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feat(nodes): add prototype heuristic image resize node

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Uses the fancy cnet resize that retains edges.
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psychedelicious 2024-04-29 22:38:29 +10:00 committed by Kent Keirsey
parent 2d7b8c2a1b
commit e822897b1c
1 changed files with 27 additions and 2 deletions
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29
invokeai/app/invocations/controlnet_image_processors.py
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@ -35,15 +35,16 @@ from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.invocations.primitives import ImageOutput from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.shared.invocation_context import InvocationContext from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
from invokeai.backend.image_util.canny import get_canny_edges from invokeai.backend.image_util.canny import get_canny_edges
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
from invokeai.backend.image_util.hed import HEDProcessor from invokeai.backend.image_util.hed import HEDProcessor
from invokeai.backend.image_util.lineart import LineartProcessor from invokeai.backend.image_util.lineart import LineartProcessor
from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
from invokeai.backend.image_util.util import np_to_pil, pil_to_np
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output from .baseinvocation import BaseInvocation, BaseInvocationOutput, Classification, invocation, invocation_output
class ControlField(BaseModel): class ControlField(BaseModel):
@ -634,3 +635,27 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
resolution=self.image_resolution, resolution=self.image_resolution,
) )
return processed_image return processed_image
@invocation(
"heuristic_resize",
title="Heuristic Resize",
tags=["image, controlnet"],
category="image",
version="1.0.0",
classification=Classification.Prototype,
)
class HeuristicResizeInvocation(BaseInvocation):
"""Resize an image using a heuristic method. Preserves edge maps."""
image: ImageField = InputField(description="The image to resize")
width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name, "RGB")
np_img = pil_to_np(image)
np_resized = heuristic_resize(np_img, (self.width, self.height))
resized = np_to_pil(np_resized)
image_dto = context.images.save(image=resized)
return ImageOutput.build(image_dto)