InvokeAI/invokeai/app/api_app.py

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feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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import asyncio
import logging
feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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import mimetypes
import socket
from contextlib import asynccontextmanager
from pathlib import Path
import torch
feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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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.responses import HTMLResponse
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
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 invokeai.app.api.dependencies import ApiDependencies
feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
from invokeai.app.api.routers import (
feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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app_info,
board_images,
boards,
download_queue,
images,
model_manager,
session_queue,
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style_presets,
feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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utilities,
workflows,
)
from invokeai.app.api.sockets import SocketIO
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.custom_openapi import get_openapi_func
from invokeai.backend.util.devices import TorchDevice
from invokeai.backend.util.logging import InvokeAILogger
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
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app_config = get_config()
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feat: single app entrypoint with CLI arg parsing We have two problems with how argparse is being utilized: - We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails. - We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths. To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this. - `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result. - `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app. The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`. The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`. CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
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if is_mps_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
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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")
torch_device_name = TorchDevice.get_torch_device_name()
logger.info(f"Using torch device: {torch_device_name}")
fix(app): use asyncio queue and existing event loop for events Around the time we (I) implemented pydantic events, I noticed a short pause between progress images every 4 or 5 steps when generating with SDXL. It didn't happen with SD1.5, but I did notice that with SD1.5, we'd get 4 or 5 progress events simultaneously. I'd expect one event every ~25ms, matching my it/s with SD1.5. Mysterious! Digging in, I found an issue is related to our use of a synchronous queue for events. When the event queue is empty, we must call `asyncio.sleep` before checking again. We were sleeping for 100ms. Said another way, every time we clear the event queue, we have to wait 100ms before another event can be dispatched, even if it is put on the queue immediately after we start waiting. In practice, this means our events get buffered into batches, dispatched once every 100ms. This explains why I was getting batches of 4 or 5 SD1.5 progress events at once, but not the intermittent SDXL delay. But this 100ms wait has another effect when the events are put on the queue in intervals that don't perfectly line up with the 100ms wait. This is most noticeable when the time between events is >100ms, and can add up to 100ms delay before the event is dispatched. For example, say the queue is empty and we start a 100ms wait. Then, immediately after - like 0.01ms later - we push an event on to the queue. We still need to wait another 99.9ms before that event will be dispatched. That's the SDXL delay. The easy fix is to reduce the sleep to something like 0.01 seconds, but this feels kinda dirty. Can't we just wait on the queue and dispatch every event immediately? Not with the normal synchronous queue - but we can with `asyncio.Queue`. I switched the events queue to use `asyncio.Queue` (as seen in this commit), which lets us asynchronous wait on the queue in a loop. Unfortunately, I ran into another issue - events now felt like their timing was inconsistent, but in a different way than with the 100ms sleep. The time between pushing events on the queue and dispatching them was not consistently ~0ms as I'd expect - it was highly variable from ~0ms up to ~100ms. This is resolved by passing the asyncio loop directly into the events service and using its methods to create the task and interact with the queue. I don't fully understand why this resolved the issue, because either way we are interacting with the same event loop (as shown by `asyncio.get_running_loop()`). I suppose there's some scheduling magic happening.
