InvokeAI/tests/test_config.py
psychedelicious c42d692ea6
feat: workflow library (#5148)
* 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>
2023-12-09 09:48:38 +11:00

216 lines
6.4 KiB
Python

import os
from pathlib import Path
from typing import Any
import pytest
from omegaconf import OmegaConf
from pydantic import ValidationError
@pytest.fixture
def patch_rootdir(tmp_path: Path, monkeypatch: Any) -> None:
"""This may be overkill since the current tests don't need the root dir to exist"""
monkeypatch.setenv("INVOKEAI_ROOT", str(tmp_path))
init1 = OmegaConf.create(
"""
InvokeAI:
Features:
always_use_cpu: false
Memory/Performance:
max_cache_size: 5
tiled_decode: false
"""
)
init2 = OmegaConf.create(
"""
InvokeAI:
Features:
always_use_cpu: true
Memory/Performance:
max_cache_size: 2
tiled_decode: true
"""
)
init3 = OmegaConf.create(
"""
InvokeAI:
Generation:
sequential_guidance: true
attention_type: xformers
attention_slice_size: 7
forced_tiled_decode: True
Device:
device: cpu
Model Cache:
ram: 1.25
"""
)
def test_use_init(patch_rootdir):
# note that we explicitly set omegaconf dict and argv here
# so that the values aren't read from ~invokeai/invokeai.yaml and
# sys.argv respectively.
from invokeai.app.services.config import InvokeAIAppConfig
conf1 = InvokeAIAppConfig.get_config()
assert conf1
conf1.parse_args(conf=init1, argv=[])
assert not conf1.tiled_decode
assert conf1.max_cache_size == 5
assert not conf1.always_use_cpu
conf2 = InvokeAIAppConfig.get_config()
assert conf2
conf2.parse_args(conf=init2, argv=[])
assert conf2.tiled_decode
assert conf2.max_cache_size == 2
assert not hasattr(conf2, "invalid_attribute")
def test_legacy():
from invokeai.app.services.config import InvokeAIAppConfig
conf = InvokeAIAppConfig.get_config()
assert conf
conf.parse_args(conf=init3, argv=[])
assert conf.xformers_enabled
assert conf.device == "cpu"
assert conf.use_cpu
assert conf.ram == 1.25
assert conf.ram_cache_size == 1.25
def test_argv_override():
from invokeai.app.services.config import InvokeAIAppConfig
conf = InvokeAIAppConfig.get_config()
conf.parse_args(conf=init1, argv=["--always_use_cpu", "--max_cache=10"])
assert conf.always_use_cpu
assert conf.max_cache_size == 10
assert conf.outdir == Path("outputs") # this is the default
def test_env_override(patch_rootdir):
from invokeai.app.services.config import InvokeAIAppConfig
# argv overrides
conf = InvokeAIAppConfig()
conf.parse_args(conf=init1, argv=["--max_cache=10"])
assert conf.always_use_cpu is False
os.environ["INVOKEAI_always_use_cpu"] = "True"
conf.parse_args(conf=init1, argv=["--max_cache=10"])
assert conf.always_use_cpu is True
# environment variables should be case insensitive
os.environ["InvokeAI_Max_Cache_Size"] = "15"
conf = InvokeAIAppConfig()
conf.parse_args(conf=init1, argv=[])
assert conf.max_cache_size == 15
conf = InvokeAIAppConfig()
conf.parse_args(conf=init1, argv=["--no-always_use_cpu", "--max_cache=10"])
assert conf.always_use_cpu is False
assert conf.max_cache_size == 10
conf = InvokeAIAppConfig.get_config(max_cache_size=20)
conf.parse_args(conf=init1, argv=[])
assert conf.max_cache_size == 20
# make sure that prefix is respected
del os.environ["INVOKEAI_always_use_cpu"]
os.environ["always_use_cpu"] = "True"
conf.parse_args(conf=init1, argv=[])
assert conf.always_use_cpu is False
def test_root_resists_cwd(patch_rootdir):
from invokeai.app.services.config import InvokeAIAppConfig
previous = os.environ["INVOKEAI_ROOT"]
cwd = Path(os.getcwd()).resolve()
os.environ["INVOKEAI_ROOT"] = "."
conf = InvokeAIAppConfig.get_config()
conf.parse_args([])
assert conf.root_path == cwd
os.chdir("..")
assert conf.root_path == cwd
os.environ["INVOKEAI_ROOT"] = previous
os.chdir(cwd)
def test_type_coercion(patch_rootdir):
from invokeai.app.services.config import InvokeAIAppConfig
conf = InvokeAIAppConfig().get_config()
conf.parse_args(argv=["--root=/tmp/foobar"])
assert conf.root == Path("/tmp/foobar")
assert isinstance(conf.root, Path)
conf = InvokeAIAppConfig.get_config(root="/tmp/different")
conf.parse_args(argv=["--root=/tmp/foobar"])
assert conf.root == Path("/tmp/different")
assert isinstance(conf.root, Path)
@pytest.mark.xfail(
reason="""
This test fails when run as part of the full test suite.
This test needs to deny nodes from being included in the InvocationsUnion by providing
an app configuration as a test fixture. Pytest executes all test files before running
tests, so the app configuration is already initialized by the time this test runs, and
the InvocationUnion is already created and the denied nodes are not omitted from it.
This test passes when `test_config.py` is tested in isolation.
Perhaps a solution would be to call `InvokeAIAppConfig.get_config().parse_args()` in
other test files?
"""
)
def test_deny_nodes(patch_rootdir):
from invokeai.app.services.config import InvokeAIAppConfig
# Allow integer, string and float, but explicitly deny float
allow_deny_nodes_conf = OmegaConf.create(
"""
InvokeAI:
Nodes:
allow_nodes:
- integer
- string
- float
deny_nodes:
- float
"""
)
# must parse config before importing Graph, so its nodes union uses the config
conf = InvokeAIAppConfig().get_config()
conf.parse_args(conf=allow_deny_nodes_conf, argv=[])
from invokeai.app.services.shared.graph import Graph
# confirm graph validation fails when using denied node
Graph(nodes={"1": {"id": "1", "type": "integer"}})
Graph(nodes={"1": {"id": "1", "type": "string"}})
with pytest.raises(ValidationError):
Graph(nodes={"1": {"id": "1", "type": "float"}})
from invokeai.app.invocations.baseinvocation import BaseInvocation
# confirm invocations union will not have denied nodes
all_invocations = BaseInvocation.get_invocations()
has_integer = len([i for i in all_invocations if i.model_fields.get("type").default == "integer"]) == 1
has_string = len([i for i in all_invocations if i.model_fields.get("type").default == "string"]) == 1
has_float = len([i for i in all_invocations if i.model_fields.get("type").default == "float"]) == 1
assert has_integer
assert has_string
assert not has_float