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import os |
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from typing import Dict, Optional, Tuple, List, Union |
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import cv2 |
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import torch |
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import modules.scripts as scripts |
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from modules import shared, script_callbacks, masking, images |
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from modules.ui_components import InputAccordion |
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from modules.api.api import decode_base64_to_image |
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import gradio as gr |
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from lib_controlnet import global_state, external_code |
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from lib_controlnet.external_code import ControlNetUnit |
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from lib_controlnet.utils import align_dim_latent, set_numpy_seed, crop_and_resize_image, \ |
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prepare_mask, judge_image_type |
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from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup |
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from lib_controlnet.controlnet_ui.photopea import Photopea |
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from lib_controlnet.logging import logger |
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from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img, \ |
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StableDiffusionProcessing |
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from lib_controlnet.infotext import Infotext |
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from modules_forge.forge_util import HWC3, numpy_to_pytorch |
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from lib_controlnet.enums import HiResFixOption |
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from lib_controlnet.api import controlnet_api |
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import numpy as np |
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import functools |
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from PIL import Image |
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from modules_forge.shared import try_load_supported_control_model |
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from modules_forge.supported_controlnet import ControlModelPatcher |
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import tempfile |
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gradio_tempfile_path = os.path.join(tempfile.gettempdir(), 'gradio') |
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os.makedirs(gradio_tempfile_path, exist_ok=True) |
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global_state.update_controlnet_filenames() |
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@functools.lru_cache(maxsize=shared.opts.data.get("control_net_model_cache_size", 5)) |
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def cached_controlnet_loader(filename): |
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return try_load_supported_control_model(filename) |
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class ControlNetCachedParameters: |
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def __init__(self): |
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self.preprocessor = None |
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self.model = None |
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self.control_cond = None |
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self.control_cond_for_hr_fix = None |
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self.control_mask = None |
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self.control_mask_for_hr_fix = None |
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class ControlNetForForgeOfficial(scripts.Script): |
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sorting_priority = 10 |
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def title(self): |
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return "ControlNet" |
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def show(self, is_img2img): |
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return scripts.AlwaysVisible |
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def ui(self, is_img2img): |
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infotext = Infotext() |
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ui_groups = [] |
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controls = [] |
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max_models = shared.opts.data.get("control_net_unit_count", 3) |
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gen_type = "img2img" if is_img2img else "txt2img" |
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elem_id_tabname = gen_type + "_controlnet" |
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default_unit = ControlNetUnit(enabled=False, module="None", model="None") |
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with gr.Group(elem_id=elem_id_tabname): |
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with gr.Accordion(f"ControlNet Integrated", open=False, elem_id="controlnet", |
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elem_classes=["controlnet"]): |
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photopea = ( |
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Photopea() |
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if not shared.opts.data.get("controlnet_disable_photopea_edit", False) |
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else None |
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) |
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with gr.Row(elem_id=elem_id_tabname + "_accordions", elem_classes="accordions"): |
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for i in range(max_models): |
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with InputAccordion( |
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value=False, |
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label=f"ControlNet Unit {i}", |
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elem_classes=["cnet-unit-enabled-accordion"], |
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): |
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group = ControlNetUiGroup(is_img2img, default_unit, photopea) |
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ui_groups.append(group) |
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controls.append(group.render(f"ControlNet-{i}", elem_id_tabname)) |
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for i, ui_group in enumerate(ui_groups): |
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infotext.register_unit(i, ui_group) |
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if shared.opts.data.get("control_net_sync_field_args", True): |
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self.infotext_fields = infotext.infotext_fields |
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self.paste_field_names = infotext.paste_field_names |
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return tuple(controls) |
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def get_enabled_units(self, units): |
