mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
233 lines
8.7 KiB
Python
233 lines
8.7 KiB
Python
import pytest
|
|
from pydantic import TypeAdapter, ValidationError
|
|
|
|
from invokeai.app.services.session_queue.session_queue_common import (
|
|
Batch,
|
|
BatchDataCollection,
|
|
BatchDatum,
|
|
NodeFieldValue,
|
|
calc_session_count,
|
|
create_session_nfv_tuples,
|
|
prepare_values_to_insert,
|
|
)
|
|
from invokeai.app.services.shared.graph import Graph, GraphExecutionState
|
|
from tests.test_nodes import PromptTestInvocation
|
|
|
|
|
|
@pytest.fixture
|
|
def batch_data_collection() -> BatchDataCollection:
|
|
return [
|
|
[
|
|
# zipped
|
|
BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi", "Grape sushi"]),
|
|
BatchDatum(node_path="2", field_name="prompt", items=["Strawberry sushi", "Blueberry sushi"]),
|
|
],
|
|
[
|
|
BatchDatum(node_path="3", field_name="prompt", items=["Orange sushi", "Apple sushi"]),
|
|
],
|
|
]
|
|
|
|
|
|
@pytest.fixture
|
|
def batch_graph() -> Graph:
|
|
g = Graph()
|
|
g.add_node(PromptTestInvocation(id="1", prompt="Chevy"))
|
|
g.add_node(PromptTestInvocation(id="2", prompt="Toyota"))
|
|
g.add_node(PromptTestInvocation(id="3", prompt="Subaru"))
|
|
g.add_node(PromptTestInvocation(id="4", prompt="Nissan"))
|
|
return g
|
|
|
|
|
|
def test_create_sessions_from_batch_with_runs(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
|
|
t = list(create_session_nfv_tuples(batch=b, maximum=1000))
|
|
# 2 list[BatchDatum] * length 2 * 2 runs = 8
|
|
assert len(t) == 8
|
|
|
|
assert t[0][0].graph.get_node("1").prompt == "Banana sushi"
|
|
assert t[0][0].graph.get_node("2").prompt == "Strawberry sushi"
|
|
assert t[0][0].graph.get_node("3").prompt == "Orange sushi"
|
|
assert t[0][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
assert t[1][0].graph.get_node("1").prompt == "Banana sushi"
|
|
assert t[1][0].graph.get_node("2").prompt == "Strawberry sushi"
|
|
assert t[1][0].graph.get_node("3").prompt == "Apple sushi"
|
|
assert t[1][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
assert t[2][0].graph.get_node("1").prompt == "Grape sushi"
|
|
assert t[2][0].graph.get_node("2").prompt == "Blueberry sushi"
|
|
assert t[2][0].graph.get_node("3").prompt == "Orange sushi"
|
|
assert t[2][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
assert t[3][0].graph.get_node("1").prompt == "Grape sushi"
|
|
assert t[3][0].graph.get_node("2").prompt == "Blueberry sushi"
|
|
assert t[3][0].graph.get_node("3").prompt == "Apple sushi"
|
|
assert t[3][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
# repeat for second run
|
|
assert t[4][0].graph.get_node("1").prompt == "Banana sushi"
|
|
assert t[4][0].graph.get_node("2").prompt == "Strawberry sushi"
|
|
assert t[4][0].graph.get_node("3").prompt == "Orange sushi"
|
|
assert t[4][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
assert t[5][0].graph.get_node("1").prompt == "Banana sushi"
|
|
assert t[5][0].graph.get_node("2").prompt == "Strawberry sushi"
|
|
assert t[5][0].graph.get_node("3").prompt == "Apple sushi"
|
|
assert t[5][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
assert t[6][0].graph.get_node("1").prompt == "Grape sushi"
|
|
assert t[6][0].graph.get_node("2").prompt == "Blueberry sushi"
|
|
assert t[6][0].graph.get_node("3").prompt == "Orange sushi"
|
|
assert t[6][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
assert t[7][0].graph.get_node("1").prompt == "Grape sushi"
|
|
assert t[7][0].graph.get_node("2").prompt == "Blueberry sushi"
|
|
assert t[7][0].graph.get_node("3").prompt == "Apple sushi"
|
|
assert t[7][0].graph.get_node("4").prompt == "Nissan"
|
|
|
|
|
|
def test_create_sessions_from_batch_without_runs(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection)
|
|
t = list(create_session_nfv_tuples(batch=b, maximum=1000))
|
|
# 2 list[BatchDatum] * length 2 * 1 runs = 8
|
|
assert len(t) == 4
|
|
|
|
|
|
def test_create_sessions_from_batch_without_batch(batch_graph):
|
|
b = Batch(graph=batch_graph, runs=2)
|
|
t = list(create_session_nfv_tuples(batch=b, maximum=1000))
|
|
# 2 runs
|
|
assert len(t) == 2
|
|
|
|
|
|
def test_create_sessions_from_batch_without_batch_or_runs(batch_graph):
|
