fix(nodes): restore type annotations for InvocationContext

This commit is contained in:
psychedelicious 2024-02-05 17:16:35 +11:00
parent 281c334531
commit 4ce21087d3
25 changed files with 158 additions and 143 deletions

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@ -174,7 +174,7 @@ class ResizeInvocation(BaseInvocation):
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image") width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image") height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context): def invoke(self, context: InvocationContext):
pass pass
``` ```
@ -203,7 +203,7 @@ class ResizeInvocation(BaseInvocation):
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image") width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image") height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
pass pass
``` ```
@ -229,7 +229,7 @@ class ResizeInvocation(BaseInvocation):
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image") width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image") height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
# Load the input image as a PIL image # Load the input image as a PIL image
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)

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@ -5,6 +5,7 @@ import numpy as np
from pydantic import ValidationInfo, field_validator from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX from invokeai.app.util.misc import SEED_MAX
from .baseinvocation import BaseInvocation, invocation from .baseinvocation import BaseInvocation, invocation
@ -27,7 +28,7 @@ class RangeInvocation(BaseInvocation):
raise ValueError("stop must be greater than start") raise ValueError("stop must be greater than start")
return v return v
def invoke(self, context) -> IntegerCollectionOutput: def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step))) return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
@ -45,7 +46,7 @@ class RangeOfSizeInvocation(BaseInvocation):
size: int = InputField(default=1, gt=0, description="The number of values") size: int = InputField(default=1, gt=0, description="The number of values")
step: int = InputField(default=1, description="The step of the range") step: int = InputField(default=1, description="The step of the range")
def invoke(self, context) -> IntegerCollectionOutput: def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
return IntegerCollectionOutput( return IntegerCollectionOutput(
collection=list(range(self.start, self.start + (self.step * self.size), self.step)) collection=list(range(self.start, self.start + (self.step * self.size), self.step))
) )
@ -72,6 +73,6 @@ class RandomRangeInvocation(BaseInvocation):
description="The seed for the RNG (omit for random)", description="The seed for the RNG (omit for random)",
) )
def invoke(self, context) -> IntegerCollectionOutput: def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
rng = np.random.default_rng(self.seed) rng = np.random.default_rng(self.seed)
return IntegerCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size))) return IntegerCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))

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@ -1,4 +1,4 @@
from typing import TYPE_CHECKING, List, Optional, Union from typing import List, Optional, Union
import torch import torch
from compel import Compel, ReturnedEmbeddingsType from compel import Compel, ReturnedEmbeddingsType
@ -12,6 +12,7 @@ from invokeai.app.invocations.fields import (
UIComponent, UIComponent,
) )
from invokeai.app.invocations.primitives import ConditioningOutput from invokeai.app.invocations.primitives import ConditioningOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ( from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo, BasicConditioningInfo,
ConditioningFieldData, ConditioningFieldData,
@ -31,10 +32,7 @@ from .baseinvocation import (
) )
from .model import ClipField from .model import ClipField
if TYPE_CHECKING: # unconditioned: Optional[torch.Tensor]
from invokeai.app.services.shared.invocation_context import InvocationContext
# unconditioned: Optional[torch.Tensor]
# class ConditioningAlgo(str, Enum): # class ConditioningAlgo(str, Enum):
@ -65,7 +63,7 @@ class CompelInvocation(BaseInvocation):
) )
@torch.no_grad() @torch.no_grad()
def invoke(self, context) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump()) tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump()) text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
@ -148,7 +146,7 @@ class CompelInvocation(BaseInvocation):
class SDXLPromptInvocationBase: class SDXLPromptInvocationBase:
def run_clip_compel( def run_clip_compel(
self, self,
context: "InvocationContext", context: InvocationContext,
clip_field: ClipField, clip_field: ClipField,
prompt: str, prompt: str,
get_pooled: bool, get_pooled: bool,
@ -288,7 +286,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2") clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
@torch.no_grad() @torch.no_grad()
def invoke(self, context) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
c1, c1_pooled, ec1 = self.run_clip_compel( c1, c1_pooled, ec1 = self.run_clip_compel(
context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
) )
@ -373,7 +371,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection) clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
@torch.no_grad() @torch.no_grad()
def invoke(self, context) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
# TODO: if there will appear lora for refiner - write proper prefix # TODO: if there will appear lora for refiner - write proper prefix
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False) c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
@ -418,7 +416,7 @@ class ClipSkipInvocation(BaseInvocation):
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP") clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers) skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)
def invoke(self, context) -> ClipSkipInvocationOutput: def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
self.clip.skipped_layers += self.skipped_layers self.clip.skipped_layers += self.skipped_layers
return ClipSkipInvocationOutput( return ClipSkipInvocationOutput(
clip=self.clip, clip=self.clip,

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@ -28,6 +28,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_valida
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, WithMetadata from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, WithMetadata
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
from invokeai.backend.model_management.models.base import BaseModelType from invokeai.backend.model_management.models.base import BaseModelType
@ -119,7 +120,7 @@ class ControlNetInvocation(BaseInvocation):
validate_begin_end_step(self.begin_step_percent, self.end_step_percent) validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self return self
def invoke(self, context) -> ControlOutput: def invoke(self, context: InvocationContext) -> ControlOutput:
return ControlOutput( return ControlOutput(
control=ControlField( control=ControlField(
image=self.image, image=self.image,
@ -143,7 +144,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
# superclass just passes through image without processing # superclass just passes through image without processing
return image return image
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
raw_image = context.images.get_pil(self.image.image_name) raw_image = context.images.get_pil(self.image.image_name)
# image type should be PIL.PngImagePlugin.PngImageFile ? # image type should be PIL.PngImagePlugin.PngImageFile ?
processed_image = self.run_processor(raw_image) processed_image = self.run_processor(raw_image)

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@ -7,6 +7,7 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.fields import ImageField from invokeai.app.invocations.fields import ImageField
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, invocation from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, WithMetadata from .fields import InputField, WithMetadata
@ -19,7 +20,7 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to inpaint") image: ImageField = InputField(description="The image to inpaint")
mask: ImageField = InputField(description="The mask to use when inpainting") mask: ImageField = InputField(description="The mask to use when inpainting")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
mask = context.images.get_pil(self.mask.image_name) mask = context.images.get_pil(self.mask.image_name)

