InvokeAI/invokeai/app/services/shared/invocation_context.py

from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING, Optional

from PIL.Image import Image
from torch import Tensor

from invokeai.app.invocations.fields import MetadataField, WithBoard, WithMetadata
from invokeai.app.invocations.constants import IMAGE_MODES
from invokeai.app.services.boards.boards_common import BoardDTO
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.invocation_services import InvocationServices
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
from invokeai.backend.model_manager.load.load_base import LoadedModel
from invokeai.backend.model_manager.metadata.metadata_base import AnyModelRepoMetadata
from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData

if TYPE_CHECKING:
    from invokeai.app.invocations.baseinvocation import BaseInvocation

"""
The InvocationContext provides access to various services and data about the current invocation.

We do not provide the invocation services directly, as their methods are both dangerous and
inconvenient to use.

For example:
- The `images` service allows nodes to delete or unsafely modify existing images.
- The `configuration` service allows nodes to change the app's config at runtime.
- The `events` service allows nodes to emit arbitrary events.

Wrapping these services provides a simpler and safer interface for nodes to use.

When a node executes, a fresh `InvocationContext` is built for it, ensuring nodes cannot interfere
with each other.

Many of the wrappers have the same signature as the methods they wrap. This allows us to write
user-facing docstrings and not need to go and update the internal services to match.

Note: The docstrings are in weird places, but that's where they must be to get IDEs to see them.
"""


@dataclass
class InvocationContextData:
    invocation: "BaseInvocation"
    """The invocation that is being executed."""
    session_id: str
    """The session that is being executed."""
    queue_id: str
    """The queue in which the session is being executed."""
    source_node_id: str
    """The ID of the node from which the currently executing invocation was prepared."""
    queue_item_id: int
    """The ID of the queue item that is being executed."""
    batch_id: str
    """The ID of the batch that is being executed."""
    workflow: Optional[WorkflowWithoutID] = None
    """The workflow associated with this queue item, if any."""


class InvocationContextInterface:
    def __init__(self, services: InvocationServices, context_data: InvocationContextData) -> None:
        self._services = services
        self._context_data = context_data


class BoardsInterface(InvocationContextInterface):
    def create(self, board_name: str) -> BoardDTO:
        """
        Creates a board.

        :param board_name: The name of the board to create.
        """
        return self._services.boards.create(board_name)

    def get_dto(self, board_id: str) -> BoardDTO:
        """
        Gets a board DTO.

        :param board_id: The ID of the board to get.
        """
        return self._services.boards.get_dto(board_id)

    def get_all(self) -> list[BoardDTO]:
        """
        Gets all boards.
        """
        return self._services.boards.get_all()

    def add_image_to_board(self, board_id: str, image_name: str) -> None:
        """
        Adds an image to a board.

        :param board_id: The ID of the board to add the image to.
        :param image_name: The name of the image to add to the board.
        """
        return self._services.board_images.add_image_to_board(board_id, image_name)

    def get_all_image_names_for_board(self, board_id: str) -> list[str]:
        """
        Gets all image names for a board.

        :param board_id: The ID of the board to get the image names for.
        """
        return self._services.board_images.get_all_board_image_names_for_board(board_id)


class LoggerInterface(InvocationContextInterface):
    def debug(self, message: str) -> None:
        """
        Logs a debug message.

        :param message: The message to log.
        """
        self._services.logger.debug(message)

    def info(self, message: str) -> None:
        """
        Logs an info message.

        :param message: The message to log.
        """
        self._services.logger.info(message)

    def warning(self, message: str) -> None:
        """
        Logs a warning message.

        :param message: The message to log.
        """
        self._services.logger.warning(message)

    def error(self, message: str) -> None:
        """
        Logs an error message.

        :param message: The message to log.
        """
        self._services.logger.error(message)


class ImagesInterface(InvocationContextInterface):
    def save(
        self,
        image: Image,
        board_id: Optional[str] = None,
        image_category: ImageCategory = ImageCategory.GENERAL,
        metadata: Optional[MetadataField] = None,
    ) -> ImageDTO:
        """
        Saves an image, returning its DTO.

        If the current queue item has a workflow or metadata, it is automatically saved with the image.

