mirror of
https://github.com/invoke-ai/InvokeAI
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93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
import numpy as np
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import torch
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from PIL import Image
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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InputField,
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InvocationContext,
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WithMetadata,
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invocation,
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)
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from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
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from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
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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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mask: 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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mask_strength: float = InputField(
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description="The strength of the mask to apply to the conditioning tensor.", default=1.0
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)
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@staticmethod
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def convert_image_to_mask(image: 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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self.conditioning.mask_strength = self.mask_strength
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return ConditioningOutput(conditioning=self.conditioning)
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@invocation(
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"rectangle_mask",
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title="Create Rectangle 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 RectangleMaskInvocation(BaseInvocation, WithMetadata):
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"""Create a mask image containing a rectangular mask region."""
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height: int = InputField(description="The height of the image.")
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width: int = InputField(description="The width of the image.")
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y_top: int = InputField(description="The top y-coordinate of the rectangle (inclusive).")
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y_bottom: int = InputField(description="The bottom y-coordinate of the rectangle (exclusive).")
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x_left: int = InputField(description="The left x-coordinate of the rectangle (inclusive).")
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x_right: int = InputField(description="The right x-coordinate of the rectangle (exclusive).")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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mask = np.zeros((self.height, self.width, 3), dtype=np.uint8)
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mask[self.y_top : self.y_bottom, self.x_left : self.x_right, :] = 255
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mask_image = Image.fromarray(mask)
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image_dto = context.services.images.create(
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image=mask_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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is_intermediate=self.is_intermediate,
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metadata=self.metadata,
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workflow=context.workflow,
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)
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return ImageOutput(
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image=ImageField(image_name=image_dto.image_name),
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width=image_dto.width,
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height=image_dto.height,
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)
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