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units = [ |
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ControlNetUnit.from_dict(unit) if isinstance(unit, dict) else unit |
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for unit in units |
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] |
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assert all(isinstance(unit, ControlNetUnit) for unit in units) |
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enabled_units = [x for x in units if x.enabled] |
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return enabled_units |
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@staticmethod |
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def try_crop_image_with_a1111_mask( |
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p: StableDiffusionProcessing, |
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unit: ControlNetUnit, |
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input_image: np.ndarray, |
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resize_mode: external_code.ResizeMode, |
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preprocessor |
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) -> np.ndarray: |
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a1111_mask_image: Optional[Image.Image] = getattr(p, "image_mask", None) |
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is_only_masked_inpaint = ( |
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issubclass(type(p), StableDiffusionProcessingImg2Img) and |
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p.inpaint_full_res and |
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a1111_mask_image is not None |
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) |
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if ( |
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preprocessor.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab |
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and is_only_masked_inpaint |
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): |
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logger.info("Crop input image based on A1111 mask.") |
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input_image = [input_image[:, :, i] for i in range(input_image.shape[2])] |
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input_image = [Image.fromarray(x) for x in input_image] |
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mask = prepare_mask(a1111_mask_image, p) |
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crop_region = masking.get_crop_region(np.array(mask), p.inpaint_full_res_padding) |
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crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height) |
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input_image = [ |
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images.resize_image(resize_mode.int_value(), i, mask.width, mask.height) |
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for i in input_image |
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] |
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input_image = [x.crop(crop_region) for x in input_image] |
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input_image = [ |
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images.resize_image(external_code.ResizeMode.OUTER_FIT.int_value(), x, p.width, p.height) |
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for x in input_image |
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] |
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input_image = [np.asarray(x)[:, :, 0] for x in input_image] |
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input_image = np.stack(input_image, axis=2) |
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return input_image |
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def get_input_data(self, p, unit, preprocessor, h, w): |
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logger.info(f'ControlNet Input Mode: {unit.input_mode}') |
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image_list = [] |
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resize_mode = external_code.resize_mode_from_value(unit.resize_mode) |
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if unit.input_mode == external_code.InputMode.MERGE: |
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for idx, item in enumerate(unit.batch_input_gallery): |
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img_path = item['name'] |
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logger.info(f'Try to read image: {img_path}') |
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img = np.ascontiguousarray(cv2.imread(img_path)[:, :, ::-1]).copy() |
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mask = None |
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if len(unit.batch_mask_gallery) > 0: |
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if len(unit.batch_mask_gallery) >= len(unit.batch_input_gallery): |
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mask_path = unit.batch_mask_gallery[idx]['name'] |
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else: |
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mask_path = unit.batch_mask_gallery[0]['name'] |
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mask = np.ascontiguousarray(cv2.imread(mask_path)[:, :, ::-1]).copy() |
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if img is not None: |
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image_list.append([img, mask]) |
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elif unit.input_mode == external_code.InputMode.BATCH: |
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image_list = [] |
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image_extensions = ['.jpg', '.jpeg', '.png', '.bmp'] |
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batch_image_files = shared.listfiles(unit.batch_image_dir) |
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for batch_modifier in getattr(unit, 'batch_modifiers', []): |
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batch_image_files = batch_modifier(batch_image_files, p) |
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for idx, filename in enumerate(batch_image_files): |
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if any(filename.lower().endswith(ext) for ext in image_extensions): |
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img_path = os.path.join(unit.batch_image_dir, filename) |
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logger.info(f'Try to read image: {img_path}') |
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img = np.ascontiguousarray(cv2.imread(img_path)[:, :, ::-1]).copy() |
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mask = None |
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if unit.batch_mask_dir: |
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batch_mask_files = shared.listfiles(unit.batch_mask_dir) |
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if len(batch_mask_files) >= len(batch_image_files): |
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mask_path = batch_mask_files[idx] |
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else: |
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mask_path = batch_mask_files[0] |
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mask_path = os.path.join(unit.batch_mask_dir, mask_path) |