        :param image: The image to save, as a PIL image.
        :param board_id: The board ID to add the image to, if it should be added. It the invocation \
            inherits from `WithBoard`, that board will be used automatically. **Use this only if \
            you want to override or provide a board manually!**
        :param image_category: The category of the image. Only the GENERAL category is added \
            to the gallery.
        :param metadata: The metadata to save with the image, if it should have any. If the \
            invocation inherits from `WithMetadata`, that metadata will be used automatically. \
            **Use this only if you want to override or provide metadata manually!**
        """

        # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
        metadata_ = None
        if metadata:
            metadata_ = metadata
        elif isinstance(self._context_data.invocation, WithMetadata):
            metadata_ = self._context_data.invocation.metadata

        # If `board_id` is provided directly, use that. Else, use the board provided by `WithBoard`, falling back to None.
        board_id_ = None
        if board_id:
            board_id_ = board_id
        elif isinstance(self._context_data.invocation, WithBoard) and self._context_data.invocation.board:
            board_id_ = self._context_data.invocation.board.board_id

        return self._services.images.create(
            image=image,
            is_intermediate=self._context_data.invocation.is_intermediate,
            image_category=image_category,
            board_id=board_id_,
            metadata=metadata_,
            image_origin=ResourceOrigin.INTERNAL,
            workflow=self._context_data.workflow,
            session_id=self._context_data.session_id,
            node_id=self._context_data.invocation.id,
        )

    def get_pil(self, image_name: str, mode: IMAGE_MODES | None = None) -> Image:
        """
        Gets an image as a PIL Image object.

        :param image_name: The name of the image to get.
        :param mode: The color mode to convert the image to. If None, the original mode is used.
        """
        image = self._services.images.get_pil_image(image_name)
        if mode and mode != image.mode:
            try:
                image = image.convert(mode)
            except ValueError:
                self._services.logger.warning(f"Could not convert image from {image.mode} to {mode}. Using original mode instead.")
        return image

    def get_metadata(self, image_name: str) -> Optional[MetadataField]:
        """
        Gets an image's metadata, if it has any.

        :param image_name: The name of the image to get the metadata for.
        """
        return self._services.images.get_metadata(image_name)

    def get_dto(self, image_name: str) -> ImageDTO:
        """
        Gets an image as an ImageDTO object.

        :param image_name: The name of the image to get.
        """
        return self._services.images.get_dto(image_name)


class TensorsInterface(InvocationContextInterface):
    def save(self, tensor: Tensor) -> str:
        """
        Saves a tensor, returning its name.

        :param tensor: The tensor to save.
        """

        name = self._services.tensors.save(obj=tensor)
        return name

    def load(self, name: str) -> Tensor:
        """
        Loads a tensor by name.

        :param name: The name of the tensor to load.
        """
        return self._services.tensors.load(name)


class ConditioningInterface(InvocationContextInterface):
    def save(self, conditioning_data: ConditioningFieldData) -> str:
        """
        Saves a conditioning data object, returning its name.

        :param conditioning_context_data: The conditioning data to save.
        """

        name = self._services.conditioning.save(obj=conditioning_data)
        return name

    def load(self, name: str) -> ConditioningFieldData:
        """
        Loads conditioning data by name.

        :param name: The name of the conditioning data to load.
        """

        return self._services.conditioning.load(name)


class ModelsInterface(InvocationContextInterface):
    def exists(self, key: str) -> bool:
        """
        Checks if a model exists.

        :param key: The key of the model.
        """
        return self._services.model_manager.store.exists(key)

    def load(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
        """
        Loads a model.

        :param key: The key of the model.
        :param submodel_type: The submodel of the model to get.
        :returns: An object representing the loaded model.
        """

        # The model manager emits events as it loads the model. It needs the context data to build
        # the event payloads.

        return self._services.model_manager.load_model_by_key(
            key=key, submodel_type=submodel_type, context_data=self._context_data
        )

    def load_by_attrs(
        self, model_name: str, base_model: BaseModelType, model_type: ModelType, submodel: Optional[SubModelType] = None
    ) -> LoadedModel:
        """
        Loads a model by its attributes.

        :param model_name: Name of to be fetched.
        :param base_model: Base model
        :param model_type: Type of the model
        :param submodel: For main (pipeline models), the submodel to fetch
        """
        return self._services.model_manager.load_model_by_attr(
            model_name=model_name,
            base_model=base_model,
            model_type=model_type,
            submodel=submodel,
            context_data=self._context_data,
        )

    def get_config(self, key: str) -> AnyModelConfig:
        """
        Gets a model's info, an dict-like object.