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@ -1,7 +1,7 @@
import math import math
import re import re
from pathlib import Path from pathlib import Path
from typing import TYPE_CHECKING, Optional, TypedDict from typing import Optional, TypedDict
import cv2 import cv2
import numpy as np import numpy as np
@ -19,9 +19,7 @@ from invokeai.app.invocations.baseinvocation import (
from invokeai.app.invocations.fields import ImageField, InputField, OutputField, WithMetadata from invokeai.app.invocations.fields import ImageField, InputField, OutputField, WithMetadata
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory from invokeai.app.services.image_records.image_records_common import ImageCategory
from invokeai.app.services.shared.invocation_context import InvocationContext
if TYPE_CHECKING:
from invokeai.app.services.shared.invocation_context import InvocationContext
@invocation_output("face_mask_output") @invocation_output("face_mask_output")
@ -176,7 +174,7 @@ def prepare_faces_list(
def generate_face_box_mask( def generate_face_box_mask(
context: "InvocationContext", context: InvocationContext,
minimum_confidence: float, minimum_confidence: float,
x_offset: float, x_offset: float,
y_offset: float, y_offset: float,
@ -275,7 +273,7 @@ def generate_face_box_mask(
def extract_face( def extract_face(
context: "InvocationContext", context: InvocationContext,
image: ImageType, image: ImageType,
face: FaceResultData, face: FaceResultData,
padding: int, padding: int,
@ -356,7 +354,7 @@ def extract_face(
def get_faces_list( def get_faces_list(
context: "InvocationContext", context: InvocationContext,
image: ImageType, image: ImageType,
should_chunk: bool, should_chunk: bool,
minimum_confidence: float, minimum_confidence: float,
@ -458,7 +456,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.", description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
) )
def faceoff(self, context: "InvocationContext", image: ImageType) -> Optional[ExtractFaceData]: def faceoff(self, context: InvocationContext, image: ImageType) -> Optional[ExtractFaceData]:
all_faces = get_faces_list( all_faces = get_faces_list(
context=context, context=context,
image=image, image=image,
@ -485,7 +483,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
return face_data return face_data
def invoke(self, context) -> FaceOffOutput: def invoke(self, context: InvocationContext) -> FaceOffOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
result = self.faceoff(context=context, image=image) result = self.faceoff(context=context, image=image)
@ -543,7 +541,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
raise ValueError('Face IDs must be a comma-separated list of integers (e.g. "1,2,3")') raise ValueError('Face IDs must be a comma-separated list of integers (e.g. "1,2,3")')
return v return v
def facemask(self, context: "InvocationContext", image: ImageType) -> FaceMaskResult: def facemask(self, context: InvocationContext, image: ImageType) -> FaceMaskResult:
all_faces = get_faces_list( all_faces = get_faces_list(
context=context, context=context,
image=image, image=image,
@ -600,7 +598,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
mask=mask_pil, mask=mask_pil,
) )
def invoke(self, context) -> FaceMaskOutput: def invoke(self, context: InvocationContext) -> FaceMaskOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
result = self.facemask(context=context, image=image) result = self.facemask(context=context, image=image)
@ -633,7 +631,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.", description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
) )
def faceidentifier(self, context: "InvocationContext", image: ImageType) -> ImageType: def faceidentifier(self, context: InvocationContext, image: ImageType) -> ImageType:
image = image.copy() image = image.copy()
all_faces = get_faces_list( all_faces = get_faces_list(
@ -674,7 +672,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
return image return image
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
result_image = self.faceidentifier(context=context, image=image) result_image = self.faceidentifier(context=context, image=image)

