Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

changes to base class for controlnet nodes

Browse Source
This commit is contained in:
user1 2023-05-04 17:06:49 -07:00 committed by Kent Keirsey
parent f2f4c37f19
commit dc12fa6cd6
1 changed files with 62 additions and 47 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -9,12 +9,24 @@ from .baseinvocation import (
InvocationConfig, InvocationConfig,
) )
from controlnet_aux import CannyDetector from controlnet_aux import (
CannyDetector,
HEDdetector,
LineartDetector,
LineartAnimeDetector,
MidasDetector,
MLSDdetector,
NormalBaeDetector,
OpenposeDetector,
PidiNetDetector,
ContentShuffleDetector,
# StyleShuffleDetector,
ZoeDetector)
from .image import ImageOutput, build_image_output, PILInvocationConfig from .image import ImageOutput, build_image_output, PILInvocationConfig
class ControlField(BaseModel): class ControlField(BaseModel):
image: ImageField = Field(default=None, description="processed image") image: ImageField = Field(default=None, description="processed image")
# width: Optional[int] = Field(default=None, description="The width of the image in pixels") # width: Optional[int] = Field(default=None, description="The width of the image in pixels")
# height: Optional[int] = Field(default=None, description="The height of the image in pixels") # height: Optional[int] = Field(default=None, description="The height of the image in pixels")
@ -38,6 +50,7 @@ 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 PreprocessedControlInvocation(BaseInvocation, PILInvocationConfig):
"""Base class for invocations that preprocess images for ControlNet""" """Base class for invocations that preprocess images for ControlNet"""
@ -48,6 +61,7 @@ class PreprocessedControlInvocation(BaseInvocation, PILInvocationConfig):
image: ImageField = Field(default=None, description="image to process") image: ImageField = Field(default=None, description="image to process")
control_model: str = Field(default=None, description="control model to use") control_model: str = Field(default=None, description="control model to use")
control_weight: float = Field(default=0.5, ge=0, le=1, description="control weight") control_weight: float = Field(default=0.5, ge=0, le=1, description="control weight")
# begin_step_percent: float = Field(default=0, ge=0, le=1, # begin_step_percent: float = Field(default=0, ge=0, le=1,
# description="% of total steps at which controlnet is first applied") # description="% of total steps at which controlnet is first applied")
# end_step_percent: float = Field(default=1, ge=0, le=1, # end_step_percent: float = Field(default=1, ge=0, le=1,
@ -58,7 +72,7 @@ class PreprocessedControlInvocation(BaseInvocation, PILInvocationConfig):
# This super class handles invoke() call, which in turn calls run_processor(image) # This super class handles invoke() call, which in turn calls run_processor(image)
# subclasses override run_processor instead of implementing their own invoke() # subclasses override run_processor instead of implementing their own invoke()
def run_processor(self, image): def run_processor(self, image):
# super class pass through of image # superclass just passes through image without processing
return image return image
def invoke(self, context: InvocationContext) -> ControlOutput: def invoke(self, context: InvocationContext) -> ControlOutput:
@ -94,10 +108,11 @@ class CannyControlInvocation(PreprocessedControlInvocation, PILInvocationConfig)
"""Canny edge detection for ControlNet""" """Canny edge detection for ControlNet"""
# fmt: off # fmt: off
type: Literal["cannycontrol"] = "cannycontrol" type: Literal["canny_control"] = "canny_control"
# Inputs # Inputs
low_threshold: float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient") 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") high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
# fmt: on # fmt: on
def run_processor(self, image): def run_processor(self, image):