# Invocations for ControlNet image preprocessors
# initial implementation by Gregg Helt, 2023
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
from builtins import float, bool
import cv2
import numpy as np
from typing import Literal, Optional, Union, List, Dict
from PIL import Image
from pydantic import BaseModel, Field, validator
from ...backend.model_management import BaseModelType, ModelType
from ..models.image import ImageField, ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InvocationContext,
InvocationConfig,
)
from controlnet_aux import (
CannyDetector,
HEDdetector,
LineartDetector,
LineartAnimeDetector,
MidasDetector,
MLSDdetector,
NormalBaeDetector,
OpenposeDetector,
PidiNetDetector,
ContentShuffleDetector,
ZoeDetector,
MediapipeFaceDetector,
SamDetector,
LeresDetector,
)
from controlnet_aux.util import HWC3, ade_palette
from .image import ImageOutput, PILInvocationConfig
CONTROLNET_DEFAULT_MODELS = [
###########################################
# lllyasviel sd v1.5, ControlNet v1.0 models
##############################################
"lllyasviel/sd-controlnet-canny",
"lllyasviel/sd-controlnet-depth",
"lllyasviel/sd-controlnet-hed",
"lllyasviel/sd-controlnet-seg",
"lllyasviel/sd-controlnet-openpose",
"lllyasviel/sd-controlnet-scribble",
"lllyasviel/sd-controlnet-normal",
"lllyasviel/sd-controlnet-mlsd",
#############################################
# lllyasviel sd v1.5, ControlNet v1.1 models
#############################################
"lllyasviel/control_v11p_sd15_canny",
"lllyasviel/control_v11p_sd15_openpose",
"lllyasviel/control_v11p_sd15_seg",
# "lllyasviel/control_v11p_sd15_depth", # broken
"lllyasviel/control_v11f1p_sd15_depth",
"lllyasviel/control_v11p_sd15_normalbae",
"lllyasviel/control_v11p_sd15_scribble",
"lllyasviel/control_v11p_sd15_mlsd",
"lllyasviel/control_v11p_sd15_softedge",
"lllyasviel/control_v11p_sd15s2_lineart_anime",
"lllyasviel/control_v11p_sd15_lineart",
"lllyasviel/control_v11p_sd15_inpaint",
# "lllyasviel/control_v11u_sd15_tile",
# problem (temporary?) with huffingface "lllyasviel/control_v11u_sd15_tile",
# so for now replace "lllyasviel/control_v11f1e_sd15_tile",
"lllyasviel/control_v11e_sd15_shuffle",
"lllyasviel/control_v11e_sd15_ip2p",
"lllyasviel/control_v11f1e_sd15_tile",
#################################################
# thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
##################################################
"thibaud/controlnet-sd21-openpose-diffusers",
"thibaud/controlnet-sd21-canny-diffusers",
"thibaud/controlnet-sd21-depth-diffusers",
"thibaud/controlnet-sd21-scribble-diffusers",
"thibaud/controlnet-sd21-hed-diffusers",
"thibaud/controlnet-sd21-zoedepth-diffusers",
"thibaud/controlnet-sd21-color-diffusers",
"thibaud/controlnet-sd21-openposev2-diffusers",
"thibaud/controlnet-sd21-lineart-diffusers",
"thibaud/controlnet-sd21-normalbae-diffusers",
"thibaud/controlnet-sd21-ade20k-diffusers",
##############################################
# ControlNetMediaPipeface, ControlNet v1.1
##############################################
# ["CrucibleAI/ControlNetMediaPipeFace", "diffusion_sd15"], # SD 1.5
# diffusion_sd15 needs to be passed to from_pretrained() as subfolder arg
# hacked t2l to split to model & subfolder if format is "model,subfolder"
"CrucibleAI/ControlNetMediaPipeFace,diffusion_sd15", # SD 1.5
"CrucibleAI/ControlNetMediaPipeFace", # SD 2.1?