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@ -18,6 +18,7 @@ from invokeai.app.invocations.fields import (
) )
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory from invokeai.app.services.image_records.image_records_common import ImageCategory
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker from invokeai.backend.image_util.safety_checker import SafetyChecker
@ -34,7 +35,7 @@ class ShowImageInvocation(BaseInvocation):
image: ImageField = InputField(description="The image to show") image: ImageField = InputField(description="The image to show")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
image.show() image.show()
@ -62,7 +63,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata):
mode: Literal["RGB", "RGBA"] = InputField(default="RGB", description="The mode of the image") mode: Literal["RGB", "RGBA"] = InputField(default="RGB", description="The mode of the image")
color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color of the image") color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color of the image")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = Image.new(mode=self.mode, size=(self.width, self.height), color=self.color.tuple()) image = Image.new(mode=self.mode, size=(self.width, self.height), color=self.color.tuple())
image_dto = context.images.save(image=image) image_dto = context.images.save(image=image)
@ -86,7 +87,7 @@ class ImageCropInvocation(BaseInvocation, WithMetadata):
width: int = InputField(default=512, gt=0, description="The width of the crop rectangle") width: int = InputField(default=512, gt=0, description="The width of the crop rectangle")
height: int = InputField(default=512, gt=0, description="The height of the crop rectangle") height: int = InputField(default=512, gt=0, description="The height of the crop rectangle")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
image_crop = Image.new(mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0)) image_crop = Image.new(mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0))
@ -125,7 +126,7 @@ class CenterPadCropInvocation(BaseInvocation):
description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)", description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)",
) )
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
# Calculate and create new image dimensions # Calculate and create new image dimensions
@ -161,7 +162,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata):
y: int = InputField(default=0, description="The top y coordinate at which to paste the image") y: int = InputField(default=0, description="The top y coordinate at which to paste the image")
crop: bool = InputField(default=False, description="Crop to base image dimensions") crop: bool = InputField(default=False, description="Crop to base image dimensions")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
base_image = context.images.get_pil(self.base_image.image_name) base_image = context.images.get_pil(self.base_image.image_name)
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
mask = None mask = None
@ -201,7 +202,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to create the mask from") image: ImageField = InputField(description="The image to create the mask from")
invert: bool = InputField(default=False, description="Whether or not to invert the mask") invert: bool = InputField(default=False, description="Whether or not to invert the mask")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
image_mask = image.split()[-1] image_mask = image.split()[-1]
@ -226,7 +227,7 @@ class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
image1: ImageField = InputField(description="The first image to multiply") image1: ImageField = InputField(description="The first image to multiply")
image2: ImageField = InputField(description="The second image to multiply") image2: ImageField = InputField(description="The second image to multiply")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image1 = context.images.get_pil(self.image1.image_name) image1 = context.images.get_pil(self.image1.image_name)
image2 = context.images.get_pil(self.image2.image_name) image2 = context.images.get_pil(self.image2.image_name)
@ -253,7 +254,7 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to get the channel from") image: ImageField = InputField(description="The image to get the channel from")
channel: IMAGE_CHANNELS = InputField(default="A", description="The channel to get") channel: IMAGE_CHANNELS = InputField(default="A", description="The channel to get")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
channel_image = image.getchannel(self.channel) channel_image = image.getchannel(self.channel)
@ -279,7 +280,7 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to convert") image: ImageField = InputField(description="The image to convert")
mode: IMAGE_MODES = InputField(default="L", description="The mode to convert to") mode: IMAGE_MODES = InputField(default="L", description="The mode to convert to")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
converted_image = image.convert(self.mode) converted_image = image.convert(self.mode)
@ -304,7 +305,7 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
# Metadata # Metadata
blur_type: Literal["gaussian", "box"] = InputField(default="gaussian", description="The type of blur") blur_type: Literal["gaussian", "box"] = InputField(default="gaussian", description="The type of blur")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
blur = ( blur = (
@ -338,7 +339,7 @@ class UnsharpMaskInvocation(BaseInvocation, WithMetadata):
def array_from_pil(self, img): def array_from_pil(self, img):
return numpy.array(img) / 255 return numpy.array(img) / 255
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
mode = image.mode mode = image.mode
@ -401,7 +402,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata):
height: int = InputField(default=512, gt=0, description="The height to resize to (px)") height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode") resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode] resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
@ -434,7 +435,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata):
) )
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode") resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode] resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
@ -465,7 +466,7 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata):
min: int = InputField(default=0, ge=0, le=255, description="The minimum output value") min: int = InputField(default=0, ge=0, le=255, description="The minimum output value")
max: int = InputField(default=255, ge=0, le=255, description="The maximum output value") max: int = InputField(default=255, ge=0, le=255, description="The maximum output value")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
image_arr = numpy.asarray(image, dtype=numpy.float32) / 255 image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
@ -492,7 +493,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
min: int = InputField(default=0, ge=0, le=255, description="The minimum input value") min: int = InputField(default=0, ge=0, le=255, description="The minimum input value")
max: int = InputField(default=255, ge=0, le=255, description="The maximum input value") max: int = InputField(default=255, ge=0, le=255, description="The maximum input value")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
image_arr = numpy.asarray(image, dtype=numpy.float32) image_arr = numpy.asarray(image, dtype=numpy.float32)
@ -517,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to check") image: ImageField = InputField(description="The image to check")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
logger = context.logger logger = context.logger
@ -553,7 +554,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to check") image: ImageField = InputField(description="The image to check")
text: str = InputField(default="InvokeAI", description="Watermark text") text: str = InputField(default="InvokeAI", description="Watermark text")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
new_image = InvisibleWatermark.add_watermark(image, self.text) new_image = InvisibleWatermark.add_watermark(image, self.text)
image_dto = context.images.save(image=new_image) image_dto = context.images.save(image=new_image)
@ -579,7 +580,7 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata):
description="Second threshold for the hysteresis procedure in Canny edge detection" description="Second threshold for the hysteresis procedure in Canny edge detection"
) )
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
mask = context.images.get_pil(self.image.image_name).convert("L") mask = context.images.get_pil(self.image.image_name).convert("L")
npimg = numpy.asarray(mask, dtype=numpy.uint8) npimg = numpy.asarray(mask, dtype=numpy.uint8)
@ -613,7 +614,7 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata):
mask1: ImageField = InputField(description="The first mask to combine") mask1: ImageField = InputField(description="The first mask to combine")
mask2: ImageField = InputField(description="The second image to combine") mask2: ImageField = InputField(description="The second image to combine")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
mask1 = context.images.get_pil(self.mask1.image_name).convert("L") mask1 = context.images.get_pil(self.mask1.image_name).convert("L")
mask2 = context.images.get_pil(self.mask2.image_name).convert("L") mask2 = context.images.get_pil(self.mask2.image_name).convert("L")
@ -642,7 +643,7 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata):
mask: Optional[ImageField] = InputField(default=None, description="Mask to use when applying color-correction") mask: Optional[ImageField] = InputField(default=None, description="Mask to use when applying color-correction")
mask_blur_radius: float = InputField(default=8, description="Mask blur radius") mask_blur_radius: float = InputField(default=8, description="Mask blur radius")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
pil_init_mask = None pil_init_mask = None
if self.mask is not None: if self.mask is not None:
pil_init_mask = context.images.get_pil(self.mask.image_name).convert("L") pil_init_mask = context.images.get_pil(self.mask.image_name).convert("L")
@ -741,7 +742,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to adjust") image: ImageField = InputField(description="The image to adjust")
hue: int = InputField(default=0, description="The degrees by which to rotate the hue, 0-360") hue: int = InputField(default=0, description="The degrees by which to rotate the hue, 0-360")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.images.get_pil(self.image.image_name) pil_image = context.images.get_pil(self.image.image_name)
# Convert image to HSV color space # Convert image to HSV color space
@ -831,7 +832,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
channel: COLOR_CHANNELS = InputField(description="Which channel to adjust") channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by") offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.images.get_pil(self.image.image_name) pil_image = context.images.get_pil(self.image.image_name)
# extract the channel and mode from the input and reference tuple # extract the channel and mode from the input and reference tuple
@ -888,7 +889,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
scale: float = InputField(default=1.0, ge=0.0, description="The amount to scale the channel by.") scale: float = InputField(default=1.0, ge=0.0, description="The amount to scale the channel by.")
invert_channel: bool = InputField(default=False, description="Invert the channel after scaling") invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.images.get_pil(self.image.image_name) pil_image = context.images.get_pil(self.image.image_name)
# extract the channel and mode from the input and reference tuple # extract the channel and mode from the input and reference tuple
@ -931,7 +932,7 @@ class SaveImageInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description=FieldDescriptions.image) image: ImageField = InputField(description=FieldDescriptions.image)
board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct) board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
image_dto = context.images.save(image=image, board_id=self.board.board_id if self.board else None) image_dto = context.images.save(image=image, board_id=self.board.board_id if self.board else None)
@ -953,7 +954,7 @@ class LinearUIOutputInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description=FieldDescriptions.image) image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct) board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image_dto = context.images.get_dto(self.image.image_name) image_dto = context.images.get_dto(self.image.image_name)
image_dto = context.images.update( image_dto = context.images.update(

View File

@ -8,6 +8,7 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.fields import ColorField, ImageField from invokeai.app.invocations.fields import ColorField, ImageField
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX from invokeai.app.util.misc import SEED_MAX
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA from invokeai.backend.image_util.lama import LaMA
@ -129,7 +130,7 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
description="The color to use to infill", description="The color to use to infill",
) )
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
solid_bg = Image.new("RGBA", image.size, self.color.tuple()) solid_bg = Image.new("RGBA", image.size, self.color.tuple())
@ -155,7 +156,7 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
description="The seed to use for tile generation (omit for random)", description="The seed to use for tile generation (omit for random)",
) )
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size) infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
@ -176,7 +177,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill") downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode") resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name).convert("RGBA") image = context.images.get_pil(self.image.image_name).convert("RGBA")
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode] resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
@ -213,7 +214,7 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
infilled = infill_lama(image.copy()) infilled = infill_lama(image.copy())
@ -229,7 +230,7 @@ class CV2InfillInvocation(BaseInvocation, WithMetadata):
image: ImageField = InputField(description="The image to infill") image: ImageField = InputField(description="The image to infill")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
infilled = infill_cv2(image.copy()) infilled = infill_cv2(image.copy())

View File

@ -13,6 +13,7 @@ from invokeai.app.invocations.baseinvocation import (
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.invocations.primitives import ImageField from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.model_management.models.base import BaseModelType, ModelType from invokeai.backend.model_management.models.base import BaseModelType, ModelType
from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
@ -92,7 +93,7 @@ class IPAdapterInvocation(BaseInvocation):
validate_begin_end_step(self.begin_step_percent, self.end_step_percent) validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self return self
def invoke(self, context) -> IPAdapterOutput: def invoke(self, context: InvocationContext) -> IPAdapterOutput:
# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model. # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
ip_adapter_info = context.models.get_info( ip_adapter_info = context.models.get_info(
self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter

