InvokeAI/invokeai/app/services/boards/boards_common.py

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feat: refactor services folder/module structure Refactor services folder/module structure. **Motivation** While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward. **Services** Services are now in their own folder with a few files: - `services/{service_name}/__init__.py`: init as needed, mostly empty now - `services/{service_name}/{service_name}_base.py`: the base class for the service - `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory` - `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename. There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`. **Shared** Things that are used across disparate services are in `services/shared/`: - `default_graphs.py`: previously in `services/` - `graphs.py`: previously in `services/` - `paginatation`: generic pagination models used in a few services - `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
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from typing import Optional
from pydantic import Field
from invokeai.app.services.board_records.board_records_common import BoardRecord
feat: refactor services folder/module structure Refactor services folder/module structure. **Motivation** While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward. **Services** Services are now in their own folder with a few files: - `services/{service_name}/__init__.py`: init as needed, mostly empty now - `services/{service_name}/{service_name}_base.py`: the base class for the service - `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory` - `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename. There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`. **Shared** Things that are used across disparate services are in `services/shared/`: - `default_graphs.py`: previously in `services/` - `graphs.py`: previously in `services/` - `paginatation`: generic pagination models used in a few services - `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
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class BoardDTO(BoardRecord):
"""Deserialized board record with cover image URL and image count."""
cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
"""The URL of the thumbnail of the most recent image in the board."""
image_count: int = Field(description="The number of images in the board.")
"""The number of images in the board."""
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
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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**board_record.model_dump(exclude={"cover_image_name"}),
feat: refactor services folder/module structure Refactor services folder/module structure. **Motivation** While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward. **Services** Services are now in their own folder with a few files: - `services/{service_name}/__init__.py`: init as needed, mostly empty now - `services/{service_name}/{service_name}_base.py`: the base class for the service - `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory` - `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename. There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`. **Shared** Things that are used across disparate services are in `services/shared/`: - `default_graphs.py`: previously in `services/` - `graphs.py`: previously in `services/` - `paginatation`: generic pagination models used in a few services - `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
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cover_image_name=cover_image_name,
image_count=image_count,
)