]
CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
# crop and fill options not ready yet
# CONTROLNET_RESIZE_VALUES = Literal[tuple(["just_resize", "crop_resize", "fill_resize"])]
class ControlNetModelField(BaseModel):
"""ControlNet model field"""
model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model")
class ControlField(BaseModel):
image: ImageField = Field(default=None, description="The control image")
control_model: Optional[ControlNetModelField] = Field(default=None, description="The ControlNet model to use")
# control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
begin_step_percent: float = Field(default=0, ge=0, le=1,
description="When the ControlNet is first applied (% of total steps)")
end_step_percent: float = Field(default=1, ge=0, le=1,
description="When the ControlNet is last applied (% of total steps)")
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
# resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight")
def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range"""
if isinstance(v, list):
for i in v:
if i < -1 or i > 2:
raise ValueError('Control weights must be within -1 to 2 range')
else:
if v < -1 or v > 2:
raise ValueError('Control weights must be within -1 to 2 range')
return v
class Config:
schema_extra = {
"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"],
"ui": {
"type_hints": {
"control_weight": "float",
"control_model": "controlnet_model",
# "control_weight": "number",
}
}
}
class ControlOutput(BaseInvocationOutput):
"""node output for ControlNet info"""
# fmt: off
type: Literal["control_output"] = "control_output"
control: ControlField = Field(default=None, description="The control info")
# fmt: on
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
# fmt: off
type: Literal["controlnet"] = "controlnet"
# Inputs
image: ImageField = Field(default=None, description="The control image")
control_model: ControlNetModelField = Field(default="lllyasviel/sd-controlnet-canny",
description="control model used")
control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
begin_step_percent: float = Field(default=0, ge=-1, le=2,
description="When the ControlNet is first applied (% of total steps)")
end_step_percent: float = Field(default=1, ge=0, le=1,
description="When the ControlNet is last applied (% of total steps)")
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode used")
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["latents"],
"type_hints": {
"model": "model",
"control": "control",
# "cfg_scale": "float",
"cfg_scale": "number",
"control_weight": "float",
}
},
}
def invoke(self, context: InvocationContext) -> ControlOutput:
return ControlOutput(
control=ControlField(
image=self.image,
control_model=self.control_model,
control_weight=self.control_weight,
begin_step_percent=self.begin_step_percent,
end_step_percent=self.end_step_percent,
control_mode=self.control_mode,
),
)
class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
"""Base class for invocations that preprocess images for ControlNet"""
# fmt: off
type: Literal["image_processor"] = "image_processor"
# Inputs
image: ImageField = Field(default=None, description="The image to process")
# fmt: on
def run_processor(self, image):
# superclass just passes through image without processing
return image
def invoke(self, context: InvocationContext) -> ImageOutput:
raw_image = context.services.images.get_pil_image(self.image.image_name)
# image type should be PIL.PngImagePlugin.PngImageFile ?
processed_image = self.run_processor(raw_image)
# FIXME: what happened to image metadata?
# metadata = context.services.metadata.build_metadata(
# session_id=context.graph_execution_state_id, node=self
# )
# currently can't see processed image in node UI without a showImage node,
# so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
image_dto = context.services.images.create(
image=processed_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.CONTROL,
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate
)
"""Builds an ImageOutput and its ImageField"""
processed_image_field = ImageField(image_name=image_dto.image_name)
return ImageOutput(
image=processed_image_field,
# width=processed_image.width,
width = image_dto.width,
# height=processed_image.height,
height = image_dto.height,
# mode=processed_image.mode,
)
class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Canny edge detection for ControlNet"""
# fmt: off
type: Literal["canny_image_processor"] = "canny_image_processor"
# Input
low_threshold: int = Field(default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)")
high_threshold: int = Field(default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)")
# fmt: on
def run_processor(self, image):
canny_processor = CannyDetector()
processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
return processed_image
class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies HED edge detection to image"""
# fmt: off
type: Literal["hed_image_processor"] = "hed_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
# safe not supported in controlnet_aux v0.0.3
# safe: bool = Field(default=False, description="whether to use safe mode")
scribble: bool = Field(default=False, description="Whether to use scribble mode")
# fmt: on
def run_processor(self, image):
hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
processed_image = hed_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
# safe not supported in controlnet_aux v0.0.3
# safe=self.safe,
scribble=self.scribble,
)
return processed_image
class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies line art processing to image"""
# fmt: off
type: Literal["lineart_image_processor"] = "lineart_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
coarse: bool = Field(default=False, description="Whether to use coarse mode")
# fmt: on
def run_processor(self, image):
lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
processed_image = lineart_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
coarse=self.coarse)
return processed_image
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies line art anime processing to image"""
# fmt: off
type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
# fmt: on
def run_processor(self, image):
processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
)
return processed_image
class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies Openpose processing to image"""
# fmt: off
type: Literal["openpose_image_processor"] = "openpose_image_processor"
# Inputs
hand_and_face: bool = Field(default=False, description="Whether to use hands and face mode")
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
# fmt: on
def run_processor(self, image):
openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = openpose_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
hand_and_face=self.hand_and_face,
)
return processed_image
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies Midas depth processing to image"""