View File

@ -3,7 +3,7 @@
import math import math
from contextlib import ExitStack from contextlib import ExitStack
from functools import singledispatchmethod from functools import singledispatchmethod
from typing import TYPE_CHECKING, List, Literal, Optional, Union from typing import List, Literal, Optional, Union
import einops import einops
import numpy as np import numpy as np
@ -42,6 +42,7 @@ from invokeai.app.invocations.primitives import (
LatentsOutput, LatentsOutput,
) )
from invokeai.app.invocations.t2i_adapter import T2IAdapterField from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.controlnet_utils import prepare_control_image from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
from invokeai.backend.model_management.models import ModelType, SilenceWarnings from invokeai.backend.model_management.models import ModelType, SilenceWarnings
@ -70,9 +71,6 @@ from .baseinvocation import (
from .controlnet_image_processors import ControlField from .controlnet_image_processors import ControlField
from .model import ModelInfo, UNetField, VaeField from .model import ModelInfo, UNetField, VaeField
if TYPE_CHECKING:
from invokeai.app.services.shared.invocation_context import InvocationContext
if choose_torch_device() == torch.device("mps"): if choose_torch_device() == torch.device("mps"):
from torch import mps from torch import mps
@ -177,7 +175,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
def get_scheduler( def get_scheduler(
context: "InvocationContext", context: InvocationContext,
scheduler_info: ModelInfo, scheduler_info: ModelInfo,
scheduler_name: str, scheduler_name: str,
seed: int, seed: int,
@ -300,7 +298,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
def get_conditioning_data( def get_conditioning_data(
self, self,
context: "InvocationContext", context: InvocationContext,
scheduler, scheduler,
unet, unet,
seed, seed,
@ -369,7 +367,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
def prep_control_data( def prep_control_data(
self, self,
context: "InvocationContext", context: InvocationContext,
control_input: Union[ControlField, List[ControlField]], control_input: Union[ControlField, List[ControlField]],
latents_shape: List[int], latents_shape: List[int],
exit_stack: ExitStack, exit_stack: ExitStack,
@ -442,7 +440,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
def prep_ip_adapter_data( def prep_ip_adapter_data(
self, self,
context: "InvocationContext", context: InvocationContext,
ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]], ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
conditioning_data: ConditioningData, conditioning_data: ConditioningData,
exit_stack: ExitStack, exit_stack: ExitStack,
@ -509,7 +507,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
def run_t2i_adapters( def run_t2i_adapters(
self, self,
context: "InvocationContext", context: InvocationContext,
t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]], t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
latents_shape: list[int], latents_shape: list[int],
do_classifier_free_guidance: bool, do_classifier_free_guidance: bool,
@ -618,7 +616,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
return num_inference_steps, timesteps, init_timestep return num_inference_steps, timesteps, init_timestep
def prep_inpaint_mask(self, context: "InvocationContext", latents): def prep_inpaint_mask(self, context: InvocationContext, latents):
if self.denoise_mask is None: if self.denoise_mask is None:
return None, None return None, None

View File

@ -7,6 +7,7 @@ from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.fields import FieldDescriptions, InputField from invokeai.app.invocations.fields import FieldDescriptions, InputField
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, invocation from .baseinvocation import BaseInvocation, invocation
@ -18,7 +19,7 @@ class AddInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1) a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2) b: int = InputField(default=0, description=FieldDescriptions.num_2)
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.a + self.b) return IntegerOutput(value=self.a + self.b)
@ -29,7 +30,7 @@ class SubtractInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1) a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2) b: int = InputField(default=0, description=FieldDescriptions.num_2)
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.a - self.b) return IntegerOutput(value=self.a - self.b)
@ -40,7 +41,7 @@ class MultiplyInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1) a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2) b: int = InputField(default=0, description=FieldDescriptions.num_2)
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.a * self.b) return IntegerOutput(value=self.a * self.b)
@ -51,7 +52,7 @@ class DivideInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1) a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2) b: int = InputField(default=0, description=FieldDescriptions.num_2)
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=int(self.a / self.b)) return IntegerOutput(value=int(self.a / self.b))
@ -69,7 +70,7 @@ class RandomIntInvocation(BaseInvocation):
low: int = InputField(default=0, description=FieldDescriptions.inclusive_low) low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high) high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=np.random.randint(self.low, self.high)) return IntegerOutput(value=np.random.randint(self.low, self.high))
@ -88,7 +89,7 @@ class RandomFloatInvocation(BaseInvocation):
high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high) high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high)
decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places) decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places)
def invoke(self, context) -> FloatOutput: def invoke(self, context: InvocationContext) -> FloatOutput:
random_float = np.random.uniform(self.low, self.high) random_float = np.random.uniform(self.low, self.high)
rounded_float = round(random_float, self.decimals) rounded_float = round(random_float, self.decimals)
return FloatOutput(value=rounded_float) return FloatOutput(value=rounded_float)
@ -110,7 +111,7 @@ class FloatToIntegerInvocation(BaseInvocation):
default="Nearest", description="The method to use for rounding" default="Nearest", description="The method to use for rounding"
) )
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
if self.method == "Nearest": if self.method == "Nearest":
return IntegerOutput(value=round(self.value / self.multiple) * self.multiple) return IntegerOutput(value=round(self.value / self.multiple) * self.multiple)
elif self.method == "Floor": elif self.method == "Floor":
@ -128,7 +129,7 @@ class RoundInvocation(BaseInvocation):
value: float = InputField(default=0, description="The float value") value: float = InputField(default=0, description="The float value")
decimals: int = InputField(default=0, description="The number of decimal places") decimals: int = InputField(default=0, description="The number of decimal places")
def invoke(self, context) -> FloatOutput: def invoke(self, context: InvocationContext) -> FloatOutput:
return FloatOutput(value=round(self.value, self.decimals)) return FloatOutput(value=round(self.value, self.decimals))
@ -196,7 +197,7 @@ class IntegerMathInvocation(BaseInvocation):
raise ValueError("Result of exponentiation is not an integer") raise ValueError("Result of exponentiation is not an integer")
return v return v
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
# Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9 # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
if self.operation == "ADD": if self.operation == "ADD":
return IntegerOutput(value=self.a + self.b) return IntegerOutput(value=self.a + self.b)
@ -270,7 +271,7 @@ class FloatMathInvocation(BaseInvocation):
raise ValueError("Root operation resulted in a complex number") raise ValueError("Root operation resulted in a complex number")
return v return v
def invoke(self, context) -> FloatOutput: def invoke(self, context: InvocationContext) -> FloatOutput:
# Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9 # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
if self.operation == "ADD": if self.operation == "ADD":
return FloatOutput(value=self.a + self.b) return FloatOutput(value=self.a + self.b)

