extensions-builtin/sd_forge_controlnet/scripts/controlnet.py

import functools
from typing import Optional, TYPE_CHECKING

if TYPE_CHECKING:
    from modules_forge.supported_preprocessor import Preprocessor

import cv2
import gradio as gr
import numpy as np
import torch
from lib_controlnet import external_code, global_state
from lib_controlnet.api import controlnet_api
from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup
from lib_controlnet.enums import HiResFixOption
from lib_controlnet.external_code import ControlNetUnit
from lib_controlnet.infotext import Infotext
from lib_controlnet.logging import logger
from lib_controlnet.utils import align_dim_latent, crop_and_resize_image, judge_image_type, prepare_mask, set_numpy_seed
from PIL import Image, ImageOps

from modules import images, masking, script_callbacks, scripts, shared
from modules.processing import StableDiffusionProcessing, StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img
from modules_forge.forge_util import HWC3, numpy_to_pytorch
from modules_forge.shared import try_load_supported_control_model
from modules_forge.supported_controlnet import ControlModelPatcher

global_state.update_controlnet_filenames()


@functools.lru_cache(maxsize=getattr(shared.opts, "control_net_model_cache_size", 1))
def cached_controlnet_loader(filename):
    return try_load_supported_control_model(filename)


class ControlNetCachedParameters:
    def __init__(self):
        self.preprocessor = None
        self.model = None
        self.control_cond = None
        self.control_cond_for_hr_fix = None
        self.control_mask = None
        self.control_mask_for_hr_fix = None


class ControlNetForForgeOfficial(scripts.Script):
    sorting_priority = 10

    def title(self):
        return "ControlNet"

    def show(self, is_img2img):
        return scripts.AlwaysVisible

    def ui(self, is_img2img):
        default_unit = ControlNetUnit(enabled=False, module="None", model="None")
        elem_id_tabname = f"{'img2img' if is_img2img else 'txt2img'}_controlnet"
        infotext = Infotext()
        ui_groups = []
        controls = []

        with gr.Group(elem_id=elem_id_tabname):
            with gr.Accordion(
                open=False,
                label="ControlNet Integrated",
                elem_id="controlnet",
                elem_classes=["controlnet"],
            ):
                with gr.Tabs(elem_classes="controlnet_tabs"):
                    max_models = shared.opts.data.get("control_net_unit_count", 3)
                    for i in range(max_models):
                        with gr.Tab(label=f"ControlNet Unit {i + 1}", id=i):
                            group = ControlNetUiGroup(is_img2img, default_unit)
                            ui_groups.append(group)
                            controls.append(group.render(f"ControlNet-{i}", elem_id_tabname))

        for i, ui_group in enumerate(ui_groups):
            infotext.register_unit(i, ui_group)

        if shared.opts.data.get("control_net_sync_field_args", True):
            self.infotext_fields = infotext.infotext_fields
            self.paste_field_names = infotext.paste_field_names

        return controls

    def get_enabled_units(self, units: list[ControlNetUnit]):  # Parse dict from API calls
        units = [ControlNetUnit.from_dict(unit) if isinstance(unit, dict) else unit for unit in units]
        assert all(isinstance(unit, ControlNetUnit) for unit in units)
        enabled_units = [x for x in units if x.enabled]
        return enabled_units

    @staticmethod
    def try_crop_image_with_a1111_mask(p: StableDiffusionProcessing, input_image: np.ndarray, resize_mode: external_code.ResizeMode, preprocessor: "Preprocessor") -> np.ndarray:
        a1111_mask_image: Optional[Image.Image] = getattr(p, "image_mask", None)
        is_only_masked_inpaint: bool = issubclass(type(p), StableDiffusionProcessingImg2Img) and p.inpaint_full_res and a1111_mask_image is not None

        if preprocessor.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab and is_only_masked_inpaint:
            logger.info("Crop input image based on A1111 mask.")
            input_image = [input_image[:, :, i] for i in range(input_image.shape[2])]
            input_image = [Image.fromarray(x) for x in input_image]

            mask = prepare_mask(a1111_mask_image, p)

            crop_region = masking.get_crop_region(np.array(mask), p.inpaint_full_res_padding)
            crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height)

