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https://github.com/invoke-ai/InvokeAI
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c238a7f18b
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.
130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
from abc import ABC, abstractmethod
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from typing import Callable, Optional
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from PIL.Image import Image as PILImageType
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from invokeai.app.invocations.metadata import ImageMetadata
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from invokeai.app.services.image_records.image_records_common import (
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ImageCategory,
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ImageRecord,
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ImageRecordChanges,
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ResourceOrigin,
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)
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from invokeai.app.services.images.images_common import ImageDTO
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from invokeai.app.services.shared.pagination import OffsetPaginatedResults
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class ImageServiceABC(ABC):
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"""High-level service for image management."""
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_on_changed_callbacks: list[Callable[[ImageDTO], None]]
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_on_deleted_callbacks: list[Callable[[str], None]]
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def __init__(self) -> None:
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self._on_changed_callbacks = list()
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self._on_deleted_callbacks = list()
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def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
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"""Register a callback for when an image is changed"""
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self._on_changed_callbacks.append(on_changed)
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def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
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"""Register a callback for when an image is deleted"""
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self._on_deleted_callbacks.append(on_deleted)
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def _on_changed(self, item: ImageDTO) -> None:
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for callback in self._on_changed_callbacks:
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callback(item)
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def _on_deleted(self, item_id: str) -> None:
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for callback in self._on_deleted_callbacks:
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callback(item_id)
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@abstractmethod
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def create(
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self,
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image: PILImageType,
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image_origin: ResourceOrigin,
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image_category: ImageCategory,
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node_id: Optional[str] = None,
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session_id: Optional[str] = None,
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board_id: Optional[str] = None,
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is_intermediate: Optional[bool] = False,
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metadata: Optional[dict] = None,
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workflow: Optional[str] = None,
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) -> ImageDTO:
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"""Creates an image, storing the file and its metadata."""
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pass
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@abstractmethod
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def update(
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self,
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image_name: str,
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changes: ImageRecordChanges,
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) -> ImageDTO:
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"""Updates an image."""
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pass
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@abstractmethod
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def get_pil_image(self, image_name: str) -> PILImageType:
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"""Gets an image as a PIL image."""
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pass
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@abstractmethod
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def get_record(self, image_name: str) -> ImageRecord:
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"""Gets an image record."""
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pass
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@abstractmethod
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def get_dto(self, image_name: str) -> ImageDTO:
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"""Gets an image DTO."""
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pass
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@abstractmethod
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def get_metadata(self, image_name: str) -> ImageMetadata:
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"""Gets an image's metadata."""
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pass
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@abstractmethod
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def get_path(self, image_name: str, thumbnail: bool = False) -> str:
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"""Gets an image's path."""
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pass
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@abstractmethod
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def validate_path(self, path: str) -> bool:
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"""Validates an image's path."""
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pass
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@abstractmethod
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def get_url(self, image_name: str, thumbnail: bool = False) -> str:
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"""Gets an image's or thumbnail's URL."""
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pass
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@abstractmethod
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def get_many(
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self,
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offset: int = 0,
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limit: int = 10,
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image_origin: Optional[ResourceOrigin] = None,
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categories: Optional[list[ImageCategory]] = None,
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is_intermediate: Optional[bool] = None,
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board_id: Optional[str] = None,
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) -> OffsetPaginatedResults[ImageDTO]:
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"""Gets a paginated list of image DTOs."""
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pass
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@abstractmethod
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def delete(self, image_name: str):
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"""Deletes an image."""
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pass
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@abstractmethod
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def delete_intermediates(self) -> int:
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"""Deletes all intermediate images."""
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pass
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@abstractmethod
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def delete_images_on_board(self, board_id: str):
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"""Deletes all images on a board."""
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pass
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