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feat: metadata refactor

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- Refactor how metadata is handled to support a user-defined metadata in graphs
- Update workflow embed handling
- Update UI to work with these changes
- Update tests to support metadata/workflow changes
This commit is contained in:
psychedelicious 2023-10-17 17:23:10 +11:00
parent c2da74c587
commit f0db4d36e4
66 changed files with 1807 additions and 1798 deletions
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33
invokeai/app/api/routers/images.py
View File

@ -5,12 +5,13 @@ from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadF
from fastapi.responses import FileResponse
from fastapi.routing import APIRouter
from PIL import Image
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, ValidationError
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.baseinvocation import MetadataField, type_adapter_MetadataField
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.workflow_records.workflow_records_common import type_adapter_WorkflowField
from ..dependencies import ApiDependencies
@ -45,8 +46,10 @@ async def upload_image(
if not file.content_type or not file.content_type.startswith("image"):
raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read()
metadata = None
workflow = None
contents = await file.read()
try:
pil_image = Image.open(io.BytesIO(contents))
if crop_visible:
@ -56,6 +59,24 @@ async def upload_image(
# Error opening the image
raise HTTPException(status_code=415, detail="Failed to read image")
# attempt to parse metadata from image
metadata_raw = pil_image.info.get("invokeai_metadata", None)
if metadata_raw:
try:
metadata = type_adapter_MetadataField.validate_json(metadata_raw)
except ValidationError:
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
pass
# attempt to parse workflow from image
workflow_raw = pil_image.info.get("invokeai_workflow", None)
if workflow_raw is not None:
try:
workflow = type_adapter_WorkflowField.validate_json(workflow_raw)
except ValidationError:
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
pass
try:
image_dto = ApiDependencies.invoker.services.images.create(
image=pil_image,
@ -63,6 +84,8 @@ async def upload_image(
image_category=image_category,
session_id=session_id,
board_id=board_id,
metadata=metadata,
workflow=workflow,
is_intermediate=is_intermediate,
)
@ -146,11 +169,11 @@ async def get_image_dto(
@images_router.get(
"/i/{image_name}/metadata",
operation_id="get_image_metadata",
response_model=ImageMetadata,
response_model=Optional[MetadataField],
)
async def get_image_metadata(
image_name: str = Path(description="The name of image to get"),
) -> ImageMetadata:
) -> Optional[MetadataField]:
"""Gets an image's metadata"""
try:

2
invokeai/app/api/routers/workflows.py
View File

@ -1,4 +1,4 @@
from fastapi import APIRouter, Body, Path
from fastapi import APIRouter, Path
from invokeai.app.api.dependencies import ApiDependencies
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowField

2
invokeai/app/api_app.py
View File

@ -44,8 +44,8 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
boards,
images,
models,
sessions,
session_queue,
sessions,
utilities,
workflows,
)

36
invokeai/app/invocations/baseinvocation.py
View File

@ -15,8 +15,8 @@ from pydantic.fields import _Unset
from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.util.misc import uuid_string
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowField
from invokeai.app.util.misc import uuid_string
if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices
@ -60,6 +60,11 @@ class FieldDescriptions:
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)"
metadata = "Optional metadata to be saved with the image"
metadata_collection = "Collection of Metadata"
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
metadata_item_label = "Label for this metadata item"
metadata_item_value = "The value for this metadata item (may be any type)"
workflow = "Optional workflow to be saved with the image"
interp_mode = "Interpolation mode"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
@ -167,8 +172,12 @@ class UIType(str, Enum):
Scheduler = "Scheduler"
WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate"
MetadataField = "MetadataField"
BoardField = "BoardField"
Any = "Any"
MetadataItem = "MetadataItem"
MetadataItemCollection = "MetadataItemCollection"
MetadataItemPolymorphic = "MetadataItemPolymorphic"
MetadataDict = "MetadataDict"
# endregion
@ -807,3 +816,26 @@ def invocation_output(
class WithWorkflow(BaseModel):
workflow: Optional[WorkflowField] = InputField(default=None, description=FieldDescriptions.workflow)
class MetadataItemField(BaseModel):
label: str = Field(description=FieldDescriptions.metadata_item_label)
value: Any = Field(description=FieldDescriptions.metadata_item_value)
class MetadataField(RootModel):
"""
Pydantic model for metadata with custom root of type dict[str, Any].
Metadata is stored without a strict schema.
"""
root: dict[str, Any] = Field(description="A dictionary of metadata, shape of which is arbitrary")
def model_dump(self, *args, **kwargs) -> dict[str, Any]:
return super().model_dump(*args, **kwargs)["root"]
type_adapter_MetadataField = TypeAdapter(MetadataField)
class WithMetadata(BaseModel):
metadata: Optional[MetadataField] = InputField(default=None, description=FieldDescriptions.metadata)

7
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -38,6 +38,8 @@ from .baseinvocation import (
InputField,
InvocationContext,
OutputField,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -127,12 +129,12 @@ class ControlNetInvocation(BaseInvocation):
# This invocation exists for other invocations to subclass it - do not register with @invocation!
class ImageProcessorInvocation(BaseInvocation):
class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Base class for invocations that preprocess images for ControlNet"""
image: ImageField = InputField(description="The image to process")
def run_processor(self, image):
def run_processor(self, image: Image.Image) -> Image.Image:
# superclass just passes through image without processing
return image
@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)

4
invokeai/app/invocations/cv.py
View File

@ -8,11 +8,11 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
class CvInpaintInvocation(BaseInvocation):
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Simple inpaint using opencv."""
image: ImageField = InputField(description="The image to inpaint")

8
invokeai/app/invocations/facetools.py
View File

@ -16,6 +16,8 @@ from invokeai.app.invocations.baseinvocation import (
InputField,
InvocationContext,
OutputField,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -437,7 +439,7 @@ def get_faces_list(
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
class FaceOffInvocation(BaseInvocation):
class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
image: ImageField = InputField(description="Image for face detection")
@ -531,7 +533,7 @@ class FaceOffInvocation(BaseInvocation):
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
class FaceMaskInvocation(BaseInvocation):
class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Face mask creation using mediapipe face detection"""
image: ImageField = InputField(description="Image to face detect")
@ -650,7 +652,7 @@ class FaceMaskInvocation(BaseInvocation):
@invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
)
class FaceIdentifierInvocation(BaseInvocation):
class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
image: ImageField = InputField(description="Image to face detect")

223
invokeai/app/invocations/image.py
View File

@ -7,13 +7,21 @@ import cv2
import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
from .baseinvocation import (
BaseInvocation,
FieldDescriptions,
Input,
InputField,
InvocationContext,
WithMetadata,
WithWorkflow,
invocation,
)
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@ -36,14 +44,8 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation(
"blank_image",
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation):
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Creates a blank image and forwards it to the pipeline"""
width: int = InputField(default=512, description="The width of the image")
@ -61,6 +63,7 @@ class BlankImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -71,14 +74,8 @@ class BlankImageInvocation(BaseInvocation):
)
@invocation(
"img_crop",
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation):
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Crops an image to a specified box. The box can be outside of the image."""
image: ImageField = InputField(description="The image to crop")
@ -100,6 +97,7 @@ class ImageCropInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -110,14 +108,8 @@ class ImageCropInvocation(BaseInvocation):
)
@invocation(
"img_paste",
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation):
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Pastes an image into another image."""
base_image: ImageField = InputField(description="The base image")
@ -159,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -169,14 +162,8 @@ class ImagePasteInvocation(BaseInvocation):
)
@invocation(
"tomask",
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation):
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Extracts the alpha channel of an image as a mask."""
image: ImageField = InputField(description="The image to create the mask from")
@ -196,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -206,14 +194,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@invocation(
"img_mul",
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation):
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
image1: ImageField = InputField(description="The first image to multiply")
@ -232,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -245,14 +228,8 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation(
"img_chan",
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation):
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Gets a channel from an image."""
image: ImageField = InputField(description="The image to get the channel from")
@ -270,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -283,14 +261,8 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation(
"img_conv",
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation):
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Converts an image to a different mode."""
image: ImageField = InputField(description="The image to convert")
@ -308,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -318,14 +291,8 @@ class ImageConvertInvocation(BaseInvocation):
)
@invocation(
"img_blur",
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation):
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Blurs an image"""
image: ImageField = InputField(description="The image to blur")
@ -348,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -378,23 +346,14 @@ PIL_RESAMPLING_MAP = {
}
@invocation(
"img_resize",
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation):
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Resizes an image to specific dimensions"""
image: ImageField = InputField(description="The image to resize")
width: int = InputField(default=512, gt=0, description="The width 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")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -413,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -424,14 +383,8 @@ class ImageResizeInvocation(BaseInvocation):
)
@invocation(
"img_scale",
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation):
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Scales an image by a factor"""
image: ImageField = InputField(description="The image to scale")
@ -461,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -471,14 +425,8 @@ class ImageScaleInvocation(BaseInvocation):
)
@invocation(
"img_lerp",
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation):
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp")
@ -500,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -510,14 +459,8 @@ class ImageLerpInvocation(BaseInvocation):
)
@invocation(
"img_ilerp",
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation):
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Inverse linear interpolation of all pixels of an image"""
image: ImageField = InputField(description="The image to lerp")
@ -539,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -549,20 +493,11 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@invocation(
"img_nsfw",
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation):
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add blur to NSFW-flagged images"""
image: ImageField = InputField(description="The image to check")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -583,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -607,14 +542,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ImageWatermarkInvocation(BaseInvocation):
class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add an invisible watermark to an image"""
image: ImageField = InputField(description="The image to check")
text: str = InputField(default="InvokeAI", description="Watermark text")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -626,7 +558,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)
@ -637,14 +569,8 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@invocation(
"mask_edge",
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation):
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Applies an edge mask to an image"""
image: ImageField = InputField(description="The image to apply the mask to")
@ -678,6 +604,7 @@ class MaskEdgeInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -695,7 +622,7 @@ class MaskEdgeInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class MaskCombineInvocation(BaseInvocation):
class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
mask1: ImageField = InputField(description="The first mask to combine")
@ -714,6 +641,7 @@ class MaskCombineInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -724,14 +652,8 @@ class MaskCombineInvocation(BaseInvocation):
)
@invocation(
"color_correct",
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation):
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""
Shifts the colors of a target image to match the reference image, optionally
using a mask to only color-correct certain regions of the target image.
@ -830,6 +752,7 @@ class ColorCorrectInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -840,14 +763,8 @@ class ColorCorrectInvocation(BaseInvocation):
)
@invocation(
"img_hue_adjust",
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation):
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Adjusts the Hue of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -875,6 +792,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow,
)
@ -950,7 +868,7 @@ CHANNEL_FORMATS = {
category="image",
version="1.0.0",
)
class ImageChannelOffsetInvocation(BaseInvocation):
class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Add or subtract a value from a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -984,6 +902,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
metadata=self.metadata,
workflow=self.workflow,
)
@ -1020,7 +939,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ImageChannelMultiplyInvocation(BaseInvocation):
class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Scale a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
@ -1060,6 +979,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
workflow=self.workflow,
metadata=self.metadata,
)
return ImageOutput(
@ -1079,16 +999,11 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
version="1.0.1",
use_cache=False,
)
class SaveImageInvocation(BaseInvocation):
class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -1101,7 +1016,7 @@ class SaveImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

21
invokeai/app/invocations/infill.py
View File

@ -13,7 +13,7 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
@ -119,7 +119,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillColorInvocation(BaseInvocation):
class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with a solid color"""
image: ImageField = InputField(description="The image to infill")
@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -154,7 +155,7 @@ class InfillColorInvocation(BaseInvocation):
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillTileInvocation(BaseInvocation):
class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill")
@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -192,7 +194,7 @@ class InfillTileInvocation(BaseInvocation):
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
)
class InfillPatchMatchInvocation(BaseInvocation):
class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill")
@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
@ -243,7 +246,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class LaMaInfillInvocation(BaseInvocation):
class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the LaMa model"""
image: ImageField = InputField(description="The image to infill")
@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
return ImageOutput(
@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation):
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class CV2InfillInvocation(BaseInvocation):
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill")
@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)
return ImageOutput(