            input_image = [images.resize_image(resize_mode.int_value(), i, mask.width, mask.height) for i in input_image]
            input_image = [x.crop(crop_region) for x in input_image]
            input_image = [images.resize_image(external_code.ResizeMode.OUTER_FIT.int_value(), x, p.width, p.height) for x in input_image]
            input_image = [np.asarray(x)[:, :, 0] for x in input_image]
            input_image = np.stack(input_image, axis=2)

        return input_image

    def get_input_data(self, p: StableDiffusionProcessing, unit: ControlNetUnit, preprocessor: "Preprocessor", h: int, w: int):
        resize_mode = external_code.resize_mode_from_value(unit.resize_mode)
        image_list = []

        assert unit.use_preview_as_input is False

        a1111_i2i_image = getattr(p, "init_images", [None])[0]
        a1111_i2i_mask = getattr(p, "image_mask", None)

        if a1111_i2i_mask is not None and getattr(p, "inpainting_mask_invert", False):
            a1111_i2i_mask = ImageOps.invert(a1111_i2i_mask)

        using_a1111_data = False

        if unit.image is None:
            if isinstance(p, StableDiffusionProcessingImg2Img):
                resize_mode = external_code.resize_mode_from_value(p.resize_mode)
                image = HWC3(np.asarray(a1111_i2i_image))
                using_a1111_data = True
            else:
                image = None
        elif (unit.image["image"] < 5).all() and (unit.image["mask"] > 5).any():
            image = unit.image["mask"]
        else:
            image = unit.image["image"]

        if not isinstance(image, np.ndarray):
            logger.error("ControlNet is enabled but no input image is given...")
            raise ValueError

        image = HWC3(image)

        if using_a1111_data:
            mask = None if a1111_i2i_mask is None else HWC3(np.asarray(a1111_i2i_mask))
        elif unit.mask_image is not None and (unit.mask_image["image"] > 5).any():
            mask = unit.mask_image["image"]
        elif unit.mask_image is not None and (unit.mask_image["mask"] > 5).any():
            mask = unit.mask_image["mask"]
        elif unit.image is not None and (unit.image["mask"] > 5).any():
            mask = unit.image["mask"]
        else:
            mask = None

        image = self.try_crop_image_with_a1111_mask(p, image, resize_mode, preprocessor)

        if mask is not None:
            mask = cv2.resize(
                HWC3(mask),
                (image.shape[1], image.shape[0]),
                interpolation=cv2.INTER_NEAREST,
            )
            mask = self.try_crop_image_with_a1111_mask(p, mask, resize_mode, preprocessor)

        image_list = [[image, mask]]

        if resize_mode == external_code.ResizeMode.OUTER_FIT and preprocessor.expand_mask_when_resize_and_fill:
            new_image_list = []
            for input_image, input_mask in image_list:
                if input_mask is None:
                    input_mask = np.zeros_like(input_image)
                input_mask = crop_and_resize_image(
                    input_mask,
                    external_code.ResizeMode.OUTER_FIT,
                    h,
                    w,
                    fill_border_with_255=True,
                )
                input_image = crop_and_resize_image(
                    input_image,
                    external_code.ResizeMode.OUTER_FIT,
                    h,
                    w,
                    fill_border_with_255=False,
                )
                new_image_list.append((input_image, input_mask))
            image_list = new_image_list

        return image_list, resize_mode

    @staticmethod
    def get_target_dimensions(p: StableDiffusionProcessing) -> tuple[int, int, int, int]:
        """Returns (h, w, hr_h, hr_w)."""
        h = align_dim_latent(p.height)
        w = align_dim_latent(p.width)

        high_res_fix = getattr(p, "enable_hr", False) and isinstance(p, StableDiffusionProcessingTxt2Img)

        if high_res_fix:
            if p.hr_resize_x == 0 and p.hr_resize_y == 0:
                hr_y = int(p.height * p.hr_scale)
                hr_x = int(p.width * p.hr_scale)
            else:
                hr_y, hr_x = p.hr_resize_y, p.hr_resize_x
            hr_y = align_dim_latent(hr_y)
            hr_x = align_dim_latent(hr_x)
        else:
            hr_y = h
            hr_x = w

        return h, w, hr_y, hr_x

    @torch.no_grad()
    def process_unit_after_click_generate(self, p: StableDiffusionProcessing, unit: ControlNetUnit, params: ControlNetCachedParameters, *args, **kwargs) -> bool:

        h, w, hr_y, hr_x = self.get_target_dimensions(p)

