mirror of
https://github.com/invoke-ai/InvokeAI
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42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
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import numpy as np
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import torch
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from PIL.Image import Image
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from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
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from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField
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@invocation(
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"add_conditioning_mask",
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title="Add Conditioning Mask",
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tags=["conditioning"],
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category="conditioning",
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version="1.0.0",
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)
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class AddConditioningMaskInvocation(BaseInvocation):
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"""Add a mask to an existing conditioning tensor."""
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conditioning: ConditioningField = InputField(description="The conditioning tensor to add a mask to.")
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image: ImageField = InputField(
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description="A mask image to add to the conditioning tensor. Only the first channel of the image is used. "
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"Pixels <128 are excluded from the mask, pixels >=128 are included in the mask."
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)
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@staticmethod
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def convert_image_to_mask(image: Image) -> torch.Tensor:
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"""Convert a PIL image to a uint8 mask tensor."""
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np_image = np.array(image)
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torch_image = torch.from_numpy(np_image[0, :, :])
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mask = torch_image >= 128
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return mask.to(dtype=torch.uint8)
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def invoke(self, context: InvocationContext) -> ConditioningOutput:
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image = context.services.images.get_pil_image(self.image.image_name)
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mask = self.convert_image_to_mask(image)
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mask_name = f"{context.graph_execution_state_id}__{self.id}_conditioning_mask"
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context.services.latents.save(mask_name, mask)
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self.conditioning.mask_name = mask_name
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return ConditioningOutput(conditioning=self.conditioning)
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