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https://github.com/invoke-ai/InvokeAI
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429 lines
20 KiB
Python
429 lines
20 KiB
Python
# InvokeAI nodes for ControlNet image preprocessors
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# initial implementation by Gregg Helt, 2023
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# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
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import numpy as np
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from typing import Literal, Optional, Union, List
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from PIL import Image, ImageFilter, ImageOps
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from pydantic import BaseModel, Field
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from ..models.image import ImageField, ImageCategory, ResourceOrigin
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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InvocationContext,
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InvocationConfig,
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)
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from controlnet_aux import (
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CannyDetector,
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HEDdetector,
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LineartDetector,
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LineartAnimeDetector,
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MidasDetector,
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MLSDdetector,
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NormalBaeDetector,
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OpenposeDetector,
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PidiNetDetector,
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ContentShuffleDetector,
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ZoeDetector,
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MediapipeFaceDetector,
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)
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from .image import ImageOutput, PILInvocationConfig
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CONTROLNET_DEFAULT_MODELS = [
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###########################################
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# lllyasviel sd v1.5, ControlNet v1.0 models
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##############################################
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"lllyasviel/sd-controlnet-canny",
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"lllyasviel/sd-controlnet-depth",
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"lllyasviel/sd-controlnet-hed",
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"lllyasviel/sd-controlnet-seg",
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"lllyasviel/sd-controlnet-openpose",
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"lllyasviel/sd-controlnet-scribble",
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"lllyasviel/sd-controlnet-normal",
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"lllyasviel/sd-controlnet-mlsd",
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#############################################
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# lllyasviel sd v1.5, ControlNet v1.1 models
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#############################################
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"lllyasviel/control_v11p_sd15_canny",
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"lllyasviel/control_v11p_sd15_openpose",
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"lllyasviel/control_v11p_sd15_seg",
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# "lllyasviel/control_v11p_sd15_depth", # broken
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"lllyasviel/control_v11f1p_sd15_depth",
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"lllyasviel/control_v11p_sd15_normalbae",
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"lllyasviel/control_v11p_sd15_scribble",
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"lllyasviel/control_v11p_sd15_mlsd",
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"lllyasviel/control_v11p_sd15_softedge",
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"lllyasviel/control_v11p_sd15s2_lineart_anime",
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"lllyasviel/control_v11p_sd15_lineart",
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"lllyasviel/control_v11p_sd15_inpaint",
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# "lllyasviel/control_v11u_sd15_tile",
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# problem (temporary?) with huffingface "lllyasviel/control_v11u_sd15_tile",
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# so for now replace "lllyasviel/control_v11f1e_sd15_tile",
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"lllyasviel/control_v11e_sd15_shuffle",
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"lllyasviel/control_v11e_sd15_ip2p",
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"lllyasviel/control_v11f1e_sd15_tile",
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#################################################
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# thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
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##################################################
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"thibaud/controlnet-sd21-openpose-diffusers",
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"thibaud/controlnet-sd21-canny-diffusers",
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"thibaud/controlnet-sd21-depth-diffusers",
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"thibaud/controlnet-sd21-scribble-diffusers",
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"thibaud/controlnet-sd21-hed-diffusers",
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"thibaud/controlnet-sd21-zoedepth-diffusers",
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"thibaud/controlnet-sd21-color-diffusers",
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"thibaud/controlnet-sd21-openposev2-diffusers",
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"thibaud/controlnet-sd21-lineart-diffusers",
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"thibaud/controlnet-sd21-normalbae-diffusers",
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"thibaud/controlnet-sd21-ade20k-diffusers",
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##############################################
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# ControlNetMediaPipeface, ControlNet v1.1
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##############################################
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# ["CrucibleAI/ControlNetMediaPipeFace", "diffusion_sd15"], # SD 1.5
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# diffusion_sd15 needs to be passed to from_pretrained() as subfolder arg
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# hacked t2l to split to model & subfolder if format is "model,subfolder"
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"CrucibleAI/ControlNetMediaPipeFace,diffusion_sd15", # SD 1.5
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"CrucibleAI/ControlNetMediaPipeFace", # SD 2.1?