12
invokeai/app/invocations/latent.py
View File

@ -23,7 +23,6 @@ from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
DenoiseMaskOutput,
@ -64,6 +63,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -792,7 +793,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
category="latents",
version="1.0.0",
)
class LatentsToImageInvocation(BaseInvocation):
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -805,11 +806,6 @@ class LatentsToImageInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -878,7 +874,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

174
invokeai/app/invocations/metadata.py
View File

@ -1,13 +1,17 @@
from typing import Optional
from typing import Any, Literal, Optional, Union
from pydantic import Field
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
InputField,
InvocationContext,
MetadataField,
MetadataItemField,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -16,116 +20,100 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
from ...version import __version__
class LoRAMetadataField(BaseModelExcludeNull):
"""LoRA metadata for an image generated in InvokeAI."""
class LoRAMetadataField(BaseModel):
"""LoRA Metadata Field"""
lora: LoRAModelField = Field(description="The LoRA model")
weight: float = Field(description="The weight of the LoRA model")
lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
weight: float = Field(description=FieldDescriptions.lora_weight)
class IPAdapterMetadataField(BaseModelExcludeNull):
class IPAdapterMetadataField(BaseModel):
"""IP Adapter Field, minus the CLIP Vision Encoder model"""
image: ImageField = Field(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
weight: float = Field(description="The weight of the IP-Adapter model")
ip_adapter_model: IPAdapterModelField = Field(
description="The IP-Adapter model.",
)
weight: Union[float, list[float]] = Field(
default=1,
ge=0,
description="The weight given to the IP-Adapter",
)
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
)
class CoreMetadata(BaseModelExcludeNull):
"""Core generation metadata for an image generated in InvokeAI."""
@invocation_output("metadata_item_output")
class MetadataItemOutput(BaseInvocationOutput):
"""Metadata Item Output"""
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
generation_mode: Optional[str] = Field(
default=None,
description="The generation mode that output this image",
)
created_by: Optional[str] = Field(default=None, description="The name of the creator of the image")
positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
width: Optional[int] = Field(default=None, description="The width parameter")
height: Optional[int] = Field(default=None, description="The height parameter")
seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = Field(
default=None,
description="The number of skipped CLIP layers",
)
model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
default=None, description="The IP Adapters used for inference"
)
t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
vae: Optional[VAEModelField] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
item: MetadataItemField = OutputField(description="Metadata Item")
@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
class MetadataItemInvocation(BaseInvocation):
"""Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
label: str = InputField(description=FieldDescriptions.metadata_item_label)
value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
def invoke(self, context: InvocationContext) -> MetadataItemOutput:
return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
@invocation_output("metadata_output")
class MetadataOutput(BaseInvocationOutput):
metadata: MetadataField = OutputField(description="Metadata Dict")
@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
class MetadataInvocation(BaseInvocation):
"""Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
items: Union[list[MetadataItemField], MetadataItemField] = InputField(
description=FieldDescriptions.metadata_item_polymorphic
)
# Latents-to-Latents
strength: Optional[float] = Field(
default=None,
description="The strength used for latents-to-latents",
)
init_image: Optional[str] = Field(default=None, description="The name of the initial image")
def invoke(self, context: InvocationContext) -> MetadataOutput:
if isinstance(self.items, MetadataItemField):
# single metadata item
data = {self.items.label: self.items.value}
else:
# collection of metadata items
data = {item.label: item.value for item in self.items}
# SDXL
positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
# SDXL Refiner
refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
refiner_cfg_scale: Optional[float] = Field(
default=None,
description="The classifier-free guidance scale parameter used for the refiner",
)
refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
refiner_positive_aesthetic_score: Optional[float] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_negative_aesthetic_score: Optional[float] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
# add app version
data.update({"app_version": __version__})
return MetadataOutput(metadata=MetadataField.model_validate(data))
class ImageMetadata(BaseModelExcludeNull):
"""An image's generation metadata"""
@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
class MergeMetadataInvocation(BaseInvocation):
"""Merged a collection of MetadataDict into a single MetadataDict."""
metadata: Optional[dict] = Field(
default=None,
description="The image's core metadata, if it was created in the Linear or Canvas UI",
)
graph: Optional[dict] = Field(default=None, description="The graph that created the image")
collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
def invoke(self, context: InvocationContext) -> MetadataOutput:
data = {}
for item in self.collection:
data.update(item.model_dump())
return MetadataOutput(metadata=MetadataField.model_validate(data))
@invocation_output("metadata_accumulator_output")
class MetadataAccumulatorOutput(BaseInvocationOutput):
"""The output of the MetadataAccumulator node"""
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
class CoreMetadataInvocation(BaseInvocation):
"""Collects core generation metadata into a MetadataField"""
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
@invocation(
"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
)
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
generation_mode: Optional[str] = InputField(
generation_mode: Literal["txt2img", "img2img", "inpaint", "outpaint"] = InputField(
default=None,
description="The generation mode that output this image",
)
@ -138,6 +126,8 @@ class MetadataAccumulatorInvocation(BaseInvocation):
cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis")
clip_skip: Optional[int] = InputField(
default=None,
description="The number of skipped CLIP layers",
@ -220,7 +210,13 @@ class MetadataAccumulatorInvocation(BaseInvocation):
description="The start value used for refiner denoising",
)
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
def invoke(self, context: InvocationContext) -> MetadataOutput:
"""Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))
return MetadataOutput(
metadata=MetadataField.model_validate(
self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
)
)
model_config = ConfigDict(extra="allow")

14
invokeai/app/invocations/onnx.py
View File

@ -4,7 +4,7 @@ import inspect
import re
# from contextlib import ExitStack
from typing import List, Literal, Optional, Union
from typing import List, Literal, Union
import numpy as np
import torch
@ -12,7 +12,6 @@ from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.step_callback import stable_diffusion_step_callback
@ -31,6 +30,8 @@ from .baseinvocation import (
OutputField,
UIComponent,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -327,7 +328,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
category="image",
version="1.0.0",
)
class ONNXLatentsToImageInvocation(BaseInvocation):
class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -338,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
description=FieldDescriptions.vae,
input=Input.Connection,
)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
# tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -381,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)

4
invokeai/app/invocations/primitives.py
View File

@ -251,7 +251,9 @@ class ImageCollectionOutput(BaseInvocationOutput):
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
class ImageInvocation(BaseInvocation):
class ImageInvocation(
BaseInvocation,
):
"""An image primitive value"""
image: ImageField = InputField(description="The image to load")

5
invokeai/app/invocations/upscale.py
View File

@ -14,7 +14,7 @@ from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
# TODO: Populate this from disk?
# TODO: Use model manager to load?
@ -30,7 +30,7 @@ if choose_torch_device() == torch.device("mps"):
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
class ESRGANInvocation(BaseInvocation):
class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image")
@ -123,6 +123,7 @@ class ESRGANInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata,
workflow=self.workflow,
)

7
invokeai/app/services/image_files/image_files_base.py
View File

@ -4,6 +4,9 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowField
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@ -30,8 +33,8 @@ class ImageFileStorageBase(ABC):
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256,
) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""

11
invokeai/app/services/image_files/image_files_disk.py
View File

@ -1,5 +1,4 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import json
from pathlib import Path
from queue import Queue
from typing import Dict, Optional, Union
@ -8,7 +7,9 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowField
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
from .image_files_base import ImageFileStorageBase
@ -55,8 +56,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
thumbnail_size: int = 256,
) -> None:
try:
@ -67,9 +68,9 @@ class DiskImageFileStorage(ImageFileStorageBase):
if metadata is not None or workflow is not None:
if metadata is not None:
pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
if workflow is not None:
pnginfo.add_text("invokeai_workflow", workflow)
pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
else:
# For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back
# TODO: retain non-invokeai metadata on save...

6
invokeai/app/services/image_records/image_records_base.py
View File

@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
from datetime import datetime
from typing import Optional
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
@ -18,7 +19,7 @@ class ImageRecordStorageBase(ABC):
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata'."""
pass
@ -78,7 +79,8 @@ class ImageRecordStorageBase(ABC):
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
metadata: Optional[MetadataField] = None,
workflow_id: Optional[str] = None,
) -> datetime:
"""Saves an image record."""
pass

8
invokeai/app/services/image_records/image_records_common.py
View File

@ -100,6 +100,7 @@ IMAGE_DTO_COLS = ", ".join(
"width",
"height",
"session_id",
"workflow_id",
"node_id",
"is_intermediate",
"created_at",
@ -140,6 +141,11 @@ class ImageRecord(BaseModelExcludeNull):
description="The session ID that generated this image, if it is a generated image.",
)
"""The session ID that generated this image, if it is a generated image."""
workflow_id: Optional[str] = Field(
default=None,
description="The workflow that generated this image.",
)
"""The workflow that generated this image."""
node_id: Optional[str] = Field(
default=None,
description="The node ID that generated this image, if it is a generated image.",
@ -184,6 +190,7 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
width = image_dict.get("width", 0)
height = image_dict.get("height", 0)
session_id = image_dict.get("session_id", None)
workflow_id = image_dict.get("workflow_id", None)
node_id = image_dict.get("node_id", None)
created_at = image_dict.get("created_at", get_iso_timestamp())
updated_at = image_dict.get("updated_at", get_iso_timestamp())
@ -198,6 +205,7 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
width=width,
height=height,
session_id=session_id,
workflow_id=workflow_id,
node_id=node_id,
created_at=created_at,
updated_at=updated_at,

35
invokeai/app/services/image_records/image_records_sqlite.py
View File

@ -1,9 +1,9 @@
import json
import sqlite3
import threading
from datetime import datetime
from typing import Optional, Union, cast
from invokeai.app.invocations.baseinvocation import MetadataField, type_adapter_MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
@ -76,6 +76,16 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
"""
)
if "workflow_id" not in columns:
self._cursor.execute(
"""--sql
ALTER TABLE images
ADD COLUMN workflow_id TEXT;
-- TODO: This requires a migration:
-- FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE SET NULL;
"""
)
# Create the `images` table indices.
self._cursor.execute(
"""--sql
@ -141,22 +151,26 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
return deserialize_image_record(dict(result))
def get_metadata(self, image_name: str) -> Optional[dict]:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT images.metadata FROM images
SELECT metadata FROM images
WHERE image_name = ?;
""",
(image_name,),
)
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
if not result or not result[0]:
return None
return json.loads(result[0])
if not result:
raise ImageRecordNotFoundException
as_dict = dict(result)
metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
return type_adapter_MetadataField.validate_json(metadata_raw) if metadata_raw is not None else None
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordNotFoundException from e
@ -408,10 +422,11 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
metadata: Optional[MetadataField] = None,
workflow_id: Optional[str] = None,
) -> datetime:
try:
metadata_json = None if metadata is None else json.dumps(metadata)
metadata_json = metadata.model_dump_json() if metadata is not None else None
self._lock.acquire()
self._cursor.execute(
"""--sql
@ -424,10 +439,11 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
node_id,
session_id,
metadata,
workflow_id,
is_intermediate,
starred
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
""",
(
image_name,
@ -438,6 +454,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
node_id,
session_id,
metadata_json,
workflow_id,
is_intermediate,
starred,
),

9
invokeai/app/services/images/images_base.py
View File

@ -3,7 +3,7 @@ from typing import Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,
@ -12,6 +12,7 @@ from invokeai.app.services.image_records.image_records_common import (
)
from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowField
class ImageServiceABC(ABC):
@ -50,8 +51,8 @@ class ImageServiceABC(ABC):
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@ -81,7 +82,7 @@ class ImageServiceABC(ABC):
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
"""Gets an image's metadata."""
pass

2
invokeai/app/services/images/images_common.py
View File

@ -25,8 +25,6 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
)
"""The id of the board the image belongs to, if one exists."""
pass
def image_record_to_dto(
image_record: ImageRecord,