        has_high_res_fix = isinstance(p, StableDiffusionProcessingTxt2Img) and getattr(p, "enable_hr", False)

        if unit.use_preview_as_input:
            unit.module = "None"

        preprocessor = global_state.get_preprocessor(unit.module)

        try:
            input_list, resize_mode = self.get_input_data(p, unit, preprocessor, h, w)
        except ValueError:
            return False

        preprocessor_outputs = []
        control_masks = []
        preprocessor_output_is_image = False
        preprocessor_output = None

        def optional_tqdm(iterable, use_tqdm):
            from tqdm import tqdm

            return tqdm(iterable) if use_tqdm else iterable

        for input_image, input_mask in optional_tqdm(input_list, len(input_list) > 1):
            if unit.pixel_perfect:
                unit.processor_res = external_code.pixel_perfect_resolution(
                    input_image,
                    target_H=h,
                    target_W=w,
                    resize_mode=resize_mode,
                )

            seed = set_numpy_seed(p)
            logger.debug(f"Use numpy seed {seed}.")
            logger.info(f"Using preprocessor: {unit.module}")
            logger.info(f"preprocessor resolution = {unit.processor_res}")

            preprocessor_output = preprocessor(
                input_image=input_image,
                input_mask=input_mask,
                resolution=unit.processor_res,
                slider_1=unit.threshold_a,
                slider_2=unit.threshold_b,
            )

            preprocessor_outputs.append(preprocessor_output)
            preprocessor_output_is_image = judge_image_type(preprocessor_output)

            if input_mask is not None:
                control_masks.append(input_mask)

            if len(input_list) > 1 and not preprocessor_output_is_image:
                logger.info("Batch wise input only support controlnet, control-lora, and t2i adapters!")
                break

        if has_high_res_fix:
            hr_option = HiResFixOption.from_value(unit.hr_option)
        else:
            hr_option = HiResFixOption.BOTH

        alignment_indices = [i % len(preprocessor_outputs) for i in range(p.batch_size)]

        def attach_extra_result_image(img: np.ndarray, is_high_res: bool = False):
            if not shared.opts.data.get("control_net_no_detectmap", False) and ((is_high_res and hr_option.high_res_enabled) or (not is_high_res and hr_option.low_res_enabled)) and unit.save_detected_map:
                p.extra_result_images.append(img)

        if preprocessor_output_is_image:
            params.control_cond = []
            params.control_cond_for_hr_fix = []

            for preprocessor_output in preprocessor_outputs:
                control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w)
                attach_extra_result_image(external_code.visualize_inpaint_mask(control_cond))
                params.control_cond.append(numpy_to_pytorch(control_cond).movedim(-1, 1))

            params.control_cond = torch.cat(params.control_cond, dim=0)[alignment_indices].contiguous()

            if has_high_res_fix:
                for preprocessor_output in preprocessor_outputs:
                    control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x)
                    attach_extra_result_image(
                        external_code.visualize_inpaint_mask(control_cond_for_hr_fix),
                        is_high_res=True,
                    )
                    params.control_cond_for_hr_fix.append(numpy_to_pytorch(control_cond_for_hr_fix).movedim(-1, 1))
                params.control_cond_for_hr_fix = torch.cat(params.control_cond_for_hr_fix, dim=0)[alignment_indices].contiguous()
            else:
                params.control_cond_for_hr_fix = params.control_cond
        else:
            params.control_cond = preprocessor_output
            params.control_cond_for_hr_fix = preprocessor_output
            attach_extra_result_image(input_image)

        if len(control_masks) > 0:
            params.control_mask = []
            params.control_mask_for_hr_fix = []

            for input_mask in control_masks:
                fill_border = preprocessor.fill_mask_with_one_when_resize_and_fill
                control_mask = crop_and_resize_image(input_mask, resize_mode, h, w, fill_border)
                attach_extra_result_image(control_mask)
                control_mask = numpy_to_pytorch(control_mask).movedim(-1, 1)[:, :1]
                params.control_mask.append(control_mask)

                if has_high_res_fix:
                    control_mask_for_hr_fix = crop_and_resize_image(input_mask, resize_mode, hr_y, hr_x, fill_border)
                    attach_extra_result_image(control_mask_for_hr_fix, is_high_res=True)
                    control_mask_for_hr_fix = numpy_to_pytorch(control_mask_for_hr_fix).movedim(-1, 1)[:, :1]
                    params.control_mask_for_hr_fix.append(control_mask_for_hr_fix)