# fmt: off
type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
# Inputs
a_mult: float = Field(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
bg_th: float = Field(default=0.1, ge=0, description="Midas parameter `bg_th`")
# depth_and_normal not supported in controlnet_aux v0.0.3
# depth_and_normal: bool = Field(default=False, description="whether to use depth and normal mode")
# fmt: on
def run_processor(self, image):
midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
processed_image = midas_processor(image,
a=np.pi * self.a_mult,
bg_th=self.bg_th,
# dept_and_normal not supported in controlnet_aux v0.0.3
# depth_and_normal=self.depth_and_normal,
)
return processed_image
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies NormalBae processing to image"""
# fmt: off
type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
# fmt: on
def run_processor(self, image):
normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = normalbae_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution)
return processed_image
class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies MLSD processing to image"""
# fmt: off
type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
thr_v: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_v`")
thr_d: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_d`")
# fmt: on
def run_processor(self, image):
mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
processed_image = mlsd_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
thr_v=self.thr_v,
thr_d=self.thr_d)
return processed_image
class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies PIDI processing to image"""
# fmt: off
type: Literal["pidi_image_processor"] = "pidi_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
safe: bool = Field(default=False, description="Whether to use safe mode")
scribble: bool = Field(default=False, description="Whether to use scribble mode")
# fmt: on
def run_processor(self, image):
pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
processed_image = pidi_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
safe=self.safe,
scribble=self.scribble)
return processed_image
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies content shuffle processing to image"""
# fmt: off
type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
# Inputs
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
h: Optional[int] = Field(default=512, ge=0, description="Content shuffle `h` parameter")
w: Optional[int] = Field(default=512, ge=0, description="Content shuffle `w` parameter")
f: Optional[int] = Field(default=256, ge=0, description="Content shuffle `f` parameter")
# fmt: on
def run_processor(self, image):
content_shuffle_processor = ContentShuffleDetector()
processed_image = content_shuffle_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
h=self.h,
w=self.w,
f=self.f
)
return processed_image
# should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies Zoe depth processing to image"""
# fmt: off
type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
# fmt: on
def run_processor(self, image):
zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = zoe_depth_processor(image)
return processed_image
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies mediapipe face processing to image"""
# fmt: off
type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
# Inputs
max_faces: int = Field(default=1, ge=1, description="Maximum number of faces to detect")
min_confidence: float = Field(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
# fmt: on
def run_processor(self, image):
# MediaPipeFaceDetector throws an error if image has alpha channel
# so convert to RGB if needed
if image.mode == 'RGBA':
image = image.convert('RGB')
mediapipe_face_processor = MediapipeFaceDetector()
processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
return processed_image
class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies leres processing to image"""
# fmt: off
type: Literal["leres_image_processor"] = "leres_image_processor"
# Inputs
thr_a: float = Field(default=0, description="Leres parameter `thr_a`")
thr_b: float = Field(default=0, description="Leres parameter `thr_b`")
boost: bool = Field(default=False, description="Whether to use boost mode")
detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
# fmt: on
def run_processor(self, image):
leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
processed_image = leres_processor(image,
thr_a=self.thr_a,
thr_b=self.thr_b,
boost=self.boost,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution)
return processed_image
class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
# fmt: off
type: Literal["tile_image_processor"] = "tile_image_processor"
# Inputs
#res: int = Field(default=512, ge=0, le=1024, description="The pixel resolution for each tile")
down_sampling_rate: float = Field(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
# fmt: on
# tile_resample copied from sd-webui-controlnet/scripts/processor.py
def tile_resample(self,
np_img: np.ndarray,
res=512, # never used?
down_sampling_rate=1.0,
):
np_img = HWC3(np_img)
if down_sampling_rate < 1.1:
return np_img
H, W, C = np_img.shape
H = int(float(H) / float(down_sampling_rate))
W = int(float(W) / float(down_sampling_rate))
np_img = cv2.resize(np_img, (W, H), interpolation=cv2.INTER_AREA)
return np_img
def run_processor(self, img):
np_img = np.array(img, dtype=np.uint8)
processed_np_image = self.tile_resample(np_img,
#res=self.tile_size,
down_sampling_rate=self.down_sampling_rate
)
processed_image = Image.fromarray(processed_np_image)
return processed_image
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies segment anything processing to image"""
# fmt: off
type: Literal["segment_anything_processor"] = "segment_anything_processor"
# fmt: on
def run_processor(self, image):
# segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
segment_anything_processor = SamDetectorReproducibleColors.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
np_img = np.array(image, dtype=np.uint8)
processed_image = segment_anything_processor(np_img)
return processed_image
class SamDetectorReproducibleColors(SamDetector):
# overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
# base class show_anns() method randomizes colors,
# which seems to also lead to non-reproducible image generation
# so using ADE20k color palette instead
def show_anns(self, anns: List[Dict]):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
h, w = anns[0]['segmentation'].shape
final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
palette = ade_palette()
for i, ann in enumerate(sorted_anns):
m = ann['segmentation']
img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
# doing modulo just in case number of annotated regions exceeds number of colors in palette
ann_color = palette[i % len(palette)]
img[:, :] = ann_color
final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
return np.array(final_img, dtype=np.uint8)