35
invokeai/app/services/images/images_default.py
View File

@ -2,10 +2,10 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowField
from ..image_files.image_files_common import (
ImageFileDeleteException,
@ -42,8 +42,8 @@ class ImageService(ImageServiceABC):
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
metadata: Optional[MetadataField] = None,
workflow: Optional[WorkflowField] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
@ -56,6 +56,12 @@ class ImageService(ImageServiceABC):
(width, height) = image.size
try:
if workflow is not None:
created_workflow = self.__invoker.services.workflow_records.create(workflow)
workflow_id = created_workflow.model_dump()["id"]
else:
workflow_id = None
# TODO: Consider using a transaction here to ensure consistency between storage and database
self.__invoker.services.image_records.save(
# Non-nullable fields
@ -69,6 +75,7 @@ class ImageService(ImageServiceABC):
# Nullable fields
node_id=node_id,
metadata=metadata,
workflow_id=workflow_id,
session_id=session_id,
)
if board_id is not None:
@ -146,25 +153,9 @@ class ImageService(ImageServiceABC):
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> ImageMetadata:
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try:
image_record = self.__invoker.services.image_records.get(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name)
if not image_record.session_id:
return ImageMetadata(metadata=metadata)
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
return self.__invoker.services.image_records.get_metadata(image_name)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise

32
invokeai/app/services/shared/graph.py
View File

@ -439,6 +439,14 @@ class Graph(BaseModel):
except Exception as e:
raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
def _is_destination_field_Any(self, edge: Edge) -> bool:
"""Checks if the destination field for an edge is of type typing.Any"""
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
"""Checks if the destination field for an edge is of type typing.Any"""
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
def _validate_edge(self, edge: Edge):
"""Validates that a new edge doesn't create a cycle in the graph"""
@ -491,8 +499,19 @@ class Graph(BaseModel):
f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector output type matches input type (if this edge results in both being set)
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
# Validate that we are not connecting collector to iterator (currently unsupported)
if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
raise InvalidEdgeError(
f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
if (
isinstance(from_node, CollectInvocation)
and edge.source.field == "collection"
and not self._is_destination_field_list_of_Any(edge)
and not self._is_destination_field_Any(edge)
):
if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
raise InvalidEdgeError(
f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
@ -725,16 +744,15 @@ class Graph(BaseModel):
# Get the input root type
input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore
# Verify that all outputs are lists
# if not all((get_origin(f) == list for f in output_fields)):
# return False
# Verify that all outputs are lists
if not all(is_list_or_contains_list(f) for f in output_fields):
return False
# Verify that all outputs match the input type (are a base class or the same class)
if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
if not all(
is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
for f in output_fields
):
return False
return True

41
invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx
View File

@ -27,7 +27,7 @@ import {
setShouldShowImageDetails,
setShouldShowProgressInViewer,
} from 'features/ui/store/uiSlice';
import { memo, useCallback, useMemo } from 'react';
import { memo, useCallback } from 'react';
import { useHotkeys } from 'react-hotkeys-hook';
import { useTranslation } from 'react-i18next';
import {
@ -40,11 +40,13 @@ import {
import { FaCircleNodes, FaEllipsis } from 'react-icons/fa6';
import {
useGetImageDTOQuery,
useGetImageMetadataFromFileQuery,
useGetImageMetadataQuery,
} from 'services/api/endpoints/images';
import { menuListMotionProps } from 'theme/components/menu';
import { useDebounce } from 'use-debounce';
import { sentImageToImg2Img } from '../../store/actions';
import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
import { useGetWorkflowQuery } from 'services/api/endpoints/workflows';
const currentImageButtonsSelector = createSelector(
[stateSelector, activeTabNameSelector],
@ -89,7 +91,6 @@ const CurrentImageButtons = () => {
shouldShowImageDetails,
lastSelectedImage,
shouldShowProgressInViewer,
shouldFetchMetadataFromApi,
} = useAppSelector(currentImageButtonsSelector);
const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
@ -104,23 +105,17 @@ const CurrentImageButtons = () => {
lastSelectedImage?.image_name ?? skipToken
);
const getMetadataArg = useMemo(() => {
if (lastSelectedImage) {
return { image: lastSelectedImage, shouldFetchMetadataFromApi };
} else {
return skipToken;
}
}, [lastSelectedImage, shouldFetchMetadataFromApi]);
const [debouncedImageName] = useDebounce(lastSelectedImage?.image_name, 300);
const [debouncedWorkflowId] = useDebounce(
lastSelectedImage?.workflow_id,
300
);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
getMetadataArg,
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const { data: metadata, isLoading: isLoadingMetadata } =
useGetImageMetadataQuery(debouncedImageName ?? skipToken);
const { data: workflow, isLoading: isLoadingWorkflow } = useGetWorkflowQuery(
debouncedWorkflowId ?? skipToken
);
const handleLoadWorkflow = useCallback(() => {
@ -257,7 +252,7 @@ const CurrentImageButtons = () => {
<ButtonGroup isAttached={true} isDisabled={shouldDisableToolbarButtons}>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingWorkflow}
icon={<FaCircleNodes />}
tooltip={`${t('nodes.loadWorkflow')} (W)`}
aria-label={`${t('nodes.loadWorkflow')} (W)`}
@ -265,7 +260,7 @@ const CurrentImageButtons = () => {
onClick={handleLoadWorkflow}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaQuoteRight />}
tooltip={`${t('parameters.usePrompt')} (P)`}
aria-label={`${t('parameters.usePrompt')} (P)`}
@ -273,7 +268,7 @@ const CurrentImageButtons = () => {
onClick={handleUsePrompt}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaSeedling />}
tooltip={`${t('parameters.useSeed')} (S)`}
aria-label={`${t('parameters.useSeed')} (S)`}
@ -281,7 +276,7 @@ const CurrentImageButtons = () => {
onClick={handleUseSeed}
/>
<IAIIconButton
isLoading={isLoading}
isLoading={isLoadingMetadata}
icon={<FaAsterisk />}
tooltip={`${t('parameters.useAll')} (A)`}
aria-label={`${t('parameters.useAll')} (A)`}

42
invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
View File

@ -1,8 +1,9 @@
import { Flex, MenuItem, Spinner } from '@chakra-ui/react';
import { useStore } from '@nanostores/react';
import { skipToken } from '@reduxjs/toolkit/dist/query';
import { useAppToaster } from 'app/components/Toaster';
import { $customStarUI } from 'app/store/nanostores/customStarUI';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { useAppDispatch } from 'app/store/storeHooks';
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
import {
imagesToChangeSelected,
@ -32,12 +33,13 @@ import {
import { FaCircleNodes } from 'react-icons/fa6';
import { MdStar, MdStarBorder } from 'react-icons/md';
import {
useGetImageMetadataFromFileQuery,
useGetImageMetadataQuery,
useStarImagesMutation,
useUnstarImagesMutation,
} from 'services/api/endpoints/images';
import { useGetWorkflowQuery } from 'services/api/endpoints/workflows';
import { ImageDTO } from 'services/api/types';
import { configSelector } from '../../../system/store/configSelectors';
import { useDebounce } from 'use-debounce';
import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
type SingleSelectionMenuItemsProps = {
@ -53,18 +55,16 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
const toaster = useAppToaster();
const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const customStarUi = useStore($customStarUI);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
{ image: imageDTO, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const [debouncedImageName] = useDebounce(imageDTO?.image_name, 300);
const [debouncedWorkflowId] = useDebounce(imageDTO?.workflow_id, 300);
const { data: metadata, isLoading: isLoadingMetadata } =
useGetImageMetadataQuery(debouncedImageName ?? skipToken);
const { data: workflow, isLoading: isLoadingWorkflow } = useGetWorkflowQuery(
debouncedWorkflowId ?? skipToken
);
const [starImages] = useStarImagesMutation();
@ -181,17 +181,17 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
{t('parameters.downloadImage')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaCircleNodes />}
icon={isLoadingWorkflow ? <SpinnerIcon /> : <FaCircleNodes />}
onClickCapture={handleLoadWorkflow}
isDisabled={isLoading || !workflow}
isDisabled={isLoadingWorkflow || !workflow}
>
{t('nodes.loadWorkflow')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaQuoteRight />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaQuoteRight />}
onClickCapture={handleRecallPrompt}
isDisabled={
isLoading ||
isLoadingMetadata ||
(metadata?.positive_prompt === undefined &&
metadata?.negative_prompt === undefined)
}
@ -199,16 +199,16 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
{t('parameters.usePrompt')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaSeedling />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaSeedling />}
onClickCapture={handleRecallSeed}
isDisabled={isLoading || metadata?.seed === undefined}
isDisabled={isLoadingMetadata || metadata?.seed === undefined}
>
{t('parameters.useSeed')}
</MenuItem>
<MenuItem
icon={isLoading ? <SpinnerIcon /> : <FaAsterisk />}
icon={isLoadingMetadata ? <SpinnerIcon /> : <FaAsterisk />}
onClickCapture={handleUseAllParameters}
isDisabled={isLoading || !metadata}
isDisabled={isLoadingMetadata || !metadata}
>
{t('parameters.useAll')}
</MenuItem>

28
invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx
View File

@ -9,16 +9,17 @@ import {
Tabs,
Text,
} from '@chakra-ui/react';
import { skipToken } from '@reduxjs/toolkit/dist/query';
import { IAINoContentFallback } from 'common/components/IAIImageFallback';
import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
import { memo } from 'react';
import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images';
import { useTranslation } from 'react-i18next';
import { useGetImageMetadataQuery } from 'services/api/endpoints/images';
import { useGetWorkflowQuery } from 'services/api/endpoints/workflows';
import { ImageDTO } from 'services/api/types';
import { useDebounce } from 'use-debounce';
import DataViewer from './DataViewer';
import ImageMetadataActions from './ImageMetadataActions';
import { useAppSelector } from '../../../../app/store/storeHooks';
import { configSelector } from '../../../system/store/configSelectors';
import { useTranslation } from 'react-i18next';
import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
type ImageMetadataViewerProps = {
image: ImageDTO;
@ -32,16 +33,15 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
// });
const { t } = useTranslation();
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const [debouncedImageName] = useDebounce(image.image_name, 300);
const [debouncedWorkflowId] = useDebounce(image.workflow_id, 300);
const { metadata, workflow } = useGetImageMetadataFromFileQuery(
{ image, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
const { data: metadata } = useGetImageMetadataQuery(
debouncedImageName ?? skipToken
);
const { data: workflow } = useGetWorkflowQuery(
debouncedWorkflowId ?? skipToken
);
return (

6
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx
View File

@ -1,13 +1,13 @@
import { Checkbox, Flex, FormControl, FormLabel } from '@chakra-ui/react';
import { useAppDispatch } from 'app/store/storeHooks';
import { useEmbedWorkflow } from 'features/nodes/hooks/useEmbedWorkflow';
import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
import { useWithWorkflow } from 'features/nodes/hooks/useWithWorkflow';
import { nodeEmbedWorkflowChanged } from 'features/nodes/store/nodesSlice';
import { ChangeEvent, memo, useCallback } from 'react';
const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
const dispatch = useAppDispatch();
const hasImageOutput = useHasImageOutput(nodeId);
const withWorkflow = useWithWorkflow(nodeId);
const embedWorkflow = useEmbedWorkflow(nodeId);
const handleChange = useCallback(
(e: ChangeEvent<HTMLInputElement>) => {
@ -21,7 +21,7 @@ const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
[dispatch, nodeId]
);
if (!hasImageOutput) {
if (!withWorkflow) {
return null;
}

4
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx
View File

@ -1,11 +1,11 @@
import { Flex } from '@chakra-ui/react';
import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants';
import { memo } from 'react';
import { useFeatureStatus } from '../../../../../system/hooks/useFeatureStatus';
import EmbedWorkflowCheckbox from './EmbedWorkflowCheckbox';
import SaveToGalleryCheckbox from './SaveToGalleryCheckbox';
import UseCacheCheckbox from './UseCacheCheckbox';
import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
import { useFeatureStatus } from '../../../../../system/hooks/useFeatureStatus';
type Props = {
nodeId: string;

31
invokeai/frontend/web/src/features/nodes/hooks/useWithWorkflow.ts Normal file
View File

