Invocations

Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.

Creating a new invocation

To create a new invocation, either find the appropriate module file in /ldm/invoke/app/invocations to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of typing and pydantic for validation and integration into the CLI and API.

An invocation looks like this:

class UpscaleInvocation(BaseInvocation):
    """Upscales an image."""
    type: Literal['upscale'] = 'upscale'

    # Inputs
    image: Union[ImageField,None] = Field(description="The input image")
    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
    level: Literal[2,4]           = Field(default=2, description = "The upscale level")

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get(self.image.image_type, self.image.image_name)
        results = context.services.generate.upscale_and_reconstruct(
            image_list     = [[image, 0]],
            upscale        = (self.level, self.strength),
            strength       = 0.0, # GFPGAN strength
            save_original  = False,
            image_callback = None,
        )

        # Results are image and seed, unwrap for now
        # TODO: can this return multiple results?
        image_type = ImageType.RESULT
        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
        context.services.images.save(image_type, image_name, results[0][0])
        return ImageOutput(
            image = ImageField(image_type = image_type, image_name = image_name)
        )

Each portion is important to implement correctly.

Class definition and type

class UpscaleInvocation(BaseInvocation):
    """Upscales an image."""
    type: Literal['upscale'] = 'upscale'

All invocations must derive from BaseInvocation. They should have a docstring that declares what they do in a single, short line. They should also have a type with a type hint that's Literal["command_name"], where command_name is what the user will type on the CLI or use in the API to create this invocation. The command_name must be unique. The type must be assigned to the value of the literal in the type hint.

Inputs

    # Inputs
    image: Union[ImageField,None] = Field(description="The input image")
    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
    level: Literal[2,4]           = Field(default=2, description="The upscale level")

Inputs consist of three parts: a name, a type hint, and a Field with default, description, and validation information. For example:

Part	Value	Description
Name	`strength`	This field is referred to as `strength`
Type Hint	`float`	This field must be of type `float`
Field	`Field(default=0.75, gt=0, le=1, description="The strength")`	The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field.

Notice that image has type Union[ImageField,None]. The Union allows this field to be parsed with None as a value, which enables linking to previous invocations. All fields should either provide a default value or allow None as a value, so that they can be overwritten with a linked output from another invocation.

The special type ImageField is also used here. All images are passed as ImageField, which protects them from pydantic validation errors (since images only ever come from links).

Finally, note that for all linking, the type of the linked fields must match. If the name also matches, then the field can be automatically linked to a previous invocation by name and matching.

Invoke Function

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get(self.image.image_type, self.image.image_name)
        results = context.services.generate.upscale_and_reconstruct(
            image_list     = [[image, 0]],
            upscale        = (self.level, self.strength),
            strength       = 0.0, # GFPGAN strength
            save_original  = False,
            image_callback = None,
        )

        # Results are image and seed, unwrap for now
        image_type = ImageType.RESULT
        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
        context.services.images.save(image_type, image_name, results[0][0])
        return ImageOutput(
            image = ImageField(image_type = image_type, image_name = image_name)
        )

The invoke function is the last portion of an invocation. It is provided an InvocationContext which contains services to perform work as well as a session_id for use as needed. It should return a class with output values that derives from BaseInvocationOutput.

Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).

Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the InvocationServices class, and make sure it can be overridden.

Outputs

class ImageOutput(BaseInvocationOutput):
    """Base class for invocations that output an image"""
    type: Literal['image'] = 'image'

    image: ImageField = Field(default=None, description="The output image")

Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

Schema Generation

Invocation, output and related classes are used to generate an OpenAPI schema.

Required Properties

The schema generation treat all properties with default values as optional. This makes sense internally, but when when using these classes via the generated schema, we end up with e.g. the ImageOutput class having its image property marked as optional.

We know that this property will always be present, so the additional logic needed to always check if the property exists adds a lot of extraneous cruft.

To fix this, we can leverage pydantic's schema customisation to mark properties that we know will always be present as required.

Here's that ImageOutput class, without the needed schema customisation:

class ImageOutput(BaseInvocationOutput):
    """Base class for invocations that output an image"""

    type: Literal["image"] = "image"
    image:      ImageField = Field(default=None, description="The output image")

The generated OpenAPI schema, and all clients/types generated from it, will have the type and image properties marked as optional, even though we know they will always have a value by the time we can interact with them via the API.

Here's the same class, but with the schema customisation added:

class ImageOutput(BaseInvocationOutput):
    """Base class for invocations that output an image"""

    type: Literal["image"] = "image"
    image:      ImageField = Field(default=None, description="The output image")

    class Config:
        schema_extra = {
            'required': [
                'type',
                'image',
            ]
        }

The resultant schema (and any API client or types generated from it) will now have see type as string literal "image" and image as an ImageField object.

See this pydantic issue for discussion on this solution: https://github.com/pydantic/pydantic/discussions/4577

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