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mask = np.ascontiguousarray(cv2.imread(mask_path)[:, :, ::-1]).copy() |
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if img is not None: |
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image_list.append([img, mask]) |
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else: |
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a1111_i2i_image = getattr(p, "init_images", [None])[0] |
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a1111_i2i_mask = getattr(p, "image_mask", None) |
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using_a1111_data = False |
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if unit.use_preview_as_input and unit.generated_image is not None: |
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image = unit.generated_image |
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elif unit.image is None: |
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resize_mode = external_code.resize_mode_from_value(p.resize_mode) |
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image = HWC3(np.asarray(a1111_i2i_image)) |
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using_a1111_data = True |
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elif (unit.image['image'] < 5).all() and (unit.image['mask'] > 5).any(): |
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image = unit.image['mask'] |
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else: |
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image = unit.image['image'] |
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if not isinstance(image, np.ndarray): |
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raise ValueError("controlnet is enabled but no input image is given") |
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image = HWC3(image) |
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if using_a1111_data: |
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mask = HWC3(np.asarray(a1111_i2i_mask)) if a1111_i2i_mask is not None else None |
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elif unit.mask_image is not None and (unit.mask_image['image'] > 5).any(): |
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mask = unit.mask_image['image'] |
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elif unit.mask_image is not None and (unit.mask_image['mask'] > 5).any(): |
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mask = unit.mask_image['mask'] |
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elif unit.image is not None and (unit.image['mask'] > 5).any(): |
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mask = unit.image['mask'] |
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else: |
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mask = None |
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image = self.try_crop_image_with_a1111_mask(p, unit, image, resize_mode, preprocessor) |
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if mask is not None: |
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mask = cv2.resize(HWC3(mask), (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST) |
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mask = self.try_crop_image_with_a1111_mask(p, unit, mask, resize_mode, preprocessor) |
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image_list = [[image, mask]] |
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if resize_mode == external_code.ResizeMode.OUTER_FIT and preprocessor.expand_mask_when_resize_and_fill: |
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new_image_list = [] |
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for input_image, input_mask in image_list: |
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if input_mask is None: |
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input_mask = np.zeros_like(input_image) |
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input_mask = crop_and_resize_image( |
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input_mask, |
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external_code.ResizeMode.OUTER_FIT, h, w, |
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fill_border_with_255=True, |
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) |
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input_image = crop_and_resize_image( |
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input_image, |
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external_code.ResizeMode.OUTER_FIT, h, w, |
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fill_border_with_255=False, |
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) |
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new_image_list.append((input_image, input_mask)) |
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image_list = new_image_list |
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return image_list, resize_mode |
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@staticmethod |
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def get_target_dimensions(p: StableDiffusionProcessing) -> Tuple[int, int, int, int]: |
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"""Returns (h, w, hr_h, hr_w).""" |
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h = align_dim_latent(p.height) |
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w = align_dim_latent(p.width) |
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high_res_fix = ( |
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isinstance(p, StableDiffusionProcessingTxt2Img) |
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and getattr(p, 'enable_hr', False) |
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) |
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if high_res_fix: |
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if p.hr_resize_x == 0 and p.hr_resize_y == 0: |
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hr_y = int(p.height * p.hr_scale) |
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hr_x = int(p.width * p.hr_scale) |
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else: |
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hr_y, hr_x = p.hr_resize_y, p.hr_resize_x |
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hr_y = align_dim_latent(hr_y) |
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hr_x = align_dim_latent(hr_x) |
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else: |
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hr_y = h |
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hr_x = w |
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return h, w, hr_y, hr_x |
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@torch.no_grad() |
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def process_unit_after_click_generate(self, |
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p: StableDiffusionProcessing, |
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unit: ControlNetUnit, |
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params: ControlNetCachedParameters, |
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*args, **kwargs): |
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h, w, hr_y, hr_x = self.get_target_dimensions(p) |
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has_high_res_fix = ( |
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isinstance(p, StableDiffusionProcessingTxt2Img) |
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and getattr(p, 'enable_hr', False) |
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) |