@ -0,0 +1,31 @@
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import { useMemo } from 'react';
import { isInvocationNode } from '../types/types';
export const useWithWorkflow = (nodeId: string) => {
const selector = useMemo(
() =>
createSelector(
stateSelector,
({ nodes }) => {
const node = nodes.nodes.find((node) => node.id === nodeId);
if (!isInvocationNode(node)) {
return false;
}
const nodeTemplate = nodes.nodeTemplates[node?.data.type ?? ''];
if (!nodeTemplate) {
return false;
}
return nodeTemplate.withWorkflow;
},
defaultSelectorOptions
),
[nodeId]
);
const withWorkflow = useAppSelector(selector);
return withWorkflow;
};

5
invokeai/frontend/web/src/features/nodes/store/util/validateSourceAndTargetTypes.ts
View File

@ -69,6 +69,8 @@ export const validateSourceAndTargetTypes = (
(sourceType === 'integer' || sourceType === 'float') &&
targetType === 'string';
const isTargetAnyType = targetType === 'Any';
return (
isCollectionItemToNonCollection ||
isNonCollectionToCollectionItem ||
@ -76,6 +78,7 @@ export const validateSourceAndTargetTypes = (
isGenericCollectionToAnyCollectionOrPolymorphic ||
isCollectionToGenericCollection ||
isIntToFloat ||
isIntOrFloatToString
isIntOrFloatToString ||
isTargetAnyType
);
};

38
invokeai/frontend/web/src/features/nodes/types/constants.ts
View File

@ -33,6 +33,8 @@ export const COLLECTION_TYPES: FieldType[] = [
'ColorCollection',
'T2IAdapterCollection',
'IPAdapterCollection',
'MetadataItemCollection',
'MetadataCollection',
];
export const POLYMORPHIC_TYPES: FieldType[] = [
@ -47,6 +49,7 @@ export const POLYMORPHIC_TYPES: FieldType[] = [
'ColorPolymorphic',
'T2IAdapterPolymorphic',
'IPAdapterPolymorphic',
'MetadataItemPolymorphic',
];
export const MODEL_TYPES: FieldType[] = [
@ -78,6 +81,8 @@ export const COLLECTION_MAP: FieldTypeMapWithNumber = {
ColorField: 'ColorCollection',
T2IAdapterField: 'T2IAdapterCollection',
IPAdapterField: 'IPAdapterCollection',
MetadataItemField: 'MetadataItemCollection',
MetadataField: 'MetadataCollection',
};
export const isCollectionItemType = (
itemType: string | undefined
@ -97,6 +102,7 @@ export const SINGLE_TO_POLYMORPHIC_MAP: FieldTypeMapWithNumber = {
ColorField: 'ColorPolymorphic',
T2IAdapterField: 'T2IAdapterPolymorphic',
IPAdapterField: 'IPAdapterPolymorphic',
MetadataItemField: 'MetadataItemPolymorphic',
};
export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
@ -111,6 +117,7 @@ export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
ColorPolymorphic: 'ColorField',
T2IAdapterPolymorphic: 'T2IAdapterField',
IPAdapterPolymorphic: 'IPAdapterField',
MetadataItemPolymorphic: 'MetadataItemField',
};
export const TYPES_WITH_INPUT_COMPONENTS: FieldType[] = [
@ -144,6 +151,37 @@ export const isPolymorphicItemType = (
Boolean(itemType && itemType in SINGLE_TO_POLYMORPHIC_MAP);
export const FIELDS: Record<FieldType, FieldUIConfig> = {
Any: {
color: 'gray.500',
description: 'Any field type is accepted.',
title: 'Any',
},
MetadataField: {
color: 'gray.500',
description: 'A metadata dict.',
title: 'Metadata Dict',
},
MetadataCollection: {
color: 'gray.500',
description: 'A collection of metadata dicts.',
title: 'Metadata Dict Collection',
},
MetadataItemField: {
color: 'gray.500',
description: 'A metadata item.',
title: 'Metadata Item',
},
MetadataItemCollection: {
color: 'gray.500',
description: 'Any field type is accepted.',
title: 'Metadata Item Collection',
},
MetadataItemPolymorphic: {
color: 'gray.500',
description:
'MetadataItem or MetadataItemCollection field types are accepted.',
title: 'Metadata Item Polymorphic',
},
boolean: {
color: 'green.500',
description: t('nodes.booleanDescription'),

120
invokeai/frontend/web/src/features/nodes/types/types.ts
View File

@ -54,6 +54,10 @@ export type InvocationTemplate = {
* The type of this node's output
*/
outputType: string; // TODO: generate a union of output types
/**
* Whether or not this invocation supports workflows
*/
withWorkflow: boolean;
/**
* The invocation's version.
*/
@ -72,6 +76,7 @@ export type FieldUIConfig = {
// TODO: Get this from the OpenAPI schema? may be tricky...
export const zFieldType = z.enum([
'Any',
'BoardField',
'boolean',
'BooleanCollection',
@ -109,6 +114,11 @@ export const zFieldType = z.enum([
'LatentsPolymorphic',
'LoRAModelField',
'MainModelField',
'MetadataField',
'MetadataCollection',
'MetadataItemField',
'MetadataItemCollection',
'MetadataItemPolymorphic',
'ONNXModelField',
'Scheduler',
'SDXLMainModelField',
@ -685,6 +695,57 @@ export type CollectionItemInputFieldValue = z.infer<
typeof zCollectionItemInputFieldValue
>;
export const zMetadataItemField = z.object({
label: z.string(),
value: z.any(),
});
export type MetadataItemField = z.infer<typeof zMetadataItemField>;
export const zMetadataItemInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('MetadataItemField'),
value: zMetadataItemField.optional(),
});
export type MetadataItemInputFieldValue = z.infer<
typeof zMetadataItemInputFieldValue
>;
export const zMetadataItemCollectionInputFieldValue =
zInputFieldValueBase.extend({
type: z.literal('MetadataItemCollection'),
value: z.array(zMetadataItemField).optional(),
});
export type MetadataItemCollectionInputFieldValue = z.infer<
typeof zMetadataItemCollectionInputFieldValue
>;
export const zMetadataItemPolymorphicInputFieldValue =
zInputFieldValueBase.extend({
type: z.literal('MetadataItemPolymorphic'),
value: z
.union([zMetadataItemField, z.array(zMetadataItemField)])
.optional(),
});
export type MetadataItemPolymorphicInputFieldValue = z.infer<
typeof zMetadataItemPolymorphicInputFieldValue
>;
export const zMetadataField = z.record(z.any());
export type MetadataField = z.infer<typeof zMetadataField>;
export const zMetadataInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('MetadataField'),
value: zMetadataField.optional(),
});
export type MetadataInputFieldValue = z.infer<typeof zMetadataInputFieldValue>;
export const zMetadataCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('MetadataCollection'),
value: z.array(zMetadataField).optional(),
});
export type MetadataCollectionInputFieldValue = z.infer<
typeof zMetadataCollectionInputFieldValue
>;
export const zColorField = z.object({
r: z.number().int().min(0).max(255),
g: z.number().int().min(0).max(255),
@ -723,7 +784,13 @@ export type SchedulerInputFieldValue = z.infer<
typeof zSchedulerInputFieldValue
>;
export const zAnyInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('Any'),
value: z.any().optional(),
});
export const zInputFieldValue = z.discriminatedUnion('type', [
zAnyInputFieldValue,
zBoardInputFieldValue,
zBooleanCollectionInputFieldValue,
zBooleanInputFieldValue,
@ -774,6 +841,11 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
zUNetInputFieldValue,
zVaeInputFieldValue,
zVaeModelInputFieldValue,
zMetadataItemInputFieldValue,
zMetadataItemCollectionInputFieldValue,
zMetadataItemPolymorphicInputFieldValue,
zMetadataInputFieldValue,
zMetadataCollectionInputFieldValue,
]);
export type InputFieldValue = z.infer<typeof zInputFieldValue>;
@ -786,6 +858,11 @@ export type InputFieldTemplateBase = {
fieldKind: 'input';
} & _InputField;
export type AnyInputFieldTemplate = InputFieldTemplateBase & {
type: 'Any';
default: undefined;
};
export type IntegerInputFieldTemplate = InputFieldTemplateBase & {
type: 'integer';
default: number;
@ -939,6 +1016,11 @@ export type UNetInputFieldTemplate = InputFieldTemplateBase & {
type: 'UNetField';
};
export type MetadataItemFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataItemField';
};
export type ClipInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'ClipField';
@ -1087,6 +1169,34 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
type: 'WorkflowField';
};
export type MetadataItemInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataItemField';
};
export type MetadataItemCollectionInputFieldTemplate =
InputFieldTemplateBase & {
default: undefined;
type: 'MetadataItemCollection';
};
export type MetadataItemPolymorphicInputFieldTemplate = Omit<
MetadataItemInputFieldTemplate,
'type'
> & {
type: 'MetadataItemPolymorphic';
};
export type MetadataInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataField';
};
export type MetadataCollectionInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'MetadataCollection';
};
/**
* An input field template is generated on each page load from the OpenAPI schema.
*
@ -1094,6 +1204,7 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
* maximum length, pattern to match, etc).
*/
export type InputFieldTemplate =
| AnyInputFieldTemplate
| BoardInputFieldTemplate
| BooleanCollectionInputFieldTemplate
| BooleanPolymorphicInputFieldTemplate
@ -1143,7 +1254,12 @@ export type InputFieldTemplate =
| T2IAdapterPolymorphicInputFieldTemplate
| UNetInputFieldTemplate
| VaeInputFieldTemplate
| VaeModelInputFieldTemplate;
| VaeModelInputFieldTemplate
| MetadataItemInputFieldTemplate
| MetadataItemCollectionInputFieldTemplate
| MetadataInputFieldTemplate
| MetadataItemPolymorphicInputFieldTemplate
| MetadataCollectionInputFieldTemplate;
export const isInputFieldValue = (
field?: InputFieldValue | OutputFieldValue
@ -1264,7 +1380,7 @@ export const isInvocationFieldSchema = (
export type InvocationEdgeExtra = { type: 'default' | 'collapsed' };
const zLoRAMetadataItem = z.object({
export const zLoRAMetadataItem = z.object({
lora: zLoRAModelField.deepPartial(),
weight: z.number(),
});

