from .services.config import InvokeAIAppConfig # parse_args() must be called before any other imports. if it is not called first, consumers of the config # which are imported/used before parse_args() is called will get the default config values instead of the # values from the command line or config file. app_config = InvokeAIAppConfig.get_config() app_config.parse_args() if True: # hack to make flake8 happy with imports coming after setting up the config import asyncio import mimetypes import socket from inspect import signature from pathlib import Path import torch import uvicorn from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html from fastapi.openapi.utils import get_openapi from fastapi.staticfiles import StaticFiles from fastapi_events.handlers.local import local_handler from fastapi_events.middleware import EventHandlerASGIMiddleware from pydantic.json_schema import models_json_schema # noinspection PyUnresolvedReferences import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import) import invokeai.frontend.web as web_dir from invokeai.version.invokeai_version import __version__ from ..backend.util.logging import InvokeAILogger from .api.dependencies import ApiDependencies from .api.routers import app_info, board_images, boards, images, models, session_queue, utilities from .api.sockets import SocketIO from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField if torch.backends.mps.is_available(): # noinspection PyUnresolvedReferences import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import) app_config = InvokeAIAppConfig.get_config() app_config.parse_args() logger = InvokeAILogger.get_logger(config=app_config) # fix for windows mimetypes registry entries being borked # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352 mimetypes.add_type("application/javascript", ".js") mimetypes.add_type("text/css", ".css") # Create the app # TODO: create this all in a method so configuration/etc. can be passed in? app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False) # Add event handler event_handler_id: int = id(app) app.add_middleware( EventHandlerASGIMiddleware, handlers=[local_handler], # TODO: consider doing this in services to support different configurations middleware_id=event_handler_id, ) socket_io = SocketIO(app) app.add_middleware( CORSMiddleware, allow_origins=app_config.allow_origins, allow_credentials=app_config.allow_credentials, allow_methods=app_config.allow_methods, allow_headers=app_config.allow_headers, ) # Add startup event to load dependencies @app.on_event("startup") async def startup_event(): ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger) # Shut down threads @app.on_event("shutdown") async def shutdown_event(): ApiDependencies.shutdown() # Include all routers # app.include_router(sessions.session_router, prefix="/api") app.include_router(utilities.utilities_router, prefix="/api") app.include_router(models.models_router, prefix="/api") app.include_router(images.images_router, prefix="/api") app.include_router(boards.boards_router, prefix="/api") app.include_router(board_images.board_images_router, prefix="/api") app.include_router(app_info.app_router, prefix="/api") app.include_router(session_queue.session_queue_router, prefix="/api") # Build a custom OpenAPI to include all outputs # TODO: can outputs be included on metadata of invocation schemas somehow? def custom_openapi(): if app.openapi_schema: return app.openapi_schema openapi_schema = get_openapi( title=app.title, description="An API for invoking AI image operations", version="1.0.0", routes=app.routes, separate_input_output_schemas=False, # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/ ) # Add all outputs all_invocations = BaseInvocation.get_invocations() output_types = set() output_type_titles = dict() for invoker in all_invocations: output_type = signature(invoker.invoke).return_annotation output_types.add(output_type) output_schemas = models_json_schema( models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}" ) for schema_key, output_schema in output_schemas[1]["$defs"].items(): # TODO: note that we assume the schema_key here is the TYPE.__name__ # This could break in some cases, figure out a better way to do it output_type_titles[schema_key] = output_schema["title"] # Add Node Editor UI helper schemas ui_config_schemas = models_json_schema( [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")], ref_template="#/components/schemas/{model}", ) for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items(): openapi_schema["components"]["schemas"][schema_key] = ui_config_schema # Add a reference to the output type to additionalProperties of the invoker schema for invoker in all_invocations: invoker_name = invoker.__name__ output_type = signature(obj=invoker.invoke).return_annotation output_type_title = output_type_titles[output_type.__name__] invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"] outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"} invoker_schema["output"] = outputs_ref invoker_schema["class"] = "invocation" openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output" from invokeai.backend.model_management.models import get_model_config_enums for model_config_format_enum in set(get_model_config_enums()): name = model_config_format_enum.__qualname__ if name in openapi_schema["components"]["schemas"]: # print(f"Config with name {name} already defined") continue # "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"} openapi_schema["components"]["schemas"][name] = dict( title=name, description="An enumeration.", type="string", enum=list(v.value for v in model_config_format_enum), ) app.openapi_schema = openapi_schema return app.openapi_schema app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment # Override API doc favicons app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static") @app.get("/docs", include_in_schema=False) def overridden_swagger(): return get_swagger_ui_html( openapi_url=app.openapi_url, title=app.title, swagger_favicon_url="/static/favicon.ico", ) @app.get("/redoc", include_in_schema=False) def overridden_redoc(): return get_redoc_html( openapi_url=app.openapi_url, title=app.title, redoc_favicon_url="/static/favicon.ico", ) # Must mount *after* the other routes else it borks em app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui") def invoke_api(): def find_port(port: int): """Find a port not in use starting at given port""" # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon! # https://github.com/WaylonWalker with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: if s.connect_ex(("localhost", port)) == 0: return find_port(port=port + 1) else: return port from invokeai.backend.install.check_root import check_invokeai_root check_invokeai_root(app_config) # note, may exit with an exception if root not set up if app_config.dev_reload: try: import jurigged except ImportError as e: logger.error( 'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.', exc_info=e, ) else: jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info) port = find_port(app_config.port) if port != app_config.port: logger.warn(f"Port {app_config.port} in use, using port {port}") # Start our own event loop for eventing usage loop = asyncio.new_event_loop() config = uvicorn.Config( app=app, host=app_config.host, port=port, loop=loop, log_level=app_config.log_level, ) server = uvicorn.Server(config) # replace uvicorn's loggers with InvokeAI's for consistent appearance for logname in ["uvicorn.access", "uvicorn"]: log = InvokeAILogger.get_logger(logname) log.handlers.clear() for ch in logger.handlers: log.addHandler(ch) loop.run_until_complete(server.serve()) if __name__ == "__main__": if app_config.version: print(f"InvokeAI version {__version__}") else: invoke_api()