Add an unsharp mask node to core nodes

Unsharp mask is an image operation that, despite its name, sharpens an image. Like a Gaussian blur, it takes a radius and strength.

invokeai/app/invocations/image.py

@@ -5,13 +5,12 @@ from typing import Literal, Optional
 
 import cv2
 import numpy
-from PIL import Image, ImageChops, ImageFilter, ImageOps
-
 from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
+from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, invocation
 
@@ -421,6 +420,64 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata):
         )
 
 
+
+@invocation(
+    "unsharp_mask",
+    title="Unsharp Mask",
+    tags=["image", "unsharp_mask"],
+    category="image",
+    version="1.2.0",
+)
+class UnsharpMaskInvocation(BaseInvocation, WithMetadata):
+    """Applies an unsharp mask filter to an image"""
+
+    image: ImageField = InputField(description="The image to use")
+    radius: float = InputField(gt=0, description="Unsharp mask radius", default=2)
+    strength: float = InputField(ge=0, description="Unsharp mask strength", default=50)
+
+    def pil_from_array(self, arr):
+        return Image.fromarray((arr * 255).astype("uint8"))
+
+    def array_from_pil(self, img):
+        return numpy.array(img) / 255
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        mode = image.mode
+
+        alpha_channel = image.getchannel("A") if mode == "RGBA" else None
+        image = image.convert("RGB")
+        image_blurred = self.array_from_pil(image.filter(ImageFilter.GaussianBlur(radius=self.radius)))
+
+        image = self.array_from_pil(image)
+        image += (image - image_blurred) * (self.strength / 100.0)
+        image = numpy.clip(image, 0, 1)
+        image = self.pil_from_array(image)
+
+        image = image.convert(mode)
+
+        # Make the image RGBA if we had a source alpha channel
+        if alpha_channel is not None:
+            image.putalpha(alpha_channel)
+
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=context.workflow,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image.width,
+            height=image.height,
+        )
+
+
 PIL_RESAMPLING_MODES = Literal[
     "nearest",
     "box",