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https://github.com/invoke-ai/InvokeAI
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714 lines
29 KiB
Python
714 lines
29 KiB
Python
# Invocations 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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from builtins import bool, float
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from typing import Dict, List, Literal, Optional, Union
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import cv2
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import numpy as np
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from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
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LeresDetector, LineartAnimeDetector,
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LineartDetector, MediapipeFaceDetector,
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MidasDetector, MLSDdetector, NormalBaeDetector,
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OpenposeDetector, PidiNetDetector, SamDetector,
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ZoeDetector)
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from controlnet_aux.util import HWC3, ade_palette
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from PIL import Image
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from pydantic import BaseModel, Field, validator
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from ...backend.model_management import BaseModelType, ModelType
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from ..models.image import ImageCategory, ImageField, ResourceOrigin
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from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
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InvocationConfig, InvocationContext)
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from ..models.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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CONTROLNET_MODE_VALUES = Literal[tuple(
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["balanced", "more_prompt", "more_control", "unbalanced"])]
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CONTROLNET_RESIZE_VALUES = Literal[tuple(
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["just_resize", "crop_resize", "fill_resize", "just_resize_simple",])]
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class ControlNetModelField(BaseModel):
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"""ControlNet model field"""
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model_name: str = Field(description="Name of the ControlNet model")
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base_model: BaseModelType = Field(description="Base model")
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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[ControlNetModelField] = Field(
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default=None, description="The ControlNet model to use")
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# control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
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control_weight: Union[float, List[float]] = Field(
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default=1, description="The weight given to the ControlNet")
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begin_step_percent: float = Field(
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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(
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default=1, ge=0, le=1,
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description="When the ControlNet is last applied (% of total steps)")
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control_mode: CONTROLNET_MODE_VALUES = Field(
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default="balanced", description="The control mode to use")
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resize_mode: CONTROLNET_RESIZE_VALUES = Field(
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default="just_resize", description="The resize mode to use")
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@validator("control_weight")
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def validate_control_weight(cls, v):
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"""Validate that all control weights in the valid range"""
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if isinstance(v, list):
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for i in v:
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if i < -1 or i > 2:
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raise ValueError(
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'Control weights must be within -1 to 2 range')
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else:
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if v < -1 or v > 2:
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raise ValueError('Control weights must be within -1 to 2 range')
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return v
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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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"ui": {
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"type_hints": {
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"control_weight": "float",
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"control_model": "controlnet_model",
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# "control_weight": "number",
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}
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}
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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 control info")
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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: ControlNetModelField = Field(default="lllyasviel/sd-controlnet-canny",
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description="control model used")
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control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
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begin_step_percent: float = Field(default=0, ge=-1, le=2,
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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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control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode used")
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resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode used")
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# fmt: on
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "ControlNet",
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"tags": ["controlnet", "latents"],
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"type_hints": {
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"model": "model",
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"control": "control",
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# "cfg_scale": "float",
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"cfg_scale": "number",
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"control_weight": "float",
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}
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},
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}
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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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control_mode=self.control_mode,
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resize_mode=self.resize_mode,
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),
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)
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Image Processor",
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"tags": ["image", "processor"]
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},
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}
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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(self.image.image_name)
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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(image_name=image_dto.image_name)
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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(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Canny Processor",
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"tags": ["controlnet", "canny", "image", "processor"]
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},
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}
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def run_processor(self, image):
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canny_processor = CannyDetector()
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processed_image = canny_processor(
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image, self.low_threshold, self.high_threshold)
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return processed_image
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class HedImageProcessorInvocation(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Softedge(HED) Processor",
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"tags": ["controlnet", "softedge", "hed", "image", "processor"]
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},
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}
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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(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Lineart Processor",
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"tags": ["controlnet", "lineart", "image", "processor"]
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},
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}
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def run_processor(self, image):
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lineart_processor = LineartDetector.from_pretrained(
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"lllyasviel/Annotators")
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processed_image = lineart_processor(
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image, detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution, coarse=self.coarse)
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return processed_image
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class LineartAnimeImageProcessorInvocation(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Lineart Anime Processor",
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"tags": ["controlnet", "lineart", "anime", "image", "processor"]
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},
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}
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def run_processor(self, image):
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processor = LineartAnimeDetector.from_pretrained(
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"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(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Openpose Processor",
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"tags": ["controlnet", "openpose", "image", "processor"]
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},
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}
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def run_processor(self, image):
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openpose_processor = OpenposeDetector.from_pretrained(
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"lllyasviel/Annotators")
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processed_image = openpose_processor(
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image, 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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return processed_image
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class MidasDepthImageProcessorInvocation(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Midas (Depth) Processor",
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"tags": ["controlnet", "midas", "depth", "image", "processor"]
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},
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}
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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(
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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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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Normal BAE Processor",
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"tags": ["controlnet", "normal", "bae", "image", "processor"]
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},
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}
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def run_processor(self, image):
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normalbae_processor = NormalBaeDetector.from_pretrained(
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"lllyasviel/Annotators")
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processed_image = normalbae_processor(
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image, 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(
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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")
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "MLSD Processor",
|
|
"tags": ["controlnet", "mlsd", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "PIDI Processor",
|
|
"tags": ["controlnet", "pidi", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "Content Shuffle Processor",
|
|
"tags": ["controlnet", "contentshuffle", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "Zoe (Depth) Processor",
|
|
"tags": ["controlnet", "zoe", "depth", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "Mediapipe Processor",
|
|
"tags": ["controlnet", "mediapipe", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "Leres (Depth) Processor",
|
|
"tags": ["controlnet", "leres", "depth", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {
|
|
"ui": {
|
|
"title": "Tile Resample Processor",
|
|
"tags": ["controlnet", "tile", "resample", "image", "processor"]
|
|
},
|
|
}
|
|
|
|
# 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
|
|
|
|
class Config(InvocationConfig):
|
|
schema_extra = {"ui": {"title": "Segment Anything Processor", "tags": [
|
|
"controlnet", "segment", "anything", "sam", "image", "processor"]}, }
|
|
|
|
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)
|