# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team from typing import Literal import numpy as np from pydantic import validator from invokeai.app.invocations.primitives import ImageCollectionOutput, ImageField, IntegerCollectionOutput from invokeai.app.util.misc import SEED_MAX, get_random_seed from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIType, tags, title @title("Integer Range") @tags("collection", "integer", "range") class RangeInvocation(BaseInvocation): """Creates a range of numbers from start to stop with step""" type: Literal["range"] = "range" # Inputs start: int = InputField(default=0, description="The start of the range") stop: int = InputField(default=10, description="The stop of the range") step: int = InputField(default=1, description="The step of the range") @validator("stop") def stop_gt_start(cls, v, values): if "start" in values and v <= values["start"]: raise ValueError("stop must be greater than start") return v def invoke(self, context: InvocationContext) -> IntegerCollectionOutput: return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step))) @title("Integer Range of Size") @tags("range", "integer", "size", "collection") class RangeOfSizeInvocation(BaseInvocation): """Creates a range from start to start + size with step""" type: Literal["range_of_size"] = "range_of_size" # Inputs start: int = InputField(default=0, description="The start of the range") size: int = InputField(default=1, description="The number of values") step: int = InputField(default=1, description="The step of the range") def invoke(self, context: InvocationContext) -> IntegerCollectionOutput: return IntegerCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step))) @title("Random Range") @tags("range", "integer", "random", "collection") class RandomRangeInvocation(BaseInvocation): """Creates a collection of random numbers""" type: Literal["random_range"] = "random_range" # Inputs low: int = InputField(default=0, description="The inclusive low value") high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value") size: int = InputField(default=1, description="The number of values to generate") seed: int = InputField( ge=0, le=SEED_MAX, description="The seed for the RNG (omit for random)", default_factory=get_random_seed, ) def invoke(self, context: InvocationContext) -> IntegerCollectionOutput: rng = np.random.default_rng(self.seed) return IntegerCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size))) @title("Image Collection") @tags("image", "collection") class ImageCollectionInvocation(BaseInvocation): """Load a collection of images and provide it as output.""" type: Literal["image_collection"] = "image_collection" # Inputs images: list[ImageField] = InputField( default=[], description="The image collection to load", ui_type=UIType.ImageCollection ) def invoke(self, context: InvocationContext) -> ImageCollectionOutput: return ImageCollectionOutput(collection=self.images)