microsoftexcel-controlnet/scripts/ui/controlnet_ui_group.py

import gradio as gr
import functools
from typing import List, Optional, Union, Dict, Callable
import numpy as np
import base64

from scripts.utils import svg_preprocess
from scripts import (
    global_state,
    external_code,
    processor,
    batch_hijack,
)
from scripts.processor import (
    preprocessor_sliders_config,
    flag_preprocessor_resolution,
    model_free_preprocessors,
    preprocessor_filters,
    HWC3,
)
from modules import shared
from modules.ui_components import FormRow


class ToolButton(gr.Button, gr.components.FormComponent):
    """Small button with single emoji as text, fits inside gradio forms"""

    def __init__(self, **kwargs):
        super().__init__(variant="tool", elem_classes=["cnet-toolbutton"], **kwargs)

    def get_block_name(self):
        return "button"


class UiControlNetUnit(external_code.ControlNetUnit):
    """The data class that stores all states of a ControlNetUnit."""

    def __init__(
        self,
        input_mode: batch_hijack.InputMode = batch_hijack.InputMode.SIMPLE,
        batch_images: Optional[Union[str, List[external_code.InputImage]]] = None,
        output_dir: str = "",
        loopback: bool = False,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.is_ui = True
        self.input_mode = input_mode
        self.batch_images = batch_images
        self.output_dir = output_dir
        self.loopback = loopback


def update_json_download_link(json_string: str, file_name: str) -> Dict:
    base64_encoded_json = base64.b64encode(json_string.encode("utf-8")).decode("utf-8")
    data_uri = f"data:application/json;base64,{base64_encoded_json}"
    style = """ 
    position: absolute;
    right: var(--size-2);
    bottom: calc(var(--size-2) * 4);
    font-size: x-small;
    font-weight: bold;
    padding: 2px;

    box-shadow: var(--shadow-drop);
    border: 1px solid var(--button-secondary-border-color);
    border-radius: var(--radius-sm);
    background: var(--background-fill-primary);
    height: var(--size-5);
    color: var(--block-label-text-color);
    """
    hint = "Download the pose as .json file"
    html = f"""<a href='{data_uri}' download='{file_name}' style="{style}" title="{hint}">
                Json</a>"""
    return gr.update(value=html, visible=(json_string != ""))


class ControlNetUiGroup(object):
    # Note: Change symbol hints mapping in `javascript/hints.js` when you change the symbol values.
    refresh_symbol = "\U0001f504"  # 🔄
    switch_values_symbol = "\U000021C5"  # ⇅
    camera_symbol = "\U0001F4F7"  # 📷
    reverse_symbol = "\U000021C4"  # ⇄
    tossup_symbol = "\u2934"
    trigger_symbol = "\U0001F4A5"  # 💥
    open_symbol = "\U0001F4DD"  # 📝

    global_batch_input_dir = gr.Textbox(
        label="Controlnet input directory",
        placeholder="Leave empty to use input directory",
        **shared.hide_dirs,
        elem_id="controlnet_batch_input_dir",
    )
    img2img_batch_input_dir = None
    img2img_batch_input_dir_callbacks = []
    img2img_batch_output_dir = None
    img2img_batch_output_dir_callbacks = []
    txt2img_submit_button = None
    img2img_submit_button = None

    # Slider controls from A1111 WebUI.
    txt2img_w_slider = None
    txt2img_h_slider = None
    img2img_w_slider = None
    img2img_h_slider = None

    def __init__(
        self,
        gradio_compat: bool,
        infotext_fields: List[str],
        default_unit: external_code.ControlNetUnit,
        preprocessors: List[Callable],
    ):
        self.gradio_compat = gradio_compat
        self.infotext_fields = infotext_fields
        self.default_unit = default_unit
        self.preprocessors = preprocessors
        self.webcam_enabled = False
        self.webcam_mirrored = False

