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Merge branch 'main' into patch-2
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‎docs/nodes/defaultNodes.md‎

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@@ -35,13 +35,13 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
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|Inverse Lerp Image | Inverse linear interpolation of all pixels of an image|
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|Image Primitive | An image primitive value|
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|Lerp Image | Linear interpolation of all pixels of an image|
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|Image Luminosity Adjustment | Adjusts the Luminosity (Value) of an image.|
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|Offset Image Channel | Add to or subtract from an image color channel by a uniform value.|
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|Multiply Image Channel | Multiply or Invert an image color channel by a scalar value.|
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|Multiply Images | Multiplies two images together using `PIL.ImageChops.multiply()`.|
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|Blur NSFW Image | Add blur to NSFW-flagged images|
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|Paste Image | Pastes an image into another image.|
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|ImageProcessor | Base class for invocations that preprocess images for ControlNet|
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|Resize Image | Resizes an image to specific dimensions|
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|Image Saturation Adjustment | Adjusts the Saturation of an image.|
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|Scale Image | Scales an image by a factor|
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|Image to Latents | Encodes an image into latents.|
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|Add Invisible Watermark | Add an invisible watermark to an image|

‎invokeai/app/invocations/image.py‎

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@@ -773,39 +773,95 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
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)
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COLOR_CHANNELS = Literal[
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"Red (RGBA)",
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"Green (RGBA)",
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"Blue (RGBA)",
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"Alpha (RGBA)",
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"Cyan (CMYK)",
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"Magenta (CMYK)",
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"Yellow (CMYK)",
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"Black (CMYK)",
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"Hue (HSV)",
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"Saturation (HSV)",
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"Value (HSV)",
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"Luminosity (LAB)",
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"A (LAB)",
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"B (LAB)",
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"Y (YCbCr)",
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"Cb (YCbCr)",
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"Cr (YCbCr)",
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]
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CHANNEL_FORMATS = {
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"Red (RGBA)": ("RGBA", 0),
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"Green (RGBA)": ("RGBA", 1),
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"Blue (RGBA)": ("RGBA", 2),
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"Alpha (RGBA)": ("RGBA", 3),
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"Cyan (CMYK)": ("CMYK", 0),
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"Magenta (CMYK)": ("CMYK", 1),
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"Yellow (CMYK)": ("CMYK", 2),
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"Black (CMYK)": ("CMYK", 3),
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"Hue (HSV)": ("HSV", 0),
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"Saturation (HSV)": ("HSV", 1),
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"Value (HSV)": ("HSV", 2),
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"Luminosity (LAB)": ("LAB", 0),
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"A (LAB)": ("LAB", 1),
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"B (LAB)": ("LAB", 2),
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"Y (YCbCr)": ("YCbCr", 0),
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"Cb (YCbCr)": ("YCbCr", 1),
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"Cr (YCbCr)": ("YCbCr", 2),
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}
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@invocation(
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"img_luminosity_adjust",
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title="Adjust Image Luminosity",
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tags=["image", "luminosity", "hsl"],
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"img_channel_offset",
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title="Offset Image Channel",
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tags=[
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"image",
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"offset",
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"red",
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"green",
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"blue",
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"alpha",
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"cyan",
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"magenta",
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"yellow",
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"black",
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"hue",
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"saturation",
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"luminosity",
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"value",
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],
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category="image",
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version="1.0.0",
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)
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class ImageLuminosityAdjustmentInvocation(BaseInvocation):
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"""Adjusts the Luminosity (Value) of an image."""
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class ImageChannelOffsetInvocation(BaseInvocation):
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"""Add or subtract a value from a specific color channel of an image."""
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image: ImageField = InputField(description="The image to adjust")
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luminosity: float = InputField(
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default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)"
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)
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channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
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offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")
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791846
def invoke(self, context: InvocationContext) -> ImageOutput:
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pil_image = context.services.images.get_pil_image(self.image.image_name)
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794-
# Convert PIL image to OpenCV format (numpy array), note color channel
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# ordering is changed from RGB to BGR
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image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
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# extract the channel and mode from the input and reference tuple
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mode = CHANNEL_FORMATS[self.channel][0]
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channel_number = CHANNEL_FORMATS[self.channel][1]
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# Convert image to HSV color space
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hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# Convert PIL image to new format
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converted_image = numpy.array(pil_image.convert(mode)).astype(int)
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image_channel = converted_image[:, :, channel_number]
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801-
# Adjust the luminosity (value)
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hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
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# Adjust the value, clipping to 0..255
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image_channel = numpy.clip(image_channel + self.offset, 0, 255)
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804-
# Convert image back to BGR color space
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image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
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# Put the channel back into the image
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converted_image[:, :, channel_number] = image_channel
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807-
# Convert back to PIL format and to original color mode
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pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
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# Convert back to RGBA format and output
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pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
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image_dto = context.services.images.create(
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image=pil_image,
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@invocation(
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"img_saturation_adjust",
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title="Adjust Image Saturation",
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tags=["image", "saturation", "hsl"],
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"img_channel_multiply",
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title="Multiply Image Channel",
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tags=[
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"image",
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"invert",
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"scale",
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"multiply",
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"red",
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"green",
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"blue",
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"alpha",
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"cyan",
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"magenta",
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"yellow",
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"black",
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"hue",
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"saturation",
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"luminosity",
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"value",
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],
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category="image",
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version="1.0.0",
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)
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class ImageSaturationAdjustmentInvocation(BaseInvocation):
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"""Adjusts the Saturation of an image."""
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class ImageChannelMultiplyInvocation(BaseInvocation):
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"""Scale a specific color channel of an image."""
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image: ImageField = InputField(description="The image to adjust")
840-
saturation: float = InputField(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
913+
channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
914+
scale: float = InputField(default=1.0, ge=0.0, description="The amount to scale the channel by.")
915+
invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")
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842917
def invoke(self, context: InvocationContext) -> ImageOutput:
843918
pil_image = context.services.images.get_pil_image(self.image.image_name)
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845-
# Convert PIL image to OpenCV format (numpy array), note color channel
846-
# ordering is changed from RGB to BGR
847-
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
920+
# extract the channel and mode from the input and reference tuple
921+
mode = CHANNEL_FORMATS[self.channel][0]
922+
channel_number = CHANNEL_FORMATS[self.channel][1]
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849-
# Convert image to HSV color space
850-
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
924+
# Convert PIL image to new format
925+
converted_image = numpy.array(pil_image.convert(mode)).astype(float)
926+
image_channel = converted_image[:, :, channel_number]
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852-
# Adjust the saturation
853-
hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
928+
# Adjust the value, clipping to 0..255
929+
image_channel = numpy.clip(image_channel * self.scale, 0, 255)
854930

855-
# Convert image back to BGR color space
856-
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
931+
# Invert the channel if requested
932+
if self.invert_channel:
933+
image_channel = 255 - image_channel
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858-
# Convert back to PIL format and to original color mode
859-
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
935+
# Put the channel back into the image
936+
converted_image[:, :, channel_number] = image_channel
937+
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# Convert back to RGBA format and output
939+
pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
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861941
image_dto = context.services.images.create(
862942
image=pil_image,

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