View File

@ -20,6 +20,7 @@ from invokeai.app.invocations.fields import (
from invokeai.app.invocations.ip_adapter import IPAdapterModelField from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.shared.invocation_context import InvocationContext
from ...version import __version__ from ...version import __version__
@ -64,7 +65,7 @@ class MetadataItemInvocation(BaseInvocation):
label: str = InputField(description=FieldDescriptions.metadata_item_label) label: str = InputField(description=FieldDescriptions.metadata_item_label)
value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any) value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
def invoke(self, context) -> MetadataItemOutput: def invoke(self, context: InvocationContext) -> MetadataItemOutput:
return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value)) return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
@ -81,7 +82,7 @@ class MetadataInvocation(BaseInvocation):
description=FieldDescriptions.metadata_item_polymorphic description=FieldDescriptions.metadata_item_polymorphic
) )
def invoke(self, context) -> MetadataOutput: def invoke(self, context: InvocationContext) -> MetadataOutput:
if isinstance(self.items, MetadataItemField): if isinstance(self.items, MetadataItemField):
# single metadata item # single metadata item
data = {self.items.label: self.items.value} data = {self.items.label: self.items.value}
@ -100,7 +101,7 @@ class MergeMetadataInvocation(BaseInvocation):
collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection) collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
def invoke(self, context) -> MetadataOutput: def invoke(self, context: InvocationContext) -> MetadataOutput:
data = {} data = {}
for item in self.collection: for item in self.collection:
data.update(item.model_dump()) data.update(item.model_dump())
@ -218,7 +219,7 @@ class CoreMetadataInvocation(BaseInvocation):
description="The start value used for refiner denoising", description="The start value used for refiner denoising",
) )
def invoke(self, context) -> MetadataOutput: def invoke(self, context: InvocationContext) -> MetadataOutput:
"""Collects and outputs a CoreMetadata object""" """Collects and outputs a CoreMetadata object"""
return MetadataOutput( return MetadataOutput(

View File

@ -4,6 +4,7 @@ from typing import List, Optional
from pydantic import BaseModel, ConfigDict, Field from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.shared.models import FreeUConfig from invokeai.app.shared.models import FreeUConfig
from ...backend.model_management import BaseModelType, ModelType, SubModelType from ...backend.model_management import BaseModelType, ModelType, SubModelType
@ -109,7 +110,7 @@ class MainModelLoaderInvocation(BaseInvocation):
model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct) model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
# TODO: precision? # TODO: precision?
def invoke(self, context) -> ModelLoaderOutput: def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
base_model = self.model.base_model base_model = self.model.base_model
model_name = self.model.model_name model_name = self.model.model_name
model_type = ModelType.Main model_type = ModelType.Main
@ -221,7 +222,7 @@ class LoraLoaderInvocation(BaseInvocation):
title="CLIP", title="CLIP",
) )
def invoke(self, context) -> LoraLoaderOutput: def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
if self.lora is None: if self.lora is None:
raise Exception("No LoRA provided") raise Exception("No LoRA provided")
@ -310,7 +311,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
title="CLIP 2", title="CLIP 2",
) )
def invoke(self, context) -> SDXLLoraLoaderOutput: def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
if self.lora is None: if self.lora is None:
raise Exception("No LoRA provided") raise Exception("No LoRA provided")
@ -393,7 +394,7 @@ class VaeLoaderInvocation(BaseInvocation):
title="VAE", title="VAE",
) )
def invoke(self, context) -> VAEOutput: def invoke(self, context: InvocationContext) -> VAEOutput:
base_model = self.vae_model.base_model base_model = self.vae_model.base_model
model_name = self.vae_model.model_name model_name = self.vae_model.model_name
model_type = ModelType.Vae model_type = ModelType.Vae
@ -448,7 +449,7 @@ class SeamlessModeInvocation(BaseInvocation):
seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless") seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless") seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
def invoke(self, context) -> SeamlessModeOutput: def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
# Conditionally append 'x' and 'y' based on seamless_x and seamless_y # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
unet = copy.deepcopy(self.unet) unet = copy.deepcopy(self.unet)
vae = copy.deepcopy(self.vae) vae = copy.deepcopy(self.vae)
@ -484,6 +485,6 @@ class FreeUInvocation(BaseInvocation):
s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1) s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1)
s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2) s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2)
def invoke(self, context) -> UNetOutput: def invoke(self, context: InvocationContext) -> UNetOutput:
self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2) self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2)
return UNetOutput(unet=self.unet) return UNetOutput(unet=self.unet)

View File

@ -5,6 +5,7 @@ import torch
from pydantic import field_validator from pydantic import field_validator
from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX from invokeai.app.util.misc import SEED_MAX
from ...backend.util.devices import choose_torch_device, torch_dtype from ...backend.util.devices import choose_torch_device, torch_dtype
@ -112,7 +113,7 @@ class NoiseInvocation(BaseInvocation):
"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range.""" """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
return v % (SEED_MAX + 1) return v % (SEED_MAX + 1)
def invoke(self, context) -> NoiseOutput: def invoke(self, context: InvocationContext) -> NoiseOutput:
noise = get_noise( noise = get_noise(
width=self.width, width=self.width,
height=self.height, height=self.height,

View File

@ -63,7 +63,7 @@ class ONNXPromptInvocation(BaseInvocation):
prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea) prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection) clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
def invoke(self, context) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.model_dump(), **self.clip.tokenizer.model_dump(),
) )
@ -201,7 +201,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
# based on # based on
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375 # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
def invoke(self, context) -> LatentsOutput: def invoke(self, context: InvocationContext) -> LatentsOutput:
c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name) c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name) uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
@ -342,7 +342,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
) )
# tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)") # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
latents = context.services.latents.get(self.latents.latents_name) latents = context.services.latents.get(self.latents.latents_name)
if self.vae.vae.submodel != SubModelType.VaeDecoder: if self.vae.vae.submodel != SubModelType.VaeDecoder:
@ -417,7 +417,7 @@ class OnnxModelLoaderInvocation(BaseInvocation):
description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel
) )
def invoke(self, context) -> ONNXModelLoaderOutput: def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
base_model = self.model.base_model base_model = self.model.base_model
model_name = self.model.model_name model_name = self.model.model_name
model_type = ModelType.ONNX model_type = ModelType.ONNX

View File

@ -40,6 +40,7 @@ from easing_functions import (
from matplotlib.ticker import MaxNLocator from matplotlib.ticker import MaxNLocator
from invokeai.app.invocations.primitives import FloatCollectionOutput from invokeai.app.invocations.primitives import FloatCollectionOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, invocation from .baseinvocation import BaseInvocation, invocation
from .fields import InputField from .fields import InputField
@ -62,7 +63,7 @@ class FloatLinearRangeInvocation(BaseInvocation):
description="number of values to interpolate over (including start and stop)", description="number of values to interpolate over (including start and stop)",
) )
def invoke(self, context) -> FloatCollectionOutput: def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps)) param_list = list(np.linspace(self.start, self.stop, self.steps))
return FloatCollectionOutput(collection=param_list) return FloatCollectionOutput(collection=param_list)
@ -130,7 +131,7 @@ class StepParamEasingInvocation(BaseInvocation):
# alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing") # alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing")
show_easing_plot: bool = InputField(default=False, description="show easing plot") show_easing_plot: bool = InputField(default=False, description="show easing plot")
def invoke(self, context) -> FloatCollectionOutput: def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
log_diagnostics = False log_diagnostics = False
# convert from start_step_percent to nearest step <= (steps * start_step_percent) # convert from start_step_percent to nearest step <= (steps * start_step_percent)
# start_step = int(np.floor(self.num_steps * self.start_step_percent)) # start_step = int(np.floor(self.num_steps * self.start_step_percent))