        # Note: All gradio elements declared in `render` will be defined as member variable.
        self.upload_tab = None
        self.input_image = None
        self.generated_image_group = None
        self.generated_image = None
        self.download_pose_link = None
        self.batch_tab = None
        self.batch_image_dir = None
        self.create_canvas = None
        self.canvas_width = None
        self.canvas_height = None
        self.canvas_create_button = None
        self.canvas_cancel_button = None
        self.open_new_canvas_button = None
        self.webcam_enable = None
        self.webcam_mirror = None
        self.send_dimen_button = None
        self.enabled = None
        self.lowvram = None
        self.pixel_perfect = None
        self.preprocessor_preview = None
        self.type_filter = None
        self.module = None
        self.trigger_preprocessor = None
        self.model = None
        self.refresh_models = None
        self.weight = None
        self.guidance_start = None
        self.guidance_end = None
        self.advanced = None
        self.processor_res = None
        self.threshold_a = None
        self.threshold_b = None
        self.control_mode = None
        self.resize_mode = None
        self.loopback = None

    def render(self, tabname: str, elem_id_tabname: str) -> None:
        """The pure HTML structure of a single ControlNetUnit. Calling this
        function will populate `self` with all gradio element declared
        in local scope.

        Args:
            tabname:
            elem_id_tabname:

        Returns:
            None
        """
        with gr.Tabs():
            with gr.Tab(label="Single Image") as self.upload_tab:
                with gr.Row().style(equal_height=True):
                    self.input_image = gr.Image(
                        source="upload",
                        brush_radius=20,
                        mirror_webcam=False,
                        type="numpy",
                        tool="sketch",
                        elem_id=f"{elem_id_tabname}_{tabname}_input_image",
                    )
                    with gr.Group(visible=False) as self.generated_image_group:
                        self.generated_image = gr.Image(
                            label="Preprocessor Preview",
                            elem_id=f"{elem_id_tabname}_{tabname}_generated_image",
                        ).style(
                            height=242
                        )  # Gradio's magic number. Only 242 works.
                        self.download_pose_link = gr.HTML(value="", visible=False)
                        preview_close_button_style = """ 
                            position: absolute;
                            right: var(--size-2);
                            bottom: var(--size-2);
                            font-size: x-small;
                            font-weight: bold;
                            padding: 2px;
                            cursor: pointer;

                            box-shadow: var(--shadow-drop);
                            border: 1px solid var(--button-secondary-border-color);
                            border-radius: var(--radius-sm);
                            background: var(--background-fill-primary);
                            height: var(--size-5);
                            color: var(--block-label-text-color);
                            """
                        preview_check_elem_id = f"{elem_id_tabname}_{tabname}_controlnet_preprocessor_preview_checkbox"
                        preview_close_button_js = f"document.querySelector('#{preview_check_elem_id} input[type=\\'checkbox\\']').click();"
                        gr.HTML(
                            value=f"""<a style="{preview_close_button_style}" title="Close Preview" onclick="{preview_close_button_js}">Close</a>""",
                            visible=True,
                        )

            with gr.Tab(label="Batch") as self.batch_tab:
                self.batch_image_dir = gr.Textbox(
                    label="Input Directory",
                    placeholder="Leave empty to use img2img batch controlnet input directory",
                    elem_id=f"{elem_id_tabname}_{tabname}_batch_image_dir",
                )

        with gr.Accordion(label="Open New Canvas", visible=False) as self.create_canvas:
            self.canvas_width = gr.Slider(
                label="New Canvas Width",
                minimum=256,
                maximum=1024,
                value=512,
                step=64,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_canvas_width",
            )
            self.canvas_height = gr.Slider(
                label="New Canvas Height",
                minimum=256,
                maximum=1024,
                value=512,
                step=64,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_canvas_height",
            )
            with gr.Row():
                self.canvas_create_button = gr.Button(
                    value="Create New Canvas",
                    elem_id=f"{elem_id_tabname}_{tabname}_controlnet_canvas_create_button",
                )
                self.canvas_cancel_button = gr.Button(
                    value="Cancel",
                    elem_id=f"{elem_id_tabname}_{tabname}_controlnet_canvas_cancel_button",
                )

        with gr.Row(elem_classes="controlnet_image_controls"):
            gr.HTML(
                value="<p>Set the preprocessor to [invert] If your image has white background and black lines.</p>",
                elem_classes="controlnet_invert_warning",
            )
            self.open_new_canvas_button = ToolButton(
                value=ControlNetUiGroup.open_symbol,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_open_new_canvas_button",
            )
            self.webcam_enable = ToolButton(
                value=ControlNetUiGroup.camera_symbol,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_webcam_enable",
            )
            self.webcam_mirror = ToolButton(
                value=ControlNetUiGroup.reverse_symbol,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_webcam_mirror",
            )
            self.send_dimen_button = ToolButton(
                value=ControlNetUiGroup.tossup_symbol,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_send_dimen_button",
            )