        :param key: The key of the model.
        """
        return self._services.model_manager.store.get_model(key=key)

    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
        """
        Gets a model's metadata, if it has any.

        :param key: The key of the model.
        """
        return self._services.model_manager.store.get_metadata(key=key)

    def search_by_path(self, path: Path) -> list[AnyModelConfig]:
        """
        Searches for models by path.

        :param path: The path to search for.
        """
        return self._services.model_manager.store.search_by_path(path)

    def search_by_attrs(
        self,
        model_name: Optional[str] = None,
        base_model: Optional[BaseModelType] = None,
        model_type: Optional[ModelType] = None,
        model_format: Optional[ModelFormat] = None,
    ) -> list[AnyModelConfig]:
        """
        Searches for models by attributes.

        :param model_name: Name of to be fetched.
        :param base_model: Base model
        :param model_type: Type of the model
        :param submodel: For main (pipeline models), the submodel to fetch
        """

        return self._services.model_manager.store.search_by_attr(
            model_name=model_name,
            base_model=base_model,
            model_type=model_type,
            model_format=model_format,
        )


class ConfigInterface(InvocationContextInterface):
    def get(self) -> InvokeAIAppConfig:
        """Gets the app's config."""

        return self._services.configuration.get_config()


class UtilInterface(InvocationContextInterface):
    def sd_step_callback(self, intermediate_state: PipelineIntermediateState, base_model: BaseModelType) -> None:
        """
        The step callback emits a progress event with the current step, the total number of
        steps, a preview image, and some other internal metadata.

        This should be called after each denoising step.

        :param intermediate_state: The intermediate state of the diffusion pipeline.
        :param base_model: The base model for the current denoising step.
        """

        # The step callback needs access to the events and the invocation queue services, but this
        # represents a dangerous level of access.
        #
        # We wrap the step callback so that nodes do not have direct access to these services.

        stable_diffusion_step_callback(
            context_data=self._context_data,
            intermediate_state=intermediate_state,
            base_model=base_model,
            invocation_queue=self._services.queue,
            events=self._services.events,
        )


class InvocationContext:
    """
    The `InvocationContext` provides access to various services and data for the current invocation.
    """

    def __init__(
        self,
        images: ImagesInterface,
        tensors: TensorsInterface,
        conditioning: ConditioningInterface,
        models: ModelsInterface,
        logger: LoggerInterface,
        config: ConfigInterface,
        util: UtilInterface,
        boards: BoardsInterface,
        context_data: InvocationContextData,
        services: InvocationServices,
    ) -> None:
        self.images = images
        """Provides methods to save, get and update images and their metadata."""
        self.tensors = tensors
        """Provides methods to save and get tensors, including image, noise, masks, and masked images."""
        self.conditioning = conditioning
        """Provides methods to save and get conditioning data."""
        self.models = models
        """Provides methods to check if a model exists, get a model, and get a model's info."""
        self.logger = logger
        """Provides access to the app logger."""
        self.config = config
        """Provides access to the app's config."""
        self.util = util
        """Provides utility methods."""
        self.boards = boards
        """Provides methods to interact with boards."""
        self._data = context_data
        """Provides data about the current queue item and invocation. This is an internal API and may change without warning."""
        self._services = services
        """Provides access to the full application services. This is an internal API and may change without warning."""


def build_invocation_context(
    services: InvocationServices,
    context_data: InvocationContextData,
) -> InvocationContext:
    """
    Builds the invocation context for a specific invocation execution.

    :param invocation_services: The invocation services to wrap.
    :param invocation_context_data: The invocation context data.
    """

    logger = LoggerInterface(services=services, context_data=context_data)
    images = ImagesInterface(services=services, context_data=context_data)
    tensors = TensorsInterface(services=services, context_data=context_data)
    models = ModelsInterface(services=services, context_data=context_data)
    config = ConfigInterface(services=services, context_data=context_data)
    util = UtilInterface(services=services, context_data=context_data)
    conditioning = ConditioningInterface(services=services, context_data=context_data)
    boards = BoardsInterface(services=services, context_data=context_data)

    ctx = InvocationContext(
        images=images,
        logger=logger,
        config=config,
        tensors=tensors,
        models=models,
        context_data=context_data,
        util=util,
        conditioning=conditioning,
        services=services,
        boards=boards,
    )

    return ctx