Files
InvokeAI/invokeai/app/run_app.py
mickr777 4c5ad1b7d7 Ruff Fix
2025-05-30 19:03:43 +10:00

104 lines
4.2 KiB
Python

def get_app():
"""Import the app and event loop. We wrap this in a function to more explicitly control when it happens, because
importing from api_app does a bunch of stuff - it's more like calling a function than importing a module.
"""
from invokeai.app.api_app import app, loop
return app, loop
def run_app() -> None:
"""The main entrypoint for the app."""
from invokeai.frontend.cli.arg_parser import InvokeAIArgs
# Parse the CLI arguments before doing anything else, which ensures CLI args correctly override settings from other
# sources like `invokeai.yaml` or env vars.
InvokeAIArgs.parse_args()
import uvicorn
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
from invokeai.backend.util.logging import InvokeAILogger
# Load config.
app_config = get_config()
logger = InvokeAILogger.get_logger(config=app_config)
# Configure the torch CUDA memory allocator.
# NOTE: It is important that this happens before torch is imported.
if app_config.pytorch_cuda_alloc_conf:
configure_torch_cuda_allocator(app_config.pytorch_cuda_alloc_conf, logger)
# This import must happen after configure_torch_cuda_allocator() is called, because the module imports torch.
from invokeai.app.invocations.baseinvocation import InvocationRegistry
from invokeai.app.invocations.load_custom_nodes import load_custom_nodes
from invokeai.backend.util.devices import TorchDevice
torch_device_name = TorchDevice.get_torch_device_name()
logger.info(f"Using torch device: {torch_device_name}")
# Import from startup_utils here to avoid importing torch before configure_torch_cuda_allocator() is called.
from invokeai.app.util.startup_utils import (
apply_monkeypatches,
check_cudnn,
enable_dev_reload,
find_open_port,
register_mime_types,
)
# Find an open port, and modify the config accordingly.
first_open_port = find_open_port(app_config.port)
if app_config.port != first_open_port:
orig_config_port = app_config.port
app_config.port = first_open_port
logger.warning(f"Port {orig_config_port} is already in use. Using port {app_config.port}.")
# Miscellaneous startup tasks.
apply_monkeypatches()
register_mime_types()
check_cudnn(logger)
# Initialize the app and event loop.
app, loop = get_app()
# Load custom nodes. This must be done after importing the Graph class, which itself imports all modules from the
# invocations module. The ordering here is implicit, but important - we want to load custom nodes after all the
# core nodes have been imported so that we can catch when a custom node clobbers a core node.
load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path, logger=logger)
# Check all invocations and ensure their outputs are registered.
for invocation in InvocationRegistry.get_invocation_classes():
invocation_type = invocation.get_type()
output_annotation = invocation.get_output_annotation()
if output_annotation not in InvocationRegistry.get_output_classes():
logger.warning(
f'Invocation "{invocation_type}" has unregistered output class "{output_annotation.__name__}"'
)
if app_config.dev_reload:
# load_custom_nodes seems to bypass jurrigged's import sniffer, so be sure to call it *after* they're already
# imported.
enable_dev_reload(custom_nodes_path=app_config.custom_nodes_path)
# Start the server.
config = uvicorn.Config(
app=app,
host=app_config.host,
port=app_config.port,
loop="asyncio",
log_level=app_config.log_level_network,
ssl_certfile=app_config.ssl_certfile,
ssl_keyfile=app_config.ssl_keyfile,
)
server = uvicorn.Server(config)
# replace uvicorn's loggers with InvokeAI's for consistent appearance
uvicorn_logger = InvokeAILogger.get_logger("uvicorn")
uvicorn_logger.handlers.clear()
for hdlr in logger.handlers:
uvicorn_logger.addHandler(hdlr)
loop.run_until_complete(server.serve())