144
invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts
View File

@ -7,6 +7,7 @@ import {
startCase,
} from 'lodash-es';
import { OpenAPIV3_1 } from 'openapi-types';
import { ControlField } from 'services/api/types';
import {
COLLECTION_MAP,
POLYMORPHIC_TYPES,
@ -15,36 +16,70 @@ import {
isPolymorphicItemType,
} from '../types/constants';
import {
AnyInputFieldTemplate,
BoardInputFieldTemplate,
BooleanCollectionInputFieldTemplate,
BooleanInputFieldTemplate,
BooleanPolymorphicInputFieldTemplate,
ClipInputFieldTemplate,
CollectionInputFieldTemplate,
CollectionItemInputFieldTemplate,
ColorCollectionInputFieldTemplate,
ColorInputFieldTemplate,
ColorPolymorphicInputFieldTemplate,
ConditioningCollectionInputFieldTemplate,
ConditioningField,
ConditioningInputFieldTemplate,
ConditioningPolymorphicInputFieldTemplate,
ControlCollectionInputFieldTemplate,
ControlInputFieldTemplate,
ControlNetModelInputFieldTemplate,
ControlPolymorphicInputFieldTemplate,
DenoiseMaskInputFieldTemplate,
EnumInputFieldTemplate,
FieldType,
FloatCollectionInputFieldTemplate,
FloatPolymorphicInputFieldTemplate,
FloatInputFieldTemplate,
FloatPolymorphicInputFieldTemplate,
IPAdapterCollectionInputFieldTemplate,
IPAdapterField,
IPAdapterInputFieldTemplate,
IPAdapterModelInputFieldTemplate,
IPAdapterPolymorphicInputFieldTemplate,
ImageCollectionInputFieldTemplate,
ImageField,
ImageInputFieldTemplate,
ImagePolymorphicInputFieldTemplate,
InputFieldTemplate,
InputFieldTemplateBase,
IntegerCollectionInputFieldTemplate,
IntegerInputFieldTemplate,
IntegerPolymorphicInputFieldTemplate,
InvocationFieldSchema,
InvocationSchemaObject,
LatentsCollectionInputFieldTemplate,
LatentsField,
LatentsInputFieldTemplate,
LatentsPolymorphicInputFieldTemplate,
LoRAModelInputFieldTemplate,
MainModelInputFieldTemplate,
MetadataCollectionInputFieldTemplate,
MetadataInputFieldTemplate,
MetadataItemCollectionInputFieldTemplate,
MetadataItemInputFieldTemplate,
MetadataItemPolymorphicInputFieldTemplate,
OpenAPIV3_1SchemaOrRef,
SDXLMainModelInputFieldTemplate,
SDXLRefinerModelInputFieldTemplate,
SchedulerInputFieldTemplate,
StringCollectionInputFieldTemplate,
StringInputFieldTemplate,
StringPolymorphicInputFieldTemplate,
T2IAdapterCollectionInputFieldTemplate,
T2IAdapterField,
T2IAdapterInputFieldTemplate,
T2IAdapterModelInputFieldTemplate,
T2IAdapterPolymorphicInputFieldTemplate,
UNetInputFieldTemplate,
VaeInputFieldTemplate,
VaeModelInputFieldTemplate,
@ -52,36 +87,7 @@ import {
isNonArraySchemaObject,
isRefObject,
isSchemaObject,
ControlPolymorphicInputFieldTemplate,
ColorPolymorphicInputFieldTemplate,
ColorCollectionInputFieldTemplate,
IntegerPolymorphicInputFieldTemplate,
StringPolymorphicInputFieldTemplate,
BooleanPolymorphicInputFieldTemplate,
ImagePolymorphicInputFieldTemplate,
LatentsPolymorphicInputFieldTemplate,
LatentsCollectionInputFieldTemplate,
ConditioningPolymorphicInputFieldTemplate,
ConditioningCollectionInputFieldTemplate,
ControlCollectionInputFieldTemplate,
ImageField,
LatentsField,
ConditioningField,
IPAdapterField,
IPAdapterInputFieldTemplate,
IPAdapterModelInputFieldTemplate,
IPAdapterPolymorphicInputFieldTemplate,
IPAdapterCollectionInputFieldTemplate,
T2IAdapterField,
T2IAdapterInputFieldTemplate,
T2IAdapterModelInputFieldTemplate,
T2IAdapterPolymorphicInputFieldTemplate,
T2IAdapterCollectionInputFieldTemplate,
BoardInputFieldTemplate,
InputFieldTemplate,
OpenAPIV3_1SchemaOrRef,
} from '../types/types';
import { ControlField } from 'services/api/types';
export type BaseFieldProperties = 'name' | 'title' | 'description';
@ -851,6 +857,78 @@ const buildCollectionItemInputFieldTemplate = ({
return template;
};
const buildAnyInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): AnyInputFieldTemplate => {
const template: AnyInputFieldTemplate = {
...baseField,
type: 'Any',
default: undefined,
};
return template;
};
const buildMetadataItemInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataItemInputFieldTemplate => {
const template: MetadataItemInputFieldTemplate = {
...baseField,
type: 'MetadataItemField',
default: undefined,
};
return template;
};
const buildMetadataItemCollectionInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataItemCollectionInputFieldTemplate => {
const template: MetadataItemCollectionInputFieldTemplate = {
...baseField,
type: 'MetadataItemCollection',
default: undefined,
};
return template;
};
const buildMetadataItemPolymorphicInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataItemPolymorphicInputFieldTemplate => {
const template: MetadataItemPolymorphicInputFieldTemplate = {
...baseField,
type: 'MetadataItemPolymorphic',
default: undefined,
};
return template;
};
const buildMetadataDictInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataInputFieldTemplate => {
const template: MetadataInputFieldTemplate = {
...baseField,
type: 'MetadataField',
default: undefined,
};
return template;
};
const buildMetadataCollectionInputFieldTemplate = ({
baseField,
}: BuildInputFieldArg): MetadataCollectionInputFieldTemplate => {
const template: MetadataCollectionInputFieldTemplate = {
...baseField,
type: 'MetadataCollection',
default: undefined,
};
return template;
};
const buildColorInputFieldTemplate = ({
schemaObject,
baseField,
@ -1012,6 +1090,7 @@ const TEMPLATE_BUILDER_MAP: {
[key in FieldType]?: (arg: BuildInputFieldArg) => InputFieldTemplate;
} = {
BoardField: buildBoardInputFieldTemplate,
Any: buildAnyInputFieldTemplate,
boolean: buildBooleanInputFieldTemplate,
BooleanCollection: buildBooleanCollectionInputFieldTemplate,
BooleanPolymorphic: buildBooleanPolymorphicInputFieldTemplate,
@ -1047,6 +1126,11 @@ const TEMPLATE_BUILDER_MAP: {
LatentsField: buildLatentsInputFieldTemplate,
LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate,
LoRAModelField: buildLoRAModelInputFieldTemplate,
MetadataItemField: buildMetadataItemInputFieldTemplate,
MetadataItemCollection: buildMetadataItemCollectionInputFieldTemplate,
MetadataItemPolymorphic: buildMetadataItemPolymorphicInputFieldTemplate,
MetadataField: buildMetadataDictInputFieldTemplate,
MetadataCollection: buildMetadataCollectionInputFieldTemplate,
MainModelField: buildMainModelInputFieldTemplate,
Scheduler: buildSchedulerInputFieldTemplate,
SDXLMainModelField: buildSDXLMainModelInputFieldTemplate,

6
invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts
View File

@ -3,6 +3,7 @@ import { FieldType, InputFieldTemplate, InputFieldValue } from '../types/types';
const FIELD_VALUE_FALLBACK_MAP: {
[key in FieldType]: InputFieldValue['value'];
} = {
Any: undefined,
enum: '',
BoardField: undefined,
boolean: false,
@ -38,6 +39,11 @@ const FIELD_VALUE_FALLBACK_MAP: {
LatentsCollection: [],
LatentsField: undefined,
LatentsPolymorphic: undefined,
MetadataItemField: undefined,
MetadataItemCollection: [],
MetadataItemPolymorphic: undefined,
MetadataField: undefined,
MetadataCollection: [],
LoRAModelField: undefined,
MainModelField: undefined,
ONNXModelField: undefined,

25
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts
View File

@ -5,14 +5,14 @@ import {
CollectInvocation,
ControlField,
ControlNetInvocation,
MetadataAccumulatorInvocation,
CoreMetadataInvocation,
} from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CONTROL_NET_COLLECT,
METADATA_ACCUMULATOR,
} from './constants';
import { upsertMetadata } from './metadata';
export const addControlNetToLinearGraph = (
state: RootState,
@ -23,9 +23,11 @@ export const addControlNetToLinearGraph = (
(ca) => ca.model?.base_model === state.generation.model?.base_model
);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
// const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
// | MetadataAccumulatorInvocation
// | undefined;
const controlNetMetadata: CoreMetadataInvocation['controlnets'] = [];
if (validControlNets.length) {
// Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect
@ -99,15 +101,9 @@ export const addControlNetToLinearGraph = (
graph.nodes[controlNetNode.id] = controlNetNode as ControlNetInvocation;
if (metadataAccumulator?.controlnets) {
// metadata accumulator only needs a control field - not the whole node
// extract what we need and add to the accumulator
const controlField = omit(controlNetNode, [
'id',
'type',
]) as ControlField;
metadataAccumulator.controlnets.push(controlField);
}
controlNetMetadata.push(
omit(controlNetNode, ['id', 'type', 'is_intermediate']) as ControlField
);
graph.edges.push({
source: { node_id: controlNetNode.id, field: 'control' },
@ -117,5 +113,6 @@ export const addControlNetToLinearGraph = (
},
});
});
upsertMetadata(graph, { controlnets: controlNetMetadata });
}
};

42
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts
View File

@ -1,25 +1,25 @@
import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
DenoiseLatentsInvocation,
ResizeLatentsInvocation,
NoiseInvocation,
LatentsToImageInvocation,
Edge,
LatentsToImageInvocation,
NoiseInvocation,
ResizeLatentsInvocation,
} from 'services/api/types';
import {
LATENTS_TO_IMAGE,
DENOISE_LATENTS,
NOISE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
LATENTS_TO_IMAGE_HRF,
DENOISE_LATENTS_HRF,
RESCALE_LATENTS,
LATENTS_TO_IMAGE,
LATENTS_TO_IMAGE_HRF,
MAIN_MODEL_LOADER,
NOISE,
NOISE_HRF,
RESCALE_LATENTS,
VAE_LOADER,
} from './constants';
import { logger } from 'app/logging/logger';
import { upsertMetadata } from './metadata';
// Copy certain connections from previous DENOISE_LATENTS to new DENOISE_LATENTS_HRF.
function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void {
@ -71,10 +71,8 @@ export const addHrfToGraph = (
}
const log = logger('txt2img');
const { vae } = state.generation;
const { vae, hrfWidth, hrfHeight, hrfStrength } = state.generation;
const isAutoVae = !vae;
const hrfWidth = state.generation.hrfWidth;
const hrfHeight = state.generation.hrfHeight;
// Pre-existing (original) graph nodes.
const originalDenoiseLatentsNode = graph.nodes[DENOISE_LATENTS] as
@ -116,7 +114,7 @@ export const addHrfToGraph = (
cfg_scale: originalDenoiseLatentsNode?.cfg_scale,
scheduler: originalDenoiseLatentsNode?.scheduler,
steps: originalDenoiseLatentsNode?.steps,
denoising_start: 1 - state.generation.hrfStrength,
denoising_start: 1 - hrfStrength,
denoising_end: 1,
};
@ -221,16 +219,6 @@ export const addHrfToGraph = (
field: 'latents',
},
},
{
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE_HRF,
field: 'metadata',
},
},
{
source: {
node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
@ -243,5 +231,11 @@ export const addHrfToGraph = (
}
);
upsertMetadata(graph, {
hrf_height: hrfHeight,
hrf_width: hrfWidth,
hrf_strength: hrfStrength,
});
copyConnectionsToDenoiseLatentsHrf(graph);
};

33
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts
View File

@ -1,16 +1,18 @@
import { RootState } from 'app/store/store';
import { selectValidIPAdapters } from 'features/controlAdapters/store/controlAdaptersSlice';
import { omit } from 'lodash-es';
import {
CollectInvocation,
CoreMetadataInvocation,
IPAdapterInvocation,
MetadataAccumulatorInvocation,
IPAdapterMetadataField,
} from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
IP_ADAPTER_COLLECT,
METADATA_ACCUMULATOR,
} from './constants';
import { upsertMetadata } from './metadata';
export const addIPAdapterToLinearGraph = (
state: RootState,
@ -21,10 +23,6 @@ export const addIPAdapterToLinearGraph = (
(ca) => ca.model?.base_model === state.generation.model?.base_model
);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (validIPAdapters.length) {
// Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect
const ipAdapterCollectNode: CollectInvocation = {
@ -50,6 +48,7 @@ export const addIPAdapterToLinearGraph = (
},
});
}
const ipAdapterMetdata: CoreMetadataInvocation['ipAdapters'] = [];
validIPAdapters.forEach((ipAdapter) => {
if (!ipAdapter.model) {
@ -76,19 +75,13 @@ export const addIPAdapterToLinearGraph = (
graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation;
if (metadataAccumulator?.ipAdapters) {
const ipAdapterField = {
image: {
image_name: ipAdapter.controlImage,
},
weight,
ip_adapter_model: model,
begin_step_percent: beginStepPct,
end_step_percent: endStepPct,
};
metadataAccumulator.ipAdapters.push(ipAdapterField);
}
ipAdapterMetdata.push(
omit(ipAdapterNode, [
'id',
'type',
'is_intermediate',
]) as IPAdapterMetadataField
);
graph.edges.push({
source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
@ -98,5 +91,7 @@ export const addIPAdapterToLinearGraph = (
},
});
});
upsertMetadata(graph, { ipAdapters: ipAdapterMetdata });
}
};

