mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
c48fd9c083
Refine concept of "parameter" nodes to "primitives": - integer - float - string - boolean - image - latents - conditioning - color Each primitive has: - A field definition, if it is not already python primitive value. The field is how this primitive value is passed between nodes. Collections are lists of the field in node definitions. ex: `ImageField` & `list[ImageField]` - A single output class. ex: `ImageOutput` - A collection output class. ex: `ImageCollectionOutput` - A node, which functions to load or pass on the primitive value. ex: `ImageInvocation` (in this case, `ImageInvocation` replaces `LoadImage`) Plus a number of related changes: - Reorganize these into `primitives.py` - Update all nodes and logic to use primitives - Consolidate "prompt" outputs into "string" & "mask" into "image" (there's no reason for these to be different, the function identically) - Update default graphs & tests - Regen frontend types & minor frontend tidy related to changes
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Literal
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import cv2 as cv
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import numpy
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from PIL import Image, ImageOps
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from invokeai.app.invocations.primitives import ImageField, ImageOutput
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from invokeai.app.models.image import ImageCategory, ResourceOrigin
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from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
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@title("OpenCV Inpaint")
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@tags("opencv", "inpaint")
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class CvInpaintInvocation(BaseInvocation):
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"""Simple inpaint using opencv."""
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type: Literal["cv_inpaint"] = "cv_inpaint"
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# Inputs
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image: ImageField = InputField(description="The image to inpaint")
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mask: ImageField = InputField(description="The mask to use when inpainting")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get_pil_image(self.image.image_name)
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mask = context.services.images.get_pil_image(self.mask.image_name)
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# Convert to cv image/mask
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# TODO: consider making these utility functions
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cv_image = cv.cvtColor(numpy.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
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cv_mask = numpy.array(ImageOps.invert(mask.convert("L")))
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# Inpaint
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cv_inpainted = cv.inpaint(cv_image, cv_mask, 3, cv.INPAINT_TELEA)
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# Convert back to Pillow
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# TODO: consider making a utility function
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image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
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image_dto = context.services.images.create(
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image=image_inpainted,
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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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)
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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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