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https://github.com/invoke-ai/InvokeAI
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fix(nodes): remove dataURL invocation
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@ -5,8 +5,7 @@ from typing import Literal, Optional
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import numpy
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import numpy
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from PIL import Image, ImageFilter, ImageOps
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from PIL import Image, ImageFilter, ImageOps
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from pydantic import BaseModel, Field, validator
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from pydantic import BaseModel, Field
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from w3lib.url import parse_data_uri
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from ..models.image import ImageField, ImageType
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from ..models.image import ImageField, ImageType
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from .baseinvocation import (
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from .baseinvocation import (
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@ -118,45 +117,6 @@ class ShowImageInvocation(BaseInvocation):
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)
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)
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class DataURLToImageInvocation(BaseInvocation, PILInvocationConfig):
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"""Outputs an image from a data URL."""
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type: Literal["dataURL_image"] = "dataURL_image"
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# Inputs
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dataURL: str = Field(description="The b64 data URL")
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@validator("dataURL")
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def must_be_valid_image_dataURL(cls, v):
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try:
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result = parse_data_uri(v)
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img = Image.open(io.BytesIO(result.data))
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img.verify()
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except Exception:
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raise ValueError("Invalid image dataURL")
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return v
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def invoke(self, context: InvocationContext) -> ImageOutput:
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# TODO: Figure out how to use pydantic validator to also transform into a different type,
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# bc this is just the same logic as we use to validate the dataURL.
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result = parse_data_uri(self.dataURL)
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image = Image.open(io.BytesIO(result.data))
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image_name = context.services.images.create_name(
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context.graph_execution_state_id, self.id
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)
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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(ImageType.RESULT, image_name, image, metadata)
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return build_image_output(
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image_type=ImageType.RESULT, image_name=image_name, image=image
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)
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class CropImageInvocation(BaseInvocation, PILInvocationConfig):
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class CropImageInvocation(BaseInvocation, PILInvocationConfig):
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"""Crops an image to a specified box. The box can be outside of the image."""
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"""Crops an image to a specified box. The box can be outside of the image."""
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