        with FormRow(
            elem_classes=["checkboxes-row", "controlnet_main_options"],
            variant="compact",
        ):
            self.enabled = gr.Checkbox(
                label="Enable",
                value=self.default_unit.enabled,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_enable_checkbox",
            )
            self.lowvram = gr.Checkbox(
                label="Low VRAM",
                value=self.default_unit.low_vram,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_low_vram_checkbox",
            )
            self.pixel_perfect = gr.Checkbox(
                label="Pixel Perfect",
                value=self.default_unit.pixel_perfect,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_pixel_perfect_checkbox",
            )
            self.preprocessor_preview = gr.Checkbox(
                label="Allow Preview", value=False, elem_id=preview_check_elem_id
            )

        if not shared.opts.data.get("controlnet_disable_control_type", False):
            with gr.Row(elem_classes="controlnet_control_type"):
                self.type_filter = gr.Radio(
                    list(preprocessor_filters.keys()),
                    label=f"Control Type",
                    value="All",
                    elem_id=f"{elem_id_tabname}_{tabname}_controlnet_type_filter_radio",
                    elem_classes="controlnet_control_type_filter_group",
                )

        with gr.Row(elem_classes="controlnet_preprocessor_model"):
            self.module = gr.Dropdown(
                global_state.ui_preprocessor_keys,
                label=f"Preprocessor",
                value=self.default_unit.module,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_preprocessor_dropdown",
            )
            self.trigger_preprocessor = ToolButton(
                value=ControlNetUiGroup.trigger_symbol,
                visible=True,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_trigger_preprocessor",
            )
            self.model = gr.Dropdown(
                list(global_state.cn_models.keys()),
                label=f"Model",
                value=self.default_unit.model,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_model_dropdown",
            )
            self.refresh_models = ToolButton(
                value=ControlNetUiGroup.refresh_symbol,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_refresh_models",
            )

        with gr.Row(elem_classes="controlnet_weight_steps"):
            self.weight = gr.Slider(
                label=f"Control Weight",
                value=self.default_unit.weight,
                minimum=0.0,
                maximum=2.0,
                step=0.05,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_control_weight_slider",
                elem_classes="controlnet_control_weight_slider",
            )
            self.guidance_start = gr.Slider(
                label="Starting Control Step",
                value=self.default_unit.guidance_start,
                minimum=0.0,
                maximum=1.0,
                interactive=True,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_start_control_step_slider",
                elem_classes="controlnet_start_control_step_slider",
            )
            self.guidance_end = gr.Slider(
                label="Ending Control Step",
                value=self.default_unit.guidance_end,
                minimum=0.0,
                maximum=1.0,
                interactive=True,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_ending_control_step_slider",
                elem_classes="controlnet_ending_control_step_slider",
            )

        # advanced options
        with gr.Column(visible=False) as self.advanced:
            self.processor_res = gr.Slider(
                label="Preprocessor resolution",
                value=self.default_unit.processor_res,
                minimum=64,
                maximum=2048,
                visible=False,
                interactive=False,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_preprocessor_resolution_slider",
            )
            self.threshold_a = gr.Slider(
                label="Threshold A",
                value=self.default_unit.threshold_a,
                minimum=64,
                maximum=1024,
                visible=False,
                interactive=False,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_threshold_A_slider",
            )
            self.threshold_b = gr.Slider(
                label="Threshold B",
                value=self.default_unit.threshold_b,
                minimum=64,
                maximum=1024,
                visible=False,
                interactive=False,
                elem_id=f"{elem_id_tabname}_{tabname}_controlnet_threshold_B_slider",
            )

        self.control_mode = gr.Radio(
            choices=[e.value for e in external_code.ControlMode],
            value=self.default_unit.control_mode.value,
            label="Control Mode",
            elem_id=f"{elem_id_tabname}_{tabname}_controlnet_control_mode_radio",
            elem_classes="controlnet_control_mode_radio",
        )

        self.resize_mode = gr.Radio(
            choices=[e.value for e in external_code.ResizeMode],
            value=self.default_unit.resize_mode.value,
            label="Resize Mode",
            elem_id=f"{elem_id_tabname}_{tabname}_controlnet_resize_mode_radio",
            elem_classes="controlnet_resize_mode_radio",
        )

        self.loopback = gr.Checkbox(
            label="[Loopback] Automatically send generated images to this ControlNet unit",
            value=self.default_unit.loopback,
            elem_id=f"{elem_id_tabname}_{tabname}_controlnet_automatically_send_generated_images_checkbox",
            elem_classes="controlnet_loopback_checkbox",
        )

    def register_send_dimensions(self, is_img2img: bool):
        """Register event handler for send dimension button."""