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts
View File

@ -2,20 +2,20 @@ import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { forEach, size } from 'lodash-es';
import {
CoreMetadataInvocation,
LoraLoaderInvocation,
MetadataAccumulatorInvocation,
} from 'services/api/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_INPAINT_GRAPH,
CANVAS_OUTPAINT_GRAPH,
CANVAS_COHERENCE_DENOISE_LATENTS,
CLIP_SKIP,
LORA_LOADER,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
POSITIVE_CONDITIONING,
} from './constants';
import { upsertMetadata } from './metadata';
export const addLoRAsToGraph = (
state: RootState,
@ -33,11 +33,11 @@ export const addLoRAsToGraph = (
const { loras } = state.lora;
const loraCount = size(loras);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (loraCount > 0) {
if (loraCount === 0) {
return;
}
// Remove modelLoaderNodeId unet connection to feed it to LoRAs
graph.edges = graph.edges.filter(
(e) =>
@ -51,11 +51,11 @@ export const addLoRAsToGraph = (
(e) =>
!(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field))
);
}
// we need to remember the last lora so we can chain from it
let lastLoraNodeId = '';
let currentLoraIndex = 0;
const loraMetadata: CoreMetadataInvocation['loras'] = [];
forEach(loras, (lora) => {
const { model_name, base_model, weight } = lora;
@ -69,13 +69,10 @@ export const addLoRAsToGraph = (
weight,
};
// add the lora to the metadata accumulator
if (metadataAccumulator?.loras) {
metadataAccumulator.loras.push({
loraMetadata.push({
lora: { model_name, base_model },
weight,
});
}
// add to graph
graph.nodes[currentLoraNodeId] = loraLoaderNode;
@ -182,4 +179,6 @@ export const addLoRAsToGraph = (
lastLoraNodeId = currentLoraNodeId;
currentLoraIndex += 1;
});
upsertMetadata(graph, { loras: loraMetadata });
};

43
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts
View File

@ -1,14 +1,14 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { forEach, size } from 'lodash-es';
import {
MetadataAccumulatorInvocation,
SDXLLoraLoaderInvocation,
} from 'services/api/types';
LoRAMetadataItem,
NonNullableGraph,
zLoRAMetadataItem,
} from 'features/nodes/types/types';
import { forEach, size } from 'lodash-es';
import { SDXLLoraLoaderInvocation } from 'services/api/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
LORA_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
POSITIVE_CONDITIONING,
SDXL_CANVAS_INPAINT_GRAPH,
@ -17,6 +17,7 @@ import {
SDXL_REFINER_INPAINT_CREATE_MASK,
SEAMLESS,
} from './constants';
import { upsertMetadata } from './metadata';
export const addSDXLLoRAsToGraph = (
state: RootState,
@ -34,9 +35,12 @@ export const addSDXLLoRAsToGraph = (
const { loras } = state.lora;
const loraCount = size(loras);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (loraCount === 0) {
return;
}
const loraMetadata: LoRAMetadataItem[] = [];
// Handle Seamless Plugs
const unetLoaderId = modelLoaderNodeId;
@ -47,7 +51,6 @@ export const addSDXLLoRAsToGraph = (
clipLoaderId = SDXL_MODEL_LOADER;
}
if (loraCount > 0) {
// Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
graph.edges = graph.edges.filter(
(e) =>
@ -57,12 +60,8 @@ export const addSDXLLoRAsToGraph = (
!(
e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field)
) &&
!(
e.source.node_id === clipLoaderId &&
['clip2'].includes(e.source.field)
)
!(e.source.node_id === clipLoaderId && ['clip2'].includes(e.source.field))
);
}
// we need to remember the last lora so we can chain from it
let lastLoraNodeId = '';
@ -80,16 +79,12 @@ export const addSDXLLoRAsToGraph = (
weight,
};
// add the lora to the metadata accumulator
if (metadataAccumulator) {
if (!metadataAccumulator.loras) {
metadataAccumulator.loras = [];
}
metadataAccumulator.loras.push({
loraMetadata.push(
zLoRAMetadataItem.parse({
lora: { model_name, base_model },
weight,
});
}
})
);
// add to graph
graph.nodes[currentLoraNodeId] = loraLoaderNode;
@ -242,4 +237,6 @@ export const addSDXLLoRAsToGraph = (
lastLoraNodeId = currentLoraNodeId;
currentLoraIndex += 1;
});
upsertMetadata(graph, { loras: loraMetadata });
};

27
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts
View File

@ -2,7 +2,6 @@ import { RootState } from 'app/store/store';
import {
CreateDenoiseMaskInvocation,
ImageDTO,
MetadataAccumulatorInvocation,
SeamlessModeInvocation,
} from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
@ -12,7 +11,6 @@ import {
LATENTS_TO_IMAGE,
MASK_COMBINE,
MASK_RESIZE_UP,
METADATA_ACCUMULATOR,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
SDXL_CANVAS_OUTPAINT_GRAPH,
@ -26,6 +24,7 @@ import {
SDXL_REFINER_SEAMLESS,
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { upsertMetadata } from './metadata';
export const addSDXLRefinerToGraph = (
state: RootState,
@ -58,21 +57,15 @@ export const addSDXLRefinerToGraph = (
return;
}
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (metadataAccumulator) {
metadataAccumulator.refiner_model = refinerModel;
metadataAccumulator.refiner_positive_aesthetic_score =
refinerPositiveAestheticScore;
metadataAccumulator.refiner_negative_aesthetic_score =
refinerNegativeAestheticScore;
metadataAccumulator.refiner_cfg_scale = refinerCFGScale;
metadataAccumulator.refiner_scheduler = refinerScheduler;
metadataAccumulator.refiner_start = refinerStart;
metadataAccumulator.refiner_steps = refinerSteps;
}
upsertMetadata(graph, {
refiner_model: refinerModel,
refiner_positive_aesthetic_score: refinerPositiveAestheticScore,
refiner_negative_aesthetic_score: refinerNegativeAestheticScore,
refiner_cfg_scale: refinerCFGScale,
refiner_scheduler: refinerScheduler,
refiner_start: refinerStart,
refiner_steps: refinerSteps,
});
const modelLoaderId = modelLoaderNodeId
? modelLoaderNodeId

27
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts
View File

@ -1,19 +1,15 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
import { SaveImageInvocation } from 'services/api/types';
import {
CANVAS_OUTPUT,
LATENTS_TO_IMAGE,
LATENTS_TO_IMAGE_HRF,
METADATA_ACCUMULATOR,
NSFW_CHECKER,
SAVE_IMAGE,
WATERMARKER,
} from './constants';
import {
MetadataAccumulatorInvocation,
SaveImageInvocation,
} from 'services/api/types';
import { RootState } from 'app/store/store';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
/**
* Set the `use_cache` field on the linear/canvas graph's final image output node to False.
@ -37,23 +33,6 @@ export const addSaveImageNode = (
graph.nodes[SAVE_IMAGE] = saveImageNode;
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (metadataAccumulator) {
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: SAVE_IMAGE,
field: 'metadata',
},
});
}
const destination = {
node_id: SAVE_IMAGE,
field: 'image',

12
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts
View File

@ -1,6 +1,7 @@
import { RootState } from 'app/store/store';
import { SeamlessModeInvocation } from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import { upsertMetadata } from './metadata';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_INPAINT_GRAPH,
@ -31,6 +32,17 @@ export const addSeamlessToLinearGraph = (
seamless_y: seamlessYAxis,
} as SeamlessModeInvocation;
if (seamlessXAxis) {
upsertMetadata(graph, {
seamless_x: seamlessXAxis,
});
}
if (seamlessYAxis) {
upsertMetadata(graph, {
seamless_y: seamlessYAxis,
});
}
let denoisingNodeId = DENOISE_LATENTS;
if (

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts
View File

@ -3,15 +3,15 @@ import { selectValidT2IAdapters } from 'features/controlAdapters/store/controlAd
import { omit } from 'lodash-es';
import {
CollectInvocation,
MetadataAccumulatorInvocation,
CoreMetadataInvocation,
T2IAdapterInvocation,
} from 'services/api/types';
import { NonNullableGraph, T2IAdapterField } from '../../types/types';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
METADATA_ACCUMULATOR,
T2I_ADAPTER_COLLECT,
} from './constants';
import { upsertMetadata } from './metadata';
export const addT2IAdaptersToLinearGraph = (
state: RootState,
@ -22,10 +22,6 @@ export const addT2IAdaptersToLinearGraph = (
(ca) => ca.model?.base_model === state.generation.model?.base_model
);
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (validT2IAdapters.length) {
// Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect
const t2iAdapterCollectNode: CollectInvocation = {
@ -51,6 +47,7 @@ export const addT2IAdaptersToLinearGraph = (
},
});
}
const t2iAdapterMetdata: CoreMetadataInvocation['t2iAdapters'] = [];
validT2IAdapters.forEach((t2iAdapter) => {
if (!t2iAdapter.model) {
@ -96,15 +93,13 @@ export const addT2IAdaptersToLinearGraph = (
graph.nodes[t2iAdapterNode.id] = t2iAdapterNode as T2IAdapterInvocation;
if (metadataAccumulator?.t2iAdapters) {
// metadata accumulator only needs a control field - not the whole node
// extract what we need and add to the accumulator
const t2iAdapterField = omit(t2iAdapterNode, [
t2iAdapterMetdata.push(
omit(t2iAdapterNode, [
'id',
'type',
]) as T2IAdapterField;
metadataAccumulator.t2iAdapters.push(t2iAdapterField);
}
'is_intermediate',
]) as T2IAdapterField
);
graph.edges.push({
source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' },
@ -114,5 +109,7 @@ export const addT2IAdaptersToLinearGraph = (
},
});
});
upsertMetadata(graph, { t2iAdapters: t2iAdapterMetdata });
}
};

10
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts
View File

@ -1,6 +1,5 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { MetadataAccumulatorInvocation } from 'services/api/types';
import {
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_IMAGE_TO_IMAGE_GRAPH,
@ -14,7 +13,6 @@ import {
INPAINT_IMAGE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
ONNX_MODEL_LOADER,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
@ -26,6 +24,7 @@ import {
TEXT_TO_IMAGE_GRAPH,
VAE_LOADER,
} from './constants';
import { upsertMetadata } from './metadata';
export const addVAEToGraph = (
state: RootState,
@ -41,9 +40,6 @@ export const addVAEToGraph = (
);
const isAutoVae = !vae;
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (!isAutoVae) {
graph.nodes[VAE_LOADER] = {
@ -181,7 +177,7 @@ export const addVAEToGraph = (
}
}
if (vae && metadataAccumulator) {
metadataAccumulator.vae = vae;
if (vae) {
upsertMetadata(graph, { vae });
}
};

25
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts
View File

@ -5,14 +5,8 @@ import {
ImageNSFWBlurInvocation,
ImageWatermarkInvocation,
LatentsToImageInvocation,
MetadataAccumulatorInvocation,
} from 'services/api/types';
import {
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NSFW_CHECKER,
WATERMARKER,
} from './constants';
import { LATENTS_TO_IMAGE, NSFW_CHECKER, WATERMARKER } from './constants';
export const addWatermarkerToGraph = (
state: RootState,
@ -32,10 +26,6 @@ export const addWatermarkerToGraph = (
| ImageNSFWBlurInvocation
| undefined;
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
| MetadataAccumulatorInvocation
| undefined;
if (!nodeToAddTo) {
// something has gone terribly awry
return;
@ -80,17 +70,4 @@ export const addWatermarkerToGraph = (
},
});
}
if (metadataAccumulator) {
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: WATERMARKER,
field: 'metadata',
},
});
}
};

9
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts
View File

@ -1,12 +1,13 @@
import { BoardId } from 'features/gallery/store/types';
import { NonNullableGraph } from 'features/nodes/types/types';
import { ESRGANModelName } from 'features/parameters/store/postprocessingSlice';
import {
Graph,
ESRGANInvocation,
Graph,
SaveImageInvocation,
} from 'services/api/types';
import { REALESRGAN as ESRGAN, SAVE_IMAGE } from './constants';
import { BoardId } from 'features/gallery/store/types';
import { addCoreMetadataNode } from './metadata';
type Arg = {
image_name: string;
@ -55,5 +56,9 @@ export const buildAdHocUpscaleGraph = ({
],
};
addCoreMetadataNode(graph, {
esrgan_model: esrganModelName,
});
return graph;
};