View File

@ -17,6 +17,7 @@ from invokeai.app.invocations.fields import (
UIComponent, UIComponent,
) )
from invokeai.app.services.images.images_common import ImageDTO from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import ( from .baseinvocation import (
BaseInvocation, BaseInvocation,
@ -59,7 +60,7 @@ class BooleanInvocation(BaseInvocation):
value: bool = InputField(default=False, description="The boolean value") value: bool = InputField(default=False, description="The boolean value")
def invoke(self, context) -> BooleanOutput: def invoke(self, context: InvocationContext) -> BooleanOutput:
return BooleanOutput(value=self.value) return BooleanOutput(value=self.value)
@ -75,7 +76,7 @@ class BooleanCollectionInvocation(BaseInvocation):
collection: list[bool] = InputField(default=[], description="The collection of boolean values") collection: list[bool] = InputField(default=[], description="The collection of boolean values")
def invoke(self, context) -> BooleanCollectionOutput: def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
return BooleanCollectionOutput(collection=self.collection) return BooleanCollectionOutput(collection=self.collection)
@ -108,7 +109,7 @@ class IntegerInvocation(BaseInvocation):
value: int = InputField(default=0, description="The integer value") value: int = InputField(default=0, description="The integer value")
def invoke(self, context) -> IntegerOutput: def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.value) return IntegerOutput(value=self.value)
@ -124,7 +125,7 @@ class IntegerCollectionInvocation(BaseInvocation):
collection: list[int] = InputField(default=[], description="The collection of integer values") collection: list[int] = InputField(default=[], description="The collection of integer values")
def invoke(self, context) -> IntegerCollectionOutput: def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
return IntegerCollectionOutput(collection=self.collection) return IntegerCollectionOutput(collection=self.collection)
@ -155,7 +156,7 @@ class FloatInvocation(BaseInvocation):
value: float = InputField(default=0.0, description="The float value") value: float = InputField(default=0.0, description="The float value")
def invoke(self, context) -> FloatOutput: def invoke(self, context: InvocationContext) -> FloatOutput:
return FloatOutput(value=self.value) return FloatOutput(value=self.value)
@ -171,7 +172,7 @@ class FloatCollectionInvocation(BaseInvocation):
collection: list[float] = InputField(default=[], description="The collection of float values") collection: list[float] = InputField(default=[], description="The collection of float values")
def invoke(self, context) -> FloatCollectionOutput: def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
return FloatCollectionOutput(collection=self.collection) return FloatCollectionOutput(collection=self.collection)
@ -202,7 +203,7 @@ class StringInvocation(BaseInvocation):
value: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea) value: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea)
def invoke(self, context) -> StringOutput: def invoke(self, context: InvocationContext) -> StringOutput:
return StringOutput(value=self.value) return StringOutput(value=self.value)
@ -218,7 +219,7 @@ class StringCollectionInvocation(BaseInvocation):
collection: list[str] = InputField(default=[], description="The collection of string values") collection: list[str] = InputField(default=[], description="The collection of string values")
def invoke(self, context) -> StringCollectionOutput: def invoke(self, context: InvocationContext) -> StringCollectionOutput:
return StringCollectionOutput(collection=self.collection) return StringCollectionOutput(collection=self.collection)
@ -261,7 +262,7 @@ class ImageInvocation(
image: ImageField = InputField(description="The image to load") image: ImageField = InputField(description="The image to load")
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
return ImageOutput( return ImageOutput(
@ -283,7 +284,7 @@ class ImageCollectionInvocation(BaseInvocation):
collection: list[ImageField] = InputField(description="The collection of image values") collection: list[ImageField] = InputField(description="The collection of image values")
def invoke(self, context) -> ImageCollectionOutput: def invoke(self, context: InvocationContext) -> ImageCollectionOutput:
return ImageCollectionOutput(collection=self.collection) return ImageCollectionOutput(collection=self.collection)
@ -346,7 +347,7 @@ class LatentsInvocation(BaseInvocation):
latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection) latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection)
def invoke(self, context) -> LatentsOutput: def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.latents.get(self.latents.latents_name) latents = context.latents.get(self.latents.latents_name)
return LatentsOutput.build(self.latents.latents_name, latents) return LatentsOutput.build(self.latents.latents_name, latents)
@ -366,7 +367,7 @@ class LatentsCollectionInvocation(BaseInvocation):
description="The collection of latents tensors", description="The collection of latents tensors",
) )
def invoke(self, context) -> LatentsCollectionOutput: def invoke(self, context: InvocationContext) -> LatentsCollectionOutput:
return LatentsCollectionOutput(collection=self.collection) return LatentsCollectionOutput(collection=self.collection)
@ -397,7 +398,7 @@ class ColorInvocation(BaseInvocation):
color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color value") color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color value")
def invoke(self, context) -> ColorOutput: def invoke(self, context: InvocationContext) -> ColorOutput:
return ColorOutput(color=self.color) return ColorOutput(color=self.color)
@ -438,7 +439,7 @@ class ConditioningInvocation(BaseInvocation):
conditioning: ConditioningField = InputField(description=FieldDescriptions.cond, input=Input.Connection) conditioning: ConditioningField = InputField(description=FieldDescriptions.cond, input=Input.Connection)
def invoke(self, context) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
return ConditioningOutput(conditioning=self.conditioning) return ConditioningOutput(conditioning=self.conditioning)
@ -457,7 +458,7 @@ class ConditioningCollectionInvocation(BaseInvocation):
description="The collection of conditioning tensors", description="The collection of conditioning tensors",
) )
def invoke(self, context) -> ConditioningCollectionOutput: def invoke(self, context: InvocationContext) -> ConditioningCollectionOutput:
return ConditioningCollectionOutput(collection=self.collection) return ConditioningCollectionOutput(collection=self.collection)

View File

@ -6,6 +6,7 @@ from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPrompt
from pydantic import field_validator from pydantic import field_validator
from invokeai.app.invocations.primitives import StringCollectionOutput from invokeai.app.invocations.primitives import StringCollectionOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, invocation from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, UIComponent from .fields import InputField, UIComponent
@ -29,7 +30,7 @@ class DynamicPromptInvocation(BaseInvocation):
max_prompts: int = InputField(default=1, description="The number of prompts to generate") max_prompts: int = InputField(default=1, description="The number of prompts to generate")
combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator") combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
def invoke(self, context) -> StringCollectionOutput: def invoke(self, context: InvocationContext) -> StringCollectionOutput:
if self.combinatorial: if self.combinatorial:
generator = CombinatorialPromptGenerator() generator = CombinatorialPromptGenerator()
prompts = generator.generate(self.prompt, max_prompts=self.max_prompts) prompts = generator.generate(self.prompt, max_prompts=self.max_prompts)
@ -91,7 +92,7 @@ class PromptsFromFileInvocation(BaseInvocation):
break break
return prompts return prompts
def invoke(self, context) -> StringCollectionOutput: def invoke(self, context: InvocationContext) -> StringCollectionOutput:
prompts = self.promptsFromFile( prompts = self.promptsFromFile(
self.file_path, self.file_path,
self.pre_prompt, self.pre_prompt,