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]
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CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
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class ControlField(BaseModel):
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image: ImageField = Field(default=None, description="The control image")
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control_model: Optional[str] = Field(default=None, description="The ControlNet model to use")
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control_weight: Optional[float] = Field(default=1, description="The weight given to the ControlNet")
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begin_step_percent: float = Field(default=0, ge=0, le=1,
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description="When the ControlNet is first applied (% of total steps)")
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end_step_percent: float = Field(default=1, ge=0, le=1,
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description="When the ControlNet is last applied (% of total steps)")
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class Config:
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schema_extra = {
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"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"]
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}
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class ControlOutput(BaseInvocationOutput):
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"""node output for ControlNet info"""
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# fmt: off
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type: Literal["control_output"] = "control_output"
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control: ControlField = Field(default=None, description="The output control image")
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# fmt: on
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class ControlNetInvocation(BaseInvocation):
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"""Collects ControlNet info to pass to other nodes"""
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# fmt: off
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type: Literal["controlnet"] = "controlnet"
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# Inputs
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image: ImageField = Field(default=None, description="The control image")
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control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
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description="The ControlNet model to use")
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control_weight: float = Field(default=1.0, ge=0, le=1, description="The weight given to the ControlNet")
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# TODO: add support in backend core for begin_step_percent, end_step_percent, guess_mode
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begin_step_percent: float = Field(default=0, ge=0, le=1,
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description="When the ControlNet is first applied (% of total steps)")
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end_step_percent: float = Field(default=1, ge=0, le=1,
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description="When the ControlNet is last applied (% of total steps)")
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# fmt: on
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def invoke(self, context: InvocationContext) -> ControlOutput:
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return ControlOutput(
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control=ControlField(
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image=self.image,
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control_model=self.control_model,
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control_weight=self.control_weight,
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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),
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)
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# TODO: move image processors to separate file (image_analysis.py
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class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
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"""Base class for invocations that preprocess images for ControlNet"""
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# fmt: off
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type: Literal["image_processor"] = "image_processor"
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# Inputs
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image: ImageField = Field(default=None, description="The image to process")
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# fmt: on
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def run_processor(self, image):
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# superclass just passes through image without processing
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return image
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def invoke(self, context: InvocationContext) -> ImageOutput:
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raw_image = context.services.images.get_pil_image(
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self.image.image_origin, self.image.image_name
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)
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# image type should be PIL.PngImagePlugin.PngImageFile ?
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processed_image = self.run_processor(raw_image)
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# FIXME: what happened to image metadata?
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# metadata = context.services.metadata.build_metadata(
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# session_id=context.graph_execution_state_id, node=self
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# )
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# currently can't see processed image in node UI without a showImage node,
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# so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
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image_dto = context.services.images.create(
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image=processed_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.CONTROL,
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session_id=context.graph_execution_state_id,
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node_id=self.id,
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is_intermediate=self.is_intermediate
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)
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"""Builds an ImageOutput and its ImageField"""
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processed_image_field = ImageField(
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image_name=image_dto.image_name,
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image_origin=image_dto.image_origin,
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)
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return ImageOutput(
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image=processed_image_field,
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# width=processed_image.width,
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width = image_dto.width,
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# height=processed_image.height,
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height = image_dto.height,
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# mode=processed_image.mode,
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)
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class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Canny edge detection for ControlNet"""
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# fmt: off
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type: Literal["canny_image_processor"] = "canny_image_processor"
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# Input
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low_threshold: int = Field(default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)")
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high_threshold: int = Field(default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)")
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# fmt: on
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def run_processor(self, image):
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canny_processor = CannyDetector()
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processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
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return processed_image
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class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies HED edge detection to image"""
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# fmt: off
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type: Literal["hed_image_processor"] = "hed_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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# safe not supported in controlnet_aux v0.0.3
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# safe: bool = Field(default=False, description="whether to use safe mode")
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scribble: bool = Field(default=False, description="Whether to use scribble mode")
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# fmt: on
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def run_processor(self, image):
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hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
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processed_image = hed_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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# safe not supported in controlnet_aux v0.0.3
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# safe=self.safe,
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scribble=self.scribble,
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)
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return processed_image
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class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies line art processing to image"""
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# fmt: off