14
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
View File

@ -20,12 +20,12 @@ import {
IMG2IMG_RESIZE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
SEAMLESS,
} from './constants';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Image to Image graph.
@ -308,10 +308,7 @@ export const buildCanvasImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'img2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -325,15 +322,10 @@ export const buildCanvasImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
clip_skip: clipSkip,
strength,
init_image: initialImage.image_name,
};
});
// Add Seamless To Graph
if (seamlessXAxis || seamlessYAxis) {

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts
View File

@ -16,7 +16,6 @@ import {
IMAGE_TO_LATENTS,
IMG2IMG_RESIZE,
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
@ -28,6 +27,7 @@ import {
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Image to Image graph.
@ -319,10 +319,7 @@ export const buildCanvasSDXLImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'img2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -336,24 +333,8 @@ export const buildCanvasSDXLImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
strength,
init_image: initialImage.image_name,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Seamless To Graph

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts
View File

@ -18,7 +18,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
@ -30,6 +29,7 @@ import {
SEAMLESS,
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Text to Image graph.
@ -301,10 +301,7 @@ export const buildCanvasSDXLTextToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'txt2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -318,22 +315,6 @@ export const buildCanvasSDXLTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Seamless To Graph

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
View File

@ -21,13 +21,13 @@ import {
DENOISE_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
POSITIVE_CONDITIONING,
SEAMLESS,
} from './constants';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Canvas tab's Text to Image graph.
@ -289,10 +289,7 @@ export const buildCanvasTextToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'txt2img',
cfg_scale,
width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@ -306,23 +303,7 @@ export const buildCanvasTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [],
clip_skip: clipSkip,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Seamless To Graph

37
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearBatchConfig.ts
View File

@ -7,10 +7,12 @@ import { components } from 'services/api/schema';
import { Batch, BatchConfig } from 'services/api/types';
import {
CANVAS_COHERENCE_NOISE,
METADATA,
METADATA_ACCUMULATOR,
NOISE,
POSITIVE_CONDITIONING,
} from './constants';
import { removeMetadata } from './metadata';
export const prepareLinearUIBatch = (
state: RootState,
@ -24,7 +26,6 @@ export const prepareLinearUIBatch = (
const data: Batch['data'] = [];
if (prompts.length === 1) {
unset(graph.nodes[METADATA_ACCUMULATOR], 'seed');
const seeds = generateSeeds({
count: iterations,
start: shouldRandomizeSeed ? undefined : seed,
@ -40,13 +41,13 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
removeMetadata(graph, 'seed');
zipped.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'seed',
items: seeds,
});
}
if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
zipped.push({
@ -77,13 +78,13 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
removeMetadata(graph, 'seed');
firstBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'seed',
items: seeds,
});
}
if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
firstBatchDatumList.push({
@ -106,13 +107,15 @@ export const prepareLinearUIBatch = (
items: seeds,
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
removeMetadata(graph, 'seed');
secondBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'seed',
items: seeds,
});
}
if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
secondBatchDatumList.push({
node_path: CANVAS_COHERENCE_NOISE,
@ -137,13 +140,13 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
removeMetadata(graph, 'positive_prompt');
firstBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'positive_prompt',
items: extendedPrompts,
});
}
if (shouldConcatSDXLStylePrompt && model?.base_model === 'sdxl') {
unset(graph.nodes[METADATA_ACCUMULATOR], 'positive_style_prompt');
@ -160,14 +163,14 @@ export const prepareLinearUIBatch = (
});
}
if (graph.nodes[METADATA_ACCUMULATOR]) {
// add to metadata
removeMetadata(graph, 'positive_style_prompt');
firstBatchDatumList.push({
node_path: METADATA_ACCUMULATOR,
node_path: METADATA,
field_name: 'positive_style_prompt',
items: stylePrompts,
items: extendedPrompts,
});
}
}
data.push(firstBatchDatumList);
}

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
View File

@ -21,13 +21,13 @@ import {
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RESIZE,
SEAMLESS,
} from './constants';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Image to Image tab graph.
@ -311,10 +311,7 @@ export const buildLinearImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'img2img',
cfg_scale,
height,
@ -326,25 +323,9 @@ export const buildLinearImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [], // populated in addT2IAdapterToLinearGraph
clip_skip: clipSkip,
strength,
init_image: initialImage.imageName,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

25
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
View File

@ -18,7 +18,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
@ -30,6 +29,7 @@ import {
SEAMLESS,
} from './constants';
import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
import { addCoreMetadataNode } from './metadata';
/**
* Builds the Image to Image tab graph.
@ -331,10 +331,7 @@ export const buildLinearSDXLImageToImageGraph = (
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'sdxl_img2img',
cfg_scale,
height,
@ -346,26 +343,10 @@ export const buildLinearSDXLImageToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined,
controlnets: [],
loras: [],
ipAdapters: [],
t2iAdapters: [],
strength: strength,
strength,
init_image: initialImage.imageName,
positive_style_prompt: positiveStylePrompt,
negative_style_prompt: negativeStylePrompt,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

23
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
View File

@ -11,9 +11,9 @@ import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import { addCoreMetadataNode } from './metadata';
import {
LATENTS_TO_IMAGE,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
@ -225,10 +225,7 @@ export const buildLinearSDXLTextToImageGraph = (
],
};
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'sdxl_txt2img',
cfg_scale,
height,
@ -240,24 +237,8 @@ export const buildLinearSDXLTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined,
controlnets: [],
loras: [],
ipAdapters: [],
t2iAdapters: [],
positive_style_prompt: positiveStylePrompt,
negative_style_prompt: negativeStylePrompt,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

30
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
View File

@ -15,12 +15,12 @@ import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import { addCoreMetadataNode } from './metadata';
import {
CLIP_SKIP,
DENOISE_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
@ -48,10 +48,6 @@ export const buildLinearTextToImageGraph = (
seamlessXAxis,
seamlessYAxis,
seed,
hrfWidth,
hrfHeight,
hrfStrength,
hrfEnabled: hrfEnabled,
} = state.generation;
const use_cpu = shouldUseCpuNoise;
@ -238,10 +234,7 @@ export const buildLinearTextToImageGraph = (
],
};
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
addCoreMetadataNode(graph, {
generation_mode: 'txt2img',
cfg_scale,
height,
@ -253,26 +246,7 @@ export const buildLinearTextToImageGraph = (
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
ipAdapters: [], // populated in addIPAdapterToLinearGraph
t2iAdapters: [], // populated in addT2IAdapterToLinearGraph
clip_skip: clipSkip,
hrf_width: hrfEnabled ? hrfWidth : undefined,
hrf_height: hrfEnabled ? hrfHeight : undefined,
hrf_strength: hrfEnabled ? hrfStrength : undefined,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'metadata',
},
});
// Add Seamless To Graph

5
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts
View File

@ -35,7 +35,7 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => {
const { nodes, edges } = nodesState;
const filteredNodes = nodes.filter(isInvocationNode);
const workflowJSON = JSON.stringify(buildWorkflow(nodesState));
// const workflowJSON = JSON.stringify(buildWorkflow(nodesState));
// Reduce the node editor nodes into invocation graph nodes
const parsedNodes = filteredNodes.reduce<NonNullable<Graph['nodes']>>(
@ -68,7 +68,8 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => {
if (embedWorkflow) {
// add the workflow to the node
Object.assign(graphNode, { workflow: workflowJSON });
// Object.assign(graphNode, { workflow: workflowJSON });
Object.assign(graphNode, { workflow: buildWorkflow(nodesState) });
}
// Add it to the nodes object

8
invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
View File

@ -56,7 +56,15 @@ export const IP_ADAPTER = 'ip_adapter';
export const DYNAMIC_PROMPT = 'dynamic_prompt';
export const IMAGE_COLLECTION = 'image_collection';
export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate';
export const METADATA = 'core_metadata';
export const BATCH_METADATA = 'batch_metadata';
export const BATCH_METADATA_COLLECT = 'batch_metadata_collect';
export const BATCH_SEED = 'batch_seed';
export const BATCH_PROMPT = 'batch_prompt';
export const BATCH_STYLE_PROMPT = 'batch_style_prompt';
export const METADATA_COLLECT = 'metadata_collect';
export const METADATA_ACCUMULATOR = 'metadata_accumulator';
export const MERGE_METADATA = 'merge_metadata';
export const REALESRGAN = 'esrgan';
export const DIVIDE = 'divide';
export const SCALE = 'scale_image';

58
invokeai/frontend/web/src/features/nodes/util/graphBuilders/metadata.ts Normal file
View File

@ -0,0 +1,58 @@
import { NonNullableGraph } from 'features/nodes/types/types';
import { CoreMetadataInvocation } from 'services/api/types';
import { JsonObject } from 'type-fest';
import { METADATA, SAVE_IMAGE } from './constants';
export const addCoreMetadataNode = (
graph: NonNullableGraph,
metadata: Partial<CoreMetadataInvocation> | JsonObject
): void => {
graph.nodes[METADATA] = {
id: METADATA,
type: 'core_metadata',
...metadata,
};
graph.edges.push({
source: {
node_id: METADATA,
field: 'metadata',
},
destination: {
node_id: SAVE_IMAGE,
field: 'metadata',
},
});
return;
};
export const upsertMetadata = (
graph: NonNullableGraph,
metadata: Partial<CoreMetadataInvocation> | JsonObject
): void => {
const metadataNode = graph.nodes[METADATA] as
| CoreMetadataInvocation
| undefined;
if (!metadataNode) {
return;
}
Object.assign(metadataNode, metadata);
};
export const removeMetadata = (
graph: NonNullableGraph,
key: keyof CoreMetadataInvocation
): void => {
const metadataNode = graph.nodes[METADATA] as
| CoreMetadataInvocation
| undefined;
if (!metadataNode) {
return;
}
delete metadataNode[key];
};

44
invokeai/frontend/web/src/features/nodes/util/parseSchema.ts
View File

@ -4,7 +4,6 @@ import { reduce, startCase } from 'lodash-es';
import { OpenAPIV3_1 } from 'openapi-types';
import { AnyInvocationType } from 'services/events/types';
import {
FieldType,
InputFieldTemplate,
InvocationSchemaObject,
InvocationTemplate,
@ -16,18 +15,11 @@ import {
} from '../types/types';
import { buildInputFieldTemplate, getFieldType } from './fieldTemplateBuilders';
const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'metadata', 'use_cache'];
const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'use_cache'];
const RESERVED_OUTPUT_FIELD_NAMES = ['type'];
const RESERVED_FIELD_TYPES = [
'WorkflowField',
'MetadataField',
'IsIntermediate',
];
const RESERVED_FIELD_TYPES = ['IsIntermediate'];
const invocationDenylist: AnyInvocationType[] = [
'graph',
'metadata_accumulator',
];
const invocationDenylist: AnyInvocationType[] = ['graph'];
const isReservedInputField = (nodeType: string, fieldName: string) => {
if (RESERVED_INPUT_FIELD_NAMES.includes(fieldName)) {
@ -42,7 +34,7 @@ const isReservedInputField = (nodeType: string, fieldName: string) => {
return false;
};
const isReservedFieldType = (fieldType: FieldType) => {
const isReservedFieldType = (fieldType: string) => {
if (RESERVED_FIELD_TYPES.includes(fieldType)) {
return true;
}
@ -86,6 +78,7 @@ export const parseSchema = (
const tags = schema.tags ?? [];
const description = schema.description ?? '';
const version = schema.version;
let withWorkflow = false;
const inputs = reduce(
schema.properties,
@ -112,7 +105,7 @@ export const parseSchema = (
const fieldType = property.ui_type ?? getFieldType(property);
if (!isFieldType(fieldType)) {
if (!fieldType) {
logger('nodes').warn(
{
node: type,
@ -120,11 +113,16 @@ export const parseSchema = (
fieldType,
field: parseify(property),
},
'Skipping unknown input field type'
'Missing input field type'
);
return inputsAccumulator;
}
if (fieldType === 'WorkflowField') {
withWorkflow = true;
return inputsAccumulator;
}
if (isReservedFieldType(fieldType)) {
logger('nodes').trace(
{
@ -133,7 +131,20 @@ export const parseSchema = (
fieldType,
field: parseify(property),
},
'Skipping reserved field type'
`Skipping reserved input field type: ${fieldType}`
);
return inputsAccumulator;
}
if (!isFieldType(fieldType)) {
logger('nodes').warn(
{
node: type,
fieldName: propertyName,
fieldType,
field: parseify(property),
},
`Skipping unknown input field type: ${fieldType}`
);
return inputsAccumulator;
}
@ -146,7 +157,7 @@ export const parseSchema = (
);
if (!field) {
logger('nodes').debug(
logger('nodes').warn(
{
node: type,
fieldName: propertyName,
@ -248,6 +259,7 @@ export const parseSchema = (
inputs,
outputs,
useCache,
withWorkflow,
};
Object.assign(invocationsAccumulator, { [type]: invocation });