View File

@ -1,4 +1,5 @@
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
from invokeai.app.services.shared.invocation_context import InvocationContext
from ...backend.model_management import ModelType, SubModelType from ...backend.model_management import ModelType, SubModelType
from .baseinvocation import ( from .baseinvocation import (
@ -38,7 +39,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
) )
# TODO: precision? # TODO: precision?
def invoke(self, context) -> SDXLModelLoaderOutput: def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
base_model = self.model.base_model base_model = self.model.base_model
model_name = self.model.model_name model_name = self.model.model_name
model_type = ModelType.Main model_type = ModelType.Main
@ -127,7 +128,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
) )
# TODO: precision? # TODO: precision?
def invoke(self, context) -> SDXLRefinerModelLoaderOutput: def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
base_model = self.model.base_model base_model = self.model.base_model
model_name = self.model.model_name model_name = self.model.model_name
model_type = ModelType.Main model_type = ModelType.Main

View File

@ -2,6 +2,8 @@
import re import re
from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import ( from .baseinvocation import (
BaseInvocation, BaseInvocation,
BaseInvocationOutput, BaseInvocationOutput,
@ -32,7 +34,7 @@ class StringSplitNegInvocation(BaseInvocation):
string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea) string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea)
def invoke(self, context) -> StringPosNegOutput: def invoke(self, context: InvocationContext) -> StringPosNegOutput:
p_string = "" p_string = ""
n_string = "" n_string = ""
brackets_depth = 0 brackets_depth = 0
@ -76,7 +78,7 @@ class StringSplitInvocation(BaseInvocation):
default="", description="Delimiter to spilt with. blank will split on the first whitespace" default="", description="Delimiter to spilt with. blank will split on the first whitespace"
) )
def invoke(self, context) -> String2Output: def invoke(self, context: InvocationContext) -> String2Output:
result = self.string.split(self.delimiter, 1) result = self.string.split(self.delimiter, 1)
if len(result) == 2: if len(result) == 2:
part1, part2 = result part1, part2 = result
@ -94,7 +96,7 @@ class StringJoinInvocation(BaseInvocation):
string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea) string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea)
string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea) string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
def invoke(self, context) -> StringOutput: def invoke(self, context: InvocationContext) -> StringOutput:
return StringOutput(value=((self.string_left or "") + (self.string_right or ""))) return StringOutput(value=((self.string_left or "") + (self.string_right or "")))
@ -106,7 +108,7 @@ class StringJoinThreeInvocation(BaseInvocation):
string_middle: str = InputField(default="", description="String Middle", ui_component=UIComponent.Textarea) string_middle: str = InputField(default="", description="String Middle", ui_component=UIComponent.Textarea)
string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea) string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
def invoke(self, context) -> StringOutput: def invoke(self, context: InvocationContext) -> StringOutput:
return StringOutput(value=((self.string_left or "") + (self.string_middle or "") + (self.string_right or ""))) return StringOutput(value=((self.string_left or "") + (self.string_middle or "") + (self.string_right or "")))
@ -125,7 +127,7 @@ class StringReplaceInvocation(BaseInvocation):
default=False, description="Use search string as a regex expression (non regex is case insensitive)" default=False, description="Use search string as a regex expression (non regex is case insensitive)"
) )
def invoke(self, context) -> StringOutput: def invoke(self, context: InvocationContext) -> StringOutput:
pattern = self.search_string or "" pattern = self.search_string or ""
new_string = self.string or "" new_string = self.string or ""
if len(pattern) > 0: if len(pattern) > 0:

View File

@ -11,6 +11,7 @@ from invokeai.app.invocations.baseinvocation import (
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.model_management.models.base import BaseModelType from invokeai.backend.model_management.models.base import BaseModelType
@ -89,7 +90,7 @@ class T2IAdapterInvocation(BaseInvocation):
validate_begin_end_step(self.begin_step_percent, self.end_step_percent) validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self return self
def invoke(self, context) -> T2IAdapterOutput: def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
return T2IAdapterOutput( return T2IAdapterOutput(
t2i_adapter=T2IAdapterField( t2i_adapter=T2IAdapterField(
image=self.image, image=self.image,

View File

@ -13,6 +13,7 @@ from invokeai.app.invocations.baseinvocation import (
) )
from invokeai.app.invocations.fields import ImageField, Input, InputField, OutputField, WithMetadata from invokeai.app.invocations.fields import ImageField, Input, InputField, OutputField, WithMetadata
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.tiles.tiles import ( from invokeai.backend.tiles.tiles import (
calc_tiles_even_split, calc_tiles_even_split,
calc_tiles_min_overlap, calc_tiles_min_overlap,
@ -56,7 +57,7 @@ class CalculateImageTilesInvocation(BaseInvocation):
description="The target overlap, in pixels, between adjacent tiles. Adjacent tiles will overlap by at least this amount", description="The target overlap, in pixels, between adjacent tiles. Adjacent tiles will overlap by at least this amount",
) )
def invoke(self, context) -> CalculateImageTilesOutput: def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
tiles = calc_tiles_with_overlap( tiles = calc_tiles_with_overlap(
image_height=self.image_height, image_height=self.image_height,
image_width=self.image_width, image_width=self.image_width,
@ -99,7 +100,7 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
description="The overlap, in pixels, between adjacent tiles.", description="The overlap, in pixels, between adjacent tiles.",
) )
def invoke(self, context) -> CalculateImageTilesOutput: def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
tiles = calc_tiles_even_split( tiles = calc_tiles_even_split(
image_height=self.image_height, image_height=self.image_height,
image_width=self.image_width, image_width=self.image_width,
@ -129,7 +130,7 @@ class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.") tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
min_overlap: int = InputField(default=128, ge=0, description="Minimum overlap between adjacent tiles, in pixels.") min_overlap: int = InputField(default=128, ge=0, description="Minimum overlap between adjacent tiles, in pixels.")
def invoke(self, context) -> CalculateImageTilesOutput: def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
tiles = calc_tiles_min_overlap( tiles = calc_tiles_min_overlap(
image_height=self.image_height, image_height=self.image_height,
image_width=self.image_width, image_width=self.image_width,
@ -174,7 +175,7 @@ class TileToPropertiesInvocation(BaseInvocation):
tile: Tile = InputField(description="The tile to split into properties.") tile: Tile = InputField(description="The tile to split into properties.")
def invoke(self, context) -> TileToPropertiesOutput: def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
return TileToPropertiesOutput( return TileToPropertiesOutput(
coords_left=self.tile.coords.left, coords_left=self.tile.coords.left,
coords_right=self.tile.coords.right, coords_right=self.tile.coords.right,
@ -211,7 +212,7 @@ class PairTileImageInvocation(BaseInvocation):
image: ImageField = InputField(description="The tile image.") image: ImageField = InputField(description="The tile image.")
tile: Tile = InputField(description="The tile properties.") tile: Tile = InputField(description="The tile properties.")
def invoke(self, context) -> PairTileImageOutput: def invoke(self, context: InvocationContext) -> PairTileImageOutput:
return PairTileImageOutput( return PairTileImageOutput(
tile_with_image=TileWithImage( tile_with_image=TileWithImage(
tile=self.tile, tile=self.tile,
@ -247,7 +248,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.", description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
) )
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
images = [twi.image for twi in self.tiles_with_images] images = [twi.image for twi in self.tiles_with_images]
tiles = [twi.tile for twi in self.tiles_with_images] tiles = [twi.tile for twi in self.tiles_with_images]