        def send_dimensions(image):
            def closesteight(num):
                rem = num % 8
                if rem <= 4:
                    return round(num - rem)
                else:
                    return round(num + (8 - rem))

            if image:
                interm = np.asarray(image.get("image"))
                return closesteight(interm.shape[1]), closesteight(interm.shape[0])
            else:
                return gr.Slider.update(), gr.Slider.update()

        outputs = (
            [
                ControlNetUiGroup.img2img_w_slider,
                ControlNetUiGroup.img2img_h_slider,
            ]
            if is_img2img
            else [
                ControlNetUiGroup.txt2img_w_slider,
                ControlNetUiGroup.txt2img_h_slider,
            ]
        )
        self.send_dimen_button.click(
            fn=send_dimensions,
            inputs=[self.input_image],
            outputs=outputs,
        )

    def register_webcam_toggle(self):
        def webcam_toggle():
            self.webcam_enabled = not self.webcam_enabled
            return {
                "value": None,
                "source": "webcam" if self.webcam_enabled else "upload",
                "__type__": "update",
            }

        self.webcam_enable.click(webcam_toggle, inputs=None, outputs=self.input_image)

    def register_webcam_mirror_toggle(self):
        def webcam_mirror_toggle():
            self.webcam_mirrored = not self.webcam_mirrored
            return {"mirror_webcam": self.webcam_mirrored, "__type__": "update"}

        self.webcam_mirror.click(
            webcam_mirror_toggle, inputs=None, outputs=self.input_image
        )

    def register_refresh_all_models(self):
        def refresh_all_models(*inputs):
            global_state.update_cn_models()

            dd = inputs[0]
            selected = dd if dd in global_state.cn_models else "None"
            return gr.Dropdown.update(
                value=selected, choices=list(global_state.cn_models.keys())
            )

        self.refresh_models.click(refresh_all_models, self.model, self.model)

    def register_build_sliders(self):
        if not self.gradio_compat:
            return

        def build_sliders(module, pp):
            grs = []
            module = global_state.get_module_basename(module)
            if module not in preprocessor_sliders_config:
                grs += [
                    gr.update(
                        label=flag_preprocessor_resolution,
                        value=512,
                        minimum=64,
                        maximum=2048,
                        step=1,
                        visible=not pp,
                        interactive=not pp,
                    ),
                    gr.update(visible=False, interactive=False),
                    gr.update(visible=False, interactive=False),
                    gr.update(visible=True),
                ]
            else:
                for slider_config in preprocessor_sliders_config[module]:
                    if isinstance(slider_config, dict):
                        visible = True
                        if slider_config["name"] == flag_preprocessor_resolution:
                            visible = not pp
                        grs.append(
                            gr.update(
                                label=slider_config["name"],
                                value=slider_config["value"],
                                minimum=slider_config["min"],
                                maximum=slider_config["max"],
                                step=slider_config["step"]
                                if "step" in slider_config
                                else 1,
                                visible=visible,
                                interactive=visible,
                            )
                        )
                    else:
                        grs.append(gr.update(visible=False, interactive=False))
                while len(grs) < 3:
                    grs.append(gr.update(visible=False, interactive=False))
                grs.append(gr.update(visible=True))
            if module in model_free_preprocessors:
                grs += [
                    gr.update(visible=False, value="None"),
                    gr.update(visible=False),
                ]
            else:
                grs += [gr.update(visible=True), gr.update(visible=True)]
            return grs

        inputs = [self.module, self.pixel_perfect]
        outputs = [
            self.processor_res,
            self.threshold_a,
            self.threshold_b,
            self.advanced,
            self.model,
            self.refresh_models,
        ]
        self.module.change(build_sliders, inputs=inputs, outputs=outputs)
        self.pixel_perfect.change(build_sliders, inputs=inputs, outputs=outputs)

        if self.type_filter is not None:

            def filter_selected(k, pp):
                default_option = preprocessor_filters[k]
                pattern = k.lower()
                preprocessor_list = global_state.ui_preprocessor_keys
                model_list = list(global_state.cn_models.keys())
                if pattern == "all":
                    return [
                        gr.Dropdown.update(value="none", choices=preprocessor_list),
                        gr.Dropdown.update(value="None", choices=model_list),
                    ] + build_sliders("none", pp)
                filtered_preprocessor_list = [
                    x
                    for x in preprocessor_list
                    if pattern in x.lower() or x.lower() == "none"
                ]
                if pattern in ["canny", "lineart", "scribble", "mlsd"]:
                    filtered_preprocessor_list += [
                        x for x in preprocessor_list if "invert" in x.lower()
                    ]
                filtered_model_list = [
                    x for x in model_list if pattern in x.lower() or x.lower() == "none"
                ]
                if default_option not in filtered_preprocessor_list:
                    default_option = filtered_preprocessor_list[0]
                if len(filtered_model_list) == 1:
                    default_model = "None"
                    filtered_model_list = model_list
                else:
                    default_model = filtered_model_list[1]
                    for x in filtered_model_list:
                        if "11" in x.split("[")[0]:
                            default_model = x
                            break
                return [
                    gr.Dropdown.update(
                        value=default_option, choices=filtered_preprocessor_list
                    ),
                    gr.Dropdown.update(
                        value=default_model, choices=filtered_model_list
                    ),
                ] + build_sliders(default_option, pp)

            self.type_filter.change(
                filter_selected,
                inputs=[self.type_filter, self.pixel_perfect],
                outputs=[self.module, self.model, *outputs],
            )

    def register_run_annotator(self, is_img2img: bool):
        def run_annotator(image, module, pres, pthr_a, pthr_b, t2i_w, t2i_h, pp, rm):
            if image is None:
                return gr.update(value=None, visible=True), gr.update(), gr.update()

            img = HWC3(image["image"])
            if not (
                (image["mask"][:, :, 0] == 0).all()
                or (image["mask"][:, :, 0] == 255).all()
            ):
                img = HWC3(image["mask"][:, :, 0])

            if "inpaint" in module:
                color = HWC3(image["image"])
                alpha = image["mask"][:, :, 0:1]
                img = np.concatenate([color, alpha], axis=2)

            module = global_state.get_module_basename(module)
            preprocessor = self.preprocessors[module]

            if pp:
                raw_H, raw_W, _ = img.shape
                target_H, target_W = t2i_h, t2i_w
                rm = str(rm)

                k0 = float(target_H) / float(raw_H)
                k1 = float(target_W) / float(raw_W)

                if rm == external_code.ResizeMode.OUTER_FIT.value:
                    estimation = min(k0, k1) * float(min(raw_H, raw_W))
                else:
                    estimation = max(k0, k1) * float(min(raw_H, raw_W))

                pres = int(np.round(estimation))
                print(f"Pixel Perfect Mode Enabled In Preview.")
                print(f"resize_mode = {rm}")
                print(f"raw_H = {raw_H}")
                print(f"raw_W = {raw_W}")
                print(f"target_H = {target_H}")
                print(f"target_W = {target_W}")
                print(f"estimation = {estimation}")

            class JsonAcceptor:
                def __init__(self) -> None:
                    self.value = ""

                def accept(self, json_string: str) -> None:
                    self.value = json_string

            json_acceptor = JsonAcceptor()

            print(f"Preview Resolution = {pres}")

            def is_openpose(module: str):
                return "openpose" in module

            # Only openpose preprocessor returns a JSON output, pass json_acceptor
            # only when a JSON output is expected. This will make preprocessor cache
            # work for all other preprocessors other than openpose ones. JSON acceptor
            # instance are different every call, which means cache will never take
            # effect.
            # TODO: Maybe we should let `preprocessor` return a Dict to alleviate this issue?
            # This requires changing all callsites though.
            result, is_image = preprocessor(
                img,
                res=pres,
                thr_a=pthr_a,
                thr_b=pthr_b,
                json_pose_callback=json_acceptor.accept
                if is_openpose(module)
                else None,
            )

            if "clip" in module:
                result = processor.clip_vision_visualization(result)
                is_image = True

            if is_image:
                if result.ndim == 3 and result.shape[2] == 4:
                    inpaint_mask = result[:, :, 3]
                    result = result[:, :, 0:3]
                    result[inpaint_mask > 127] = 0
                return (
                    # Update to `generated_image`
                    gr.update(value=result, visible=True, interactive=False),
                    # Update to `download_pose_link`
                    update_json_download_link(json_acceptor.value, "pose.json"),
                    # preprocessor_preview
                    gr.update(value=True),
                )

