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https://github.com/invoke-ai/InvokeAI.git
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b3d60bd56a
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager. All code related to the invocation now lives in the Invoke repo. Unfortunately, this includes a whole git repo for EfficientNet. I believe we could use the package `timm` instead of this, but it's beyond me.
32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import 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.normal_bae import NormalMapDetector
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from invokeai.backend.image_util.normal_bae.nets.NNET import NNET
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@invocation(
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"normal_map",
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title="Normal Map",
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tags=["controlnet", "normal"],
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category="controlnet",
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version="1.0.0",
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)
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class NormalMapInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Generates a normal map."""
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image: ImageField = InputField(description="The image to process")
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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(NormalMapDetector.get_model_url(), NormalMapDetector.load_model)
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with loaded_model as model:
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assert isinstance(model, NNET)
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detector = NormalMapDetector(model)
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normal_map = detector.run(image=image)
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image_dto = context.images.save(image=normal_map)
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return ImageOutput.build(image_dto)
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