18
invokeai/frontend/web/src/services/api/endpoints/images.ts
View File

@ -1,6 +1,7 @@
import { EntityState, Update } from '@reduxjs/toolkit';
import { fetchBaseQuery } from '@reduxjs/toolkit/dist/query';
import { PatchCollection } from '@reduxjs/toolkit/dist/query/core/buildThunks';
import { logger } from 'app/logging/logger';
import {
ASSETS_CATEGORIES,
BoardId,
@ -8,6 +9,7 @@ import {
IMAGE_LIMIT,
} from 'features/gallery/store/types';
import {
CoreMetadata,
ImageMetadataAndWorkflow,
zCoreMetadata,
} from 'features/nodes/types/types';
@ -23,7 +25,6 @@ import {
ListImagesArgs,
OffsetPaginatedResults_ImageDTO_,
PostUploadAction,
UnsafeImageMetadata,
} from '../types';
import {
getCategories,
@ -114,11 +115,24 @@ export const imagesApi = api.injectEndpoints({
],
keepUnusedDataFor: 86400, // 24 hours
}),
getImageMetadata: build.query<UnsafeImageMetadata, string>({
getImageMetadata: build.query<CoreMetadata | undefined, string>({
query: (image_name) => ({ url: `images/i/${image_name}/metadata` }),
providesTags: (result, error, image_name) => [
{ type: 'ImageMetadata', id: image_name },
],
transformResponse: (
response: paths['/api/v1/images/i/{image_name}/metadata']['get']['responses']['200']['content']['application/json']
) => {
if (response) {
const result = zCoreMetadata.safeParse(response);
if (result.success) {
return result.data;
} else {
logger('images').warn('Problem parsing metadata');
}
}
return;
},
keepUnusedDataFor: 86400, // 24 hours
}),
getImageMetadataFromFile: build.query<

31
invokeai/frontend/web/src/services/api/endpoints/workflows.ts Normal file
View File

@ -0,0 +1,31 @@
import { logger } from 'app/logging/logger';
import { Workflow, zWorkflow } from 'features/nodes/types/types';
import { api } from '..';
import { paths } from '../schema';
export const workflowsApi = api.injectEndpoints({
endpoints: (build) => ({
getWorkflow: build.query<Workflow | undefined, string>({
query: (workflow_id) => `workflows/i/${workflow_id}`,
keepUnusedDataFor: 86400, // 24 hours
providesTags: (result, error, workflow_id) => [
{ type: 'Workflow', id: workflow_id },
],
transformResponse: (
response: paths['/api/v1/workflows/i/{workflow_id}']['get']['responses']['200']['content']['application/json']
) => {
if (response) {
const result = zWorkflow.safeParse(response);
if (result.success) {
return result.data;
} else {
logger('images').warn('Problem parsing metadata');
}
}
return;
},
}),
}),
});
export const { useGetWorkflowQuery } = workflowsApi;

1
invokeai/frontend/web/src/services/api/index.ts
View File

@ -37,6 +37,7 @@ export const tagTypes = [
'ControlNetModel',
'LoRAModel',
'SDXLRefinerModel',
'Workflow',
] as const;
export type ApiTagDescription = TagDescription<(typeof tagTypes)[number]>;
export const LIST_TAG = 'LIST';

1487
invokeai/frontend/web/src/services/api/schema.d.ts vendored
View File

File diff suppressed because one or more lines are too long

17
invokeai/frontend/web/src/services/api/types.ts
View File

@ -27,14 +27,6 @@ export type BatchConfig =
export type EnqueueBatchResult = components['schemas']['EnqueueBatchResult'];
/**
* This is an unsafe type; the object inside is not guaranteed to be valid.
*/
export type UnsafeImageMetadata = {
metadata: s['CoreMetadata'];
graph: NonNullable<s['Graph']>;
};
export type _InputField = s['_InputField'];
export type _OutputField = s['_OutputField'];
@ -50,7 +42,6 @@ export type ImageChanges = s['ImageRecordChanges'];
export type ImageCategory = s['ImageCategory'];
export type ResourceOrigin = s['ResourceOrigin'];
export type ImageField = s['ImageField'];
export type ImageMetadata = s['ImageMetadata'];
export type OffsetPaginatedResults_BoardDTO_ =
s['OffsetPaginatedResults_BoardDTO_'];
export type OffsetPaginatedResults_ImageDTO_ =
@ -145,13 +136,19 @@ export type ImageCollectionInvocation = s['ImageCollectionInvocation'];
export type MainModelLoaderInvocation = s['MainModelLoaderInvocation'];
export type OnnxModelLoaderInvocation = s['OnnxModelLoaderInvocation'];
export type LoraLoaderInvocation = s['LoraLoaderInvocation'];
export type MetadataAccumulatorInvocation = s['MetadataAccumulatorInvocation'];
export type ESRGANInvocation = s['ESRGANInvocation'];
export type DivideInvocation = s['DivideInvocation'];
export type ImageNSFWBlurInvocation = s['ImageNSFWBlurInvocation'];
export type ImageWatermarkInvocation = s['ImageWatermarkInvocation'];
export type SeamlessModeInvocation = s['SeamlessModeInvocation'];
export type SaveImageInvocation = s['SaveImageInvocation'];
export type MetadataInvocation = s['MetadataInvocation'];
export type CoreMetadataInvocation = s['CoreMetadataInvocation'];
export type MetadataItemInvocation = s['MetadataItemInvocation'];
export type MergeMetadataInvocation = s['MergeMetadataInvocation'];
export type IPAdapterMetadataField = s['IPAdapterMetadataField'];
export type T2IAdapterField = s['T2IAdapterField'];
export type LoRAMetadataField = s['LoRAMetadataField'];
// ControlNet Nodes
export type ControlNetInvocation = s['ControlNetInvocation'];

148
tests/nodes/test_node_graph.py
View File

@ -10,7 +10,12 @@ from invokeai.app.invocations.baseinvocation import (
)
from invokeai.app.invocations.image import ShowImageInvocation
from invokeai.app.invocations.math import AddInvocation, SubtractInvocation
from invokeai.app.invocations.primitives import FloatInvocation, IntegerInvocation
from invokeai.app.invocations.primitives import (
FloatCollectionInvocation,
FloatInvocation,
IntegerInvocation,
StringInvocation,
)
from invokeai.app.invocations.upscale import ESRGANInvocation
from invokeai.app.services.shared.default_graphs import create_text_to_image
from invokeai.app.services.shared.graph import (
@ -27,8 +32,11 @@ from invokeai.app.services.shared.graph import (
)
from .test_nodes import (
AnyTypeTestInvocation,
ImageToImageTestInvocation,
ListPassThroughInvocation,
PolymorphicStringTestInvocation,
PromptCollectionTestInvocation,
PromptTestInvocation,
TextToImageTestInvocation,
)
@ -692,6 +700,144 @@ def test_ints_do_not_accept_floats():
g.add_edge(e)
def test_polymorphic_accepts_single():
g = Graph()
n1 = StringInvocation(id="1", value="banana")
n2 = PolymorphicStringTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e1 = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e1)
def test_polymorphic_accepts_collection_of_same_base_type():
g = Graph()
n1 = PromptCollectionTestInvocation(id="1", collection=["banana", "sundae"])
n2 = PolymorphicStringTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e1 = create_edge(n1.id, "collection", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e1)
def test_polymorphic_does_not_accept_collection_of_different_base_type():
g = Graph()
n1 = FloatCollectionInvocation(id="1", collection=[1.0, 2.0, 3.0])
n2 = PolymorphicStringTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e1 = create_edge(n1.id, "collection", n2.id, "value")
with pytest.raises(InvalidEdgeError):
g.add_edge(e1)
def test_polymorphic_does_not_accept_generic_collection():
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = IntegerInvocation(id="2", value=2)
n3 = CollectInvocation(id="3")
n4 = PolymorphicStringTestInvocation(id="4")
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
e1 = create_edge(n1.id, "value", n3.id, "item")
e2 = create_edge(n2.id, "value", n3.id, "item")
e3 = create_edge(n3.id, "collection", n4.id, "value")
g.add_edge(e1)
g.add_edge(e2)
with pytest.raises(InvalidEdgeError):
g.add_edge(e3)
def test_any_accepts_integer():
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_any_accepts_string():
g = Graph()
n1 = StringInvocation(id="1", value="banana sundae")
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_any_accepts_generic_collection():
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = IntegerInvocation(id="2", value=2)
n3 = CollectInvocation(id="3")
n4 = AnyTypeTestInvocation(id="4")
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
e1 = create_edge(n1.id, "value", n3.id, "item")
e2 = create_edge(n2.id, "value", n3.id, "item")
e3 = create_edge(n3.id, "collection", n4.id, "value")
g.add_edge(e1)
g.add_edge(e2)
# Not throwing on this line is sufficient
g.add_edge(e3)
def test_any_accepts_prompt_collection():
g = Graph()
n1 = PromptCollectionTestInvocation(id="1", collection=["banana", "sundae"])
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "collection", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_any_accepts_any():
g = Graph()
n1 = AnyTypeTestInvocation(id="1")
n2 = AnyTypeTestInvocation(id="2")
g.add_node(n1)
g.add_node(n2)
e = create_edge(n1.id, "value", n2.id, "value")
# Not throwing on this line is sufficient
g.add_edge(e)
def test_iterate_accepts_collection():
"""We need to update the validation for Collect -> Iterate to traverse to the Iterate
node's output and compare that against the item type of the Collect node's collection. Until
then, Collect nodes may not output into Iterate nodes."""
g = Graph()
n1 = IntegerInvocation(id="1", value=1)
n2 = IntegerInvocation(id="2", value=2)
n3 = CollectInvocation(id="3")
n4 = IterateInvocation(id="4")
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
e1 = create_edge(n1.id, "value", n3.id, "item")
e2 = create_edge(n2.id, "value", n3.id, "item")
e3 = create_edge(n3.id, "collection", n4.id, "collection")
g.add_edge(e1)
g.add_edge(e2)
# Once we fix the validation logic as described, this should should not raise an error
with pytest.raises(InvalidEdgeError, match="Cannot connect collector to iterator"):
g.add_edge(e3)
def test_graph_can_generate_schema():
# Not throwing on this line is sufficient
# NOTE: if this test fails, it's PROBABLY because a new invocation type is breaking schema generation

23
tests/nodes/test_nodes.py
View File

@ -81,6 +81,29 @@ class PromptCollectionTestInvocation(BaseInvocation):
return PromptCollectionTestInvocationOutput(collection=self.collection.copy())
@invocation_output("test_any_output")
class AnyTypeTestInvocationOutput(BaseInvocationOutput):
value: Any = Field()
@invocation("test_any")
class AnyTypeTestInvocation(BaseInvocation):
value: Any = Field(default=None)
def invoke(self, context: InvocationContext) -> AnyTypeTestInvocationOutput:
return AnyTypeTestInvocationOutput(value=self.value)
@invocation("test_polymorphic")
class PolymorphicStringTestInvocation(BaseInvocation):
value: Union[str, list[str]] = Field(default="")
def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput:
if isinstance(self.value, str):
return PromptCollectionTestInvocationOutput(collection=[self.value])
return PromptCollectionTestInvocationOutput(collection=self.value)
# Importing these must happen after test invocations are defined or they won't register
from invokeai.app.services.events.events_base import EventServiceBase # noqa: E402
from invokeai.app.services.shared.graph import Edge, EdgeConnection # noqa: E402