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if unit.use_preview_as_input: |
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unit.module = 'None' |
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preprocessor = global_state.get_preprocessor(unit.module) |
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input_list, resize_mode = self.get_input_data(p, unit, preprocessor, h, w) |
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preprocessor_outputs = [] |
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control_masks = [] |
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preprocessor_output_is_image = False |
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preprocessor_output = None |
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|
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def optional_tqdm(iterable, use_tqdm): |
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from tqdm import tqdm |
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return tqdm(iterable) if use_tqdm else iterable |
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for input_image, input_mask in optional_tqdm(input_list, len(input_list) > 1): |
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if unit.pixel_perfect: |
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unit.processor_res = external_code.pixel_perfect_resolution( |
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input_image, |
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target_H=h, |
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target_W=w, |
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resize_mode=resize_mode, |
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) |
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seed = set_numpy_seed(p) |
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logger.debug(f"Use numpy seed {seed}.") |
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logger.info(f"Using preprocessor: {unit.module}") |
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logger.info(f'preprocessor resolution = {unit.processor_res}') |
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preprocessor_output = preprocessor( |
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input_image=input_image, |
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input_mask=input_mask, |
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resolution=unit.processor_res, |
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slider_1=unit.threshold_a, |
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slider_2=unit.threshold_b, |
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) |
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preprocessor_outputs.append(preprocessor_output) |
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preprocessor_output_is_image = judge_image_type(preprocessor_output) |
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|
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if input_mask is not None: |
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control_masks.append(input_mask) |
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|
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if len(input_list) > 1 and not preprocessor_output_is_image: |
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logger.info('Batch wise input only support controlnet, control-lora, and t2i adapters!') |
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break |
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|
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if has_high_res_fix: |
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hr_option = HiResFixOption.from_value(unit.hr_option) |
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else: |
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hr_option = HiResFixOption.BOTH |
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alignment_indices = [i % len(preprocessor_outputs) for i in range(p.batch_size)] |
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def attach_extra_result_image(img: np.ndarray, is_high_res: bool = False): |
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if ( |
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(is_high_res and hr_option.high_res_enabled) or |
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(not is_high_res and hr_option.low_res_enabled) |
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) and unit.save_detected_map: |
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p.extra_result_images.append(img) |
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|
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if preprocessor_output_is_image: |
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params.control_cond = [] |
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params.control_cond_for_hr_fix = [] |
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|
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for preprocessor_output in preprocessor_outputs: |
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control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w) |
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attach_extra_result_image(external_code.visualize_inpaint_mask(control_cond)) |
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params.control_cond.append(numpy_to_pytorch(control_cond).movedim(-1, 1)) |
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params.control_cond = torch.cat(params.control_cond, dim=0)[alignment_indices].contiguous() |
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|
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if has_high_res_fix: |
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for preprocessor_output in preprocessor_outputs: |
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control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x) |
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attach_extra_result_image(external_code.visualize_inpaint_mask(control_cond_for_hr_fix), is_high_res=True) |
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params.control_cond_for_hr_fix.append(numpy_to_pytorch(control_cond_for_hr_fix).movedim(-1, 1)) |
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params.control_cond_for_hr_fix = torch.cat(params.control_cond_for_hr_fix, dim=0)[alignment_indices].contiguous() |
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else: |
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params.control_cond_for_hr_fix = params.control_cond |
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else: |
|
params.control_cond = preprocessor_output |
|
params.control_cond_for_hr_fix = preprocessor_output |
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attach_extra_result_image(input_image) |
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|
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if len(control_masks) > 0: |
|
params.control_mask = [] |
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params.control_mask_for_hr_fix = [] |
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|
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for input_mask in control_masks: |
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fill_border = preprocessor.fill_mask_with_one_when_resize_and_fill |
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control_mask = crop_and_resize_image(input_mask, resize_mode, h, w, fill_border) |
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attach_extra_result_image(control_mask) |