            params.control_mask = torch.cat(params.control_mask, dim=0)[alignment_indices].contiguous()
            if has_high_res_fix:
                params.control_mask_for_hr_fix = torch.cat(params.control_mask_for_hr_fix, dim=0)[alignment_indices].contiguous()
            else:
                params.control_mask_for_hr_fix = params.control_mask

        if preprocessor.do_not_need_model:
            model_filename = "Not Needed"
            params.model = ControlModelPatcher()
        else:
            if unit.model == "None":
                logger.error("You have not selected any control model!")
                return False
            model_filename = global_state.get_controlnet_filename(unit.model)
            params.model = cached_controlnet_loader(model_filename)
            if params.model is None:
                logger.error(f"Failed to recognize {model_filename}...")
                return False

        params.preprocessor = preprocessor

        params.preprocessor.process_after_running_preprocessors(process=p, params=params, **kwargs)
        params.model.process_after_running_preprocessors(process=p, params=params, **kwargs)

        logger.info(f"{type(params.model).__name__}: {model_filename}")
        return True

    @torch.no_grad()
    def process_unit_before_every_sampling(self, p: StableDiffusionProcessing, unit: ControlNetUnit, params: ControlNetCachedParameters, *args, **kwargs):

        is_hr_pass = getattr(p, "is_hr_pass", False)

        has_high_res_fix = isinstance(p, StableDiffusionProcessingTxt2Img) and getattr(p, "enable_hr", False)

        if has_high_res_fix:
            hr_option = HiResFixOption.from_value(unit.hr_option)
        else:
            hr_option = HiResFixOption.BOTH

        if has_high_res_fix and is_hr_pass and (not hr_option.high_res_enabled):
            logger.info("ControlNet Skipped High-res pass.")
            return

        if has_high_res_fix and (not is_hr_pass) and (not hr_option.low_res_enabled):
            logger.info("ControlNet Skipped Low-res pass.")
            return

        if is_hr_pass:
            cond = params.control_cond_for_hr_fix
            mask = params.control_mask_for_hr_fix
        else:
            cond = params.control_cond
            mask = params.control_mask

        kwargs.update(
            dict(
                unit=unit,
                params=params,
                cond_original=cond.clone() if isinstance(cond, torch.Tensor) else cond,
                mask_original=mask.clone() if isinstance(mask, torch.Tensor) else mask,
            )
        )

        params.model.strength = float(unit.weight)
        params.model.start_percent = float(unit.guidance_start)
        params.model.end_percent = float(unit.guidance_end)
        params.model.positive_advanced_weighting = None
        params.model.negative_advanced_weighting = None
        params.model.advanced_frame_weighting = None
        params.model.advanced_sigma_weighting = None

        soft_weighting = {
            "input": [
                0.09941396206337118,
                0.12050177219802567,
                0.14606275417942507,
                0.17704576264172736,
                0.214600924414215,
                0.26012233262329093,
                0.3152997971191405,
                0.3821815722656249,
                0.4632503906249999,
                0.561515625,
                0.6806249999999999,
                0.825,
            ],
            "middle": [0.561515625] if p.sd_model.is_sdxl else [1.0],
            "output": [
                0.09941396206337118,
                0.12050177219802567,
                0.14606275417942507,
                0.17704576264172736,
                0.214600924414215,
                0.26012233262329093,
                0.3152997971191405,
                0.3821815722656249,
                0.4632503906249999,
                0.561515625,
                0.6806249999999999,
                0.825,
            ],
        }

        zero_weighting = {"input": [0.0] * 12, "middle": [0.0], "output": [0.0] * 12}

        if unit.control_mode == external_code.ControlMode.CONTROL.value:
            params.model.positive_advanced_weighting = soft_weighting.copy()
            params.model.negative_advanced_weighting = zero_weighting.copy()

        if unit.control_mode == external_code.ControlMode.PROMPT.value:
            params.model.positive_advanced_weighting = soft_weighting.copy()
            params.model.negative_advanced_weighting = soft_weighting.copy()

        if is_hr_pass and params.preprocessor.use_soft_projection_in_hr_fix:
            params.model.positive_advanced_weighting = soft_weighting.copy()
            params.model.negative_advanced_weighting = soft_weighting.copy()

        cond, mask = params.preprocessor.process_before_every_sampling(p, cond, mask, *args, **kwargs)

        params.model.advanced_mask_weighting = mask

        params.model.process_before_every_sampling(p, cond, mask, *args, **kwargs)

        logger.info(f"ControlNet Method {params.preprocessor.name} patched.")