            return (
                # Update to `generated_image`
                gr.update(value=None, visible=True),
                # Update to `download_pose_link`
                update_json_download_link(json_acceptor.value, "pose.json"),
                # preprocessor_preview
                gr.update(value=True),
            )

        self.trigger_preprocessor.click(
            fn=run_annotator,
            inputs=[
                self.input_image,
                self.module,
                self.processor_res,
                self.threshold_a,
                self.threshold_b,
                ControlNetUiGroup.img2img_w_slider
                if is_img2img
                else ControlNetUiGroup.txt2img_w_slider,
                ControlNetUiGroup.img2img_h_slider
                if is_img2img
                else ControlNetUiGroup.txt2img_h_slider,
                self.pixel_perfect,
                self.resize_mode,
            ],
            outputs=[
                self.generated_image,
                self.download_pose_link,
                self.preprocessor_preview,
            ],
        )

    def register_shift_preview(self):
        def shift_preview(is_on):
            return (
                # generated_image
                gr.update() if is_on else gr.update(value=None),
                # generated_image_group
                gr.update(visible=is_on),
                # download_pose_link
                gr.update() if is_on else gr.update(value=None),
            )

        self.preprocessor_preview.change(
            fn=shift_preview,
            inputs=[self.preprocessor_preview],
            outputs=[
                self.generated_image,
                self.generated_image_group,
                self.download_pose_link,
            ],
        )

    def register_create_canvas(self):
        self.open_new_canvas_button.click(
            lambda: gr.Accordion.update(visible=True),
            inputs=None,
            outputs=self.create_canvas,
        )
        self.canvas_cancel_button.click(
            lambda: gr.Accordion.update(visible=False),
            inputs=None,
            outputs=self.create_canvas,
        )

        def fn_canvas(h, w):
            return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255, gr.Accordion.update(
                visible=False
            )

        self.canvas_create_button.click(
            fn=fn_canvas,
            inputs=[self.canvas_height, self.canvas_width],
            outputs=[self.input_image, self.create_canvas],
        )

    def register_callbacks(self, is_img2img: bool):
        """Register callbacks on the UI elements.

        Args:
            is_img2img: Whether ControlNet is under img2img. False when in txt2img mode.

        Returns:
            None
        """
        self.register_send_dimensions(is_img2img)
        self.register_webcam_toggle()
        self.register_webcam_mirror_toggle()
        self.register_refresh_all_models()
        self.register_build_sliders()
        self.register_run_annotator(is_img2img)
        self.register_shift_preview()
        self.register_create_canvas()

    def register_modules(self, tabname: str, params):
        enabled, module, model, weight = params[4:8]
        guidance_start, guidance_end, pixel_perfect, control_mode = params[-4:]

        self.infotext_fields.extend(
            [
                (enabled, f"{tabname} Enabled"),
                (module, f"{tabname} Preprocessor"),
                (model, f"{tabname} Model"),
                (weight, f"{tabname} Weight"),
                (guidance_start, f"{tabname} Guidance Start"),
                (guidance_end, f"{tabname} Guidance End"),
            ]
        )

    def render_and_register_unit(self, tabname: str, is_img2img: bool):
        """Render the invisible states elements for misc persistent
        purposes. Register callbacks on loading/unloading the controlnet
        unit and handle batch processes.

        Args:
            tabname:
            is_img2img:

        Returns:
            The data class "ControlNetUnit" representing this ControlNetUnit.
        """
        input_mode = gr.State(batch_hijack.InputMode.SIMPLE)
        batch_image_dir_state = gr.State("")
        output_dir_state = gr.State("")
        unit_args = (
            input_mode,
            batch_image_dir_state,
            output_dir_state,
            self.loopback,
            self.enabled,
            self.module,
            self.model,
            self.weight,
            self.input_image,
            self.resize_mode,
            self.lowvram,
            self.processor_res,
            self.threshold_a,
            self.threshold_b,
            self.guidance_start,
            self.guidance_end,
            self.pixel_perfect,
            self.control_mode,
        )
        self.register_modules(tabname, unit_args)

        self.input_image.preprocess = functools.partial(
            svg_preprocess, preprocess=self.input_image.preprocess
        )

        unit = gr.State(self.default_unit)
        for comp in unit_args:
            event_subscribers = []
            if hasattr(comp, "edit"):
                event_subscribers.append(comp.edit)
            elif hasattr(comp, "click"):
                event_subscribers.append(comp.click)
            elif isinstance(comp, gr.Slider) and hasattr(comp, "release"):
                event_subscribers.append(comp.release)
            elif hasattr(comp, "change"):
                event_subscribers.append(comp.change)

            if hasattr(comp, "clear"):
                event_subscribers.append(comp.clear)

            for event_subscriber in event_subscribers:
                event_subscriber(
                    fn=UiControlNetUnit, inputs=list(unit_args), outputs=unit
                )