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type: Literal["lineart_image_processor"] = "lineart_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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coarse: bool = Field(default=False, description="Whether to use coarse mode")
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# fmt: on
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def run_processor(self, image):
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lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = lineart_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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coarse=self.coarse)
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return processed_image
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class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies line art anime processing to image"""
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# fmt: off
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type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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# fmt: on
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def run_processor(self, image):
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processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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)
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return processed_image
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class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies Openpose processing to image"""
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# fmt: off
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type: Literal["openpose_image_processor"] = "openpose_image_processor"
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# Inputs
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hand_and_face: bool = Field(default=False, description="Whether to use hands and face mode")
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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# fmt: on
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def run_processor(self, image):
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openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = openpose_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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hand_and_face=self.hand_and_face,
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)
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return processed_image
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class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies Midas depth processing to image"""
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# fmt: off
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type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
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# Inputs
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a_mult: float = Field(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
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bg_th: float = Field(default=0.1, ge=0, description="Midas parameter `bg_th`")
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# depth_and_normal not supported in controlnet_aux v0.0.3
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# depth_and_normal: bool = Field(default=False, description="whether to use depth and normal mode")
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# fmt: on
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def run_processor(self, image):
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midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = midas_processor(image,
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a=np.pi * self.a_mult,
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bg_th=self.bg_th,
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# dept_and_normal not supported in controlnet_aux v0.0.3
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# depth_and_normal=self.depth_and_normal,
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)
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return processed_image
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class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies NormalBae processing to image"""
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# fmt: off
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type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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# fmt: on
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def run_processor(self, image):
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normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = normalbae_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution)
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return processed_image
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class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies MLSD processing to image"""
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# fmt: off
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type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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thr_v: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_v`")
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thr_d: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_d`")
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# fmt: on
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def run_processor(self, image):
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mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
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processed_image = mlsd_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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thr_v=self.thr_v,
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thr_d=self.thr_d)
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return processed_image
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class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies PIDI processing to image"""
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# fmt: off
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type: Literal["pidi_image_processor"] = "pidi_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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safe: bool = Field(default=False, description="Whether to use safe mode")
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scribble: bool = Field(default=False, description="Whether to use scribble mode")
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# fmt: on
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def run_processor(self, image):
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pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = pidi_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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safe=self.safe,
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scribble=self.scribble)
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return processed_image
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class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies content shuffle processing to image"""
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# fmt: off
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type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
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# Inputs
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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h: Union[int, None] = Field(default=512, ge=0, description="Content shuffle `h` parameter")
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w: Union[int, None] = Field(default=512, ge=0, description="Content shuffle `w` parameter")
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f: Union[int, None] = Field(default=256, ge=0, description="Content shuffle `f` parameter")
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# fmt: on
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def run_processor(self, image):
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content_shuffle_processor = ContentShuffleDetector()
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processed_image = content_shuffle_processor(image,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution,
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h=self.h,
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w=self.w,
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f=self.f
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)
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return processed_image
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# should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
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class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies Zoe depth processing to image"""
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# fmt: off
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type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
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# fmt: on
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def run_processor(self, image):
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zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = zoe_depth_processor(image)
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return processed_image
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class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies mediapipe face processing to image"""
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# fmt: off
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type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
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# Inputs
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max_faces: int = Field(default=1, ge=1, description="Maximum number of faces to detect")
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min_confidence: float = Field(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
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# fmt: on
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def run_processor(self, image):
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mediapipe_face_processor = MediapipeFaceDetector()
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processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
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return processed_image
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