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loop = asyncio.new_event_loop()
@asynccontextmanager
async def lifespan(app: FastAPI):
# Add startup event to load dependencies
fix(app): use asyncio queue and existing event loop for events Around the time we (I) implemented pydantic events, I noticed a short pause between progress images every 4 or 5 steps when generating with SDXL. It didn't happen with SD1.5, but I did notice that with SD1.5, we'd get 4 or 5 progress events simultaneously. I'd expect one event every ~25ms, matching my it/s with SD1.5. Mysterious! Digging in, I found an issue is related to our use of a synchronous queue for events. When the event queue is empty, we must call `asyncio.sleep` before checking again. We were sleeping for 100ms. Said another way, every time we clear the event queue, we have to wait 100ms before another event can be dispatched, even if it is put on the queue immediately after we start waiting. In practice, this means our events get buffered into batches, dispatched once every 100ms. This explains why I was getting batches of 4 or 5 SD1.5 progress events at once, but not the intermittent SDXL delay. But this 100ms wait has another effect when the events are put on the queue in intervals that don't perfectly line up with the 100ms wait. This is most noticeable when the time between events is >100ms, and can add up to 100ms delay before the event is dispatched. For example, say the queue is empty and we start a 100ms wait. Then, immediately after - like 0.01ms later - we push an event on to the queue. We still need to wait another 99.9ms before that event will be dispatched. That's the SDXL delay. The easy fix is to reduce the sleep to something like 0.01 seconds, but this feels kinda dirty. Can't we just wait on the queue and dispatch every event immediately? Not with the normal synchronous queue - but we can with `asyncio.Queue`. I switched the events queue to use `asyncio.Queue` (as seen in this commit), which lets us asynchronous wait on the queue in a loop. Unfortunately, I ran into another issue - events now felt like their timing was inconsistent, but in a different way than with the 100ms sleep. The time between pushing events on the queue and dispatching them was not consistently ~0ms as I'd expect - it was highly variable from ~0ms up to ~100ms. This is resolved by passing the asyncio loop directly into the events service and using its methods to create the task and interact with the queue. I don't fully understand why this resolved the issue, because either way we are interacting with the same event loop (as shown by `asyncio.get_running_loop()`). I suppose there's some scheduling magic happening.
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ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, loop=loop, logger=logger)
yield
# Shut down threads
ApiDependencies.shutdown()
# Create the app
# TODO: create this all in a method so configuration/etc. can be passed in?
app = FastAPI(
title="Invoke - Community Edition",
docs_url=None,
redoc_url=None,
separate_input_output_schemas=False,
lifespan=lifespan,
)
# Add event handler
event_handler_id: int = id(app)
app.add_middleware(
EventHandlerASGIMiddleware,
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handlers=[local_handler], # TODO: consider doing this in services to support different configurations
middleware_id=event_handler_id,
)
socket_io = SocketIO(app)
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
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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)
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# Include all routers
feat: queued generation (#4502) * fix(config): fix typing issues in `config/` `config/invokeai_config.py`: - use `Optional` for things that are optional - fix typing of `ram_cache_size()` and `vram_cache_size()` - remove unused and incorrectly typed method `autoconvert_path` - fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere `config/base.py`: - use `cls` for first arg of class methods - use `Optional` for things that are optional - fix minor type issue related to setting of `env_prefix` - remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`) * feat: queued generation and batches Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes. * chore: flake8, isort, black * fix(nodes): fix incorrect service stop() method * fix(nodes): improve names of a few variables * fix(tests): fix up tests after changes to batches/queue * feat(tests): add unit tests for session queue helper functions * feat(ui): dynamic prompts is always enabled * feat(queue): add queue_status_changed event * feat(ui): wip queue graphs * feat(nodes): move cleanup til after invoker startup * feat(nodes): add cancel_by_batch_ids * feat(ui): wip batch graphs & UI * fix(nodes): remove `Batch.batch_id` from required * fix(ui): cleanup and use fixedCacheKey for all mutations * fix(ui): remove orphaned nodes from canvas graphs * fix(nodes): fix cancel_by_batch_ids result count * fix(ui): only show cancel batch tooltip when batches were canceled * chore: isort * fix(api): return `[""]` when dynamic prompts generates no prompts Just a simple fallback so we always have a prompt. * feat(ui): dynamicPrompts.combinatorial is always on There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so. * feat: add queue_id & support logic * feat(ui): fix upscale button It prepends the upscale operation to queue * feat(nodes): return queue item when enqueuing a single graph This facilitates one-off graph async workflows in the client. * feat(ui): move controlnet autoprocess to queue * fix(ui): fix non-serializable DOMRect in redux state * feat(ui): QueueTable performance tweaks * feat(ui): update queue list Queue items expand to show the full queue item. Just as JSON for now. * wip threaded session_processor * feat(nodes,ui): fully migrate queue to session_processor * feat(nodes,ui): add processor events * feat(ui): ui tweaks * feat(nodes,ui): consolidate events, reduce network requests * feat(ui): cleanup & abstract queue hooks * feat(nodes): optimize batch permutation Use a generator to do only as much work as is needed. Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory. The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory. * feat(ui): add seed behaviour parameter This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration: - Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion - Per prompt: Use a different seed for every single prompt "Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation. * fix(ui): remove extraneous random seed nodes from linear graphs * fix(ui): fix controlnet autoprocess not working when queue is running * feat(queue): add timestamps to queue status updates Also show execution time in queue list * feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem This allows for much simpler handling of queue items. * feat(api): deprecate sessions router * chore(backend): tidy logging in `dependencies.py` * fix(backend): respect `use_memory_db` * feat(backend): add `config.log_sql` (enables sql trace logging) * feat: add invocation cache Supersedes #4574 The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned. ## Results This feature provides anywhere some significant to massive performance improvement. The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal. ## Overview A new `invocation_cache` service is added to handle the caching. There's not much to it. All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching. The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic. To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key. ## In-Memory Implementation An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts. Max node cache size is added as `node_cache_size` under the `Generation` config category. It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher. Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them. ## Node Definition The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`. Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`. The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something. ## One Gotcha Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again. If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit. ## Linear UI The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs. This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default. This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`. ## Workflow Editor All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user. The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes. Users should consider saving their workflows after loading them in and having them updated. ## Future Enhancements - Callback A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not. This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field. ## Future Enhancements - Persisted Cache Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future. * fix(ui): fix queue list item width * feat(nodes): do not send the whole node on every generator progress * feat(ui): strip out old logic related to sessions Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed... * feat(ui): fix up param collapse labels * feat(ui): click queue count to go to queue tab * tidy(queue): update comment, query format * feat(ui): fix progress bar when canceling * fix(ui): fix circular dependency * feat(nodes): bail on node caching logic if `node_cache_size == 0` * feat(nodes): handle KeyError on node cache pop * feat(nodes): bypass cache codepath if caches is disabled more better no do thing * fix(ui): reset api cache on connect/disconnect * feat(ui): prevent enqueue when no prompts generated * feat(ui): add queue controls to workflow editor * feat(ui): update floating buttons & other incidental UI tweaks * fix(ui): fix missing/incorrect translation keys * fix(tests): add config service to mock invocation services invoking needs access to `node_cache_size` to occur * optionally remove pause/resume buttons from queue UI * option to disable prepending * chore(ui): remove unused file * feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
2023-09-20 05:09:24 +00:00
app.include_router(utilities.utilities_router, prefix="/api")
app.include_router(model_manager.model_manager_router, prefix="/api")
[feature] Download Queue (#5225) * add base definition of download manager * basic functionality working * add unit tests for download queue * add documentation and FastAPI route * fix docs * add missing test dependency; fix import ordering * fix file path length checking on windows * fix ruff check error * move release() into the __del__ method * disable testing of stderr messages due to issues with pytest capsys fixture * fix unsorted imports * harmonized implementation of start() and stop() calls in download and & install modules * Update invokeai/app/services/download/download_base.py Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> * replace test datadir fixture with tmp_path * replace DownloadJobBase->DownloadJob in download manager documentation * make source and dest arguments to download_queue.download() an AnyHttpURL and Path respectively * fix pydantic typecheck errors in the download unit test * ruff formatting * add "job cancelled" as an event rather than an exception * fix ruff errors * Update invokeai/app/services/download/download_default.py Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com> * use threading.Event to stop service worker threads; handle unfinished job edge cases * remove dangling STOP job definition * fix ruff complaint * fix ruff check again * avoid race condition when start() and stop() are called simultaneously from different threads * avoid race condition in stop() when a job becomes active while shutting down --------- Co-authored-by: Lincoln Stein <lstein@gmail.com> Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com> Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
