|
latents=None, # type: ignore
|
|
logger=logging, # type: ignore
|
|
model_manager=None, # type: ignore
|
|
model_records=None, # type: ignore
|
|
names=None, # type: ignore
|
|
performance_statistics=InvocationStatsService(),
|
|
processor=DefaultInvocationProcessor(),
|
|
queue=MemoryInvocationQueue(),
|
|
session_processor=None, # type: ignore
|
|
session_queue=None, # type: ignore
|
|
urls=None, # type: ignore
|
|
workflow_records=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(
|
|
session_queue_batch_id="1",
|
|
session_queue_item_id=1,
|
|
session_queue_id=DEFAULT_QUEUE_ID,
|
|
graph_execution_state=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(
|
|
session_queue_batch_id="1",
|
|
session_queue_item_id=1,
|
|
session_queue_id=DEFAULT_QUEUE_ID,
|
|
graph_execution_state=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(
|
|
session_queue_batch_id="1",
|
|
session_queue_item_id=1,
|
|
session_queue_id=DEFAULT_QUEUE_ID,
|
|
graph_execution_state=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"]))
|