View File

@ -10,6 +10,7 @@ from pydantic import ConfigDict
from invokeai.app.invocations.fields import ImageField from invokeai.app.invocations.fields import ImageField
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
from invokeai.backend.util.devices import choose_torch_device from invokeai.backend.util.devices import choose_torch_device
@ -42,7 +43,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
model_config = ConfigDict(protected_namespaces=()) model_config = ConfigDict(protected_namespaces=())
def invoke(self, context) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name) image = context.images.get_pil(self.image.image_name)
models_path = context.config.get().models_path models_path = context.config.get().models_path

View File

@ -17,6 +17,7 @@ from invokeai.app.invocations.baseinvocation import (
invocation_output, invocation_output,
) )
from invokeai.app.invocations.fields import Input, InputField, OutputField, UIType from invokeai.app.invocations.fields import Input, InputField, OutputField, UIType
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import uuid_string from invokeai.app.util.misc import uuid_string
# in 3.10 this would be "from types import NoneType" # in 3.10 this would be "from types import NoneType"
@ -201,7 +202,7 @@ class GraphInvocation(BaseInvocation):
# TODO: figure out how to create a default here # TODO: figure out how to create a default here
graph: "Graph" = InputField(description="The graph to run", default=None) graph: "Graph" = InputField(description="The graph to run", default=None)
def invoke(self, context) -> GraphInvocationOutput: def invoke(self, context: InvocationContext) -> GraphInvocationOutput:
"""Invoke with provided services and return outputs.""" """Invoke with provided services and return outputs."""
return GraphInvocationOutput() return GraphInvocationOutput()
@ -227,7 +228,7 @@ class IterateInvocation(BaseInvocation):
) )
index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True) index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True)
def invoke(self, context) -> IterateInvocationOutput: def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
"""Produces the outputs as values""" """Produces the outputs as values"""
return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection)) return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
@ -254,7 +255,7 @@ class CollectInvocation(BaseInvocation):
description="The collection, will be provided on execution", default=[], ui_hidden=True description="The collection, will be provided on execution", default=[], ui_hidden=True
) )
def invoke(self, context) -> CollectInvocationOutput: def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
"""Invoke with provided services and return outputs.""" """Invoke with provided services and return outputs."""
return CollectInvocationOutput(collection=copy.copy(self.collection)) return CollectInvocationOutput(collection=copy.copy(self.collection))

View File

@ -8,6 +8,7 @@ from invokeai.app.invocations.baseinvocation import (
) )
from invokeai.app.invocations.fields import InputField, OutputField from invokeai.app.invocations.fields import InputField, OutputField
from invokeai.app.invocations.image import ImageField from invokeai.app.invocations.image import ImageField
from invokeai.app.services.shared.invocation_context import InvocationContext
# Define test invocations before importing anything that uses invocations # Define test invocations before importing anything that uses invocations
@ -20,7 +21,7 @@ class ListPassThroughInvocationOutput(BaseInvocationOutput):
class ListPassThroughInvocation(BaseInvocation): class ListPassThroughInvocation(BaseInvocation):
collection: list[ImageField] = InputField(default=[]) collection: list[ImageField] = InputField(default=[])
def invoke(self, context) -> ListPassThroughInvocationOutput: def invoke(self, context: InvocationContext) -> ListPassThroughInvocationOutput:
return ListPassThroughInvocationOutput(collection=self.collection) return ListPassThroughInvocationOutput(collection=self.collection)
@ -33,13 +34,13 @@ class PromptTestInvocationOutput(BaseInvocationOutput):
class PromptTestInvocation(BaseInvocation): class PromptTestInvocation(BaseInvocation):
prompt: str = InputField(default="") prompt: str = InputField(default="")
def invoke(self, context) -> PromptTestInvocationOutput: def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
return PromptTestInvocationOutput(prompt=self.prompt) return PromptTestInvocationOutput(prompt=self.prompt)
@invocation("test_error", version="1.0.0") @invocation("test_error", version="1.0.0")
class ErrorInvocation(BaseInvocation): class ErrorInvocation(BaseInvocation):
def invoke(self, context) -> PromptTestInvocationOutput: def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
raise Exception("This invocation is supposed to fail") raise Exception("This invocation is supposed to fail")
@ -53,7 +54,7 @@ class TextToImageTestInvocation(BaseInvocation):
prompt: str = InputField(default="") prompt: str = InputField(default="")
prompt2: str = InputField(default="") prompt2: str = InputField(default="")
def invoke(self, context) -> ImageTestInvocationOutput: def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
return ImageTestInvocationOutput(image=ImageField(image_name=self.id)) return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
@ -62,7 +63,7 @@ class ImageToImageTestInvocation(BaseInvocation):
prompt: str = InputField(default="") prompt: str = InputField(default="")
image: Union[ImageField, None] = InputField(default=None) image: Union[ImageField, None] = InputField(default=None)
def invoke(self, context) -> ImageTestInvocationOutput: def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
return ImageTestInvocationOutput(image=ImageField(image_name=self.id)) return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
@ -75,7 +76,7 @@ class PromptCollectionTestInvocationOutput(BaseInvocationOutput):
class PromptCollectionTestInvocation(BaseInvocation): class PromptCollectionTestInvocation(BaseInvocation):
collection: list[str] = InputField() collection: list[str] = InputField()
def invoke(self, context) -> PromptCollectionTestInvocationOutput: def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput:
return PromptCollectionTestInvocationOutput(collection=self.collection.copy()) return PromptCollectionTestInvocationOutput(collection=self.collection.copy())
@ -88,7 +89,7 @@ class AnyTypeTestInvocationOutput(BaseInvocationOutput):
class AnyTypeTestInvocation(BaseInvocation): class AnyTypeTestInvocation(BaseInvocation):
value: Any = InputField(default=None) value: Any = InputField(default=None)
def invoke(self, context) -> AnyTypeTestInvocationOutput: def invoke(self, context: InvocationContext) -> AnyTypeTestInvocationOutput:
return AnyTypeTestInvocationOutput(value=self.value) return AnyTypeTestInvocationOutput(value=self.value)
@ -96,7 +97,7 @@ class AnyTypeTestInvocation(BaseInvocation):
class PolymorphicStringTestInvocation(BaseInvocation): class PolymorphicStringTestInvocation(BaseInvocation):
value: Union[str, list[str]] = InputField(default="") value: Union[str, list[str]] = InputField(default="")
def invoke(self, context) -> PromptCollectionTestInvocationOutput: def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput:
if isinstance(self.value, str): if isinstance(self.value, str):
return PromptCollectionTestInvocationOutput(collection=[self.value]) return PromptCollectionTestInvocationOutput(collection=[self.value])
return PromptCollectionTestInvocationOutput(collection=self.value) return PromptCollectionTestInvocationOutput(collection=self.value)