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Refactored ControlNet nodes so they subclass from PreprocessedControlInvocation, and only need to override run_processor(image) (instead of reimplementing invoke())

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user1 2023-05-04 16:01:22 -07:00 committed by Kent Keirsey
parent 0864fca641
commit f2f4c37f19
1 changed files with 58 additions and 36 deletions
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42
invokeai/app/invocations/controlnet_image_processors.py
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@ -30,7 +30,7 @@ class ControlField(BaseModel):
class ControlOutput(BaseInvocationOutput): class ControlOutput(BaseInvocationOutput):
"""Base class for invocations that output ControlNet info""" """node output for ControlNet info"""
# fmt: off # fmt: off
type: Literal["control_output"] = "control_output" type: Literal["control_output"] = "control_output"
@ -38,12 +38,11 @@ class ControlOutput(BaseInvocationOutput):
# image: ImageField = Field(default=None, description="outputs just them image info (which is also included in control output)") # image: ImageField = Field(default=None, description="outputs just them image info (which is also included in control output)")
# fmt: on # fmt: on
class PreprocessedControlInvocation(BaseInvocation, PILInvocationConfig):
class CannyControlInvocation(BaseInvocation, PILInvocationConfig): """Base class for invocations that preprocess images for ControlNet"""
"""Canny edge detection for ControlNet"""
# fmt: off # fmt: off
type: Literal["cannycontrol"] = "cannycontrol" type: Literal["preprocessed_control"] = "preprocessed_control"
# Inputs # Inputs
image: ImageField = Field(default=None, description="image to process") image: ImageField = Field(default=None, description="image to process")
@ -54,17 +53,20 @@ class CannyControlInvocation(BaseInvocation, PILInvocationConfig):
# end_step_percent: float = Field(default=1, ge=0, le=1, # end_step_percent: float = Field(default=1, ge=0, le=1,
# description="% of total steps at which controlnet is last applied") # description="% of total steps at which controlnet is last applied")
# guess_mode: bool = Field(default=False, description="use guess mode (controlnet ignores prompt)") # guess_mode: bool = Field(default=False, description="use guess mode (controlnet ignores prompt)")
low_threshold: float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient")
high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
# fmt: on # fmt: on
# This super class handles invoke() call, which in turn calls run_processor(image)
# subclasses override run_processor instead of implementing their own invoke()
def run_processor(self, image):
# super class pass through of image
return image
def invoke(self, context: InvocationContext) -> ControlOutput: def invoke(self, context: InvocationContext) -> ControlOutput:
image = context.services.images.get( image = context.services.images.get(
self.image.image_type, self.image.image_name self.image.image_type, self.image.image_name
) )
canny_processor = CannyDetector() # image type should be PIL.PngImagePlugin.PngImageFile ?
processed_image = canny_processor(image, self.low_threshold, self.high_threshold) processed_image = self.run_processor(image)
image_type = ImageType.INTERMEDIATE image_type = ImageType.INTERMEDIATE
image_name = context.services.images.create_name( image_name = context.services.images.create_name(
context.graph_execution_state_id, self.id context.graph_execution_state_id, self.id
@ -87,3 +89,23 @@ class CannyControlInvocation(BaseInvocation, PILInvocationConfig):
) )
) )
class CannyControlInvocation(PreprocessedControlInvocation, PILInvocationConfig):
"""Canny edge detection for ControlNet"""
# fmt: off
type: Literal["cannycontrol"] = "cannycontrol"
# Inputs
low_threshold: float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient")
high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
# fmt: on
def run_processor(self, image):
print("**** running Canny processor ****")
print("image type: ", type(image))
canny_processor = CannyDetector()
processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
print("processed image type: ", type(image))
return processed_image