InvokeAI/invokeai/app/api_app.py
psychedelicious 86a74e929a feat(ui): add support for custom field types
Node authors may now create their own arbitrary/custom field types. Any pydantic model is supported.

Two notes:
1. Your field type's class name must be unique.

Suggest prefixing fields with something related to the node pack as a kind of namespace.

2. Custom field types function as connection-only fields.

For example, if your custom field has string attributes, you will not get a text input for that attribute when you give a node a field with your custom type.

This is the same behaviour as other complex fields that don't have custom UIs in the workflow editor - like, say, a string collection.

feat(ui): fix tooltips for custom types

We need to hold onto the original type of the field so they don't all just show up as "Unknown".

fix(ui): fix ts error with custom fields

feat(ui): custom field types connection validation

In the initial commit, a custom field's original type was added to the *field templates* only as `originalType`. Custom fields' `type` property was `"Custom"`*. This allowed for type safety throughout the UI logic.

*Actually, it was `"Unknown"`, but I changed it to custom for clarity.

Connection validation logic, however, uses the *field instance* of the node/field. Like the templates, *field instances* with custom types have their `type` set to `"Custom"`, but they didn't have an `originalType` property. As a result, all custom fields could be connected to all other custom fields.

To resolve this, we need to add `originalType` to the *field instances*, then switch the validation logic to use this instead of `type`.

This ended up needing a bit of fanagling:

- If we make `originalType` a required property on field instances, existing workflows will break during connection validation, because they won't have this property. We'd need a new layer of logic to migrate the workflows, adding the new `originalType` property.

While this layer is probably needed anyways, typing `originalType` as optional is much simpler. Workflow migration logic can come layer.

(Technically, we could remove all references to field types from the workflow files, and let the templates hold all this information. This feels like a significant change and I'm reluctant to do it now.)

- Because `originalType` is optional, anywhere we care about the type of a field, we need to use it over `type`. So there are a number of `field.originalType ?? field.type` expressions. This is a bit of a gotcha, we'll need to remember this in the future.

- We use `Array.prototype.includes()` often in the workflow editor, e.g. `COLLECTION_TYPES.includes(type)`. In these cases, the const array is of type `FieldType[]`, and `type` is is `FieldType`.

Because we now support custom types, the arg `type` is now widened from `FieldType` to `string`.

This causes a TS error. This behaviour is somewhat controversial (see https://github.com/microsoft/TypeScript/issues/14520). These expressions are now rewritten as `COLLECTION_TYPES.some((t) => t === type)` to satisfy TS. It's logically equivalent.

fix(ui): typo

feat(ui): add CustomCollection and CustomPolymorphic field types

feat(ui): add validation for CustomCollection & CustomPolymorphic types

- Update connection validation for custom types
- Use simple string parsing to determine if a field is a collection or polymorphic type.
- No longer need to keep a list of collection and polymorphic types.
- Added runtime checks in `baseinvocation.py` to ensure no fields are named in such a way that it could mess up the new parsing

chore(ui): remove errant console.log

fix(ui): rename 'nodes.currentConnectionFieldType' -> 'nodes.connectionStartFieldType'

This was confusingly named and kept tripping me up. Renamed to be consistent with the `reactflow` `ConnectionStartParams` type.

fix(ui): fix ts error

feat(nodes): add runtime check for custom field names

"Custom", "CustomCollection" and "CustomPolymorphic" are reserved field names.

chore(ui): add TODO for revising field type names

wip refactor fieldtype structured

wip refactor field types

wip refactor types

wip refactor types

fix node layout

refactor field types

chore: mypy

organisation

organisation

organisation

fix(nodes): fix field orig_required, field_kind and input statuses

feat(nodes): remove broken implementation of default_factory on InputField

Use of this could break connection validation due to the difference in node schemas required fields and invoke() required args.

Removed entirely for now. It wasn't ever actually used by the system, because all graphs always had values provided for fields where default_factory was used.

Also, pydantic is smart enough to not reuse the same object when specifying a default value - it clones the object first. So, the common pattern of `default_factory=list` is extraneous. It can just be `default=[]`.

fix(nodes): fix InputField name validation

workflow validation

validation

chore: ruff

feat(nodes): fix up baseinvocation comments

fix(ui): improve typing & logic of buildFieldInputTemplate

improved error handling in parseFieldType

fix: back compat for deprecated default_factory and UIType

feat(nodes): do not show node packs loaded log if none loaded

chore(ui): typegen
2023-11-29 10:49:31 +11:00

289 lines
10 KiB
Python

# 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.
import sys
from invokeai.version.invokeai_version import __version__
from .services.config import InvokeAIAppConfig
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
if app_config.version:
print(f"InvokeAI version {__version__}")
sys.exit(0)
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
from typing import Any
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
from fastapi.openapi.utils import get_openapi
from fastapi.responses import FileResponse, HTMLResponse
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
from torch.backends.mps import is_available as is_mps_available
# for PyCharm:
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.frontend.web as web_dir
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
from .api.routers import (
app_info,
board_images,
boards,
images,
model_records,
models,
session_queue,
sessions,
utilities,
workflows,
)
from .api.sockets import SocketIO
from .invocations.baseinvocation import (
BaseInvocation,
InputFieldJSONSchemaExtra,
OutputFieldJSONSchemaExtra,
UIConfigBase,
)
if is_mps_available():
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,
)
app.add_middleware(GZipMiddleware, minimum_size=1000)
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event() -> None:
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
# Shut down threads
@app.on_event("shutdown")
async def shutdown_event() -> None:
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(model_records.model_records_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")
app.include_router(workflows.workflows_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() -> dict[str, Any]:
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 = {}
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]["$defs"].items():
# TODO: note that we assume the schema_key here is the TYPE.__name__
# This could break in some cases, figure out a better way to do it
output_type_titles[schema_key] = output_schema["title"]
# Add Node Editor UI helper schemas
ui_config_schemas = models_json_schema(
[
(UIConfigBase, "serialization"),
(InputFieldJSONSchemaExtra, "serialization"),
(OutputFieldJSONSchemaExtra, "serialization"),
],
ref_template="#/components/schemas/{model}",
)
for schema_key, ui_config_schema in ui_config_schemas[1]["$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__ # type: ignore [attr-defined] # this is a valid attribute
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
openapi_schema["components"]["schemas"][name] = {
"title": name,
"description": "An enumeration.",
"type": "string",
"enum": [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
@app.get("/docs", include_in_schema=False)
def overridden_swagger() -> HTMLResponse:
return get_swagger_ui_html(
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
title=app.title,
swagger_favicon_url="/static/docs/favicon.ico",
)
@app.get("/redoc", include_in_schema=False)
def overridden_redoc() -> HTMLResponse:
return get_redoc_html(
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
title=app.title,
redoc_favicon_url="/static/docs/favicon.ico",
)
web_root_path = Path(list(web_dir.__path__)[0])
# Cannot add headers to StaticFiles, so we must serve index.html with a custom route
# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
@app.get("/", include_in_schema=False, name="ui_root")
def get_index() -> FileResponse:
return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
# # Must mount *after* the other routes else it borks em
app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static") # docs favicon is in here
app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
def invoke_api() -> None:
def find_port(port: int) -> 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="asyncio",
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__":
invoke_api()