|
b = Batch(graph=batch_graph)
|
|
t = list(create_session_nfv_tuples(batch=b, maximum=1000))
|
|
# 1 run
|
|
assert len(t) == 1
|
|
|
|
|
|
def test_create_sessions_from_batch_with_runs_and_max(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
|
|
t = list(create_session_nfv_tuples(batch=b, maximum=5))
|
|
# 2 list[BatchDatum] * length 2 * 2 runs = 8, but max is 5
|
|
assert len(t) == 5
|
|
|
|
|
|
def test_calc_session_count(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
|
|
# 2 list[BatchDatum] * length 2 * 2 runs = 8
|
|
assert calc_session_count(batch=b) == 8
|
|
|
|
|
|
def test_prepare_values_to_insert(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
|
|
values = prepare_values_to_insert(queue_id="default", batch=b, priority=0, max_new_queue_items=1000)
|
|
assert len(values) == 8
|
|
|
|
GraphExecutionStateValidator = TypeAdapter(GraphExecutionState)
|
|
# graph should be serialized
|
|
ges = GraphExecutionStateValidator.validate_json(values[0].session)
|
|
|
|
# graph values should be populated
|
|
assert ges.graph.get_node("1").prompt == "Banana sushi"
|
|
assert ges.graph.get_node("2").prompt == "Strawberry sushi"
|
|
assert ges.graph.get_node("3").prompt == "Orange sushi"
|
|
assert ges.graph.get_node("4").prompt == "Nissan"
|
|
|
|
# session ids should match deserialized graph
|
|
assert [v.session_id for v in values] == [GraphExecutionStateValidator.validate_json(v.session).id for v in values]
|
|
|
|
# should unique session ids
|
|
sids = [v.session_id for v in values]
|
|
assert len(sids) == len(set(sids))
|
|
|
|
NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
|
|
# should have 3 node field values
|
|
assert isinstance(values[0].field_values, str)
|
|
assert len(NodeFieldValueValidator.validate_json(values[0].field_values)) == 3
|
|
|
|
# should have batch id and priority
|
|
assert all(v.batch_id == b.batch_id for v in values)
|
|
assert all(v.priority == 0 for v in values)
|
|
|
|
|
|
def test_prepare_values_to_insert_with_priority(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
|
|
values = prepare_values_to_insert(queue_id="default", batch=b, priority=1, max_new_queue_items=1000)
|
|
assert all(v.priority == 1 for v in values)
|
|
|
|
|
|
def test_prepare_values_to_insert_with_max(batch_data_collection, batch_graph):
|
|
b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
|
|
values = prepare_values_to_insert(queue_id="default", batch=b, priority=1, max_new_queue_items=5)
|
|
assert len(values) == 5
|
|
|
|
|
|
def test_cannot_create_bad_batch_items_length(batch_graph):
|
|
with pytest.raises(ValidationError, match="Zipped batch items must all have the same length"):
|
|
Batch(
|
|
graph=batch_graph,
|
|
data=[
|
|
[
|
|
BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]), # 1 item
|
|
BatchDatum(node_path="2", field_name="prompt", items=["Toyota", "Nissan"]), # 2 items
|
|
],
|
|
],
|
|
)
|
|
|
|
|
|
def test_cannot_create_bad_batch_items_type(batch_graph):
|
|
with pytest.raises(ValidationError, match="All items in a batch must have the same type"):
|
|
Batch(
|
|
graph=batch_graph,
|
|
data=[
|
|
[
|
|
BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi", 123]),
|
|
]
|
|
],
|
|
)
|
|
|
|
|
|
def test_cannot_create_bad_batch_unique_ids(batch_graph):
|
|
with pytest.raises(ValidationError, match="Each batch data must have unique node_id and field_name"):
|
|
Batch(
|
|
graph=batch_graph,
|
|
data=[
|
|
[
|
|
BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]),
|
|
],
|
|
[
|
|
BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]),
|
|
],
|
|
],
|
|
)
|
|
|
|
|
|
def test_cannot_create_bad_batch_nodes_exist(
|
|
batch_graph,
|
|
):
|
|
with pytest.raises(ValidationError, match=r"Node .* not found in graph"):
|
|
Batch(
|
|
graph=batch_graph,
|
|
data=[
|
|
[
|
|
BatchDatum(node_path="batman", field_name="prompt", items=["Banana sushi"]),
|
|
],
|
|
],
|
|
)
|
|
|
|
|
|
def test_cannot_create_bad_batch_fields_exist(
|
|
batch_graph,
|
|
):
|
|
with pytest.raises(ValidationError, match=r"Field .* not found in node"):
|
|
Batch(
|
|
graph=batch_graph,
|
|
data=[
|
|
[
|
|
BatchDatum(node_path="1", field_name="batman", items=["Banana sushi"]),
|
|
],
|
|
],
|
|
)
|