        # keep input_mode in sync
        def ui_controlnet_unit_for_input_mode(input_mode, *args):
            args = list(args)
            args[0] = input_mode
            return input_mode, UiControlNetUnit(*args)

        for input_tab in (
            (self.upload_tab, batch_hijack.InputMode.SIMPLE),
            (self.batch_tab, batch_hijack.InputMode.BATCH),
        ):
            input_tab[0].select(
                fn=ui_controlnet_unit_for_input_mode,
                inputs=[gr.State(input_tab[1])] + list(unit_args),
                outputs=[input_mode, unit],
            )

        def determine_batch_dir(batch_dir, fallback_dir, fallback_fallback_dir):
            if batch_dir:
                return batch_dir
            elif fallback_dir:
                return fallback_dir
            else:
                return fallback_fallback_dir

        # keep batch_dir in sync with global batch input textboxes
        def subscribe_for_batch_dir():
            batch_dirs = [
                self.batch_image_dir,
                ControlNetUiGroup.global_batch_input_dir,
                ControlNetUiGroup.img2img_batch_input_dir,
            ]
            for batch_dir_comp in batch_dirs:
                subscriber = getattr(batch_dir_comp, "blur", None)
                if subscriber is None:
                    continue
                subscriber(
                    fn=determine_batch_dir,
                    inputs=batch_dirs,
                    outputs=[batch_image_dir_state],
                    queue=False,
                )

        if ControlNetUiGroup.img2img_batch_input_dir is None:
            # we are too soon, subscribe later when available
            ControlNetUiGroup.img2img_batch_input_dir_callbacks.append(
                subscribe_for_batch_dir
            )
        else:
            subscribe_for_batch_dir()

        # keep output_dir in sync with global batch output textbox
        def subscribe_for_output_dir():
            ControlNetUiGroup.img2img_batch_output_dir.blur(
                fn=lambda a: a,
                inputs=[ControlNetUiGroup.img2img_batch_output_dir],
                outputs=[output_dir_state],
                queue=False,
            )

        if ControlNetUiGroup.img2img_batch_input_dir is None:
            # we are too soon, subscribe later when available
            ControlNetUiGroup.img2img_batch_output_dir_callbacks.append(
                subscribe_for_output_dir
            )
        else:
            subscribe_for_output_dir()

        (
            ControlNetUiGroup.img2img_submit_button
            if is_img2img
            else ControlNetUiGroup.txt2img_submit_button
        ).click(
            fn=UiControlNetUnit,
            inputs=list(unit_args),
            outputs=unit,
            queue=False,
        )

        return unit

    @staticmethod
    def on_after_component(component, **_kwargs):
        elem_id = getattr(component, "elem_id", None)

        if elem_id == "txt2img_generate":
            ControlNetUiGroup.txt2img_submit_button = component
            return

        if elem_id == "img2img_generate":
            ControlNetUiGroup.img2img_submit_button = component
            return

        if elem_id == "img2img_batch_input_dir":
            ControlNetUiGroup.img2img_batch_input_dir = component
            for callback in ControlNetUiGroup.img2img_batch_input_dir_callbacks:
                callback()
            return

        if elem_id == "img2img_batch_output_dir":
            ControlNetUiGroup.img2img_batch_output_dir = component
            for callback in ControlNetUiGroup.img2img_batch_output_dir_callbacks:
                callback()
            return

        if elem_id == "img2img_batch_inpaint_mask_dir":
            ControlNetUiGroup.global_batch_input_dir.render()
            return

        if elem_id == "txt2img_width":
            ControlNetUiGroup.txt2img_w_slider = component
            return

        if elem_id == "txt2img_height":
            ControlNetUiGroup.txt2img_h_slider = component
            return

        if elem_id == "img2img_width":
            ControlNetUiGroup.img2img_w_slider = component
            return

        if elem_id == "img2img_height":
            ControlNetUiGroup.img2img_h_slider = component
            return