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34 lines
1.6 KiB
Python
34 lines
1.6 KiB
Python
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.pidi import PIDINetDetector
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from invokeai.backend.image_util.pidi.model import PiDiNet
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@invocation(
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"pidi_edge_detection",
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title="PiDiNet Edge Detection",
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tags=["controlnet", "edge"],
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category="controlnet_preprocessors",
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version="1.0.0",
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)
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class PiDiNetEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Generates an edge map using PiDiNet."""
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image: ImageField = InputField(description="The image to process")
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quantize_edges: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
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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(PIDINetDetector.get_model_url(), PIDINetDetector.load_model)
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with loaded_model as model:
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assert isinstance(model, PiDiNet)
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detector = PIDINetDetector(model)
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edge_map = detector.run(image=image, quantize_edges=self.quantize_edges, 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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