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https://github.com/invoke-ai/InvokeAI.git
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1cffcc02a5
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
from builtins import bool
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, WithBoard, WithMetadata
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from invokeai.app.invocations.primitives import ImageOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.image_util.hed import ControlNetHED_Apache2, HEDEdgeDetector
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@invocation(
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"hed_edge_detection",
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title="HED Edge Detection",
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tags=["controlnet", "hed", "softedge"],
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category="controlnet",
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version="1.0.0",
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)
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class HEDEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Geneartes an edge map using the HED (softedge) model."""
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image: ImageField = InputField(description="The image to process")
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scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.images.get_pil(self.image.image_name, "RGB")
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loaded_model = context.models.load_remote_model(HEDEdgeDetector.get_model_url(), HEDEdgeDetector.load_model)
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with loaded_model as model:
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assert isinstance(model, ControlNetHED_Apache2)
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hed_processor = HEDEdgeDetector(model)
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edge_map = hed_processor.run(image=image, scribble=self.scribble)
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image_dto = context.images.save(image=edge_map)
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return ImageOutput.build(image_dto)
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