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control_mask = numpy_to_pytorch(control_mask).movedim(-1, 1)[:, :1] |
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params.control_mask.append(control_mask) |
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|
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if has_high_res_fix: |
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control_mask_for_hr_fix = crop_and_resize_image(input_mask, resize_mode, hr_y, hr_x, fill_border) |
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attach_extra_result_image(control_mask_for_hr_fix, is_high_res=True) |
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control_mask_for_hr_fix = numpy_to_pytorch(control_mask_for_hr_fix).movedim(-1, 1)[:, :1] |
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params.control_mask_for_hr_fix.append(control_mask_for_hr_fix) |
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|
|
params.control_mask = torch.cat(params.control_mask, dim=0)[alignment_indices].contiguous() |
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if has_high_res_fix: |
|
params.control_mask_for_hr_fix = torch.cat(params.control_mask_for_hr_fix, dim=0)[alignment_indices].contiguous() |
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else: |
|
params.control_mask_for_hr_fix = params.control_mask |
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|
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if preprocessor.do_not_need_model: |
|
model_filename = 'Not Needed' |
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params.model = ControlModelPatcher() |
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else: |
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assert unit.model != 'None', 'You have not selected any control model!' |
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model_filename = global_state.get_controlnet_filename(unit.model) |
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params.model = cached_controlnet_loader(model_filename) |
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assert params.model is not None, logger.error(f"Recognizing Control Model failed: {model_filename}") |
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|
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params.preprocessor = preprocessor |
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|
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params.preprocessor.process_after_running_preprocessors(process=p, params=params, **kwargs) |
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params.model.process_after_running_preprocessors(process=p, params=params, **kwargs) |
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|
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logger.info(f"Current ControlNet {type(params.model).__name__}: {model_filename}") |
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return |
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|
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@torch.no_grad() |
|
def process_unit_before_every_sampling(self, |
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p: StableDiffusionProcessing, |
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unit: ControlNetUnit, |
|
params: ControlNetCachedParameters, |
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*args, **kwargs): |
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|
|
is_hr_pass = getattr(p, 'is_hr_pass', False) |
|
|
|
has_high_res_fix = ( |
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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(f"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(f"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, |
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)) |
|
|
|
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.") |
|
return |
|
|
|
@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)) |
|
|
|
return |
|
|
|
@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) |
|
return |
|
|
|
@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() |
|
self.process_unit_after_click_generate(p, unit, params, *args, **kwargs) |
|
self.current_params[i] = params |
|
return |
|
|
|
@torch.no_grad() |
|
def process_before_every_sampling(self, p, *args, **kwargs): |
|
for i, unit in enumerate(self.get_enabled_units(args)): |
|
self.process_unit_before_every_sampling(p, unit, self.current_params[i], *args, **kwargs) |
|
return |
|
|
|
@torch.no_grad() |
|
def postprocess_batch_list(self, p, pp, *args, **kwargs): |
|
for i, unit in enumerate(self.get_enabled_units(args)): |
|
self.process_unit_after_every_sampling(p, unit, self.current_params[i], pp, *args, **kwargs) |
|
return |
|
|
|
def postprocess(self, p, processed, *args): |
|
self.current_params = {} |
|
return |
|
|
|
|
|
def on_ui_settings(): |
|
section = ('control_net', "ControlNet") |
|
shared.opts.add_option("control_net_detectedmap_dir", shared.OptionInfo( |
|
"detected_maps", "Directory for detected maps auto saving", section=section)) |
|
shared.opts.add_option("control_net_models_path", shared.OptionInfo( |
|
"", "Extra path to scan for ControlNet models (e.g. training output directory)", section=section)) |
|
shared.opts.add_option("control_net_modules_path", shared.OptionInfo( |
|
"", |
|
"Path to directory containing annotator model directories (requires restart, overrides corresponding command line flag)", |
|
section=section)) |
|
shared.opts.add_option("control_net_unit_count", shared.OptionInfo( |
|
3, "Multi-ControlNet: ControlNet unit number (requires restart)", gr.Slider, |
|
{"minimum": 1, "maximum": 10, "step": 1}, section=section)) |
|
shared.opts.add_option("control_net_model_cache_size", shared.OptionInfo( |
|
5, "Model cache size (requires restart)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, section=section)) |
|
shared.opts.add_option("control_net_no_detectmap", shared.OptionInfo( |
|
False, "Do not append detectmap to output", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("control_net_detectmap_autosaving", shared.OptionInfo( |
|
False, "Allow detectmap auto saving", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("control_net_allow_script_control", shared.OptionInfo( |
|
False, "Allow other script to control this extension", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("control_net_sync_field_args", shared.OptionInfo( |
|
True, "Paste ControlNet parameters in infotext", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("controlnet_show_batch_images_in_ui", shared.OptionInfo( |
|
False, "Show batch images in gradio gallery output", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("controlnet_increment_seed_during_batch", shared.OptionInfo( |
|
False, "Increment seed after each controlnet batch iteration", gr.Checkbox, {"interactive": True}, |
|
section=section)) |
|
shared.opts.add_option("controlnet_disable_openpose_edit", shared.OptionInfo( |
|
False, "Disable openpose edit", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("controlnet_disable_photopea_edit", shared.OptionInfo( |
|
False, "Disable photopea edit", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("controlnet_photopea_warning", shared.OptionInfo( |
|
True, "Photopea popup warning", gr.Checkbox, {"interactive": True}, section=section)) |
|
shared.opts.add_option("controlnet_input_thumbnail", shared.OptionInfo( |
|
True, "Input image thumbnail on unit header", gr.Checkbox, {"interactive": True}, section=section)) |
|
|
|
|
|
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) |
|
script_callbacks.on_app_started(controlnet_api) |
|
|