    @staticmethod
    def bound_check_params(unit: ControlNetUnit) -> None:
        """
        Checks and corrects negative parameters in ControlNetUnit 'unit'.
        Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
        their default values if negative.

        Args:
            unit (ControlNetUnit): The ControlNetUnit instance to check.
        """
        preprocessor = global_state.get_preprocessor(unit.module)

        if unit.processor_res < 0:
            unit.processor_res = int(preprocessor.slider_resolution.gradio_update_kwargs.get("value", 512))
        if unit.threshold_a < 0:
            unit.threshold_a = int(preprocessor.slider_1.gradio_update_kwargs.get("value", 1.0))
        if unit.threshold_b < 0:
            unit.threshold_b = int(preprocessor.slider_2.gradio_update_kwargs.get("value", 1.0))

    @torch.no_grad()
    def process_unit_after_every_sampling(self, p: StableDiffusionProcessing, unit: ControlNetUnit, params: ControlNetCachedParameters, *args, **kwargs):
        params.preprocessor.process_after_every_sampling(p, params, *args, **kwargs)
        params.model.process_after_every_sampling(p, params, *args, **kwargs)

    @torch.no_grad()
    def process(self, p, *args, **kwargs):
        self.current_params = {}
        enabled_units = self.get_enabled_units(args)
        Infotext.write_infotext(enabled_units, p)
        for i, unit in enumerate(enabled_units):
            self.bound_check_params(unit)
            params = ControlNetCachedParameters()
            if self.process_unit_after_click_generate(p, unit, params, *args, **kwargs):
                self.current_params[i] = params

    @torch.no_grad()
    def process_before_every_sampling(self, p, *args, **kwargs):
        for i, unit in enumerate(self.get_enabled_units(args)):
            if i not in self.current_params:
                logger.warning(f"ControlNet Unit {i + 1} is skipped...")
                continue
            self.process_unit_before_every_sampling(p, unit, self.current_params[i], *args, **kwargs)

    @torch.no_grad()
    def postprocess_batch_list(self, p, pp, *args, **kwargs):
        for i, unit in enumerate(self.get_enabled_units(args)):
            if i in self.current_params:
                self.process_unit_after_every_sampling(p, unit, self.current_params[i], pp, *args, **kwargs)

    def postprocess(self, *args):
        self.current_params = {}


def on_ui_settings():
    section = ("control_net", "ControlNet")
    category_id = "sd"

    shared.opts.add_option(
        "control_net_models_path",
        shared.OptionInfo(
            "",
            "Extra Path to look for ControlNet Models",
            section=section,
            category_id=category_id,
        ).info("e.g. training output directory"),
    )
    shared.opts.add_option(
        "control_net_unit_count",
        shared.OptionInfo(
            3,
            "Number of ControlNet Units",
            gr.Slider,
            {"minimum": 1, "maximum": 5, "step": 1},
            section=section,
            category_id=category_id,
        ).needs_reload_ui(),
    )
    shared.opts.add_option(
        "control_net_model_cache_size",
        shared.OptionInfo(
            3,
            "Number of Models to Cache in Memory",
            gr.Slider,
            {"minimum": 0, "maximum": 10, "step": 1},
            section=section,
            category_id=category_id,
        ).needs_reload_ui(),
    )
    shared.opts.add_option(
        "control_net_sync_field_args",
        shared.OptionInfo(
            True,
            "Read ControlNet parameters from Infotext",
            section=section,
            category_id=category_id,
        ).needs_reload_ui(),
    )
    shared.opts.add_option(
        "control_net_no_detectmap",
        shared.OptionInfo(
            False,
            "Do not append detectmap to output",
            section=section,
            category_id=category_id,
        ),
    )


script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_infotext_pasted(Infotext.on_infotext_pasted)
script_callbacks.on_after_component(ControlNetUiGroup.on_after_component)
script_callbacks.on_before_reload(ControlNetUiGroup.reset)

if shared.cmd_opts.api:
    script_callbacks.on_app_started(controlnet_api)