2023-12-22 17:35:57 +00:00
app.include_router(download_queue.download_queue_router, prefix="/api")
2023-05-21 13:03:52 +00:00
app.include_router(images.images_router, prefix="/api")
2023-06-14 18:20:23 +00:00
app.include_router(boards.boards_router, prefix="/api")
app.include_router(board_images.board_images_router, prefix="/api")
2023-07-08 09:31:17 +00:00
app.include_router(app_info.app_router, prefix="/api")
feat: queued generation (#4502) * fix(config): fix typing issues in `config/` `config/invokeai_config.py`: - use `Optional` for things that are optional - fix typing of `ram_cache_size()` and `vram_cache_size()` - remove unused and incorrectly typed method `autoconvert_path` - fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere `config/base.py`: - use `cls` for first arg of class methods - use `Optional` for things that are optional - fix minor type issue related to setting of `env_prefix` - remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`) * feat: queued generation and batches Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes. * chore: flake8, isort, black * fix(nodes): fix incorrect service stop() method * fix(nodes): improve names of a few variables * fix(tests): fix up tests after changes to batches/queue * feat(tests): add unit tests for session queue helper functions * feat(ui): dynamic prompts is always enabled * feat(queue): add queue_status_changed event * feat(ui): wip queue graphs * feat(nodes): move cleanup til after invoker startup * feat(nodes): add cancel_by_batch_ids * feat(ui): wip batch graphs & UI * fix(nodes): remove `Batch.batch_id` from required * fix(ui): cleanup and use fixedCacheKey for all mutations * fix(ui): remove orphaned nodes from canvas graphs * fix(nodes): fix cancel_by_batch_ids result count * fix(ui): only show cancel batch tooltip when batches were canceled * chore: isort * fix(api): return `[""]` when dynamic prompts generates no prompts Just a simple fallback so we always have a prompt. * feat(ui): dynamicPrompts.combinatorial is always on There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so. * feat: add queue_id & support logic * feat(ui): fix upscale button It prepends the upscale operation to queue * feat(nodes): return queue item when enqueuing a single graph This facilitates one-off graph async workflows in the client. * feat(ui): move controlnet autoprocess to queue * fix(ui): fix non-serializable DOMRect in redux state * feat(ui): QueueTable performance tweaks * feat(ui): update queue list Queue items expand to show the full queue item. Just as JSON for now. * wip threaded session_processor * feat(nodes,ui): fully migrate queue to session_processor * feat(nodes,ui): add processor events * feat(ui): ui tweaks * feat(nodes,ui): consolidate events, reduce network requests * feat(ui): cleanup & abstract queue hooks * feat(nodes): optimize batch permutation Use a generator to do only as much work as is needed. Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory. The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory. * feat(ui): add seed behaviour parameter This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration: - Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion - Per prompt: Use a different seed for every single prompt "Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation. * fix(ui): remove extraneous random seed nodes from linear graphs * fix(ui): fix controlnet autoprocess not working when queue is running * feat(queue): add timestamps to queue status updates Also show execution time in queue list * feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem This allows for much simpler handling of queue items. * feat(api): deprecate sessions router * chore(backend): tidy logging in `dependencies.py` * fix(backend): respect `use_memory_db` * feat(backend): add `config.log_sql` (enables sql trace logging) * feat: add invocation cache Supersedes #4574 The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned. ## Results This feature provides anywhere some significant to massive performance improvement. The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal. ## Overview A new `invocation_cache` service is added to handle the caching. There's not much to it. All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching. The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic. To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key. ## In-Memory Implementation An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts. Max node cache size is added as `node_cache_size` under the `Generation` config category. It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher. Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them. ## Node Definition The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`. Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`. The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something. ## One Gotcha Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again. If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit. ## Linear UI The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs. This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default. This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`. ## Workflow Editor All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user. The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes. Users should consider saving their workflows after loading them in and having them updated. ## Future Enhancements - Callback A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not. This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field. ## Future Enhancements - Persisted Cache Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future. * fix(ui): fix queue list item width * feat(nodes): do not send the whole node on every generator progress * feat(ui): strip out old logic related to sessions Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed... * feat(ui): fix up param collapse labels * feat(ui): click queue count to go to queue tab * tidy(queue): update comment, query format * feat(ui): fix progress bar when canceling * fix(ui): fix circular dependency * feat(nodes): bail on node caching logic if `node_cache_size == 0` * feat(nodes): handle KeyError on node cache pop * feat(nodes): bypass cache codepath if caches is disabled more better no do thing * fix(ui): reset api cache on connect/disconnect * feat(ui): prevent enqueue when no prompts generated * feat(ui): add queue controls to workflow editor * feat(ui): update floating buttons & other incidental UI tweaks * fix(ui): fix missing/incorrect translation keys * fix(tests): add config service to mock invocation services invoking needs access to `node_cache_size` to occur * optionally remove pause/resume buttons from queue UI * option to disable prepending * chore(ui): remove unused file * feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
2023-09-20 05:09:24 +00:00
app.include_router(session_queue.session_queue_router, prefix="/api")
2023-10-17 06:02:15 +00:00
app.include_router(workflows.workflows_router, prefix="/api")
app.include_router(style_presets.style_presets_router, prefix="/api")
feat: queued generation (#4502) * fix(config): fix typing issues in `config/` `config/invokeai_config.py`: - use `Optional` for things that are optional - fix typing of `ram_cache_size()` and `vram_cache_size()` - remove unused and incorrectly typed method `autoconvert_path` - fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere `config/base.py`: - use `cls` for first arg of class methods - use `Optional` for things that are optional - fix minor type issue related to setting of `env_prefix` - remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`) * feat: queued generation and batches Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes. * chore: flake8, isort, black * fix(nodes): fix incorrect service stop() method * fix(nodes): improve names of a few variables * fix(tests): fix up tests after changes to batches/queue * feat(tests): add unit tests for session queue helper functions * feat(ui): dynamic prompts is always enabled * feat(queue): add queue_status_changed event * feat(ui): wip queue graphs * feat(nodes): move cleanup til after invoker startup * feat(nodes): add cancel_by_batch_ids * feat(ui): wip batch graphs & UI * fix(nodes): remove `Batch.batch_id` from required * fix(ui): cleanup and use fixedCacheKey for all mutations * fix(ui): remove orphaned nodes from canvas graphs * fix(nodes): fix cancel_by_batch_ids result count * fix(ui): only show cancel batch tooltip when batches were canceled * chore: isort * fix(api): return `[""]` when dynamic prompts generates no prompts Just a simple fallback so we always have a prompt. * feat(ui): dynamicPrompts.combinatorial is always on There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so. * feat: add queue_id & support logic * feat(ui): fix upscale button It prepends the upscale operation to queue * feat(nodes): return queue item when enqueuing a single graph This facilitates one-off graph async workflows in the client. * feat(ui): move controlnet autoprocess to queue * fix(ui): fix non-serializable DOMRect in redux state * feat(ui): QueueTable performance tweaks * feat(ui): update queue list Queue items expand to show the full queue item. Just as JSON for now. * wip threaded session_processor * feat(nodes,ui): fully migrate queue to session_processor * feat(nodes,ui): add processor events * feat(ui): ui tweaks * feat(nodes,ui): consolidate events, reduce network requests * feat(ui): cleanup & abstract queue hooks * feat(nodes): optimize batch permutation Use a generator to do only as much work as is needed. Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory. The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory. * feat(ui): add seed behaviour parameter This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration: - Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion - Per prompt: Use a different seed for every single prompt "Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation. * fix(ui): remove extraneous random seed nodes from linear graphs * fix(ui): fix controlnet autoprocess not working when queue is running * feat(queue): add timestamps to queue status updates Also show execution time in queue list * feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem This allows for much simpler handling of queue items. * feat(api): deprecate sessions router * chore(backend): tidy logging in `dependencies.py` * fix(backend): respect `use_memory_db` * feat(backend): add `config.log_sql` (enables sql trace logging) * feat: add invocation cache Supersedes #4574 The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned. ## Results This feature provides anywhere some significant to massive performance improvement. The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal. ## Overview A new `invocation_cache` service is added to handle the caching. There's not much to it. All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching. The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic. To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key. ## In-Memory Implementation An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts. Max node cache size is added as `node_cache_size` under the `Generation` config category. It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher. Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them. ## Node Definition The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`. Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`. The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something. ## One Gotcha Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again. If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit. ## Linear UI The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs. This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default. This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`. ## Workflow Editor All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user. The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes. Users should consider saving their workflows after loading them in and having them updated. ## Future Enhancements - Callback A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not. This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field. ## Future Enhancements - Persisted Cache Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future. * fix(ui): fix queue list item width * feat(nodes): do not send the whole node on every generator progress * feat(ui): strip out old logic related to sessions Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed... * feat(ui): fix up param collapse labels * feat(ui): click queue count to go to queue tab * tidy(queue): update comment, query format * feat(ui): fix progress bar when canceling * fix(ui): fix circular dependency * feat(nodes): bail on node caching logic if `node_cache_size == 0` * feat(nodes): handle KeyError on node cache pop * feat(nodes): bypass cache codepath if caches is disabled more better no do thing * fix(ui): reset api cache on connect/disconnect * feat(ui): prevent enqueue when no prompts generated * feat(ui): add queue controls to workflow editor * feat(ui): update floating buttons & other incidental UI tweaks * fix(ui): fix missing/incorrect translation keys * fix(tests): add config service to mock invocation services invoking needs access to `node_cache_size` to occur * optionally remove pause/resume buttons from queue UI * option to disable prepending * chore(ui): remove unused file * feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
2023-09-20 05:09:24 +00:00
app.openapi = get_openapi_func(app)
2023-03-03 06:02:00 +00:00
@app.get("/docs", include_in_schema=False)
def overridden_swagger() -> HTMLResponse:
2023-03-03 06:02:00 +00:00
return get_swagger_ui_html(
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
title=f"{app.title} - Swagger UI",
swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
)
2023-03-03 06:02:00 +00:00
@app.get("/redoc", include_in_schema=False)
def overridden_redoc() -> HTMLResponse:
2023-03-03 06:02:00 +00:00
return get_redoc_html(
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
title=f"{app.title} - Redoc",
redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
)
web_root_path = Path(list(web_dir.__path__)[0])
try:
app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
except RuntimeError:
logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
2024-01-16 07:32:31 +00:00
app.mount(
"/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
) # docs favicon is in here
2023-07-27 14:54:01 +00:00
2023-03-03 06:02:00 +00:00
def check_cudnn(logger: logging.Logger) -> None:
"""Check for cuDNN issues that could be causing degraded performance."""
if torch.backends.cudnn.is_available():
try:
# Note: At the time of writing (torch 2.2.1), torch.backends.cudnn.version() only raises an error the first
# time it is called. Subsequent calls will return the version number without complaining about a mismatch.
cudnn_version = torch.backends.cudnn.version()
logger.info(f"cuDNN version: {cudnn_version}")
except RuntimeError as e:
logger.warning(
"Encountered a cuDNN version issue. This may result in degraded performance. This issue is usually "
"caused by an incompatible cuDNN version installed in your python environment, or on the host "
f"system. Full error message:\n{e}"
)
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:
s.settimeout(1)
if s.connect_ex(("localhost", port)) == 0:
return find_port(port=port + 1)
else:
return port
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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}")
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check_cudnn(logger)
config = uvicorn.Config(
app=app,
host=app_config.host,
port=port,
loop="asyncio",
log_level=app_config.log_level,
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
for logname in ["uvicorn.access", "uvicorn"]:
log = InvokeAILogger.get_logger(logname)
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log.handlers.clear()
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for ch in logger.handlers:
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log.addHandler(ch)
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loop.run_until_complete(server.serve())
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if __name__ == "__main__":
invoke_api()